From 17526aa6b9e2d725fcdabcf897f10948ae5b5a4f Mon Sep 17 00:00:00 2001 From: mrmer1 Date: Wed, 4 Sep 2024 17:43:53 +0800 Subject: [PATCH 01/11] add v2 docs --- .gitignore | 1 - notebooks/Vanilla_RAG_v2.ipynb | 755 ++++++++++++++++++ notebooks/agents/Vanilla_Tool_Use_v2.ipynb | 639 +++++++++++++++ .../getting-started/v2/tutorial_pt1_v2.ipynb | 225 ++++++ .../getting-started/v2/tutorial_pt2_v2.ipynb | 529 ++++++++++++ .../getting-started/v2/tutorial_pt3_v2.ipynb | 381 +++++++++ .../getting-started/v2/tutorial_pt4_v2.ipynb | 442 ++++++++++ .../getting-started/v2/tutorial_pt5_v2.ipynb | 493 ++++++++++++ .../getting-started/v2/tutorial_pt6_v2.ipynb | 531 ++++++++++++ .../getting-started/v2/tutorial_pt7_v2.ipynb | 549 +++++++++++++ 10 files changed, 4544 insertions(+), 1 deletion(-) create mode 100644 notebooks/Vanilla_RAG_v2.ipynb create mode 100644 notebooks/agents/Vanilla_Tool_Use_v2.ipynb create mode 100644 notebooks/guides/getting-started/v2/tutorial_pt1_v2.ipynb create mode 100644 notebooks/guides/getting-started/v2/tutorial_pt2_v2.ipynb create mode 100644 notebooks/guides/getting-started/v2/tutorial_pt3_v2.ipynb create mode 100644 notebooks/guides/getting-started/v2/tutorial_pt4_v2.ipynb create mode 100644 notebooks/guides/getting-started/v2/tutorial_pt5_v2.ipynb create mode 100644 notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb create mode 100644 notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb diff --git a/.gitignore b/.gitignore index b73165d..50f7c60 100644 --- a/.gitignore +++ b/.gitignore @@ -86,7 +86,6 @@ target/ # Jupyter Notebook .ipynb_checkpoints -notebooks/ # IPython profile_default/ diff --git a/notebooks/Vanilla_RAG_v2.ipynb b/notebooks/Vanilla_RAG_v2.ipynb new file mode 100644 index 0000000..48de1f4 --- /dev/null +++ b/notebooks/Vanilla_RAG_v2.ipynb @@ -0,0 +1,755 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "mz33G3t6gbOl" + }, + "source": [ + "# RAG\n", + "\n", + "Retrieval-Augmented Generation (RAG) is a technique that combines the strengths of pre-trained language models with the ability to retrieve information from a large corpus of documents. RAG **enables the language model to produce more informed, accurate, and contextually relevant answers** than by relying on its pre-trained knowledge alone.\n", + "\n", + "At Cohere, all RAG calls come with... **precise citations**! 🎉\n", + "The model cites which groups of words, in the RAG chunks, were used to generate the final answer. \n", + "These citations make it easy to check where the model’s generated response claims are coming from and they help users gain visibility into the model reasoning. \n", + "\n", + "RAG consists of 3 steps:\n", + "- Step 1: Indexing and given a user query, retrieve the relevant chunks from the index\n", + "- Step 2: Optionally, rerank the retrieved chunks\n", + "- Step 3: Generate the model final answer with **precise citations**, given the retrieved and reranked chunks\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nSB0pnt0gbOo" + }, + "source": [ + "## Step 0 - Imports & Getting some data\n", + "\n", + "In this example, we'll use a recent piece of text, that wasn't in the training data: the Wikipedia page of the movie \"Dune 2\". \n", + "\n", + "In practice, you would typically do RAG on much longer text, that doesn't fit in the context window of the model." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "rACbepFGgbOo" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/meor/anaconda3/lib/python3.11/site-packages/pydantic/_internal/_config.py:341: UserWarning: Valid config keys have changed in V2:\n", + "* 'allow_population_by_field_name' has been renamed to 'populate_by_name'\n", + "* 'smart_union' has been removed\n", + " warnings.warn(message, UserWarning)\n" + ] + } + ], + "source": [ + "# pip install cohere\n", + "\n", + "import cohere\n", + "\n", + "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QdvbqfFrgbOq", + "outputId": "3882c95c-46bf-4dcc-99a2-453b3c2fc7c4" + }, + "outputs": [], + "source": [ + "# we'll get some wikipedia data\n", + "! pip install wikipedia -qq\n", + "\n", + "import wikipedia" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xP-bWt9XgbOq", + "outputId": "72276fb2-0d6b-415d-af74-452a013ae84b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The text has roughly 5812 words.\n" + ] + } + ], + "source": [ + "# let's get the wikipedia article about Dune Part Two\n", + "article = wikipedia.page('Dune Part Two')\n", + "text = article.content\n", + "print(f\"The text has roughly {len(text.split())} words.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-1aJ7hKGgbOr" + }, + "source": [ + "## Step 1 - Indexing and given a user query, retrieve the relevant chunks from the index\n", + "\n", + "We index the document in a vector database. This requires getting the documents, chunking them, embedding, and indexing them in a vector database. Then we retrieved relevant results based on the users' query.\n", + "\n", + "### We split the document into chunks of roughly 512 words" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZUph1JX41665", + "outputId": "6c63a93f-6999-47af-e704-d4a88727bc75" + }, + "outputs": [], + "source": [ + "# For chunking let's use langchain to help us split the text\n", + "! pip install -qU langchain-text-splitters -qq\n", + "\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uhXW7iHC1-Q6", + "outputId": "d68ac348-4b73-4c6a-a445-6c510bdb0881" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The text has been broken down in 107 chunks.\n" + ] + } + ], + "source": [ + "# Create basic configurations to chunk the text\n", + "text_splitter = RecursiveCharacterTextSplitter(\n", + " chunk_size=512,\n", + " chunk_overlap=50,\n", + " length_function=len,\n", + " is_separator_regex=False,\n", + ")\n", + "\n", + "# Split the text into chunks with some overlap\n", + "chunks_ = text_splitter.create_documents([text])\n", + "chunks = [c.page_content for c in chunks_]\n", + "print(f\"The text has been broken down in {len(chunks)} chunks.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P8g0sE2hgbOs" + }, + "source": [ + "### Embed every text chunk\n", + "\n", + "Cohere embeddings are state-of-the-art." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KEarMPEqgbOs", + "outputId": "7da0e06d-f637-4470-8e01-6de8249be64b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We just computed 107 embeddings.\n" + ] + } + ], + "source": [ + "# Because the texts being embedded are the chunks we are searching over, we set the input type as search_doc\n", + "model=\"embed-english-v3.0\"\n", + "response = co.embed(\n", + " texts= chunks,\n", + " model=model,\n", + " input_type=\"search_document\",\n", + " embedding_types=['float']\n", + ")\n", + "embeddings = response.embeddings.float\n", + "print(f\"We just computed {len(embeddings)} embeddings.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HM6vKeypgbOs" + }, + "source": [ + "### Store the embeddings in a vector database\n", + "\n", + "We use the simplest vector database ever: a python dictionary using `np.array()`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "sdW7M8HLvB-9" + }, + "outputs": [], + "source": [ + "# We use the simplest vector database ever: a python dictionary\n", + "! pip install numpy -qq" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "H2srFH-IgbOs" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "vector_database = {i: np.array(embedding) for i, embedding in enumerate(embeddings)}\n", + "# { 0: array([...]), 1: array([...]), 2: array([...]), ..., 10: array([...]) }" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q6NGVurZgbOs" + }, + "source": [ + "## Given a user query, retrieve the relevant chunks from the vector database\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eC05yJQ7jlek" + }, + "source": [ + "### Define the user question" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "Y2HTxspKgbOs" + }, + "outputs": [], + "source": [ + "query = \"Name everyone involved in writing the script, directing, and producing 'Dune: Part Two'?\"\n", + "\n", + "# Note: the relevant passage in the wikipedia page we're looking for is:\n", + "# \"[...] Dune: Part Two was originally scheduled to be released on October 20, 2023, but was delayed to November 17, 2023, before moving forward two weeks to November 3, 2023, to adjust to changes in release schedules from other studios. It was later postponed by over four months to March 15, 2024, due to the 2023 Hollywood labor disputes. After the strikes were resolved, the film moved once more up two weeks to March 1, 2024. [...]\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9oULg1tOjjOW" + }, + "source": [ + "### Embed the user question\n", + "\n", + "Cohere embeddings are state-of-the-art." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yrUuS6vXgbOs", + "outputId": "0c64a930-f817-43c2-d775-1d9145cb304e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query_embedding: [-0.068603516, -0.02947998, -0.06274414, -0.015449524, -0.033294678, 0.0056877136, -0.047210693, 0.04714966, -0.024871826, 0.008148193, '...']\n" + ] + } + ], + "source": [ + "# Because the text being embedded is the search query, we set the input type as search_query\n", + "response = co.embed(\n", + " texts=[query],\n", + " model=model,\n", + " input_type=\"search_query\",\n", + " embedding_types=['float']\n", + ")\n", + "query_embedding = response.embeddings.float[0]\n", + "print(\"query_embedding: \", query_embedding[:10] + [\"...\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8K8B87CGgbOt" + }, + "source": [ + "### Retrieve the most relevant chunks from the vector database\n", + "\n", + "We use cosine similarity to find the most similar chunks" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nik3es32gbOt", + "outputId": "a1c30024-52e1-42c7-8836-a2c590559aca" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "similarity scores: [0.6880419226352857, 0.3923392545434105, 0.6386815248269733, 0.43505965112768996, 0.35780784367307705, 0.3923210354894715, 0.3406860631010847, 0.2957357005793907, 0.4255159555931599, 0.14503223489330883, 0.3997846789722547, 0.3743933002525113, 0.4034607365399171, 0.3244752223188998, 0.3206445944107157, 0.3409994933473336, 0.23203650717727414, 0.497148799385201, 0.34487158221487835, 0.28824423308146263, 0.5782290303802001, 0.5489319961327446, 0.778415400224538, 0.5236541350938922, 0.5481962732642285, 0.7135549552247115, 0.5205999648021981, 0.5872268968480292, 0.26489409375108064, 0.6410858426784221, 0.5367759890829636, 0.6816158875713925, 0.39144361423311, 0.4827300300834518, 0.4502725966061075, 0.2462873309174519, 0.443300705486635, 0.39120172781317253, 0.20620678706300988, 0.4371599199261209, 0.37572638119299867, 0.4614621126654143, 0.29906070488176123, 0.36477353501504023, 0.36024747243912564, 0.3929914083901088, 0.23114430973151348, 0.4646991530089856, 0.1187422018303958, 0.4217084543208151, 0.36510789830148477, 0.26769253214700306, 0.36598547319281477, 0.3792997473359336, 0.4596196436425069, 0.43869634271638114, 0.20089603894036867, 0.42191807980549356, 0.4387291730859993, 0.4488941445207048, 0.1299006327023146, 0.3832393275291005, 0.14269299802866453, 0.5386575762368317, 0.29891892602821285, 0.4128023121085502, 0.15622874884471297, 0.5037411178038066, 0.3403791753246366, 0.4518658626088099, 0.3588444212378783, 0.39024780342329135, 0.48874362388129355, 0.2970250944589432, 0.4890213355379532, 0.2601457789171118, 0.5098062623890619, 0.41745989801233, 0.18695460607524733, 0.5309005848922221, 0.5226886534429271, 0.5478914338259878, 0.5004863890173412, 0.1968363843203233, 0.4210806632601615, 0.5351498045642535, 0.28640413417722466, 0.31654174678868613, 0.21754347066657245, 0.46012838286736324, 0.14089744261122422, 0.20637564356989352, 0.5713764499847603, 0.3808494424653835, 0.5832486106225127, 0.41007417198464485, 0.2611055925841932, 0.3805828196439077, 0.28780942000368676, 0.3138126314582887, 0.3471942628542812, 0.21954342602333704, 0.14159076456040556, 0.4685623412304875, 0.4516605901453241, 0.5117576674528891, 0.4176107168986097]\n", + "Here are the indices of the top 10 chunks after retrieval: [22 25 0 31 29 2 27 94 20 92]\n", + "Here are the top 10 chunks after retrieval: \n", + "== Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there's a logical place to stop the [first] movie before the book is over\".\n", + "== On October 26, 2021, Legendary officially greenlit Dune: Part Two, with a spokesperson for the company stating, \"We would not have gotten to this point without the extraordinary vision of Denis and the amazing work of his talented crew, the writers, our stellar cast, our partners at Warner Bros., and of course the fans! Here's to more Dune.\" Production work had occurred back-to-back with the first film, as Villeneuve and his wife Lapointe immediately took a flight to Budapest in order to begin\n", + "== Dune: Part Two is a 2024 American epic science fiction film directed and co-produced by Denis Villeneuve, who co-wrote the screenplay with Jon Spaihts. The sequel to Dune (2021), it is the second of a two-part adaptation of the 1965 novel Dune by Frank Herbert. It follows Paul Atreides as he unites with the Fremen people of the desert planet Arrakis to wage war against House Harkonnen. TimothĂ©e Chalamet, Rebecca Ferguson, Josh Brolin, Stellan SkarsgĂ„rd, Dave Bautista, Zendaya, Charlotte Rampling, and Javier\n", + "== Between the release of Dune and the confirmation of Dune: Part Two, Villeneuve started working the script in a way that production could begin immediately once the film was greenlit. By February 2021, Roth created a full treatment for the sequel, with writing beginning that August. He confirmed that Feyd-Rautha would appear in the film, and stated he will be a \"very important character\". In March 2022, Villeneuve had mostly finished writing the screenplay. Craig Mazin and Roth wrote additional literary\n", + "== Eric Roth was hired to co-write the screenplay in April 2017 for the Dune films, and Jon Spaihts was later confirmed to be co-writing the script alongside Roth and Villeneuve. Game of Thrones language creator David Peterson was confirmed to be developing languages for the film in April 2019. Villeneuve and Peterson had created the Chakobsa language, which was used by actors on set. In November 2019, Spaihts stepped down as show-runner for Dune: Prophecy to focus on Dune: Part Two. In June 2020, Greig\n", + "== Development began after Legendary Entertainment acquired film and television rights for the Dune franchise in 2016. Villeneuve signed on as director in 2017, intending to make a two-part adaptation of the novel due to its complexity. Production contracts were only secured for the first film before the second film was greenlit by Legendary in October 2021, subject to the success of the first. Principal photography took place in Budapest, Italy, Jordan, and Abu Dhabi between July and December 2022.\n", + "== theatrical experience is at the very heart of the cinematic language for me\". With Dune: Part Two being greenlit, Villeneuve said that his primary concern was to complete the filming as soon as possible, with the earliest he expected to start in the last quarter of 2022. He noted that production would be expedited by the work already done for the first film.\n", + "== Richard Roeper, writing for the Chicago Sun-Times, gave the film three stars out of four, praising the technical and narrative aspects, saying, \"Even as we marvel at the stunning and immersive and Oscar-level cinematography, editing, score, visual effects, production design and sound in Denis Villeneuve's Dune: Part Two, we're reminded at every turn that this is an absolutely bat-bleep [sic] crazy story.\"\n", + "== In November 2016, Legendary Pictures obtained the film and TV rights for the Dune franchise, based on the eponymous 1965 novel by Frank Herbert. Vice chair of worldwide production for Legendary Mary Parent began discussing with Denis Villeneuve about directing a film adaptation, quickly hiring him after realizing his passion for Dune. In February 2018, Villeneuve was confirmed to be hired as director, and intended to adapt the novel as a two-part film series. Villeneuve ultimately secured a two-film deal\n", + "== The film \"largely received rave reviews from critics\", and was praised for its visual effects and cast performances. Some reviews considered it one of the greatest science fiction films ever made. On the review aggregator website Rotten Tomatoes, 92% of 442 critics' reviews are positive, with an average rating of 8.3/10. The website's consensus reads: \"Visually thrilling and narratively epic, Dune: Part Two continues Denis Villeneuve's adaptation of the beloved sci-fi series in spectacular form.\"\n" + ] + } + ], + "source": [ + "def cosine_similarity(a, b):\n", + " return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))\n", + "\n", + "# Calculate similarity between the user question & each chunk\n", + "similarities = [cosine_similarity(query_embedding, chunk) for chunk in embeddings]\n", + "print(\"similarity scores: \", similarities)\n", + "\n", + "# Get indices of the top 10 most similar chunks\n", + "sorted_indices = np.argsort(similarities)[::-1]\n", + "\n", + "# Keep only the top 10 indices\n", + "top_indices = sorted_indices[:10]\n", + "print(\"Here are the indices of the top 10 chunks after retrieval: \", top_indices)\n", + "\n", + "# Retrieve the top 10 most similar chunks\n", + "top_chunks_after_retrieval = [chunks[i] for i in top_indices]\n", + "print(\"Here are the top 10 chunks after retrieval: \")\n", + "for t in top_chunks_after_retrieval:\n", + " print(\"== \" + t)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qzcpds3VgbOt" + }, + "source": [ + "## Step 2 - Rerank the chunks retrieved from the vector database\n", + "\n", + "We rerank the 10 chunks retrieved from the vector database. Reranking boosts retrieval accuracy.\n", + "\n", + "Reranking lets us go from 10 chunks retrieved from the vector database, to the 3 most relevant chunks." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2J4LywVygbOt", + "outputId": "7a4c89bf-fc5e-409f-9304-fce006b9d8bf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Here are the top 3 chunks after rerank: \n", + "== Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there's a logical place to stop the [first] movie before the book is over\".\n", + "== Dune: Part Two is a 2024 American epic science fiction film directed and co-produced by Denis Villeneuve, who co-wrote the screenplay with Jon Spaihts. The sequel to Dune (2021), it is the second of a two-part adaptation of the 1965 novel Dune by Frank Herbert. It follows Paul Atreides as he unites with the Fremen people of the desert planet Arrakis to wage war against House Harkonnen. TimothĂ©e Chalamet, Rebecca Ferguson, Josh Brolin, Stellan SkarsgĂ„rd, Dave Bautista, Zendaya, Charlotte Rampling, and Javier\n", + "== On October 26, 2021, Legendary officially greenlit Dune: Part Two, with a spokesperson for the company stating, \"We would not have gotten to this point without the extraordinary vision of Denis and the amazing work of his talented crew, the writers, our stellar cast, our partners at Warner Bros., and of course the fans! Here's to more Dune.\" Production work had occurred back-to-back with the first film, as Villeneuve and his wife Lapointe immediately took a flight to Budapest in order to begin\n" + ] + } + ], + "source": [ + "response = co.rerank(\n", + " query=query,\n", + " documents=top_chunks_after_retrieval,\n", + " top_n=3,\n", + " model=\"rerank-english-v3.0\",\n", + ")\n", + "\n", + "# top_chunks_after_rerank = [result.document['text'] for result in response]\n", + "\n", + "top_chunks_after_rerank = [top_chunks_after_retrieval[result.index] for result in response.results]\n", + "\n", + "print(\"Here are the top 3 chunks after rerank: \")\n", + "for t in top_chunks_after_rerank:\n", + " print(\"== \" + t)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KuPL0VUXgbOt" + }, + "source": [ + "## Step 3 - Generate the model final answer, given the retrieved and reranked chunks" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "oCNXWH8GgbOt" + }, + "outputs": [], + "source": [ + "# preamble containing instructions about the task and the desired style for the output.\n", + "preamble = \"\"\"\n", + "## Task & Context\n", + "You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.\n", + "\n", + "## Style Guide\n", + "Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BevatShtgbOt", + "outputId": "af71f4a9-787a-4ee3-9598-20692fb3bf16" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final answer:\n", + "*Dune: Part Two* is a 2024 American epic science fiction film directed and co-produced by Denis Villeneuve, who also co-wrote the screenplay with Jon Spaihts.\n", + "\n", + "The film was produced by Villeneuve, Mary Parent, and Cale Boyter, with the following serving as executive producers:\n", + "- Tanya Lapointe\n", + "- Brian Herbert\n", + "- Byron Merritt\n", + "- Kim Herbert\n", + "- Thomas Tull\n", + "- Richard P. Rubinstein\n", + "- John Harrison\n", + "- Herbert W. Gain\n", + "- Kevin J. Anderson (creative consultant)\n" + ] + } + ], + "source": [ + "# retrieved documents\n", + "documents = [\n", + " {\"title\": \"chunk 0\", \"snippet\": top_chunks_after_rerank[0]},\n", + " {\"title\": \"chunk 1\", \"snippet\": top_chunks_after_rerank[1]},\n", + " {\"title\": \"chunk 2\", \"snippet\": top_chunks_after_rerank[2]},\n", + " ]\n", + "\n", + "# get model response\n", + "response = co.chat(\n", + " model=\"command-r-08-2024\",\n", + " messages=[{\"role\" : \"system\", \"content\" : preamble},\n", + " {\"role\" : \"user\", \"content\" : query, \"documents\" : documents}],\n", + " temperature=0.3\n", + ")\n", + "\n", + "print(\"Final answer:\")\n", + "print(response.message.content[0].text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "20wcn-EjlXZd" + }, + "source": [ + "Note: this is indeed the answer you'd expect, and here was the passage of text in wikipedia explaining it!\n", + "\n", + "\" [...] Dune: Part Two was originally scheduled to be released on October 20, 2023, but was delayed to November 17, 2023, before moving forward two weeks to November 3, 2023, to adjust to changes in release schedules from other studios. It was later postponed by over four months to March 15, 2024, due to the 2023 Hollywood labor disputes. After the strikes were resolved, the film moved once more up two weeks to March 1, 2024. [...]\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RoSVDXSsgbOt" + }, + "source": [ + "## Bonus: Citations come for free with Cohere! 🎉\n", + "\n", + "At Cohere, all RAG calls come with... precise citations! 🎉\n", + "The model cites which groups of words, in the RAG chunks, were used to generate the final answer. \n", + "These citations make it easy to check where the model’s generated response claims are coming from. \n", + "They help users gain visibility into the model reasoning, and sanity check the final model generation. \n", + "These citations are optional — you can decide to ignore them.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BVTuQdmDgbOt", + "outputId": "f843b262-d8bb-45ba-cbfb-9915da104eda" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Citations that support the final answer:\n", + "start=22 end=61 text='2024 American epic science fiction film' sources=[Source_Document(id='doc:1:1', document={'id': 'doc:1:1', 'snippet': 'Dune: Part Two is a 2024 American epic science fiction film directed and co-produced by Denis Villeneuve, who co-wrote the screenplay with Jon Spaihts. The sequel to Dune (2021), it is the second of a two-part adaptation of the 1965 novel Dune by Frank Herbert. It follows Paul Atreides as he unites with the Fremen people of the desert planet Arrakis to wage war against House Harkonnen. TimothĂ©e Chalamet, Rebecca Ferguson, Josh Brolin, Stellan SkarsgĂ„rd, Dave Bautista, Zendaya, Charlotte Rampling, and Javier', 'title': 'chunk 1'}, type='document')]\n", + "start=62 end=106 text='directed and co-produced by Denis Villeneuve' sources=[Source_Document(id='doc:1:1', document={'id': 'doc:1:1', 'snippet': 'Dune: Part Two is a 2024 American epic science fiction film directed and co-produced by Denis Villeneuve, who co-wrote the screenplay with Jon Spaihts. The sequel to Dune (2021), it is the second of a two-part adaptation of the 1965 novel Dune by Frank Herbert. It follows Paul Atreides as he unites with the Fremen people of the desert planet Arrakis to wage war against House Harkonnen. TimothĂ©e Chalamet, Rebecca Ferguson, Josh Brolin, Stellan SkarsgĂ„rd, Dave Bautista, Zendaya, Charlotte Rampling, and Javier', 'title': 'chunk 1'}, type='document')]\n", + "start=117 end=158 text='co-wrote the screenplay with Jon Spaihts.' sources=[Source_Document(id='doc:1:1', document={'id': 'doc:1:1', 'snippet': 'Dune: Part Two is a 2024 American epic science fiction film directed and co-produced by Denis Villeneuve, who co-wrote the screenplay with Jon Spaihts. The sequel to Dune (2021), it is the second of a two-part adaptation of the 1965 novel Dune by Frank Herbert. It follows Paul Atreides as he unites with the Fremen people of the desert planet Arrakis to wage war against House Harkonnen. TimothĂ©e Chalamet, Rebecca Ferguson, Josh Brolin, Stellan SkarsgĂ„rd, Dave Bautista, Zendaya, Charlotte Rampling, and Javier', 'title': 'chunk 1'}, type='document')]\n", + "start=173 end=225 text='produced by Villeneuve, Mary Parent, and Cale Boyter' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", + "start=257 end=276 text='executive producers' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", + "start=280 end=294 text='Tanya Lapointe' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", + "start=297 end=310 text='Brian Herbert' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", + "start=313 end=326 text='Byron Merritt' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", + "start=329 end=340 text='Kim Herbert' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", + "start=343 end=354 text='Thomas Tull' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", + "start=357 end=378 text='Richard P. Rubinstein' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", + "start=381 end=394 text='John Harrison' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", + "start=397 end=412 text='Herbert W. Gain' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", + "start=415 end=454 text='Kevin J. Anderson (creative consultant)' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n" + ] + } + ], + "source": [ + "print(\"Citations that support the final answer:\")\n", + "for cite in response.message.citations:\n", + " print(cite)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IueXaIJggbOu", + "outputId": "c816af51-74be-42c9-e94e-9820bbf95f79" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "*Dune: Part Two* is a 2024 American epic science fiction film[1] directed and co-produced by Denis Villeneuve[1], who also co-wrote the screenplay with Jon Spaihts.[1]\n", + "\n", + "The film was produced by Villeneuve, Mary Parent, and Cale Boyter[0], with the following serving as executive producers[0]:\n", + "- Tanya Lapointe[0]\n", + "- Brian Herbert[0]\n", + "- Byron Merritt[0]\n", + "- Kim Herbert[0]\n", + "- Thomas Tull[0]\n", + "- Richard P. Rubinstein[0]\n", + "- John Harrison[0]\n", + "- Herbert W. Gain[0]\n", + "- Kevin J. Anderson (creative consultant)[0]\n", + "\n", + "Source documents:\n", + "[0] \n", + "[1] \n" + ] + } + ], + "source": [ + "def insert_inline_citations(text, citations, field='text'):\n", + " sorted_citations = sorted(citations, key=lambda c: c.start, reverse=True)\n", + " \n", + " for citation in sorted_citations:\n", + " source_ids = [source.id.split(':')[-1] for source in citation.sources]\n", + " citation_text = f\"[{','.join(source_ids)}]\"\n", + " text = text[:citation.end] + citation_text + text[citation.end:]\n", + " \n", + " return text\n", + "\n", + "def list_sources(citations, fields=['text']):\n", + " unique_sources = set()\n", + " for citation in citations:\n", + " for source in citation.sources:\n", + " source_data = tuple((field, source.document[field]) for field in fields if field in source.document)\n", + " unique_sources.add((source.id.split(':')[-1], source_data))\n", + " \n", + " footnotes = []\n", + " for source_id, source_data in sorted(unique_sources):\n", + " footnote = f\"[{source_id}] \" + \", \".join(f\"{key}: {value}\" for key, value in source_data)\n", + " footnotes.append(footnote)\n", + " \n", + " return \"\\n\".join(footnotes)\n", + "\n", + "# Use the functions\n", + "cited_text = insert_inline_citations(response.message.content[0].text, response.message.citations)\n", + "\n", + "# Print the result with inline citations\n", + "print(cited_text)\n", + "\n", + "# Print footnotes\n", + "if response.message.citations:\n", + " print(\"\\nSource documents:\")\n", + " print(list_sources(response.message.citations, fields=['text', 'another_field'])) # Specify the fields you want to include" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "Kp4c_HkYIEn_" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "*Dune: Part Two* is a 2024 American epic science fiction film[1] directed and co-produced by Denis Villeneuve[1], who also co-wrote the screenplay with Jon Spaihts.[1]\n", + "\n", + "The film was produced by Villeneuve, Mary Parent, and Cale Boyter[0], with the following serving as executive producers[0]:\n", + "- Tanya Lapointe[0]\n", + "- Brian Herbert[0]\n", + "- Byron Merritt[0]\n", + "- Kim Herbert[0]\n", + "- Thomas Tull[0]\n", + "- Richard P. Rubinstein[0]\n", + "- John Harrison[0]\n", + "- Herbert W. Gain[0]\n", + "- Kevin J. Anderson (creative consultant)[0]\n", + "\n", + "Source documents:\n", + "[0] snippet: Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there's a logical place to stop the [first] movie before the book is over\"., title: chunk 0\n", + "[1] snippet: Dune: Part Two is a 2024 American epic science fiction film directed and co-produced by Denis Villeneuve, who co-wrote the screenplay with Jon Spaihts. The sequel to Dune (2021), it is the second of a two-part adaptation of the 1965 novel Dune by Frank Herbert. It follows Paul Atreides as he unites with the Fremen people of the desert planet Arrakis to wage war against House Harkonnen. TimothĂ©e Chalamet, Rebecca Ferguson, Josh Brolin, Stellan SkarsgĂ„rd, Dave Bautista, Zendaya, Charlotte Rampling, and Javier, title: chunk 1\n" + ] + } + ], + "source": [ + "def insert_inline_citations(text, citations, field='text'):\n", + " sorted_citations = sorted(citations, key=lambda c: c.start, reverse=True)\n", + " \n", + " for citation in sorted_citations:\n", + " source_ids = [source.id.split(':')[-1] for source in citation.sources]\n", + " citation_text = f\"[{','.join(source_ids)}]\"\n", + " text = text[:citation.end] + citation_text + text[citation.end:]\n", + " \n", + " return text\n", + "\n", + "def list_sources(citations):\n", + " unique_sources = set()\n", + " for citation in citations:\n", + " for source in citation.sources:\n", + " source_data = tuple((key, value) for key, value in source.document.items() if key != 'id')\n", + " unique_sources.add((source.id.split(':')[-1], source_data))\n", + " \n", + " footnotes = []\n", + " for source_id, source_data in sorted(unique_sources):\n", + " footnote = f\"[{source_id}] \" + \", \".join(f\"{key}: {value}\" for key, value in source_data)\n", + " footnotes.append(footnote)\n", + " \n", + " return \"\\n\".join(footnotes)\n", + "\n", + "# Use the functions\n", + "cited_text = insert_inline_citations(response.message.content[0].text, response.message.citations)\n", + "\n", + "# Print the result with inline citations\n", + "print(cited_text)\n", + "\n", + "# Print footnotes\n", + "if response.message.citations:\n", + " print(\"\\nSource documents:\")\n", + " print(list_sources(response.message.citations))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "base" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/agents/Vanilla_Tool_Use_v2.ipynb b/notebooks/agents/Vanilla_Tool_Use_v2.ipynb new file mode 100644 index 0000000..6cda79f --- /dev/null +++ b/notebooks/agents/Vanilla_Tool_Use_v2.ipynb @@ -0,0 +1,639 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YN-eakfxtLGd" + }, + "source": [ + "# Tool Use\n", + "\n", + "Tool use allows customers to **connect their large language models to external tools like search engines, APIs, functions, databases**, etc.\n", + "\n", + "This allows the customer's model to unlock a richer set of behaviors by leveraging data stored in tools, taking actions through APIs, interacting with a vector database, querying a search engine, etc.\n", + "\n", + "This is particularly valuable for enterprise customers, since a lot of enterprise data lives in external sources.\n", + "\n", + "Tool Use consists of 4 steps:\n", + "- Step 1: the user configures the request to the model\n", + "- Step 2: the **model smartly decides which tool(s) to use and how**\n", + "- Step 3: the tool calls are executed to mock database\n", + "- Step 4: the **model generates a final answer with precise citations based on the tool results**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "us5dkKrLCbXW", + "outputId": "94c97f62-77fb-4492-a4e4-d9eeee4e438c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.8/52.8 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m28.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ], + "source": [ + "# we'll use Cohere to do Tool Use\n", + "# TODO: upgrade to \"cohere>5\"\n", + "# ! pip install cohere --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "j0DC3iPftLGo" + }, + "outputs": [], + "source": [ + "import cohere, json\n", + "\n", + "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0T7yc1PltLGp" + }, + "source": [ + "## Step 0: Create a mock database\n", + "First, we'll define the mock data that our tools will query. This data represents sales reports and a product catalog." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "id": "yZffY8xItLGp" + }, + "outputs": [], + "source": [ + "# Mock database containing daily sales reports\n", + "sales_database = {\n", + " '2023-09-28': {\n", + " 'total_sales_amount': 5000,\n", + " 'total_units_sold': 100,\n", + " },\n", + " '2023-09-29': {\n", + " 'total_sales_amount': 10000,\n", + " 'total_units_sold': 250,\n", + " },\n", + " '2023-09-30': {\n", + " 'total_sales_amount': 8000,\n", + " 'total_units_sold': 200,\n", + " }\n", + "}\n", + "\n", + "# Mock product catalog\n", + "product_catalog = {\n", + " 'Electronics': [\n", + " {'product_id': 'E1001', 'name': 'Smartphone', 'price': 500, 'stock_level': 20},\n", + " {'product_id': 'E1002', 'name': 'Laptop', 'price': 1000, 'stock_level': 15},\n", + " {'product_id': 'E1003', 'name': 'Tablet', 'price': 300, 'stock_level': 25},\n", + " ],\n", + " 'Clothing': [\n", + " {'product_id': 'C1001', 'name': 'T-Shirt', 'price': 20, 'stock_level': 100},\n", + " {'product_id': 'C1002', 'name': 'Jeans', 'price': 50, 'stock_level': 80},\n", + " {'product_id': 'C1003', 'name': 'Jacket', 'price': 100, 'stock_level': 40},\n", + " ]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6TGWYiOdtLGp" + }, + "source": [ + "Now, we'll define the tools that simulate querying this database. \n", + "You could for example use the API of an enterprise sales platform.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "id": "YuIH4us8tLGp" + }, + "outputs": [], + "source": [ + "def query_daily_sales_report(day: str) -> dict:\n", + " \"\"\"\n", + " Function to retrieve the sales report for the given day\n", + " \"\"\"\n", + " report = sales_database.get(day, {})\n", + " if report:\n", + " return {\n", + " 'date': day,\n", + " 'summary': f\"Total Sales Amount: {report['total_sales_amount']}, Total Units Sold: {report['total_units_sold']}\"\n", + " }\n", + " else:\n", + " return {'date': day, 'summary': 'No sales data available for this day.'}\n", + "\n", + "\n", + "def query_product_catalog(category: str) -> dict:\n", + " \"\"\"\n", + " Function to retrieve products for the given category\n", + " \"\"\"\n", + " products = product_catalog.get(category, [])\n", + " return {\n", + " 'category': category,\n", + " 'products': products\n", + " }\n", + "\n", + "\n", + "functions_map = {\n", + " \"query_daily_sales_report\": query_daily_sales_report,\n", + " \"query_product_catalog\": query_product_catalog\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HZRhTu4ftLGp" + }, + "source": [ + "## Step 1 - the user configures the request to the model\n", + "\n", + "The developer provides a few things to the model:\n", + "- A preamble containing instructions about the task and the desired style for the output.\n", + "- The user request.\n", + "- A list of tools to the model.\n", + "- (Optionally) a chat history for the model to work with.\n", + "\n", + "\n", + "You can specify one or many tools to the model. Every tool needs to be described with a JSON schema, indicating the tool name, description, and parameters (code snippets below).\n", + "\n", + "In our example, we provide two tools to the model: `daily_sales_report` and `product_catalog`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "id": "aIk-of_OtLGp" + }, + "outputs": [], + "source": [ + "# tool descriptions that the model has access to\n", + "# note: Cohere always adds a \"directly_answer\" tool under the hood, so that the model can decide to not leverage any tool, if they're not needed.\n", + "tools = [\n", + " {\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"query_daily_sales_report\",\n", + " \"description\": \"Connects to a database to retrieve overall sales volumes and sales information for a given day.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"day\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"Retrieves sales data for this day, formatted as YYYY-MM-DD.\"\n", + " }\n", + " },\n", + " \"required\": [\"day\"]\n", + " }\n", + " }\n", + " },\n", + " {\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"query_product_catalog\",\n", + " \"description\": \"Connects to a product catalog with information about all the products being sold, including categories, prices, and stock levels.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"category\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"Retrieves product information data for all products in this category.\"\n", + " }\n", + " },\n", + " \"required\": [\"category\"]\n", + " }\n", + " }\n", + " }\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "k2AHJRnztLGp" + }, + "source": [ + "Now let's define the user request. \n", + "\n", + "In our example we'll use: \"Can you provide a sales summary for 29th September 2023, and also give me the details of all products in the 'Electronics' category that were sold that day, including their prices and stock levels?\"\n", + "\n", + "Only a langage model with Tool Use can answer this request: it requires looking up information in the right external tools (step 2), and then providing a final answer based on the tool results (step 4)." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "id": "JuDgJ7fjtLGq" + }, + "outputs": [], + "source": [ + "# preamble containing instructions about the task and the desired style for the output.\n", + "preamble = \"\"\"\n", + "## Task and Context\n", + "You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.\n", + "\n", + "## Style Guide\n", + "Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.\n", + "\"\"\"\n", + "\n", + "# user request\n", + "message = \"Can you provide a sales summary for 29th September 2023, and also give me some details about the products in the 'Electronics' category, for example their prices and stock levels?\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1NhW-G_JtLGq" + }, + "source": [ + "## Step 2 – the model smartly decides which tool(s) to use and how\n", + "The model intelligently selects the right tool(s) to call -- and the right parameters for each tool call -- based on the content of the user message." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "o79_n99GtLGq", + "outputId": "81789d00-01b9-4c17-d1b0-1668d75a2b86" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The model recommends doing the following tool calls:\n", + "\n", + "Tool plan:\n", + "I will search for the sales summary for 29th September 2023 and details about the products in the 'Electronics' category. \n", + "\n", + "Tool calls:\n", + "Tool name: query_daily_sales_report | Parameters: {\"day\":\"2023-09-29\"}\n", + "Tool name: query_product_catalog | Parameters: {\"category\":\"Electronics\"}\n" + ] + } + ], + "source": [ + "messages=[{\"role\": \"system\", \"content\": preamble},\n", + " {\"role\": \"user\", \"content\": message}]\n", + "\n", + "response = co.chat(\n", + " model=\"command-r-plus\",\n", + " messages=messages,\n", + " tools=tools\n", + ")\n", + "\n", + "print(\"The model recommends doing the following tool calls:\\n\")\n", + "print(\"Tool plan:\")\n", + "print(response.message.tool_plan,\"\\n\")\n", + "print(\"Tool calls:\")\n", + "for tc in response.message.tool_calls:\n", + " print(f\"Tool name: {tc.function.name} | Parameters: {tc.function.arguments}\")\n", + " \n", + "# append the chat history\n", + "messages.append({'role': 'assistant', 'tool_calls': response.message.tool_calls, 'tool_plan': response.message.tool_plan})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "md_9QPcxtLGq" + }, + "source": [ + "## Step 3 – the tool calls are executed\n", + "\n", + "You can then execute the appropriate calls, using the tool calls and tool parameters generated by the model. \n", + "These tool calls return tool results that will be fed to the model in Step 4." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1LuDCRpFtLGr", + "outputId": "42ead35e-225a-4b9a-c954-b526f2865350" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tool results that will be fed back to the model in step 4:\n", + "{\n", + " \"date\": \"2023-09-29\",\n", + " \"summary\": \"Total Sales Amount: 10000, Total Units Sold: 250\"\n", + "}\n", + "{\n", + " \"category\": \"Electronics\",\n", + " \"products\": [\n", + " {\n", + " \"product_id\": \"E1001\",\n", + " \"name\": \"Smartphone\",\n", + " \"price\": 500,\n", + " \"stock_level\": 20\n", + " },\n", + " {\n", + " \"product_id\": \"E1002\",\n", + " \"name\": \"Laptop\",\n", + " \"price\": 1000,\n", + " \"stock_level\": 15\n", + " },\n", + " {\n", + " \"product_id\": \"E1003\",\n", + " \"name\": \"Tablet\",\n", + " \"price\": 300,\n", + " \"stock_level\": 25\n", + " }\n", + " ]\n", + "}\n" + ] + } + ], + "source": [ + "tool_content = []\n", + "# Iterate over the tool calls generated by the model\n", + "for tc in response.message.tool_calls:\n", + " # here is where you would call the tool recommended by the model, using the parameters recommended by the model\n", + " tool_result= functions_map[tc.function.name](**json.loads(tc.function.arguments))\n", + " # store the output in a list\n", + " tool_content.append(cohere.ToolContent(output=tool_result))\n", + " # append the chat history\n", + " messages.append({'role': 'tool', 'tool_call_id': tc.id, 'tool_content': tool_content}) \n", + "\n", + "print(\"Tool results that will be fed back to the model in step 4:\")\n", + "for result in tool_content:\n", + " print(json.dumps(result.output, indent=4))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8cKlLk18tLGr" + }, + "source": [ + "## Step 4 - the model generates a final answer based on the tool results\n", + "Finally, the developer calls the Cohere model, providing the tools results, in order to generate the model's final answer. \n", + "\n", + "Bonus: At Cohere, all Tool Use calls come with... **precise citations**! 🎉\n", + "The model cites which tool results were used to generate the final answer. \n", + "These citations make it easy to check where the model’s generated response claims are coming from. \n", + "They help users gain visibility into the model reasoning, and sanity check the final model generation. \n", + "These citations are optional — you can decide to ignore them.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "id": "MKnjXVfXtLGr" + }, + "outputs": [], + "source": [ + "response = co.chat(\n", + " model=\"command-r-plus\",\n", + " messages=messages,\n", + " tools=tools\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jlxKTsaztLGr", + "outputId": "b2cd8667-bca9-4928-c423-61930b4b49fa" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final answer:\n", + "On 29 September 2023, the total sales amount was $10,000, and 250 units were sold. \n", + "\n", + "Here are the details for the products in the 'Electronics' category:\n", + "- Smartphone: $500, 20 in stock\n", + "- Laptop: $1,000, 15 in stock\n", + "- Tablet: $300, 25 in stock\n" + ] + } + ], + "source": [ + "print(\"Final answer:\")\n", + "print(response.message.content[0].text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "niMkAr2PN9j4" + }, + "source": [ + "## Bonus: Citations come for free with Cohere! 🎉\n", + "\n", + "At Cohere, model generations come with... precise citations! 🎉\n", + "The model cites which groups of words, in the tool results, were used to generate the final answer. \n", + "These citations make it easy to check where the model’s generated response claims are coming from. \n", + "They help users gain visibility into the model reasoning, and sanity check the final model generation. \n", + "These citations are optional — you can decide to ignore them." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9wuoCUBwtLGr", + "outputId": "da3c0dc5-6b87-42ea-d64b-e7e85c40273e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Citations that support the final answer:\n", + "start=26 end=56 text='total sales amount was $10,000' sources=[Source_Tool(id='query_daily_sales_report_6c5v4phf8d4f:0', tool_output={'date': '2023-09-29', 'summary': 'Total Sales Amount: 10000, Total Units Sold: 250'}, type='tool'), Source_Tool(id='query_product_catalog_7395zjrh8m8w:0', tool_output={'date': '2023-09-29', 'summary': 'Total Sales Amount: 10000, Total Units Sold: 250'}, type='tool')]\n", + "Start: 26 | End: 56 | Text: 'total sales amount was $10,000'\n", + "start=62 end=81 text='250 units were sold' sources=[Source_Tool(id='query_daily_sales_report_6c5v4phf8d4f:0', tool_output={'date': '2023-09-29', 'summary': 'Total Sales Amount: 10000, Total Units Sold: 250'}, type='tool'), Source_Tool(id='query_product_catalog_7395zjrh8m8w:0', tool_output={'date': '2023-09-29', 'summary': 'Total Sales Amount: 10000, Total Units Sold: 250'}, type='tool')]\n", + "Start: 62 | End: 81 | Text: '250 units were sold'\n", + "start=156 end=185 text='Smartphone: $500, 20 in stock' sources=[Source_Tool(id='query_daily_sales_report_6c5v4phf8d4f:1', tool_output={'category': 'Electronics', 'products': '[{\"name\":\"Smartphone\",\"price\":500,\"product_id\":\"E1001\",\"stock_level\":20},{\"name\":\"Laptop\",\"price\":1000,\"product_id\":\"E1002\",\"stock_level\":15},{\"name\":\"Tablet\",\"price\":300,\"product_id\":\"E1003\",\"stock_level\":25}]'}, type='tool'), Source_Tool(id='query_product_catalog_7395zjrh8m8w:1', tool_output={'category': 'Electronics', 'products': '[{\"name\":\"Smartphone\",\"price\":500,\"product_id\":\"E1001\",\"stock_level\":20},{\"name\":\"Laptop\",\"price\":1000,\"product_id\":\"E1002\",\"stock_level\":15},{\"name\":\"Tablet\",\"price\":300,\"product_id\":\"E1003\",\"stock_level\":25}]'}, type='tool')]\n", + "Start: 156 | End: 185 | Text: 'Smartphone: $500, 20 in stock'\n", + "start=188 end=215 text='Laptop: $1,000, 15 in stock' sources=[Source_Tool(id='query_daily_sales_report_6c5v4phf8d4f:1', tool_output={'category': 'Electronics', 'products': '[{\"name\":\"Smartphone\",\"price\":500,\"product_id\":\"E1001\",\"stock_level\":20},{\"name\":\"Laptop\",\"price\":1000,\"product_id\":\"E1002\",\"stock_level\":15},{\"name\":\"Tablet\",\"price\":300,\"product_id\":\"E1003\",\"stock_level\":25}]'}, type='tool'), Source_Tool(id='query_product_catalog_7395zjrh8m8w:1', tool_output={'category': 'Electronics', 'products': '[{\"name\":\"Smartphone\",\"price\":500,\"product_id\":\"E1001\",\"stock_level\":20},{\"name\":\"Laptop\",\"price\":1000,\"product_id\":\"E1002\",\"stock_level\":15},{\"name\":\"Tablet\",\"price\":300,\"product_id\":\"E1003\",\"stock_level\":25}]'}, type='tool')]\n", + "Start: 188 | End: 215 | Text: 'Laptop: $1,000, 15 in stock'\n", + "start=218 end=243 text='Tablet: $300, 25 in stock' sources=[Source_Tool(id='query_daily_sales_report_6c5v4phf8d4f:1', tool_output={'category': 'Electronics', 'products': '[{\"name\":\"Smartphone\",\"price\":500,\"product_id\":\"E1001\",\"stock_level\":20},{\"name\":\"Laptop\",\"price\":1000,\"product_id\":\"E1002\",\"stock_level\":15},{\"name\":\"Tablet\",\"price\":300,\"product_id\":\"E1003\",\"stock_level\":25}]'}, type='tool'), Source_Tool(id='query_product_catalog_7395zjrh8m8w:1', tool_output={'category': 'Electronics', 'products': '[{\"name\":\"Smartphone\",\"price\":500,\"product_id\":\"E1001\",\"stock_level\":20},{\"name\":\"Laptop\",\"price\":1000,\"product_id\":\"E1002\",\"stock_level\":15},{\"name\":\"Tablet\",\"price\":300,\"product_id\":\"E1003\",\"stock_level\":25}]'}, type='tool')]\n", + "Start: 218 | End: 243 | Text: 'Tablet: $300, 25 in stock'\n" + ] + } + ], + "source": [ + "print(\"Citations that support the final answer:\")\n", + "for citation in response.message.citations:\n", + " print(citation)\n", + " print(f\"Start: {citation.start} | End: {citation.end} | Text: '{citation.text}'\")" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "On 29 September 2023, the total sales amount was $10,000[3,4], and 250 units were sold[3,4]. \n", + "\n", + "Here are the details for the products in the 'Electronics' category:\n", + "- Smartphone: $500, 20 in stock[1,2]\n", + "- Laptop: $1,000, 15 in stock[1,2]\n", + "- Tablet: $300, 25 in stock[1,2]\n", + "\n", + "Source tools:\n", + "[1]: query_daily_sales_report_6c5v4phf8d4f:1 | {'category': 'Electronics', 'products': '[{\"name\":\"Smartphone\",\"price\":500,\"product_id\":\"E1001\",\"stock_level\":20},{\"name\":\"Laptop\",\"price\":1000,\"product_id\":\"E1002\",\"stock_level\":15},{\"name\":\"Tablet\",\"price\":300,\"product_id\":\"E1003\",\"stock_level\":25}]'}\n", + "[2]: query_product_catalog_7395zjrh8m8w:1 | {'category': 'Electronics', 'products': '[{\"name\":\"Smartphone\",\"price\":500,\"product_id\":\"E1001\",\"stock_level\":20},{\"name\":\"Laptop\",\"price\":1000,\"product_id\":\"E1002\",\"stock_level\":15},{\"name\":\"Tablet\",\"price\":300,\"product_id\":\"E1003\",\"stock_level\":25}]'}\n", + "[3]: query_daily_sales_report_6c5v4phf8d4f:0 | {'date': '2023-09-29', 'summary': 'Total Sales Amount: 10000, Total Units Sold: 250'}\n", + "[4]: query_product_catalog_7395zjrh8m8w:0 | {'date': '2023-09-29', 'summary': 'Total Sales Amount: 10000, Total Units Sold: 250'}\n" + ] + } + ], + "source": [ + "def insert_inline_citations(text, citations):\n", + " sorted_citations = sorted(citations, key=lambda c: c.start, reverse=True)\n", + " source_index = {}\n", + " current_index = 1\n", + " \n", + " for citation in sorted_citations:\n", + " citation_ids = []\n", + " for source in citation.sources:\n", + " if source.id not in source_index:\n", + " source_index[source.id] = current_index\n", + " current_index += 1\n", + " citation_ids.append(str(source_index[source.id]))\n", + " \n", + " citation_text = f\"[{','.join(citation_ids)}]\"\n", + " text = text[:citation.end] + citation_text + text[citation.end:]\n", + " \n", + " return text, source_index\n", + "\n", + "def list_sources(citations, source_index):\n", + " unique_sources = {}\n", + " for citation in citations:\n", + " for source in citation.sources:\n", + " if hasattr(source, 'tool_output'):\n", + " source_text = str(source.tool_output)\n", + " else:\n", + " source_text = str(source.document)\n", + " unique_sources[source.id] = source_text\n", + " \n", + " footnotes = []\n", + " for source_id, source_text in sorted(unique_sources.items(), key=lambda x: source_index[x[0]]):\n", + " index = source_index[source_id]\n", + " footnotes.append(f\"[{index}]: {source_id} | {source_text}\")\n", + " \n", + " return \"Source tools:\\n\" + \"\\n\".join(footnotes)\n", + "\n", + "# Usage example:\n", + "cited_text, source_index = insert_inline_citations(response.message.content[0].text, response.message.citations)\n", + "\n", + "print(cited_text)\n", + "\n", + "print(\"\\n\" + list_sources(response.message.citations, source_index))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RwH2O3FptLGr" + }, + "source": [ + "Yiha. You've used Cohere for Tool Use. Tool use opens up a wide range of new use cases. Here are a few examples:\n", + "\n", + "- **Function calling**: It's now possible to ask the model to output a JSON object with specific function parameters.\n", + "For instance, this allows your chatbot to interact with your CRM to change the status of a deal, or to engage with a Python interpreter to conduct data science analyses.\n", + "\n", + "- **Query transformation**: You can transform a user message into a search query for a vector database or any search engine.\n", + "For instance, this enables your work assistant to automatically retrieve the appropriate data from your company's documentation by creating the right query for your vector database.\n", + "\n", + "- **Advanced searches**: You can transform a user message into one-or-many queries, to do multiple subtasks based on the content of the message.\n", + "For instance, this allows your chatbot to search across different databases and platforms to retrieve relevant information or to conduct comparative analysis.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FXOSzfqRCLBH" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "base" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/guides/getting-started/v2/tutorial_pt1_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt1_v2.ipynb new file mode 100644 index 0000000..cfaa4bf --- /dev/null +++ b/notebooks/guides/getting-started/v2/tutorial_pt1_v2.ipynb @@ -0,0 +1,225 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cohere Tutorial\n", + "\n", + "#### Build your first Cohere application: An onboarding assistant for new hires" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Welcome to the Cohere tutorial – a hands-on introduction to Cohere!\n", + "\n", + "In this tutorial, you will learn how to use the Cohere API, specifically three endpoints: Chat, Embed, and Rerank.\n", + "\n", + "This tutorial is split over seven parts, with each part focusing on one use case, as follows:\n", + "\n", + "- Part 1: Installation and Setup (Pre-requisite)\n", + "- Part 2: Text Generation\n", + "- Part 3: Chatbots\n", + "- Part 4: Semantic Search\n", + "- Part 5: Reranking\n", + "- Part 6: Retrieval-Augmented Generation (RAG)\n", + "- Part 7: Agents with Tool Use\n", + "\n", + "You'll learn about these use cases by building an onboarding assistant that helps new hires onboard to a fictitious company called Co1t. The assistant can help write introductions, answer user questions about the company, search for information from e-mails, and create meeting appointments.\n", + "\n", + "We recommend that you follow the parts sequentially. However, feel free to skip to specific parts if you want (apart from Part 1, which is a pre-requisite) because each part also works as a standalone tutorial.\n", + "\n", + "Total Duration: ~15 minutes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installation and Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Cohere platform lets developers access large language model (LLM) capabilities with a few lines of code. These LLMs can solve a broad spectrum of natural language use cases, including classification, semantic search, paraphrasing, summarization, and content generation.\n", + "\n", + "Cohere's models can be accessed through the playground, SDK, and CLI tool. We support SDKs in four different languages: Python, Typescript, Java, and Go.\n", + "\n", + "This tutorial uses the Python SDK and accesses the models through the Cohere platform.\n", + "\n", + "To get started, first install the Cohere Python SDK." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! pip install -U cohere" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we'll import the `cohere` library and create a client to be used throughout the examples. We create a client by passing the Cohere API key as an argument. To get an API key, [sign up with Cohere](https://dashboard.cohere.com/welcome/register) and get the API key [from the dashboard](https://dashboard.cohere.com/api-keys)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import cohere\n", + "\n", + "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Accessing Cohere from Other Platforms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Cohere platform is the fastest way to access Cohere's models and get started.\n", + "\n", + "However, if you prefer other options, you can access Cohere's models through other platforms such as Amazon Bedrock, Amazon SageMaker, Azure AI Studio, and Oracle Cloud Infrastructure (OCI) Generative AI Service.\n", + "\n", + "Read this documentation on [Cohere SDK cloud platform compatibility](https://docs.cohere.com/docs/cohere-works-everywhere)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Amazon Bedrock" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following is how you can create a Cohere client on Amazon Bedrock.\n", + "\n", + "For further information, read this documentation on [Cohere on Bedrock](https://docs.cohere.com/docs/cohere-on-aws#amazon-bedrock)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cohere\n", + "\n", + "co = cohere.BedrockClient(\n", + " aws_region=\"...\",\n", + " aws_access_key=\"...\",\n", + " aws_secret_key=\"...\",\n", + " aws_session_token=\"...\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Amazon SageMaker" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following is how you can create a Cohere client on Amazon SageMaker.\n", + "\n", + "For further information, read this documentation on [Cohere on SageMaker](https://docs.cohere.com/docs/cohere-on-aws#amazon-sagemaker)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cohere\n", + "\n", + "co = cohere.SagemakerClient(\n", + " aws_region=\"us-east-1\",\n", + " aws_access_key=\"...\",\n", + " aws_secret_key=\"...\",\n", + " aws_session_token=\"...\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Microsoft Azure" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following is how you can create a Cohere client on Microsoft Azure.\n", + "\n", + "For further information, read this documentation on [Cohere on Azure](https://docs.cohere.com/docs/cohere-on-microsoft-azure)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cohere\n", + "\n", + "co = cohere.Client(\n", + " api_key=\"...\",\n", + " base_url=\"...\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Part 2, we'll get started with the first use case - text generation." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "base" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/guides/getting-started/v2/tutorial_pt2_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt2_v2.ipynb new file mode 100644 index 0000000..f187af9 --- /dev/null +++ b/notebooks/guides/getting-started/v2/tutorial_pt2_v2.ipynb @@ -0,0 +1,529 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Text Generation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Command is Cohere’s flagship LLM. It generates a response based on a user message or prompt. It is trained to follow user commands and to be instantly useful in practical business applications, like summarization, copywriting, extraction, and question-answering.\n", + "\n", + "Command R and Command R+ are the most recent models in the Command family. They are the market-leading models that balance high efficiency with strong accuracy to enable enterprises to move from proof of concept into production-grade AI.\n", + "\n", + "You'll use Chat, the Cohere endpoint for accessing the Command models.\n", + "\n", + "In this tutorial, you'll learn about:\n", + "- Basic text generation\n", + "- Prompt engineering\n", + "- Parameters for controlling output\n", + "- Structured output generation\n", + "- Streamed output\n", + "\n", + "You'll learn these by building an onboarding assistant for new hires." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "To get started, first we need to install the `cohere` library and create a Cohere client." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# pip install cohere\n", + "\n", + "import cohere\n", + "import json\n", + "\n", + "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic text generation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get started with Chat, we need to pass two parameters, `model` for the LLM model ID and `messages`, which we add a single user message. We then call the Chat endpoint through the client we created earlier.\n", + "\n", + "The response contains several objects. For simplicity, what we want right now is the `message.content[0].text` object.\n", + "\n", + "Here's an example of the assistant responding to a new hire's query asking for help to make introductions." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sure! Here is a short introduction message: \n", + "\n", + "\"Hi everyone! My name is [Your Name], and I am thrilled to be joining the Co1t team today. I am excited to get to know you all and contribute to the amazing work being done at this startup. I look forward to collaborating with you and being a part of Co1t's journey. Feel free to reach out, and I can't wait to meet everyone!\" \n", + "\n", + "This message expresses your enthusiasm for joining the team and serves as an introduction to your teammates. It also invites them to reach out and connect, fostering an open and friendly work environment. \n", + "\n", + "Is there anything else I can help with as you begin your journey at Co1t?\n" + ] + } + ], + "source": [ + "# Add the user message\n", + "message = \"I'm joining a new startup called Co1t today. Could you help me write a short introduction message to my teammates.\"\n", + "\n", + "# Generate the response\n", + "response = co.chat(model=\"command-r-plus\",\n", + " messages=[{\"role\": \"user\", \"content\": message}])\n", + " # messages=[cohere.UserMessage(content=message)])\n", + "\n", + "print(response.message.content[0].text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further reading:\n", + "- [Chat endpoint API reference](https://docs.cohere.com/v2/reference/chat)\n", + "- [Documentation on Chat fine-tuning](https://docs.cohere.com/docs/chat-fine-tuning)\n", + "- [Documentation on Command R+](https://docs.cohere.com/docs/command-r-plus)\n", + "- [LLM University module on text generation](https://cohere.com/llmu#text-generation)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prompt engineering" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Prompting is at the heart of working with LLMs. The prompt provides context for the text that we want the model to generate. The prompts we create can be anything from simple instructions to more complex pieces of text, and they are used to encourage the model to produce a specific type of output.\n", + "\n", + "In this section, we'll look at a couple of prompting techniques.\n", + "\n", + "The first is to add more specific instructions to the prompt. The more instructions you provide in the prompt, the closer you can get to the response you need.\n", + "\n", + "The limit of how long a prompt can be is dependent on the maximum context length that a model can support (in the case Command R/R+, it's 128k tokens).\n", + "\n", + "Below, we'll add one additional instruction to the earlier prompt: the length we need the response to be." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\"I'm thrilled to join the Co1t team and look forward to contributing my skills and ideas to drive innovation and success.\"\n" + ] + } + ], + "source": [ + "# Add the user message\n", + "message = \"I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.\"\n", + "\n", + "# Generate the response\n", + "response = co.chat(model=\"command-r-plus\",\n", + " messages=[{\"role\": \"user\", \"content\": message}])\n", + " # messages=[cohere.UserMessage(content=message)])\n", + "\n", + "print(response.message.content[0].text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All our prompts so far use what is called zero-shot prompting, which means that provide instruction without any example. But in many cases, it is extremely helpful to provide examples to the model to guide its response. This is called few-shot prompting.\n", + "\n", + "Few-shot prompting is especially useful when we want the model response to follow a particular style or format. Also, it is sometimes hard to explain what you want in an instruction, and easier to show examples.\n", + "\n", + "Below, we want the response to be similar in style and length to the convention, as we show in the examples." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ticket title: \"Server Access Permissions Issue\"\n" + ] + } + ], + "source": [ + "# Add the user message\n", + "user_input = \"Why can't I access the server? Is it a permissions issue?\"\n", + "\n", + "# Create a prompt containing example outputs\n", + "message=f\"\"\"Write a ticket title for the following user request:\n", + "\n", + "User request: Where are the usual storage places for project files?\n", + "Ticket title: Project File Storage Location\n", + "\n", + "User request: Emails won't send. What could be the issue?\n", + "Ticket title: Email Sending Issues\n", + "\n", + "User request: How can I set up a connection to the office printer?\n", + "Ticket title: Printer Connection Setup\n", + "\n", + "User request: {user_input}\n", + "Ticket title:\"\"\"\n", + "\n", + "# Generate the response\n", + "response = co.chat(model=\"command-r-plus\",\n", + " messages=[{\"role\": \"user\", \"content\": message}])\n", + "\n", + "print(response.message.content[0].text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further reading:\n", + "- [Documentation on prompt engineering](https://docs.cohere.com/docs/crafting-effective-prompts)\n", + "- [LLM University module on prompt engineering](https://cohere.com/llmu#prompt-engineering)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parameters for controlling output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Chat endpoint provides developers with an array of options and parameters.\n", + "\n", + "For example, you can choose from several variations of the Command model. Different models produce different output profiles, such as quality and latency." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\"Hi everyone, my name is [Your Name] and I am thrilled to join the Co1t team today as their newest member, bringing my skills and passion to the table, and I look forward to contributing to the team's success and being part of an innovative and dynamic startup environment.\"\n" + ] + } + ], + "source": [ + "# Add the user message\n", + "message = \"I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.\"\n", + "\n", + "# Generate the response\n", + "response = co.chat(model=\"command-r-plus\",\n", + " messages=[{\"role\": \"user\", \"content\": message}])\n", + "\n", + "print(response.message.content[0].text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Often, you’ll need to control the level of randomness of the output. You can control this using a few parameters.\n", + "\n", + "The most commonly used parameter is `temperature`, which is a number used to tune the degree of randomness. You can enter values between 0.0 to 1.0.\n", + "\n", + "A lower temperature gives more predictable outputs, and a higher temperature gives more \"creative\" outputs.\n", + "\n", + "Here's an example of setting `temperature` to 0." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1: \"Revolution Enthusiast\"\n", + "\n", + "2: \"Revolution Enthusiast\"\n", + "\n", + "3: \"Revolution Enthusiast\"\n", + "\n" + ] + } + ], + "source": [ + "# Add the user message\n", + "message = \"I like learning about the industrial revolution and how it shapes the modern world. How I can introduce myself in five words or less.\"\n", + "\n", + "# Generate the response multiple times by specifying a low temperature value\n", + "for idx in range(3):\n", + " response = co.chat(model=\"command-r-plus\",\n", + " messages=[{\"role\": \"user\", \"content\": message}],\n", + " temperature=0)\n", + "\n", + " print(f\"{idx+1}: {response.message.content[0].text}\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's an example of setting `temperature` to 1." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1: \"Revolution enthusiast\"\n", + "\n", + "2: Here is a suggestion for your request: \n", + "\n", + "\"IR Nerd, shaping modernity.\"\n", + "\n", + "3: \"Revolution Enthusiast, Shaping Modernity.\"\n", + "\n" + ] + } + ], + "source": [ + "# Add the user message\n", + "message = \"I like learning about the industrial revolution and how it shapes the modern world. How I can introduce myself in five words or less.\"\n", + "\n", + "# Generate the response multiple times by specifying a low temperature value\n", + "for idx in range(3):\n", + " response = co.chat(model=\"command-r-plus\",\n", + " messages=[{\"role\": \"user\", \"content\": message}],\n", + " temperature=1)\n", + "\n", + " print(f\"{idx+1}: {response.message.content[0].text}\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further reading:\n", + "- [Available models for the Chat endpoint](https://docs.cohere.com/docs/models#command)\n", + "- [Documentation on predictable outputs](https://docs.cohere.com/v2/docs/predictable-outputs)\n", + "- [Documentation on advanced generation parameters](https://docs.cohere.com/docs/advanced-generation-hyperparameters)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Structured output generation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By adding the `response_format` parameter, you can get the model to generate the output as a JSON object. By generating JSON objects, you can structure and organize the model's responses in a way that can be used in downstream applications.\n", + "\n", + "The `response_format` parameter allows you to specify the schema the JSON object must follow. It takes the following parameters:\n", + "- `message`: The user message\n", + "- `response_format`: The schema of the JSON object" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The `response_format.schema` parameter is an experimental feature and may change in future releases.\n", + "To suppress this warning, set `log_warning_experimental_features=False` when initializing the client.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'title': 'Unable to Access Server', 'category': 'access', 'status': 'open'}\n" + ] + } + ], + "source": [ + "# Add the user message\n", + "user_input = \"Why can't I access the server? Is it a permissions issue?\"\n", + "message = f\"\"\"Create an IT ticket for the following user request. Generate a JSON object.\n", + "{user_input}\"\"\"\n", + "\n", + "# Generate the response multiple times by adding the JSON schema\n", + "response = co.chat(\n", + " model=\"command-r-plus\",\n", + " messages=[{\"role\": \"user\", \"content\": message}],\n", + " response_format={\n", + " \"type\": \"json_object\",\n", + " \"schema\": {\n", + " \"type\": \"object\",\n", + " \"required\": [\"title\", \"category\", \"status\"],\n", + " \"properties\": {\n", + " \"title\": { \"type\": \"string\"},\n", + " \"category\": { \"type\" : \"string\", \"enum\" : [\"access\", \"software\"]},\n", + " \"status\": { \"type\" : \"string\" , \"enum\" : [\"open\", \"closed\"]}\n", + " }\n", + " }\n", + " },\n", + ")\n", + "\n", + "json_object = json.loads(response.message.content[0].text)\n", + "\n", + "print(json_object)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further reading:\n", + "- [Documentation on Structured Generations (JSON)](https://docs.cohere.com/docs/structured-outputs-json)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Streaming responses" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All the previous examples above generate responses in a non-streamed manner. This means that the endpoint would return a response object only after the model has generated the text in full.\n", + "\n", + "The Chat endpoint also provides streaming support. In a streamed response, the endpoint would return a response object for each token as it is being generated. This means you can display the text incrementally without having to wait for the full completion.\n", + "\n", + "To activate it, use `co.chat_stream()` instead of `co.chat()`.\n", + "\n", + "In streaming mode, the endpoint will generate a series of objects. To get the actual text contents, we take objects whose `event_type` is `content-delta`." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\"Hi everyone, my name is [Your Name] and I am thrilled to join the Co1t team today as their newest member, bringing my skills and passion to the table, and I look forward to contributing to the team's success and being part of an innovative and dynamic startup environment.\"" + ] + } + ], + "source": [ + "# Add the user message\n", + "message = \"I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.\"\n", + "\n", + "# Generate the response by streaming it\n", + "response = co.chat_stream(model=\"command-r-plus\",\n", + " messages=[{\"role\": \"user\", \"content\": message}])\n", + "\n", + "for event in response:\n", + " if event:\n", + " if event.type == \"content-delta\":\n", + " print(event.delta.message.content.text, end=\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further reading:\n", + "- [Documentation on streaming responses](https://docs.cohere.com/docs/streaming)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, you learned about:\n", + "- How to get started with a basic text generation\n", + "- How to improve outputs with prompt engineering\n", + "- How to control outputs using parameter changes\n", + "- How to generate structured outputs\n", + "- How to stream text generation outputs\n", + "\n", + "However, we have only done all this using direct text generations. As its name implies, the Chat endpoint can also support building chatbots, which require features to support multi-turn conversations and maintain the conversation state. \n", + "\n", + "In Part 3, you'll learn how to build chatbots with the Chat endpoint." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "base" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/guides/getting-started/v2/tutorial_pt3_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt3_v2.ipynb new file mode 100644 index 0000000..e77e680 --- /dev/null +++ b/notebooks/guides/getting-started/v2/tutorial_pt3_v2.ipynb @@ -0,0 +1,381 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chatbots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As its name implies, the Chat endpoint enables developers to build chatbots that can handle conversations. At the core of a conversation is a multi-turn dialog between the user and the chatbot. This requires the chatbot to have the state (or “memory”) of all the previous turns to maintain the state of the conversation.\n", + "\n", + "In this tutorial, you'll learn about:\n", + "- Creating a custom preamble\n", + "- Creating a single-turn conversation\n", + "- Building the conversation memory\n", + "- Running a multi-turn conversation\n", + "- Viewing the chat history\n", + "\n", + "You'll learn these by building an onboarding assistant for new hires." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "To get started, first we need to install the `cohere` library and create a Cohere client." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# pip install cohere\n", + "\n", + "import cohere\n", + "\n", + "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating a custom preamble" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A conversation starts with a system message, or a preamble, to help steer a chatbot’s response toward certain characteristics.\n", + "\n", + "For example, if we want the chatbot to adopt a formal style, the preamble can be used to encourage the generation of more business-like and professional responses.\n", + "\n", + "The recommended approach is to use two H2 Markdown headers: \"Task and Context\" and \"Style Guide\" in the exact order.\n", + "\n", + "In the example below, the preamble provides context for the assistant's task (task and context) and encourages the generation of rhymes as much as possible (style guide)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sure, here's a rhyme to break the ice,\n", + "A warm welcome, nice to greet,\n", + "\n", + "Hi, I'm [Your Name], bright and bold,\n", + "Stepping into this startup, a story to unfold,\n", + "At Co1t, a journey begins, a path unknown,\n", + "But together, we'll create a future that's ours to own. \n", + "\n", + "I bring [Your Skill Set] and a will to learn and grow,\n", + "Ready to take on challenges, and face the flow,\n", + "Excited to meet you all, a team united,\n", + "Let's innovate and collaborate, and make our mark, ignited! \n", + "\n", + "Cheers to the power of shared dreams,\n", + "Here's to a great start, so it seems! \n", + "\n", + "Looking forward to connecting with you all,\n", + "Let's turn ideas into reality's call! \n", + "\n", + "Regards, \n", + "[Your Name] \n", + "\n", + "A new colleague, eager and keen,\n", + "Ready to rock at Co1t's machine!\n" + ] + } + ], + "source": [ + "# Add the user message\n", + "message = \"I'm joining a new startup called Co1t today. Could you help me write a short introduction message to my teammates.\"\n", + "\n", + "# Create a custom system message\n", + "system_message=\"\"\"## Task and Context\n", + "You are an assistant who assist new employees of Co1t with their first week.\n", + "\n", + "## Style Guide\n", + "Try to speak in rhymes as much as possible. Be professional.\"\"\"\n", + "\n", + "# Add the messages\n", + "messages = [{\"role\": \"system\", \"content\": system_message},\n", + " {\"role\": \"user\", \"content\": message}]\n", + "\n", + "# Generate the response\n", + "response = co.chat(model=\"command-r-plus\",\n", + " messages=messages)\n", + "\n", + "print(response.message.content[0].text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further reading:\n", + "- [Documentation on preambles](https://docs.cohere.com/docs/preambles)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Starting the first conversation turn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start with the first conversation turn.\n", + "\n", + "Here, we are also adding a custom preamble or system message for generating a concise response, just to keep the outputs brief for this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\"Hello, teammates! Thrilled to join the Co1t family today and looking forward to collaborating with this dynamic team to drive innovation and create impact.\"\n" + ] + } + ], + "source": [ + "# Add the user message\n", + "message = \"I'm joining a new startup called Co1t today. Could you help me write a short introduction message to my teammates.\"\n", + "\n", + "# Create a custom system message\n", + "system_message=\"\"\"## Task and Context\n", + "Generate concise responses, with maximum one-sentence.\"\"\"\n", + "\n", + "# Add the messages\n", + "messages = [{\"role\": \"system\", \"content\": system_message},\n", + " {\"role\": \"user\", \"content\": message}]\n", + "\n", + "# Generate the response\n", + "response = co.chat(model=\"command-r-plus\",\n", + " messages=messages)\n", + "\n", + "print(response.message.content[0].text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building the conversation memory" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we want the model to refine the earlier response. This requires the next generation to have access to the state, or memory, of the conversation.\n", + "\n", + "To do this, we append the `messages` with the model's previous response using the `assistant` role.\n", + "\n", + "Next, we also append a new user message (for the second turn) to the `messages` list.\n", + "\n", + "Looking at the response, we see that the model is able to get the context from the chat history. The model is able to capture that \"it\" in the user message refers to the introduction message it had generated earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\"Hey, future Co1t teammates! Stoked to join this awesome crew and can't wait to dive into some game-changing ideas and collaborations—let's make some magic happen!\"\n" + ] + } + ], + "source": [ + "# Append the previous response\n", + "messages.append({'role' : 'assistant', 'content': response.message.content[0].text})\n", + "\n", + "# Add the user message\n", + "message = \"Make it more upbeat and conversational.\"\n", + "\n", + "# Append the user message\n", + "messages.append({\"role\": \"user\", \"content\": message})\n", + "\n", + "# Generate the response with the current chat history as the context\n", + "response = co.chat(model=\"command-r-plus\",\n", + " messages=messages)\n", + "\n", + "print(response.message.content[0].text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further reading:\n", + "- [Documentation on using the Chat endpoint](https://docs.cohere.com/docs/chat-api)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running a multi-turn conversation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "You can continue doing this for any number of turns by continuing to append the chatbot's response and the new user message to the `messages` list." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\"Super excited to be part of the Co1t family and can't wait to contribute my skills and ideas to the team's success; looking forward to an amazing journey ahead under your leadership and guidance!\"\n" + ] + } + ], + "source": [ + "# Append the previous response\n", + "messages.append({\"role\": \"assistant\", \"content\": response.message.content[0].text})\n", + "\n", + "# Add the user message\n", + "message = \"Thanks. Could you create another one for my DM to my manager.\"\n", + "\n", + "# Append the user message\n", + "messages.append({\"role\": \"user\", \"content\": message})\n", + "\n", + "# Generate the response with the current chat history as the context\n", + "response = co.chat(model=\"command-r-plus\",\n", + " messages=messages)\n", + "\n", + "print(response.message.content[0].text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Viewing the chat history" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To look at the current chat history, you can print the `messages` list, which contains a list of `user` and `assistant` turns in the same sequence as they were created." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'role': 'system', 'content': '## Task and Context\\nGenerate concise responses, with maximum one-sentence.'} \n", + "\n", + "{'role': 'user', 'content': \"I'm joining a new startup called Co1t today. Could you help me write a short introduction message to my teammates.\"} \n", + "\n", + "{'role': 'assistant', 'content': '\"Hello, teammates! Thrilled to join the Co1t family today and looking forward to collaborating with this dynamic team to drive innovation and create impact.\"'} \n", + "\n", + "{'role': 'user', 'content': 'Make it more upbeat and conversational.'} \n", + "\n", + "{'role': 'assistant', 'content': '\"Hey, future Co1t teammates! Stoked to join this awesome crew and can\\'t wait to dive into some game-changing ideas and collaborations—let\\'s make some magic happen!\"'} \n", + "\n", + "{'role': 'user', 'content': 'Thanks. Could you create another one for my DM to my manager.'} \n", + "\n", + "{'role': 'assistant', 'content': '\"Super excited to be part of the Co1t family and can\\'t wait to contribute my skills and ideas to the team\\'s success; looking forward to an amazing journey ahead under your leadership and guidance!\"'} \n", + "\n" + ] + } + ], + "source": [ + "# Append the previous response\n", + "messages.append({\"role\": \"assistant\", \"content\": response.message.content[0].text})\n", + "\n", + "# View the chat history\n", + "for message in messages:\n", + " print(message,\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, you learned about:\n", + "- How to create a custom preamble\n", + "- How to create a single-turn conversation\n", + "- How to build the conversation memory\n", + "- How to run a multi-turn conversation\n", + "- How to view the chat history\n", + "\n", + "You will use the same method for running a multi-turn conversation when you learn about other use cases such as RAG (Part 6) and tool use (Part 7).\n", + "\n", + "But to fully leverage these other capabilities, you will need another type of language model that generates text representations, or embeddings.\n", + "\n", + "In Part 4, you will learn how text embeddings can power an important use case for RAG, which is semantic search." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "base" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/guides/getting-started/v2/tutorial_pt4_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt4_v2.ipynb new file mode 100644 index 0000000..ecb6ee7 --- /dev/null +++ b/notebooks/guides/getting-started/v2/tutorial_pt4_v2.ipynb @@ -0,0 +1,442 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Semantic Search" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Text embeddings are a list of numbers that represent the context or meaning inside a piece of text. This is particularly useful in search or information retrieval applications. With text embeddings, this is called semantic search.\n", + "\n", + "Semantic search solves the problem faced by the more traditional approach of lexical search, which is great at finding keyword matches, but struggles to capture the context or meaning of a piece of text.\n", + "\n", + "With Cohere, you can generate text embeddings through the Embed endpoint (Embed v3 being the latest model), which supports over 100 languages.\n", + "\n", + "In this tutorial, you'll learn about:\n", + "- Embedding the documents\n", + "- Embedding the query\n", + "- Performing semantic search\n", + "- Multilingual semantic search\n", + "- Changing embedding compression types\n", + "\n", + "You'll learn these by building an onboarding assistant for new hires." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "To get started, first we need to install the `cohere` library and create a Cohere client." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# pip install cohere\n", + "\n", + "import cohere\n", + "import numpy as np\n", + "\n", + "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Embedding the documents" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Embed endpoint takes in texts as input and returns embeddings as output.\n", + "\n", + "For semantic search, there are two types of documents we need to turn into embeddings.\n", + "- The list of documents that we want to search from.\n", + "- The query that will be used to search the documents.\n", + "\n", + "Right now, we are doing the former. We call the Embed endpoint using `co.embed()` and pass the following arguments:\n", + "- `model`: Here we choose `embed-english-v3.0`, which generates embeddings of size 1024\n", + "- `input_type`: We choose `search_document` to ensure the model treats these as the documents for search\n", + "- `texts`: The list of texts (the FAQs)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the documents\n", + "faqs_long = [\n", + " {\"text\": \"Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.\"},\n", + " {\"text\": \"Finding Coffee Spots: For your caffeine fix, head to the break room's coffee machine or cross the street to the cafĂ© for artisan coffee.\"},\n", + " {\"text\": \"Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!\"},\n", + " {\"text\": \"Working Hours Flexibility: We prioritize work-life balance. While our core hours are 9 AM to 5 PM, we offer flexibility to adjust as needed.\"},\n", + " {\"text\": \"Side Projects Policy: We encourage you to pursue your passions. Just be mindful of any potential conflicts of interest with our business.\"},\n", + " {\"text\": \"Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward.\"},\n", + " {\"text\": \"Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.\"},\n", + " {\"text\": \"Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.\"},\n", + " {\"text\": \"Performance Reviews Frequency: We conduct informal check-ins every quarter and formal performance reviews twice a year.\"},\n", + " {\"text\": \"Proposing New Ideas: Innovation is welcomed! Share your brilliant ideas at our weekly team meetings or directly with your team lead.\"},\n", + "]\n", + "\n", + "# Embed the documents\n", + "doc_emb = co.embed(\n", + " model=\"embed-english-v3.0\",\n", + " input_type=\"search_document\",\n", + " texts=[doc['text'] for doc in faqs_long]).embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further reading:\n", + "- [Embed endpoint API reference](https://docs.cohere.com/reference/embed)\n", + "- [Documentation on the Embed endpoint](https://docs.cohere.com/docs/embeddings)\n", + "- [Documentation on the models available on the Embed endpoint](https://docs.cohere.com/docs/cohere-embed)\n", + "- [LLM University module on Text Representation](https://cohere.com/llmu#text-representation)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Embedding the query" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we add a query, which asks about how to stay connected to company updates.\n", + "\n", + "We choose `search_query` as the `input_type` to ensure the model treats this as the query (instead of documents) for search." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Add the user query\n", + "query = \"How do I stay connected to what's happening at the company?\"\n", + "\n", + "# Embed the query\n", + "query_emb = co.embed(\n", + " model=\"embed-english-v3.0\",\n", + " input_type=\"search_query\",\n", + " texts=[query]).embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perfoming semantic search" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we want to search for the most relevant documents to the query. We do this by computing the similarity between the embeddings of the query and each of the documents.\n", + "\n", + "There are various approaches to compute similarity between embeddings, and we'll choose the dot product approach. For this, we use the `numpy` library which comes with the implementation.\n", + "\n", + "Each query-document pair returns a score, which represents how similar the pair is. We then sort these scores in descending order and select the top-most similar pairs, which we choose 2 (this is an arbitrary choice, you can choose any number).\n", + "\n", + "Here, we show the most relevant documents with their similarity scores." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank: 1\n", + "Score: 0.44259421762799145\n", + "Document: {'text': \"Finding Coffee Spots: For your caffeine fix, head to the break room's coffee machine or cross the street to the cafĂ© for artisan coffee.\"}\n", + "\n", + "Rank: 2\n", + "Score: 0.3277577200136209\n", + "Document: {'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}\n", + "\n" + ] + } + ], + "source": [ + "# Compute dot product similarity and display results\n", + "def return_results(query_emb, doc_emb, documents):\n", + " n = 2\n", + " scores = np.dot(query_emb, np.transpose(doc_emb))[0]\n", + " max_idx = np.argsort(-scores)[:n]\n", + " \n", + " for rank, idx in enumerate(max_idx):\n", + " print(f\"Rank: {rank+1}\")\n", + " print(f\"Score: {scores[idx]}\")\n", + " print(f\"Document: {documents[idx]}\\n\")\n", + " \n", + "return_results(query_emb, doc_emb, faqs_long)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multilingual semantic search" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Embed endpoint also supports multilingual semantic search via the `embed-multilingual-...` models. This means you can perform semantic search on texts in different languages.\n", + "\n", + "Specifically, you can do both multilingual and cross-lingual searches using one single model.\n", + "\n", + "Multilingual search happens when the query and the result are of the same language. For example, an English query of “places to eat” returning an English result of “Bob's Burgers.” You can replace English with other languages and use the same model for performing search.\n", + "\n", + "Cross-lingual search happens when the query and the result are of a different language. For example, a Hindi query of â€œà€–à€Ÿà€šà„‡ à€•à„€ à€œà€—à€č” (places to eat) returning an English result of “Bob's Burgers.”\n", + "\n", + "In the example below, we repeat the steps of performing semantic search with one difference – changing the model type to the multilingual version. Here, we use the `embed-multilingual-v3.0` model. Here, we are searching a French version of the FAQ list using an English query." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank: 1\n", + "Score: 0.442758615743984\n", + "Document: {'text': \"Travailler de l'Ă©tranger : Il est possible de travailler Ă  distance depuis un autre pays. Il suffit de coordonner avec votre responsable et de vous assurer d'ĂȘtre disponible pendant les heures de travail.\"}\n", + "\n", + "Rank: 2\n", + "Score: 0.32783563708365726\n", + "Document: {'text': 'Avantages pour la santĂ© et le bien-ĂȘtre : Nous nous soucions de votre bien-ĂȘtre et proposons des adhĂ©sions Ă  des salles de sport, des cours de yoga sur site et une assurance santĂ© complĂšte.'}\n", + "\n" + ] + } + ], + "source": [ + "# Define the documents\n", + "faqs_short_fr = [\n", + " {\"text\" : \"Remboursement des frais de voyage : GĂ©rez facilement vos frais de voyage en les soumettant via notre outil financier. Les approbations sont rapides et simples.\"},\n", + " {\"text\" : \"Travailler de l'Ă©tranger : Il est possible de travailler Ă  distance depuis un autre pays. Il suffit de coordonner avec votre responsable et de vous assurer d'ĂȘtre disponible pendant les heures de travail.\"},\n", + " {\"text\" : \"Avantages pour la santĂ© et le bien-ĂȘtre : Nous nous soucions de votre bien-ĂȘtre et proposons des adhĂ©sions Ă  des salles de sport, des cours de yoga sur site et une assurance santĂ© complĂšte.\"},\n", + " {\"text\" : \"FrĂ©quence des Ă©valuations de performance : Nous organisons des bilans informels tous les trimestres et des Ă©valuations formelles deux fois par an.\"}\n", + "]\n", + "\n", + "# Embed the documents\n", + "doc_emb = co.embed(\n", + " model=\"embed-multilingual-v3.0\",\n", + " input_type=\"search_document\",\n", + " texts=[doc['text'] for doc in faqs_short_fr]).embeddings\n", + "\n", + "# Add the user query\n", + "query = \"What's your remote-working policy?\"\n", + "\n", + "# Embed the query\n", + "query_emb = co.embed(\n", + " model=\"embed-multilingual-v3.0\",\n", + " input_type=\"search_query\",\n", + " texts=[query]).embeddings\n", + "\n", + "# Compute dot product similarity and display results\n", + "return_results(query_emb, doc_emb, faqs_short_fr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further reading:\n", + "- [The list of supported languages for multilingual Embed](https://docs.cohere.com/docs/cohere-embed#list-of-supported-languages)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Changing embedding compression types" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Semantic search over large datasets can require a lot of memory, which is expensive to host in a vector database. Changing the embeddings compression type can help reduce the memory footprint.\n", + "\n", + "A typical embedding model generates embeddings as float32 format (consuming 4 bytes). By compressing the embeddings to int8 format (1 byte), we can reduce the memory 4x while keeping 99.99% of the original search quality.\n", + "\n", + "We can go even further and use the binary format (1 bit), which reduces the needed memory 32x while keeping 90-98% of the original search quality.\n", + "\n", + "The Embed endpoint supports the following formats: `float`, `int8`, `unint8`, `binary`, and `ubinary`. You can get these different compression levels by passing the `embedding_types` parameter.\n", + "\n", + "In the example below, we embed the documents in two formats: `float` and `int8`." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Embed the documents with the given embedding types\n", + "doc_emb = co.embed(\n", + " model=\"embed-english-v3.0\",\n", + " embedding_types=[\"float\",\"int8\"],\n", + " input_type=\"search_document\",\n", + " texts=[doc['text'] for doc in faqs_long]).embeddings\n", + "\n", + "# Add the user query\n", + "query = \"How do I stay connected to what's happening at the company?\"\n", + "\n", + "# Embed the query\n", + "query_emb = co.embed(\n", + " model=\"embed-english-v3.0\",\n", + " embedding_types=[\"float\",\"int8\"],\n", + " input_type=\"search_query\",\n", + " texts=[query]).embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are the search results of using the `float` embeddings." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank: 1\n", + "Score: 0.352135965228231\n", + "Document: {'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}\n", + "\n", + "Rank: 2\n", + "Score: 0.31995661889273097\n", + "Document: {'text': 'Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.'}\n", + "\n" + ] + } + ], + "source": [ + "# Compute dot product similarity and display results\n", + "return_results(query_emb.float_, doc_emb.float_, faqs_long)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here are the search results of using the `int8` embeddings." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank: 1\n", + "Score: 563583\n", + "Document: {'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}\n", + "\n", + "Rank: 2\n", + "Score: 508692\n", + "Document: {'text': 'Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.'}\n", + "\n" + ] + } + ], + "source": [ + "# Compute dot product similarity and display results\n", + "return_results(query_emb.int8, doc_emb.int8, faqs_long)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further reading:\n", + "- [Documentation on embeddings compression levels](https://docs.cohere.com/docs/embeddings#compression-levels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, you learned about:\n", + "- How to embed documents for search\n", + "- How to embed queries\n", + "- How to perform semantic search\n", + "- How to perform multilingual semantic search\n", + "- How to change the embedding compression types\n", + "\n", + "A high-performance and modern search system typically includes a reranking stage, which further boosts the search results.\n", + "\n", + "In Part 5, you will learn how to add reranking to a search system." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "base" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/guides/getting-started/v2/tutorial_pt5_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt5_v2.ipynb new file mode 100644 index 0000000..e6c51f4 --- /dev/null +++ b/notebooks/guides/getting-started/v2/tutorial_pt5_v2.ipynb @@ -0,0 +1,493 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reranking" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reranking is a technique that leverages embeddings as the last stage of a retrieval process, and is especially useful in RAG systems.\n", + "\n", + "We can rerank results from semantic search as well as any other search systems such as lexical search. This means that companies can retain an existing keyword-based (also called “lexical”) or semantic search system for the first-stage retrieval and integrate the Rerank endpoint in the second-stage reranking.\n", + "\n", + "In this tutorial, you'll learn about:\n", + "- Reranking lexical/semantic search results\n", + "- Reranking semi-structured data\n", + "- Reranking tabular data\n", + "- Multilingual reranking\n", + "\n", + "You'll learn these by building an onboarding assistant for new hires." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "To get started, first we need to install the `cohere` library and create a Cohere client." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# pip install cohere\n", + "\n", + "import cohere\n", + "\n", + "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reranking lexical/semantic search results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rerank requires just a single line of code to implement.\n", + "\n", + "Suppose we have a list of search results of an FAQ list, which can come from semantic, lexical, or any other types of search systems. But this list may not be optimally ranked for relevance to the user query.\n", + "\n", + "This is where Rerank can help. We call the endpoint using `co.rerank()` and pass the following arguments:\n", + "- `query`: The user query\n", + "- `documents`: The list of documents\n", + "- `top_n`: The top reranked documents to select\n", + "- `model`: We choose Rerank English 3" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the documents\n", + "faqs_short = [\n", + " {\"text\": \"Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward.\"},\n", + " {\"text\": \"Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.\"},\n", + " {\"text\": \"Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.\"},\n", + " {\"text\": \"Performance Reviews Frequency: We conduct informal check-ins every quarter and formal performance reviews twice a year.\"}\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "id='37015cd2-a6a7-465e-a1dc-a3994851848f' results=[RerankResponseResultsItem(document=None, index=2, relevance_score=0.01798621), RerankResponseResultsItem(document=None, index=3, relevance_score=8.463939e-06)] meta=ApiMeta(api_version=ApiMetaApiVersion(version='1', is_deprecated=None, is_experimental=None), billed_units=ApiMetaBilledUnits(input_tokens=None, output_tokens=None, search_units=1.0, classifications=None), tokens=None, warnings=None)\n" + ] + } + ], + "source": [ + "# Add the user query\n", + "query = \"Are there fitness-related perks?\"\n", + "\n", + "# Rerank the documents\n", + "results = co.rerank(query=query,\n", + " documents=faqs_short,\n", + " top_n=2,\n", + " model='rerank-english-v3.0')\n", + "\n", + "print(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank: 1\n", + "Score: 0.01798621\n", + "Document: {'text': 'Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.'}\n", + "\n", + "Rank: 2\n", + "Score: 8.463939e-06\n", + "Document: {'text': 'Performance Reviews Frequency: We conduct informal check-ins every quarter and formal performance reviews twice a year.'}\n", + "\n" + ] + } + ], + "source": [ + "# Display the reranking results\n", + "def return_results(results, documents): \n", + " for idx, result in enumerate(results.results):\n", + " print(f\"Rank: {idx+1}\") \n", + " print(f\"Score: {result.relevance_score}\")\n", + " print(f\"Document: {documents[result.index]}\\n\")\n", + " \n", + "return_results(results, faqs_short)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further reading:\n", + "- [Rerank endpoint API reference](https://docs.cohere.com/reference/rerank)\n", + "- [Documentation on Rerank](https://docs.cohere.com/docs/overview)\n", + "- [Documentation on Rerank fine-tuning](https://docs.cohere.com/docs/rerank-fine-tuning)\n", + "- [Documentation on Rerank best practices](https://docs.cohere.com/docs/reranking-best-practices)\n", + "- [LLM University module on Text Representation](https://cohere.com/llmu#text-representation)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reranking semi-structured data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Rerank 3 model supports multi-aspect and semi-structured data like emails, invoices, JSON documents, code, and tables. By setting the rank fields, you can select which fields the model should consider for reranking.\n", + "\n", + "In the following example, we'll use an email data example. It is a semi-stuctured data that contains a number of fields – `from`, `to`, `date`, `subject`, and `text`. \n", + "\n", + "Suppose the new hire now wants to search for any emails about check-in sessions. Let's pretend we have a list of 5 emails retrieved from the email provider's API.\n", + "\n", + "To perform reranking over semi-structured data, we add an additional parameter, `rank_fields`, which contains the list of available fields.\n", + "\n", + "The model will rerank based on order of the fields passed in. For example, given rank_fields=['title','author','text'], the model will rerank using the values in title, author, and text sequentially. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the documents\n", + "emails = [\n", + " {\"from\": \"hr@co1t.com\", \"to\": \"david@co1t.com\", \"date\": \"2024-06-24\", \"subject\": \"A Warm Welcome to Co1t!\", \"text\": \"We are delighted to welcome you to the team! As you embark on your journey with us, you'll find attached an agenda to guide you through your first week.\"},\n", + " {\"from\": \"it@co1t.com\", \"to\": \"david@co1t.com\", \"date\": \"2024-06-24\", \"subject\": \"Setting Up Your IT Needs\", \"text\": \"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\"},\n", + " {\"from\": \"john@co1t.com\", \"to\": \"david@co1t.com\", \"date\": \"2024-06-24\", \"subject\": \"First Week Check-In\", \"text\": \"Hello! I hope you're settling in well. Let's connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it's a great opportunity to get to know your colleagues!\"}\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank: 1\n", + "Score: 0.1979091\n", + "Document: {'from': 'john@co1t.com', 'to': 'david@co1t.com', 'date': '2024-06-24', 'subject': 'First Week Check-In', 'text': \"Hello! I hope you're settling in well. Let's connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it's a great opportunity to get to know your colleagues!\"}\n", + "\n", + "Rank: 2\n", + "Score: 9.535461e-05\n", + "Document: {'from': 'hr@co1t.com', 'to': 'david@co1t.com', 'date': '2024-06-24', 'subject': 'A Warm Welcome to Co1t!', 'text': \"We are delighted to welcome you to the team! As you embark on your journey with us, you'll find attached an agenda to guide you through your first week.\"}\n", + "\n" + ] + } + ], + "source": [ + "# Add the user query\n", + "query = \"Any email about check ins?\"\n", + "\n", + "# Rerank the documents\n", + "results = co.rerank(query=query,\n", + " documents=emails,\n", + " top_n=2,\n", + " model='rerank-english-v3.0',\n", + " rank_fields=[\"from\", \"to\", \"date\", \"subject\", \"body\"])\n", + "\n", + "return_results(results, emails)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reranking tabular data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many enterprises rely on tabular data, such as relational databases, CSVs, and Excel. To perform reranking, you can transform a dataframe into a list of JSON records and use Rerank 3's JSON capabilities to rank them.\n", + "\n", + "Here's an example of reranking a CSV file that contains employee information." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/meor/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n", + " from pandas.core import (\n" + ] + }, + { + "data": { + "text/html": [ + "
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namerolejoin_dateemailstatus
0Rebecca LeeSenior Software Engineer2024-07-01rebecca@co1t.comFull-time
1Emma WilliamsProduct Designer2024-06-15emma@co1t.comFull-time
2Michael JonesMarketing Manager2024-05-20michael@co1t.comFull-time
3Amelia ThompsonSales Representative2024-05-20amelia@co1t.comPart-time
4Ethan DavisProduct Designer2024-05-25ethan@co1t.comContractor
\n", + "
" + ], + "text/plain": [ + " name role join_date email \\\n", + "0 Rebecca Lee Senior Software Engineer 2024-07-01 rebecca@co1t.com \n", + "1 Emma Williams Product Designer 2024-06-15 emma@co1t.com \n", + "2 Michael Jones Marketing Manager 2024-05-20 michael@co1t.com \n", + "3 Amelia Thompson Sales Representative 2024-05-20 amelia@co1t.com \n", + "4 Ethan Davis Product Designer 2024-05-25 ethan@co1t.com \n", + "\n", + " status \n", + "0 Full-time \n", + "1 Full-time \n", + "2 Full-time \n", + "3 Part-time \n", + "4 Contractor " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "from io import StringIO\n", + "\n", + "# Create a demo CSV file\n", + "data = \"\"\"name,role,join_date,email,status\n", + "Rebecca Lee,Senior Software Engineer,2024-07-01,rebecca@co1t.com,Full-time\n", + "Emma Williams,Product Designer,2024-06-15,emma@co1t.com,Full-time\n", + "Michael Jones,Marketing Manager,2024-05-20,michael@co1t.com,Full-time\n", + "Amelia Thompson,Sales Representative,2024-05-20,amelia@co1t.com,Part-time\n", + "Ethan Davis,Product Designer,2024-05-25,ethan@co1t.com,Contractor\"\"\"\n", + "data_csv = StringIO(data)\n", + "\n", + "# Load the CSV file\n", + "df = pd.read_csv(data_csv)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank: 1\n", + "Score: 0.986828\n", + "Document: {'name': 'Emma Williams', 'role': 'Product Designer', 'join_date': '2024-06-15', 'email': 'emma@co1t.com', 'status': 'Full-time'}\n", + "\n" + ] + } + ], + "source": [ + "# Define the documents and rank fields\n", + "employees = df.to_dict('records')\n", + "rank_fields = df.columns.tolist()\n", + "\n", + "# Add the user query\n", + "query = \"Any full-time product designers who joined recently?\"\n", + "\n", + "# Rerank the documents\n", + "results = co.rerank(query=query,\n", + " documents=employees,\n", + " top_n=1,\n", + " model='rerank-english-v3.0',\n", + " rank_fields=rank_fields)\n", + "\n", + "return_results(results, employees)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multilingual reranking" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Rerank endpoint also supports multilingual semantic search via the `rerank-multilingual-...` models. This means you can perform semantic search on texts in different languages.\n", + "\n", + "In the example below, we repeat the steps of performing reranking with one difference – changing the model type to a multilingual one. Here, we use the `rerank-multilingual-v3.0` model. Here, we are reranking the FAQ list using an Arabic query." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank: 1\n", + "Score: 0.42232594\n", + "Document: {'text': 'Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.'}\n", + "\n", + "Rank: 2\n", + "Score: 0.00025118678\n", + "Document: {'text': 'Performance Reviews Frequency: We conduct informal check-ins every quarter and formal performance reviews twice a year.'}\n", + "\n" + ] + } + ], + "source": [ + "# Define the query\n", + "query = \"هل Ù‡Ù†Ű§Ùƒ مŰČŰ§ÙŠŰ§ ŰȘŰȘŰčلق ŰšŰ§Ù„Ù„ÙŠŰ§Ù‚Ű© Ű§Ù„ŰšŰŻÙ†ÙŠŰ©ŰŸ\" # Are there fitness benefits?\n", + "\n", + "# Rerank the documents\n", + "results = co.rerank(query=query,\n", + " documents=faqs_short,\n", + " top_n=2,\n", + " model='rerank-multilingual-v3.0')\n", + "\n", + "return_results(results, faqs_short)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, you learned about:\n", + "- How to rerank lexical/semantic search results\n", + "- How to rerank semi-structured data\n", + "- How to rerank tabular data\n", + "- How to perform Multilingual reranking\n", + "\n", + "We have now seen two critical components of a powerful search system - semantic search, or dense retrieval (Part 4) and reranking (Part 5). These building blocks are essential for implementing RAG solutions.\n", + "\n", + "In Part 6, you will learn how to implement RAG." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "base" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb new file mode 100644 index 0000000..1bc9c0b --- /dev/null +++ b/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb @@ -0,0 +1,531 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note for reviewers - Compared to `tutorial_pt6_v2` which returned correct search query decisions this one doesn't because it's sending a chat history that contains documents to the search query generation tool (documents and tools can't be in the same chat history?)." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# pip install cohere\n", + "\n", + "import cohere\n", + "import numpy as np\n", + "import json\n", + "from typing import List\n", + "\n", + "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic RAG\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To see how RAG works, let's define the documents that the application has access to. We'll use a short list of documents consisting of internal FAQs about the fictitious company Co1t (in production, these documents are massive).\n", + "\n", + "In this example, each document is a dictionary with one field, `text`. But we can define any number of fields we want, depending on the nature of the documents. For example, emails could contain `title` and `text` fields." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "documents = [\n", + " {\"text\": \"Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward.\"},\n", + " {\"text\": \"Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.\"},\n", + " {\"text\": \"Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.\"}\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To use these documents, we create a `user` message containing two content fields:\n", + "- `content` for the user's query.\n", + "- `documents` for storing all the documents.\n", + "\n", + "The `documents` field consists of a list of dictionaries where each dictionary represents a document. A document can have different fields depending on its nature, such as `title`, `text`, `url`, etc. Optionally, you can also pass your own IDs for each document using the `id` field. Otherwise, the API will automatically generate the IDs based on the documents position in the list.\n", + "\n", + "We then pass this to the `messages` parameter in the Chat endpoint call. This tells the model to run in RAG-mode and use these documents in its response.\n", + "\n", + "Let's create a query asking about the company's support for personal well-being, which is not going to be available to the model based on the data its trained on. It will need to use external documents.\n", + "\n", + "RAG introduces additional objects in the Chat response. One of them is `citations`, which contains details about:\n", + "- specific text spans from the retrieved documents on which the response is grounded.\n", + "- the documents referenced in the citations." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Yes, we offer gym memberships, on-site yoga classes, and comprehensive health insurance.\n", + "\n", + "CITATIONS:\n", + "start=14 end=29 text='gym memberships' sources=[Source_Document(id='doc:0:2', document={'id': 'doc:0:2', 'text': 'Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.'}, type='document')] \n", + "\n", + "start=31 end=51 text='on-site yoga classes' sources=[Source_Document(id='doc:0:2', document={'id': 'doc:0:2', 'text': 'Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.'}, type='document')] \n", + "\n", + "start=57 end=88 text='comprehensive health insurance.' sources=[Source_Document(id='doc:0:2', document={'id': 'doc:0:2', 'text': 'Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.'}, type='document')] \n", + "\n" + ] + } + ], + "source": [ + "# Add the user query\n", + "query = \"Are there health benefits?\"\n", + "\n", + "## OPTIONAL: Define document IDs\n", + "# for i, doc in enumerate(documents):\n", + "# doc['id'] = str(i)\n", + "\n", + "# Add the user message containing the query and documents\n", + "messages = [{'role': 'user', 'content': query, \"documents\": documents}]\n", + "\n", + "# Generate the response\n", + "response = co.chat(model=\"command-r-plus\",\n", + " messages=messages)\n", + "\n", + "# Display the response\n", + "print(response.message.content[0].text)\n", + "\n", + "# Display the citations and source documents\n", + "if response.message.citations:\n", + " print(\"\\nCITATIONS:\")\n", + " for citation in response.message.citations:\n", + " print(citation, \"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Search query generation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The previous example showed how to get started with RAG, and in particular, the augmented generation portion of RAG. But as its name implies, RAG consists of other steps, such as retrieval. \n", + "\n", + "In a basic RAG application, the steps involved are:\n", + "\n", + "- Transforming the user message into search queries\n", + "- Retrieving relevant documents for a given search query\n", + "- Generating the response and citations\n", + "\n", + "Let's now look at the first step—search query generation. The chatbot needs to generate an optimal set of search queries to use for retrieval. \n", + "\n", + "There are different possible approaches to this. In this example, we'll take a tool use approach.\n", + "\n", + "Here, we build a tool that takes a user query and returns a list of relevant document snippets for that query. The tool can generate one or multiple search queries depending on the user query.\n", + "\n", + "We'll not do a detailed walkthrough of tool use here as we'll do that in the next tutorial, which is a dedicated tutorial on tool use." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_search_queries(message: str) -> List[str]:\n", + " \n", + " tools = [{\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"document_search\",\n", + " \"description\" : \"Searches for textual documents based on a user query.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"query\": {\"type\": \"string\", \"description\": \"The search query\"}\n", + " },\n", + " \"required\": [\"query\"]\n", + " }\n", + " }\n", + " }]\n", + " \n", + " response = co.chat(\n", + " model=\"command-r\",\n", + " messages=[{\"role\": \"user\", \"content\": message}],\n", + " tools=tools\n", + " )\n", + " \n", + " search_queries = []\n", + " \n", + " if response.message.tool_calls:\n", + " res = response.message\n", + " documents = []\n", + " for tc in res.tool_calls:\n", + " query = json.loads(tc.function.arguments)[\"query\"]\n", + " search_queries.append(query)\n", + " \n", + " return search_queries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the example below, the tool breaks down the user message into two separate queries." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['stay connected with the company', 'team events']\n" + ] + } + ], + "source": [ + "query = \"How to stay connected with the company and do you organize team events?\"\n", + "queries_for_search = generate_search_queries(query)\n", + "print(queries_for_search)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And in the example below, the tool decides that one query is sufficient." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['working hours flexibility']\n" + ] + } + ], + "source": [ + "query = \"How flexible are the working hours\"\n", + "queries_for_search = generate_search_queries(query)\n", + "print(queries_for_search)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Retrieval with Embed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given the search query, we need a way to retrieve the most relevant documents from a large collection of documents.\n", + "\n", + "This is where we can leverage text embeddings through the Embed endpoint. It enables semantic search, which lets us to compare the semantic meaning of the documents and the query. It solves the problem faced by the more traditional approach of lexical search, which is great at finding keyword matches, but struggles at capturing the context or meaning of a piece of text.\n", + "\n", + "The Embed endpoint takes in texts as input and returns embeddings as output.\n", + "\n", + "First, we need to embed the documents to search from. We call the Embed endpoint using `co.embed()` and pass the following arguments:\n", + "\n", + "- `model`: Here we choose `embed-english-v3.0`, which generates embeddings of size 1024\n", + "- `input_type`: We choose `search_document` to ensure the model treats these as the documents (instead of the query) for search\n", + "- `texts`: The list of texts (the FAQs)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the documents\n", + "faqs_long = [\n", + " {\"text\": \"Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.\"},\n", + " {\"text\": \"Finding Coffee Spots: For your caffeine fix, head to the break room's coffee machine or cross the street to the cafĂ© for artisan coffee.\"},\n", + " {\"text\": \"Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!\"},\n", + " {\"text\": \"Working Hours Flexibility: We prioritize work-life balance. While our core hours are 9 AM to 5 PM, we offer flexibility to adjust as needed.\"},\n", + " {\"text\": \"Side Projects Policy: We encourage you to pursue your passions. Just be mindful of any potential conflicts of interest with our business.\"},\n", + " {\"text\": \"Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward.\"},\n", + " {\"text\": \"Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.\"},\n", + " {\"text\": \"Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.\"},\n", + " {\"text\": \"Performance Reviews Frequency: We conduct informal check-ins every quarter and formal performance reviews twice a year.\"},\n", + " {\"text\": \"Proposing New Ideas: Innovation is welcomed! Share your brilliant ideas at our weekly team meetings or directly with your team lead.\"},\n", + "]\n", + "\n", + "# Embed the documents\n", + "doc_emb = co.embed(\n", + " model=\"embed-english-v3.0\",\n", + " input_type=\"search_document\",\n", + " texts=[doc['text'] for doc in faqs_long]).embeddings\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we add a query, which asks about how to get to know the team.\n", + "\n", + "We choose `search_query` as the `input_type` to ensure the model treats this as the query (instead of the documents) for search." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Search query: how to get to know teammates\n" + ] + } + ], + "source": [ + "# Add the user query\n", + "query = \"How to get to know my teammates\"\n", + "\n", + "# Generate the search query\n", + "# Note: For simplicity, we are assuming only one query generated. For actual implementations, you will need to perform search for each query.\n", + "queries_for_search = generate_search_queries(query)[0]\n", + "print(\"Search query: \", queries_for_search)\n", + "\n", + "# Embed the search query\n", + "query_emb = co.embed(\n", + " model=\"embed-english-v3.0\",\n", + " input_type=\"search_query\",\n", + " texts=[queries_for_search]).embeddings\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we want to search for the most relevant documents to the query. For this, we make use of the `numpy` library to compute the similarity between each query-document pair using the dot product approach.\n", + "\n", + "Each query-document pair returns a score, which represents how similar the pair are. We then sort these scores in descending order and select the top most similar pairs, which we choose 5 (this is an arbitrary choice, you can choose any number).\n", + "\n", + "Here, we show the most relevant documents with their similarity scores." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank: 1\n", + "Score: 0.34212792245283796\n", + "Document: {'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}\n", + "\n", + "Rank: 2\n", + "Score: 0.2883222063024371\n", + "Document: {'text': 'Proposing New Ideas: Innovation is welcomed! Share your brilliant ideas at our weekly team meetings or directly with your team lead.'}\n", + "\n", + "Rank: 3\n", + "Score: 0.278128283997032\n", + "Document: {'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}\n", + "\n", + "Rank: 4\n", + "Score: 0.19474858706643985\n", + "Document: {'text': \"Finding Coffee Spots: For your caffeine fix, head to the break room's coffee machine or cross the street to the cafĂ© for artisan coffee.\"}\n", + "\n", + "Rank: 5\n", + "Score: 0.13713692506528824\n", + "Document: {'text': 'Side Projects Policy: We encourage you to pursue your passions. Just be mindful of any potential conflicts of interest with our business.'}\n", + "\n" + ] + } + ], + "source": [ + "# Compute dot product similarity and display results\n", + "n = 5\n", + "scores = np.dot(query_emb, np.transpose(doc_emb))[0]\n", + "max_idx = np.argsort(-scores)[:n]\n", + "\n", + "retrieved_documents = [faqs_long[item] for item in max_idx]\n", + "\n", + "for rank, idx in enumerate(max_idx):\n", + " print(f\"Rank: {rank+1}\")\n", + " print(f\"Score: {scores[idx]}\")\n", + " print(f\"Document: {retrieved_documents[rank]}\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reranking can boost the results from semantic or lexical search further. The Rerank endpoint takes a list of search results and reranks them according to the most relevant documents to a query. This requires just a single line of code to implement.\n", + "\n", + "We call the endpoint using `co.rerank()` and pass the following arguments:\n", + "\n", + "- `query`: The user query\n", + "- `documents`: The list of documents we get from the semantic search results\n", + "- `top_n`: The top reranked documents to select\n", + "- `model`: We choose Rerank English 3\n", + "\n", + "Looking at the results, we see that the given a query about getting to know the team, the document that talks about joining Slack channels is now ranked higher (1st) compared to earlier (3rd).\n", + "\n", + "Here we select `top_n` to be 2, which will be the documents we will pass next for response generation." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank: 1\n", + "Score: 0.0020507434\n", + "Document: {'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}\n", + "\n", + "Rank: 2\n", + "Score: 0.0014158706\n", + "Document: {'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}\n", + "\n" + ] + } + ], + "source": [ + "# Rerank the documents\n", + "results = co.rerank(query=queries_for_search,\n", + " documents=retrieved_documents,\n", + " top_n=2,\n", + " model='rerank-english-v3.0')\n", + "\n", + "# Display the reranking results\n", + "for idx, result in enumerate(results.results):\n", + " print(f\"Rank: {idx+1}\") \n", + " print(f\"Score: {result.relevance_score}\")\n", + " print(f\"Document: {retrieved_documents[result.index]}\\n\")\n", + " \n", + "reranked_documents = [retrieved_documents[result.index] for result in results.results]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we reach the step that we saw in the earlier `Basic RAG` section.\n", + "\n", + "To use these documents, we create a `user` message containing two content types:\n", + "- `text` for the user's query.\n", + "- `document` for storing all the documents.\n", + "\n", + "We then pass this to the `messages` parameter in the Chat endpoint call. This tells the model to run in RAG-mode and use these documents in its response.\n", + "\n", + "The response is then generated based on the the query and the documents retrieved.\n", + "\n", + "RAG introduces additional objects in the Chat response. One of them is `citations`, which contains details about:\n", + "- specific text spans from the retrieved documents on which the response is grounded.\n", + "- the documents referenced in the citations." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can get to know your teammates by joining your company's Slack channels and taking part in team-building activities. You will receive an invite to join relevant Slack channels via email. Your company also organises monthly outings and weekly game nights to foster team spirit.\n", + "\n", + "CITATIONS:\n", + "start=38 end=75 text=\"joining your company's Slack channels\" sources=[Source_Document(id='doc:0:0', document={'id': 'doc:0:0', 'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}, type='document')] \n", + "\n", + "start=95 end=120 text='team-building activities.' sources=[Source_Document(id='doc:0:1', document={'id': 'doc:0:1', 'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}, type='document')] \n", + "\n", + "start=130 end=190 text='receive an invite to join relevant Slack channels via email.' sources=[Source_Document(id='doc:0:0', document={'id': 'doc:0:0', 'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}, type='document')] \n", + "\n", + "start=219 end=280 text='monthly outings and weekly game nights to foster team spirit.' sources=[Source_Document(id='doc:0:1', document={'id': 'doc:0:1', 'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}, type='document')] \n", + "\n" + ] + } + ], + "source": [ + "# Add the user message containing the query and documents\n", + "messages = [{'role': 'user', 'content': query, \"documents\": reranked_documents}]\n", + "\n", + "# Generate the response\n", + "response = co.chat(model=\"command-r-plus\",\n", + " messages=messages)\n", + "\n", + "# Display the response\n", + "print(response.message.content[0].text)\n", + "\n", + "# Display the citations and source documents\n", + "if response.message.citations:\n", + " print(\"\\nCITATIONS:\")\n", + " for citation in response.message.citations:\n", + " print(citation, \"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "base" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb new file mode 100644 index 0000000..797120f --- /dev/null +++ b/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb @@ -0,0 +1,549 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Agents with Tool Use" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tool use extends the ideas from RAG, where external systems are used to guide the response of an LLM, but by leveraging a much bigger set of tools than what’s possible with RAG. The concept of tool use leverages LLMs' useful feature of being able to act as a reasoning and decision-making engine.\n", + "\n", + "While RAG enables applications that can *answer questions*, tool use enables those that can *automate tasks*.\n", + "\n", + "Tool use also enables developers to build agentic applications that can take actions, that is, doing both read and write operations on an external system.\n", + "\n", + "In this tutorial, you'll learn about:\n", + "- Creating tools\n", + "- Tool planning and calling\n", + "- Tool execution\n", + "- Response and citation generation\n", + "- Multi-step tool use\n", + "\n", + "You'll learn these by building an onboarding assistant for new hires." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "To get started, first we need to install the `cohere` library and create a Cohere client." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# pip install cohere\n", + "\n", + "import cohere\n", + "import json\n", + "\n", + "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating tools" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The pre-requisite, before we can run a tool use workflow, is to set up the tools. Let's create three tools:\n", + "- `search_faqs`: A tool for searching the FAQs. For simplicity, we'll not implement any retrieval logic, but we'll simply pass a list of pre-defined documents, which are the FAQ documents we had used in the Text Embeddings section.\n", + "- `search_emails`: A tool for searching the emails. Same as above, we'll simply pass a list of pre-defined emails from the Reranking section.\n", + "- `create_calendar_event`: A tool for creating new calendar events. Again, for simplicity, we'll not implement actual event bookings, but will return a mock success event. In practice, we can connect to a calendar service API and implement all the necessary logic here.\n", + "\n", + "Here, we are defining a Python function for each tool, but more broadly, the tool can be any function or service that can receive and send objects." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the tools\n", + "def search_faqs(query):\n", + " faqs = [\n", + " {\"text\": \"Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward.\"},\n", + " {\"text\": \"Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.\"}\n", + " ]\n", + " return {\"faqs\" : faqs}\n", + "\n", + "def search_emails(query):\n", + " emails = [\n", + " {\"from\": \"it@co1t.com\", \"to\": \"david@co1t.com\", \"date\": \"2024-06-24\", \"subject\": \"Setting Up Your IT Needs\", \"text\": \"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\"},\n", + " {\"from\": \"john@co1t.com\", \"to\": \"david@co1t.com\", \"date\": \"2024-06-24\", \"subject\": \"First Week Check-In\", \"text\": \"Hello! I hope you're settling in well. Let's connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it's a great opportunity to get to know your colleagues!\"}\n", + " ]\n", + " return {\"emails\" : emails}\n", + " \n", + "def create_calendar_event(date: str, time: str, duration: int):\n", + " # You can implement any logic here\n", + " return {\"is_success\": True,\n", + " \"message\": f\"Created a {duration} hour long event at {time} on {date}\"}\n", + " \n", + "functions_map = {\n", + " \"search_faqs\": search_faqs,\n", + " \"search_emails\": search_emails,\n", + " \"create_calendar_event\": create_calendar_event\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second and final setup step is to define the tool schemas in a format that can be passed to the Chat endpoint. The schema must contain the following fields: `name`, `description`, and `parameters` in the format shown below. \n", + "\n", + "This schema informs the LLM about what the tool does, and the LLM decides whether to use a particular tool based on it. Therefore, the more descriptive and specific the schema, the more likely the LLM will make the right tool call decisions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further reading:\n", + "- [Documentation on parameter types in tool use](https://docs.cohere.com/v2/docs/parameter-types-in-tool-use)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the tools\n", + "tools = [\n", + " {\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"search_faqs\",\n", + " \"description\": \"Given a user query, searches a company's frequently asked questions (FAQs) list and returns the most relevant matches to the query.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"query\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"The query from the user\"\n", + " }\n", + " },\n", + " \"required\": [\"query\"]\n", + " }\n", + " }\n", + " },\n", + " {\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"search_emails\",\n", + " \"description\": \"Given a user query, searches a person's emails and returns the most relevant matches to the query.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"query\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"The query from the user\"\n", + " }\n", + " },\n", + " \"required\": [\"query\"]\n", + " }\n", + " }\n", + " },\n", + " {\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"create_calendar_event\",\n", + " \"description\": \"Creates a new calendar event of the specified duration at the specified time and date. A new event cannot be created on the same time as an existing event.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"date\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"the date on which the event starts, formatted as mm/dd/yy\"\n", + " },\n", + " \"time\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"the time of the event, formatted using 24h military time formatting\"\n", + " },\n", + " \"duration\": {\n", + " \"type\": \"float\",\n", + " \"description\": \"the number of hours the event lasts for\"\n", + " }\n", + " },\n", + " \"required\": [\"date\", \"time\", \"duration\"]\n", + " }\n", + " }\n", + " }\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tool planning and calling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now run the tool use workflow. We can think of a tool use system as consisting of four components:\n", + "- The user\n", + "- The application\n", + "- The LLM\n", + "- The tools\n", + "\n", + "At its most basic, these four components interact in a workflow through four steps:\n", + "- **Step 1: Get user message** – The LLM gets the user message (via the application)\n", + "- **Step 2: Tool planning and calling** – The LLM makes a decision on the tools to call (if any) and generates - the tool calls\n", + "- **Step 3: Tool execution** - The application executes the tools and the results are sent to the LLM\n", + "- **Step 4: Response and citation generation** – The LLM generates the response and citations to back to the user" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tool plan:\n", + "I will search the user's emails for any messages about getting set up with IT. \n", + "\n", + "Tool calls:\n", + "Tool name: search_emails | Parameters: {\"query\":\"getting setup with IT\"}\n" + ] + } + ], + "source": [ + "# Create custom system message\n", + "system_message=\"\"\"## Task and Context\n", + "You are an assistant who assist new employees of Co1t with their first week. You respond to their questions and assist them with their needs. Today is Monday, June 24, 2024\"\"\"\n", + "\n", + "\n", + "# Step 1: Get user message\n", + "message = \"Is there any message about getting setup with IT?\"\n", + "\n", + "# Add the system and user messages to the chat history\n", + "messages = [{\"role\": \"system\", \"content\": system_message},\n", + " {\"role\": \"user\", \"content\": message}]\n", + "\n", + "# Step 2: Tool planning and calling\n", + "response = co.chat(\n", + " model=\"command-r-plus\",\n", + " messages=messages,\n", + " tools=tools\n", + " )\n", + "\n", + "if response.message.tool_calls: \n", + " print(\"Tool plan:\")\n", + " print(response.message.tool_plan,\"\\n\")\n", + " print(\"Tool calls:\")\n", + " for tc in response.message.tool_calls:\n", + " print(f\"Tool name: {tc.function.name} | Parameters: {tc.function.arguments}\")\n", + " \n", + " # Append tool calling details to the chat history\n", + " messages.append({\"role\": \"assistant\", \"tool_calls\": response.message.tool_calls, \"tool_plan\": response.message.tool_plan})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given three tools to choose from, the model is able to pick the right tool (in this case, `search_emails`) based on what the user is asking for.\n", + "\n", + "Also, notice that the model first generates a plan about what it should do (\"I will do ...\") before actually generating the tool call(s)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tool execution" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tool results:\n", + "output={'emails': [{'from': 'it@co1t.com', 'to': 'david@co1t.com', 'date': '2024-06-24', 'subject': 'Setting Up Your IT Needs', 'text': 'Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.'}, {'from': 'john@co1t.com', 'to': 'david@co1t.com', 'date': '2024-06-24', 'subject': 'First Week Check-In', 'text': \"Hello! I hope you're settling in well. Let's connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it's a great opportunity to get to know your colleagues!\"}]}\n" + ] + } + ], + "source": [ + "# Step 3: Tool execution\n", + "tool_content = []\n", + "for tc in response.message.tool_calls:\n", + " tool_result = functions_map[tc.function.name](**json.loads(tc.function.arguments))\n", + " tool_content.append(cohere.ToolContent(output=tool_result))\n", + " # Append tool results to the chat history\n", + " messages.append({\"role\": \"tool\", \"tool_call_id\": tc.id, \"tool_content\": tool_content}) \n", + "\n", + "print(\"Tool results:\") \n", + "for result in tool_content:\n", + " print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Response and citation generation" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Response:\n", + "Yes, there is an email from IT with the subject \"Setting Up Your IT Needs\". It includes an attached comprehensive guide to help you set up your work accounts.\n", + "==================================================\n", + "\n", + "CITATIONS:\n", + "start=17 end=30 text='email from IT' sources=[Source_Tool(id='search_emails_191n4etevvf3:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'}, type='tool')] \n", + "\n", + "start=40 end=73 text='subject \"Setting Up Your IT Needs' sources=[Source_Tool(id='search_emails_191n4etevvf3:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'}, type='tool')] \n", + "\n", + "start=91 end=119 text='attached comprehensive guide' sources=[Source_Tool(id='search_emails_191n4etevvf3:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'}, type='tool')] \n", + "\n", + "start=132 end=157 text='set up your work accounts' sources=[Source_Tool(id='search_emails_191n4etevvf3:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'}, type='tool')] \n", + "\n" + ] + } + ], + "source": [ + "# Step 4: Response and citation generation\n", + "response = co.chat(\n", + " model=\"command-r-plus\",\n", + " messages=messages,\n", + " tools=tools\n", + ")\n", + "\n", + "# Append assistant response to the chat history\n", + "messages.append({\"role\": \"assistant\", \"content\": response.message.content})\n", + "\n", + "# Print final response\n", + "print(\"Response:\")\n", + "print(response.message.content[0].text)\n", + "print(\"=\"*50)\n", + "\n", + "# Print citations (if any)\n", + "if response.message.citations:\n", + " print(\"\\nCITATIONS:\")\n", + " for citation in response.message.citations:\n", + " print(citation, \"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-step tool use" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model can execute more complex tasks in tool use – tasks that require tool calls to happen in a sequence. This is referred to as \"multi-step\" tool use. \n", + "\n", + "Let's create a function to called `run_assistant` to implement these steps, and along the way, print out the key events and messages. Optionally, this function also accepts the chat history as an argument to keep the state in a multi-turn conversation. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "model = \"command-r-plus\"\n", + "\n", + "system_message=\"\"\"## Task and Context\n", + "You are an assistant who assists new employees of Co1t with their first week. You respond to their questions and assist them with their needs. Today is Monday, June 24, 2024\"\"\"\n", + "\n", + "def run_assistant(query, messages=None):\n", + " if messages is None:\n", + " messages = []\n", + " \n", + " if \"system\" not in {m.get(\"role\") for m in messages}:\n", + " messages.append({\"role\": \"system\", \"content\": system_message})\n", + " \n", + " # Step 1: get user message\n", + " print(f\"Question:\\n{query}\")\n", + " print(\"=\"*50)\n", + " \n", + " messages.append({\"role\": \"user\", \"content\": query})\n", + "\n", + " # Step 2: Generate tool calls (if any)\n", + " response = co.chat(\n", + " model=model,\n", + " messages=messages,\n", + " tools=tools\n", + " )\n", + "\n", + " while response.message.tool_calls:\n", + " \n", + " print(\"Tool plan:\")\n", + " print(response.message.tool_plan,\"\\n\")\n", + " print(\"Tool calls:\")\n", + " for tc in response.message.tool_calls:\n", + " print(f\"Tool name: {tc.function.name} | Parameters: {tc.function.arguments}\")\n", + " print(\"=\"*50)\n", + "\n", + " messages.append({\"role\": \"assistant\", \"tool_calls\": response.message.tool_calls, \"tool_plan\": response.message.tool_plan}) \n", + " \n", + " # Step 3: Get tool results\n", + " tool_content = []\n", + " for idx, tc in enumerate(response.message.tool_calls):\n", + " tool_result= functions_map[tc.function.name](**json.loads(tc.function.arguments))\n", + " tool_content.append(cohere.ToolContent(output=tool_result))\n", + " messages.append({\"role\": \"tool\", \"tool_call_id\": tc.id, \"tool_content\": tool_content})\n", + " \n", + " # Step 4: Generate response and citations \n", + " response = co.chat(\n", + " model=model,\n", + " messages=messages,\n", + " tools=tools\n", + " )\n", + " \n", + " messages.append({\"role\": \"assistant\", \"content\": response.message.content})\n", + " \n", + " # Print final response\n", + " print(\"Response:\")\n", + " print(response.message.content[0].text)\n", + " print(\"=\"*50)\n", + " \n", + " # Print citations (if any)\n", + " if response.message.citations:\n", + " print(\"\\nCITATIONS:\")\n", + " for citation in response.message.citations:\n", + " print(citation, \"\\n\")\n", + " \n", + " return messages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To illustrate the concept of multi-step tool user, let's ask the assistant to block time for any lunch invites received in the email.\n", + "\n", + "This requires tasks to happen over multiple steps in a sequence. Here, we see the assistant running these steps:\n", + "- First, it calls the `search_emails` tool to find any lunch invites, which it found one.\n", + "- Next, it calls the `create_calendar_event` tool to create an event to block the person's calendar on the day mentioned by the email.\n", + "\n", + "This is also an example of tool use enabling a write operation instead of just a read operation that we saw with RAG." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question:\n", + "Can you check if there are any lunch invites, and for those days, create a one-hour event on my calendar at 12PM.\n", + "==================================================\n", + "Tool plan:\n", + "I will search the user's emails for lunch invites and then create a calendar event for each day that they have been invited to lunch. \n", + "\n", + "Tool calls:\n", + "Tool name: search_emails | Parameters: {\"query\":\"lunch invites\"}\n", + "==================================================\n", + "Tool plan:\n", + "I found an email from john@co1t.com inviting the user to a welcoming lunch on Thursday at noon. I will now create a calendar event for this. \n", + "\n", + "Tool calls:\n", + "Tool name: create_calendar_event | Parameters: {\"date\":\"06/27/24\",\"duration\":1,\"time\":\"12:00\"}\n", + "==================================================\n", + "Response:\n", + "I found an email from John inviting you to a welcoming lunch this Thursday at noon. I've created a one-hour event on your calendar for Thursday at 12 PM.\n", + "==================================================\n", + "\n", + "CITATIONS:\n", + "start=11 end=26 text='email from John' sources=[Source_Tool(id='search_emails_2jxdbc1v4ga6:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'}, type='tool')] \n", + "\n", + "start=45 end=82 text='welcoming lunch this Thursday at noon' sources=[Source_Tool(id='search_emails_2jxdbc1v4ga6:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'}, type='tool')] \n", + "\n", + "start=99 end=152 text='one-hour event on your calendar for Thursday at 12 PM' sources=[Source_Tool(id='create_calendar_event_8g3d1nfc0mms:0', tool_output={'is_success': 'true', 'message': 'Created a 1 hour long event at 12:00 on 06/27/24'}, type='tool')] \n", + "\n" + ] + } + ], + "source": [ + "messages = run_assistant(\"Can you check if there are any lunch invites, and for those days, create a one-hour event on my calendar at 12PM.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, you learned about:\n", + "- How to create tools\n", + "- How tool planning and calling happens\n", + "- How tool execution happens\n", + "- How to generate the response and citations\n", + "- How to run tool use in a multi-step scenario\n", + "\n", + "And that concludes our 7-part Cohere tutorial. We hope that they have provided you with a foundational understanding of the Cohere API, the available models and endpoints, and the types of use cases that you can build with them.\n", + "\n", + "To continue your learning, check out:\n", + "- [LLM University - A range of courses and step-by-step guides to help you start building](https://cohere.com/llmu)\n", + "- [Cookbooks - A collection of basic to advanced example applications](https://docs.cohere.com/page/cookbooks)\n", + "- [Cohere's documentation](https://docs.cohere.com/docs/the-cohere-platform)\n", + "- [The Cohere API reference](https://docs.cohere.com/reference/about)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "base" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 0b142f399968297c7fe93dda008ac6befa58454e Mon Sep 17 00:00:00 2001 From: mrmer1 Date: Mon, 9 Sep 2024 22:07:27 +0800 Subject: [PATCH 02/11] update rag nb --- .../getting-started/v2/tutorial_pt6_v2.ipynb | 33 ++++++++++++++++++- 1 file changed, 32 insertions(+), 1 deletion(-) diff --git a/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb index 1bc9c0b..a7da6f2 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb @@ -4,7 +4,38 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note for reviewers - Compared to `tutorial_pt6_v2` which returned correct search query decisions this one doesn't because it's sending a chat history that contains documents to the search query generation tool (documents and tools can't be in the same chat history?)." + "# RAG" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Chat endpoint provides comprehensive support for various text generation use cases, including retrieval-augmented generation (RAG). \n", + "\n", + "While LLMs are good at maintaining the context of the conversation and generating responses, they can be prone to hallucinate and include factually incorrect or incomplete information in their responses.\n", + "\n", + "RAG enables a model to access and utilize supplementary information from external documents, thereby improving the accuracy of its responses.\n", + "\n", + "When using RAG with the Chat endpoint, these responses are backed by fine-grained citations linking to the source documents. This makes the responses easily verifiable.\n", + "\n", + "In this tutorial, you'll learn about:\n", + "- Basic RAG\n", + "- Search query generation\n", + "- Retrieval with Embed\n", + "- Reranking with Rerank\n", + "- Response and citation generation\n", + "\n", + "You'll learn these by building an onboarding assistant for new hires." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "To get started, first we need to install the `cohere` library and create a Cohere client." ] }, { From 9cbfb1c5968d92e64c15b2c11d037e1cce186c60 Mon Sep 17 00:00:00 2001 From: mrmer1 Date: Mon, 16 Sep 2024 12:59:07 +0800 Subject: [PATCH 03/11] update getting started nbs --- .../getting-started/v2/tutorial_pt1_v2.ipynb | 9 +++ .../getting-started/v2/tutorial_pt2_v2.ipynb | 9 +++ .../getting-started/v2/tutorial_pt3_v2.ipynb | 9 +++ .../getting-started/v2/tutorial_pt4_v2.ipynb | 9 +++ .../getting-started/v2/tutorial_pt5_v2.ipynb | 9 +++ .../getting-started/v2/tutorial_pt6_v2.ipynb | 9 +++ .../getting-started/v2/tutorial_pt7_v2.ipynb | 66 +++++++++---------- 7 files changed, 86 insertions(+), 34 deletions(-) diff --git a/notebooks/guides/getting-started/v2/tutorial_pt1_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt1_v2.ipynb index cfaa4bf..d747e9d 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt1_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt1_v2.ipynb @@ -1,5 +1,14 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/notebooks/guides/getting-started/v2/tutorial_pt2_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt2_v2.ipynb index f187af9..6355819 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt2_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt2_v2.ipynb @@ -1,5 +1,14 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/notebooks/guides/getting-started/v2/tutorial_pt3_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt3_v2.ipynb index e77e680..103bf4a 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt3_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt3_v2.ipynb @@ -1,5 +1,14 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/notebooks/guides/getting-started/v2/tutorial_pt4_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt4_v2.ipynb index ecb6ee7..1b08da0 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt4_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt4_v2.ipynb @@ -1,5 +1,14 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/notebooks/guides/getting-started/v2/tutorial_pt5_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt5_v2.ipynb index e6c51f4..190b49f 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt5_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt5_v2.ipynb @@ -1,5 +1,14 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb index a7da6f2..1c656ef 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb @@ -1,5 +1,14 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb index 797120f..43ecfa6 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb @@ -1,5 +1,14 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -38,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -47,8 +56,9 @@ "\n", "import cohere\n", "import json\n", + "import os\n", "\n", - "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + "co = cohere.ClientV2(api_key=os.environ[\"COHERE_API_KEY\"]) # Get your free API key: https://dashboard.cohere.com/api-keys" ] }, { @@ -179,7 +189,7 @@ " \"description\": \"the time of the event, formatted using 24h military time formatting\"\n", " },\n", " \"duration\": {\n", - " \"type\": \"float\",\n", + " \"type\": \"number\",\n", " \"description\": \"the number of hours the event lasts for\"\n", " }\n", " },\n", @@ -216,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -227,7 +237,7 @@ "I will search the user's emails for any messages about getting set up with IT. \n", "\n", "Tool calls:\n", - "Tool name: search_emails | Parameters: {\"query\":\"getting setup with IT\"}\n" + "Tool name: search_emails | Parameters: {\"query\":\"IT setup\"}\n" ] } ], @@ -280,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -288,7 +298,7 @@ "output_type": "stream", "text": [ "Tool results:\n", - "output={'emails': [{'from': 'it@co1t.com', 'to': 'david@co1t.com', 'date': '2024-06-24', 'subject': 'Setting Up Your IT Needs', 'text': 'Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.'}, {'from': 'john@co1t.com', 'to': 'david@co1t.com', 'date': '2024-06-24', 'subject': 'First Week Check-In', 'text': \"Hello! I hope you're settling in well. Let's connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it's a great opportunity to get to know your colleagues!\"}]}\n" + "{\"emails\": [{\"from\": \"it@co1t.com\", \"to\": \"david@co1t.com\", \"date\": \"2024-06-24\", \"subject\": \"Setting Up Your IT Needs\", \"text\": \"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\"}, {\"from\": \"john@co1t.com\", \"to\": \"david@co1t.com\", \"date\": \"2024-06-24\", \"subject\": \"First Week Check-In\", \"text\": \"Hello! I hope you're settling in well. Let's connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon\\u2014it's a great opportunity to get to know your colleagues!\"}]}\n" ] } ], @@ -297,7 +307,7 @@ "tool_content = []\n", "for tc in response.message.tool_calls:\n", " tool_result = functions_map[tc.function.name](**json.loads(tc.function.arguments))\n", - " tool_content.append(cohere.ToolContent(output=tool_result))\n", + " tool_content.append(json.dumps(tool_result))\n", " # Append tool results to the chat history\n", " messages.append({\"role\": \"tool\", \"tool_call_id\": tc.id, \"tool_content\": tool_content}) \n", "\n", @@ -315,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -323,17 +333,15 @@ "output_type": "stream", "text": [ "Response:\n", - "Yes, there is an email from IT with the subject \"Setting Up Your IT Needs\". It includes an attached comprehensive guide to help you set up your work accounts.\n", + "Yes, there is an email from IT with a comprehensive guide attached to help you set up your work accounts.\n", "==================================================\n", "\n", "CITATIONS:\n", - "start=17 end=30 text='email from IT' sources=[Source_Tool(id='search_emails_191n4etevvf3:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'}, type='tool')] \n", + "start=17 end=30 text='email from IT' sources=[Source_Tool(type='tool', id='search_emails_vrv2xt48qcgn:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'})] \n", "\n", - "start=40 end=73 text='subject \"Setting Up Your IT Needs' sources=[Source_Tool(id='search_emails_191n4etevvf3:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'}, type='tool')] \n", + "start=38 end=66 text='comprehensive guide attached' sources=[Source_Tool(type='tool', id='search_emails_vrv2xt48qcgn:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'})] \n", "\n", - "start=91 end=119 text='attached comprehensive guide' sources=[Source_Tool(id='search_emails_191n4etevvf3:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'}, type='tool')] \n", - "\n", - "start=132 end=157 text='set up your work accounts' sources=[Source_Tool(id='search_emails_191n4etevvf3:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'}, type='tool')] \n", + "start=79 end=104 text='set up your work accounts' sources=[Source_Tool(type='tool', id='search_emails_vrv2xt48qcgn:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'})] \n", "\n" ] } @@ -347,7 +355,7 @@ ")\n", "\n", "# Append assistant response to the chat history\n", - "messages.append({\"role\": \"assistant\", \"content\": response.message.content})\n", + "messages.append({\"role\": \"assistant\", \"content\": response.message.content[0].text})\n", "\n", "# Print final response\n", "print(\"Response:\")\n", @@ -379,7 +387,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -422,8 +430,8 @@ " # Step 3: Get tool results\n", " tool_content = []\n", " for idx, tc in enumerate(response.message.tool_calls):\n", - " tool_result= functions_map[tc.function.name](**json.loads(tc.function.arguments))\n", - " tool_content.append(cohere.ToolContent(output=tool_result))\n", + " tool_result = functions_map[tc.function.name](**json.loads(tc.function.arguments))\n", + " tool_content.append(json.dumps(tool_result))\n", " messages.append({\"role\": \"tool\", \"tool_call_id\": tc.id, \"tool_content\": tool_content})\n", " \n", " # Step 4: Generate response and citations \n", @@ -433,7 +441,7 @@ " tools=tools\n", " )\n", " \n", - " messages.append({\"role\": \"assistant\", \"content\": response.message.content})\n", + " messages.append({\"role\": \"assistant\", \"content\": response.message.content[0].text})\n", " \n", " # Print final response\n", " print(\"Response:\")\n", @@ -464,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -475,27 +483,17 @@ "Can you check if there are any lunch invites, and for those days, create a one-hour event on my calendar at 12PM.\n", "==================================================\n", "Tool plan:\n", - "I will search the user's emails for lunch invites and then create a calendar event for each day that they have been invited to lunch. \n", + "I will search the user's emails for lunch invites and then create a calendar event for each day they are invited to lunch. \n", "\n", "Tool calls:\n", "Tool name: search_emails | Parameters: {\"query\":\"lunch invites\"}\n", "==================================================\n", - "Tool plan:\n", - "I found an email from john@co1t.com inviting the user to a welcoming lunch on Thursday at noon. I will now create a calendar event for this. \n", - "\n", - "Tool calls:\n", - "Tool name: create_calendar_event | Parameters: {\"date\":\"06/27/24\",\"duration\":1,\"time\":\"12:00\"}\n", - "==================================================\n", "Response:\n", - "I found an email from John inviting you to a welcoming lunch this Thursday at noon. I've created a one-hour event on your calendar for Thursday at 12 PM.\n", + "Sure, I can see that you have a welcoming lunch this Thursday at noon. I've created a one-hour calendar event for you at this time.\n", "==================================================\n", "\n", "CITATIONS:\n", - "start=11 end=26 text='email from John' sources=[Source_Tool(id='search_emails_2jxdbc1v4ga6:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'}, type='tool')] \n", - "\n", - "start=45 end=82 text='welcoming lunch this Thursday at noon' sources=[Source_Tool(id='search_emails_2jxdbc1v4ga6:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'}, type='tool')] \n", - "\n", - "start=99 end=152 text='one-hour event on your calendar for Thursday at 12 PM' sources=[Source_Tool(id='create_calendar_event_8g3d1nfc0mms:0', tool_output={'is_success': 'true', 'message': 'Created a 1 hour long event at 12:00 on 06/27/24'}, type='tool')] \n", + "start=32 end=69 text='welcoming lunch this Thursday at noon' sources=[Source_Tool(type='tool', id='search_emails_92q6pjvjhehe:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'})] \n", "\n" ] } From 3e1a80f00280bb2dc2638bd6ee7c1a79cc0b5f21 Mon Sep 17 00:00:00 2001 From: mrmer1 Date: Mon, 16 Sep 2024 13:00:48 +0800 Subject: [PATCH 04/11] update tool use nb --- notebooks/agents/Vanilla_Tool_Use_v2.ipynb | 80 +++++++++++++--------- 1 file changed, 46 insertions(+), 34 deletions(-) diff --git a/notebooks/agents/Vanilla_Tool_Use_v2.ipynb b/notebooks/agents/Vanilla_Tool_Use_v2.ipynb index 6cda79f..dcae7ff 100644 --- a/notebooks/agents/Vanilla_Tool_Use_v2.ipynb +++ b/notebooks/agents/Vanilla_Tool_Use_v2.ipynb @@ -66,8 +66,20 @@ "outputs": [], "source": [ "import cohere, json\n", - "\n", - "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + "API_KEY = \"...\" # fill in your Cohere API key here\n", + "co = cohere.Client(API_KEY)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import cohere\n", + "import os\n", + "import json\n", + "co = cohere.ClientV2(os.getenv(\"COHERE_API_KEY\"))" ] }, { @@ -82,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 3, "metadata": { "id": "yZffY8xItLGp" }, @@ -132,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 4, "metadata": { "id": "YuIH4us8tLGp" }, @@ -191,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 5, "metadata": { "id": "aIk-of_OtLGp" }, @@ -252,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 6, "metadata": { "id": "JuDgJ7fjtLGq" }, @@ -283,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -299,7 +311,7 @@ "The model recommends doing the following tool calls:\n", "\n", "Tool plan:\n", - "I will search for the sales summary for 29th September 2023 and details about the products in the 'Electronics' category. \n", + "I will search for the sales summary for 29th September 2023 and the details of products in the 'Electronics' category. \n", "\n", "Tool calls:\n", "Tool name: query_daily_sales_report | Parameters: {\"day\":\"2023-09-29\"}\n", @@ -342,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -357,31 +369,31 @@ "text": [ "Tool results that will be fed back to the model in step 4:\n", "{\n", - " \"date\": \"2023-09-29\",\n", - " \"summary\": \"Total Sales Amount: 10000, Total Units Sold: 250\"\n", + " \"date\": \"2023-09-29\",\n", + " \"summary\": \"Total Sales Amount: 10000, Total Units Sold: 250\"\n", "}\n", "{\n", - " \"category\": \"Electronics\",\n", - " \"products\": [\n", - " {\n", - " \"product_id\": \"E1001\",\n", - " \"name\": \"Smartphone\",\n", - " \"price\": 500,\n", - " \"stock_level\": 20\n", - " },\n", - " {\n", - " \"product_id\": \"E1002\",\n", - " \"name\": \"Laptop\",\n", - " \"price\": 1000,\n", - " \"stock_level\": 15\n", - " },\n", - " {\n", - " \"product_id\": \"E1003\",\n", - " \"name\": \"Tablet\",\n", - " \"price\": 300,\n", - " \"stock_level\": 25\n", - " }\n", - " ]\n", + " \"category\": \"Electronics\",\n", + " \"products\": [\n", + " {\n", + " \"product_id\": \"E1001\",\n", + " \"name\": \"Smartphone\",\n", + " \"price\": 500,\n", + " \"stock_level\": 20\n", + " },\n", + " {\n", + " \"product_id\": \"E1002\",\n", + " \"name\": \"Laptop\",\n", + " \"price\": 1000,\n", + " \"stock_level\": 15\n", + " },\n", + " {\n", + " \"product_id\": \"E1003\",\n", + " \"name\": \"Tablet\",\n", + " \"price\": 300,\n", + " \"stock_level\": 25\n", + " }\n", + " ]\n", "}\n" ] } @@ -393,13 +405,13 @@ " # here is where you would call the tool recommended by the model, using the parameters recommended by the model\n", " tool_result= functions_map[tc.function.name](**json.loads(tc.function.arguments))\n", " # store the output in a list\n", - " tool_content.append(cohere.ToolContent(output=tool_result))\n", + " tool_content.append(json.dumps(tool_result))\n", " # append the chat history\n", " messages.append({'role': 'tool', 'tool_call_id': tc.id, 'tool_content': tool_content}) \n", "\n", "print(\"Tool results that will be fed back to the model in step 4:\")\n", "for result in tool_content:\n", - " print(json.dumps(result.output, indent=4))\n" + " print(json.dumps(json.loads(result), indent=2))\n" ] }, { From 41252bcb3d3dff77e5ead9183da166259f2202ac Mon Sep 17 00:00:00 2001 From: mrmer1 Date: Thu, 19 Sep 2024 10:19:35 +0800 Subject: [PATCH 05/11] add multi step tool use --- .../Multi_Step_Tool_Use_Spotify_v2.ipynb | 605 ++++++++++++++++++ 1 file changed, 605 insertions(+) create mode 100644 notebooks/agents/Multi_Step_Tool_Use_Spotify_v2.ipynb diff --git a/notebooks/agents/Multi_Step_Tool_Use_Spotify_v2.ipynb b/notebooks/agents/Multi_Step_Tool_Use_Spotify_v2.ipynb new file mode 100644 index 0000000..bb1960b --- /dev/null +++ b/notebooks/agents/Multi_Step_Tool_Use_Spotify_v2.ipynb @@ -0,0 +1,605 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-Step Tool Use with Spotify Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates an agent that performs analysis on a Spotify tracks dataset (via a Python interpreter tool) while also having access to a web search tool." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "\n", + "import cohere\n", + "\n", + "co = cohere.ClientV2(\"COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "! pip install tavily-python --q\n", + "\n", + "from tavily import TavilyClient\n", + "tavily_client = TavilyClient(api_key=\"TAVILY_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Define the tools" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we define the web search tool, which uses the Tavily Python client to perform web searches.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# here's a web search engine\n", + "def web_search(query: str) -> list[dict]:\n", + " response = tavily_client.search(query, max_results=3)['results']\n", + " return {\"results\": response}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# the LLM is equipped with a description of the web search engine\n", + "web_search_tool = {\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"web_search\",\n", + " \"description\": \"Returns a list of relevant document snippets for a textual query retrieved from the internet\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"query\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"Query to search the internet with\"\n", + " }\n", + " },\n", + " \"required\": [\"query\"]\n", + " }\n", + " }\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we define the Python interpreter tool, which uses the `exec` function to execute Python code." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# here's a python console, which can be used to access the spreadsheet, but also more generally to code and plot stuff\n", + "import io, contextlib\n", + "def python_interpreter(code: str) -> list[dict]:\n", + " output = io.StringIO()\n", + " try:\n", + " # Redirect stdout to capture print statements\n", + " with contextlib.redirect_stdout(output):\n", + " exec(code, globals())\n", + " except Exception as e:\n", + " return {\n", + " \"error\": str(e),\n", + " \"executed_code\": code\n", + " }\n", + " # Get stdout\n", + " return {\n", + " \t\t\"console_output\": output.getvalue(),\n", + " \"executed_code\": code\n", + " \t}\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# the LLM is equipped with a description of a python console\n", + "python_interpreter_tool = {\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"python_interpreter\",\n", + " \"description\": \"Executes python code and returns the result. The code runs in a static sandbox without internet access and without interactive mode, so print output or save output to a file.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"code\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"Python code to execute\"\n", + " }\n", + " },\n", + " \"required\": [\"code\"]\n", + " }\n", + " }\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "functions_map = {\n", + " \"web_search\": web_search,\n", + " \"python_interpreter\": python_interpreter,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll also need the spotify_data dataset, which contains information about Spotify tracks such as the track information, release information, popularity metrics, and musical characteristics. You can find the [dataset here](https://github.com/cohere-ai/notebooks/blob/main/notebooks/guides/advanced_rag/spotify_dataset.csv)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/meor/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n", + " from pandas.core import (\n" + ] + }, + { + "data": { + "text/html": [ + "
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track_nameartist(s)_nameartist_countreleased_yearreleased_monthreleased_dayin_spotify_playlistsin_spotify_chartsstreamsin_apple_playlists...keymodedanceabilityvalenceenergyacousticnessinstrumentalnesslivenessspeechinessrelease_date
0Seven (feat. Latto) (Explicit Ver.)Latto, Jung Kook22023714553147141381703.043...BMajor808983310842023-07-14
1LALAMyke Towers12023323147448133716286.048...C#Major716174701042023-03-23
2vampireOlivia Rodrigo120236301397113140003974.094...FMajor5132531703162023-06-30
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" + ], + "text/plain": [ + " track_name artist(s)_name artist_count \\\n", + "0 Seven (feat. Latto) (Explicit Ver.) Latto, Jung Kook 2 \n", + "1 LALA Myke Towers 1 \n", + "2 vampire Olivia Rodrigo 1 \n", + "\n", + " released_year released_month released_day in_spotify_playlists \\\n", + "0 2023 7 14 553 \n", + "1 2023 3 23 1474 \n", + "2 2023 6 30 1397 \n", + "\n", + " in_spotify_charts streams in_apple_playlists ... key mode \\\n", + "0 147 141381703.0 43 ... B Major \n", + "1 48 133716286.0 48 ... C# Major \n", + "2 113 140003974.0 94 ... F Major \n", + "\n", + " danceability valence energy acousticness instrumentalness liveness \\\n", + "0 80 89 83 31 0 8 \n", + "1 71 61 74 7 0 10 \n", + "2 51 32 53 17 0 31 \n", + "\n", + " speechiness release_date \n", + "0 4 2023-07-14 \n", + "1 4 2023-03-23 \n", + "2 6 2023-06-30 \n", + "\n", + "[3 rows x 25 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the first few rows of the dataset\n", + "import pandas as pd\n", + "file_path = './spotify_dataset.csv'\n", + "spotify_data = pd.read_csv(file_path)\n", + "spotify_data.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is the task that the agent needs to perform:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "message = \"\"\"What's the age and citizenship of the artists who had the top 3 most streamed songs on Spotify in 2023?\n", + "\n", + "You have access to a dataset with information about Spotify songs from the past 10 years, located at ./spotify_dataset.csv.\n", + "You also have access to the internet to search for information not available in the dataset.\n", + "You must use the dataset when you can, and if stuck you can use the internet.\n", + "Remember to inspect the dataset and get a list of its columnsto understand its structure before trying to query it. Take it step by step.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Run the tool use workflow\n", + "\n", + "Next, we run the tool use workflow involving for steps:\n", + "\n", + "- Get the user message\n", + "- Model generates tool calls, if any\n", + "- Execute tools based on the tool calls generated by the model\n", + "- Model either generates more tool calls or returns a response with citations\n", + "\n", + "Looking at the example output, the agent performs the task in a sequence of 3 steps:\n", + "\n", + "- Inspect the dataset and get a list of its columns.\n", + "- Write and execute Python code to find the top 3 most streamed songs on Spotify in 2023 and their respective artists.\n", + "- Search for the age and citizenship of each artist on the internet." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "USER MESSAGE:\n", + "What's the age and citizenship of the artists who had the top 3 most streamed songs on Spotify in 2023?\n", + "\n", + "You have access to a dataset with information about Spotify songs from the past 10 years, located at ./spotify_dataset.csv.\n", + "You also have access to the internet to search for information not available in the dataset.\n", + "You must use the dataset when you can, and if stuck you can use the internet.\n", + "Remember to inspect the dataset and get a list of its columnsto understand its structure before trying to query it. Take it step by step.\n", + "\n", + "==================================================\n", + "\n", + "TOOL PLAN:\n", + "I will first inspect the dataset to understand its structure and the columns it contains. Then, I will write and execute Python code to find the top 3 most streamed songs on Spotify in 2023 and their respective artists. Finally, I will search for the age and citizenship of each artist online.\n", + "\n", + "TOOL CALLS:\n", + "Tool name: python_interpreter\n", + " import pandas as pd\n", + " \n", + " df = pd.read_csv('spotify_dataset.csv')\n", + " \n", + " print(df.columns)\n", + "None\n", + "\n", + "TOOL RESULTS:\n", + "{'console_output': \"Index(['track_name', 'artist(s)_name', 'artist_count', 'released_year',\\n 'released_month', 'released_day', 'in_spotify_playlists',\\n 'in_spotify_charts', 'streams', 'in_apple_playlists', 'in_apple_charts',\\n 'in_deezer_playlists', 'in_deezer_charts', 'in_shazam_charts', 'bpm',\\n 'key', 'mode', 'danceability', 'valence', 'energy', 'acousticness',\\n 'instrumentalness', 'liveness', 'speechiness', 'release_date'],\\n dtype='object')\\n\", 'executed_code': \"import pandas as pd\\n\\ndf = pd.read_csv('spotify_dataset.csv')\\n\\nprint(df.columns)\"} \n", + "\n", + "\n", + "TOOL PLAN:\n", + "I now know the column names of the dataset. I will now find the top 3 most streamed songs on Spotify in 2023 and then find the age and citizenship of the artists of those songs.\n", + "\n", + "TOOL CALLS:\n", + "Tool name: python_interpreter\n", + " import pandas as pd\n", + " \n", + " df = pd.read_csv('spotify_dataset.csv')\n", + " \n", + " # Filter for songs released in 2023\n", + " df_2023 = df[df['released_year'] == 2023]\n", + " \n", + " # Sort by streams in descending order\n", + " df_2023 = df_2023.sort_values(by='streams', ascending=False)\n", + " \n", + " # Get the top 3 songs\n", + " top_3_songs = df_2023.head(3)\n", + " \n", + " print(\"Top 3 most streamed songs on Spotify in 2023:\")\n", + " print(top_3_songs[['track_name', 'artist(s)_name']])\n", + "None\n", + "\n", + "TOOL RESULTS:\n", + "{'console_output': 'Top 3 most streamed songs on Spotify in 2023:\\n track_name artist(s)_name\\n12 Flowers Miley Cyrus\\n6 Ella Baila Sola Eslabon Armado, Peso Pluma\\n133 Shakira: Bzrp Music Sessions, Vol. 53 Shakira, Bizarrap\\n', 'executed_code': 'import pandas as pd\\n\\ndf = pd.read_csv(\\'spotify_dataset.csv\\')\\n\\n# Filter for songs released in 2023\\ndf_2023 = df[df[\\'released_year\\'] == 2023]\\n\\n# Sort by streams in descending order\\ndf_2023 = df_2023.sort_values(by=\\'streams\\', ascending=False)\\n\\n# Get the top 3 songs\\ntop_3_songs = df_2023.head(3)\\n\\nprint(\"Top 3 most streamed songs on Spotify in 2023:\")\\nprint(top_3_songs[[\\'track_name\\', \\'artist(s)_name\\']])'} \n", + "\n", + "\n", + "RESPONSE:\n", + "The top 3 most streamed songs on Spotify in 2023 were:\n", + "1. *Flowers* by Miley Cyrus\n", + "2. *Ella Baila Sola* by Eslabon Armado and Peso Pluma\n", + "3. *Shakira: Bzrp Music Sessions, Vol. 53* by Shakira and Bizarrap\n", + "\n", + "Miley Cyrus is 30 years old and American. Eslabon Armado is a Mexican group, and Peso Pluma is a Mexican singer. Shakira is 46 years old and Colombian.\n", + "\n", + "CITATIONS:\n", + "Start: 59 | End: 66 | Text: 'Flowers'\n", + "Sources:\n", + "python_interpreter_ryqfeye95gay:0\n", + "--------------------------------------------------\n", + "Start: 71 | End: 82 | Text: 'Miley Cyrus'\n", + "Sources:\n", + "python_interpreter_ryqfeye95gay:0\n", + "--------------------------------------------------\n", + "Start: 87 | End: 102 | Text: 'Ella Baila Sola'\n", + "Sources:\n", + "python_interpreter_ryqfeye95gay:0\n", + "--------------------------------------------------\n", + "Start: 107 | End: 121 | Text: 'Eslabon Armado'\n", + "Sources:\n", + "python_interpreter_ryqfeye95gay:0\n", + "--------------------------------------------------\n", + "Start: 126 | End: 136 | Text: 'Peso Pluma'\n", + "Sources:\n", + "python_interpreter_ryqfeye95gay:0\n", + "--------------------------------------------------\n", + "Start: 141 | End: 178 | Text: 'Shakira: Bzrp Music Sessions, Vol. 53'\n", + "Sources:\n", + "python_interpreter_ryqfeye95gay:0\n", + "--------------------------------------------------\n", + "Start: 183 | End: 190 | Text: 'Shakira'\n", + "Sources:\n", + "python_interpreter_ryqfeye95gay:0\n", + "--------------------------------------------------\n", + "Start: 195 | End: 203 | Text: 'Bizarrap'\n", + "Sources:\n", + "python_interpreter_ryqfeye95gay:0\n", + "--------------------------------------------------\n" + ] + } + ], + "source": [ + "model = \"command-r-plus-08-2024\"\n", + "tools = [web_search_tool,python_interpreter_tool]\n", + "\n", + "# Step 1: get user message\n", + "print(f\"USER MESSAGE:\\n{message}\")\n", + "print(\"=\"*50)\n", + "\n", + "messages = [{'role': 'user','content': message}]\n", + "\n", + "# 2 - Model generates tool calls, if any\n", + "res = co.chat(model=model,\n", + " messages=messages,\n", + " tools=tools,\n", + " temperature=0)\n", + "\n", + "# Keep invoking tools as long as the model generates tool calls\n", + "while res.message.tool_calls:\n", + " # Tool plan and tool calls\n", + " print(\"\\nTOOL PLAN:\")\n", + " print(res.message.tool_plan)\n", + "\n", + " print(\"\\nTOOL CALLS:\")\n", + " for tc in res.message.tool_calls:\n", + " if tc.function.name == \"python_interpreter\":\n", + " print(f\"Tool name: {tc.function.name}\")\n", + " tool_call_prettified = print(\"\\n\".join(f\" {line}\" for line_num, line in enumerate(json.loads(tc.function.arguments)[\"code\"].splitlines())))\n", + " print(tool_call_prettified)\n", + " else:\n", + " print(f\"Tool name: {tc.function.name} | Parameters: {tc.function.arguments}\")\n", + "\n", + " messages.append({'role': 'assistant',\n", + " 'tool_calls': res.message.tool_calls,\n", + " 'tool_plan': res.message.tool_plan})\n", + "\n", + " # 3 - Execute tools based on the tool calls generated by the model\n", + " print(\"\\nTOOL RESULTS:\")\n", + " for tc in res.message.tool_calls:\n", + " tool_result = functions_map[tc.function.name](**json.loads(tc.function.arguments))\n", + " tool_content = [json.dumps(tool_result)]\n", + " print(tool_result, \"\\n\")\n", + " \n", + " messages.append({'role': 'tool',\n", + " 'tool_call_id': tc.id,\n", + " 'tool_content': tool_content}) \n", + "\n", + " # 4 - Model either generates more tool calls or returns a response\n", + " res = co.chat(model=model,\n", + " messages=messages,\n", + " tools=tools,\n", + " temperature=0)\n", + " \n", + "messages.append({\"role\": \"assistant\", \"content\": res.message.content[0].text})\n", + "\n", + "print(\"\\nRESPONSE:\")\n", + "print(res.message.content[0].text)\n", + "\n", + "if res.message.citations:\n", + " print(\"\\nCITATIONS:\")\n", + " for citation in res.message.citations:\n", + " print(f\"Start: {citation.start} | End: {citation.end} | Text: '{citation.text}'\")\n", + " print(\"Sources:\")\n", + " if citation.sources:\n", + " for source in citation.sources:\n", + " print(source.id)\n", + " print(\"-\"*50)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "base" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From e87f67b278b59f16d644b3d22a1e60cd4d06c26b Mon Sep 17 00:00:00 2001 From: mrmer1 Date: Thu, 19 Sep 2024 13:07:39 +0800 Subject: [PATCH 06/11] update tutorials --- .../getting-started/v2/tutorial_pt1_v2.ipynb | 112 ---------- .../getting-started/v2/tutorial_pt2_v2.ipynb | 69 +++--- .../getting-started/v2/tutorial_pt3_v2.ipynb | 77 +++---- .../getting-started/v2/tutorial_pt4_v2.ipynb | 73 ++++--- .../getting-started/v2/tutorial_pt5_v2.ipynb | 24 +-- .../getting-started/v2/tutorial_pt6_v2.ipynb | 199 ++++++++++-------- .../getting-started/v2/tutorial_pt7_v2.ipynb | 45 ++-- 7 files changed, 249 insertions(+), 350 deletions(-) diff --git a/notebooks/guides/getting-started/v2/tutorial_pt1_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt1_v2.ipynb index d747e9d..4a2d6d5 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt1_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt1_v2.ipynb @@ -90,118 +90,6 @@ "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Accessing Cohere from Other Platforms" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Cohere platform is the fastest way to access Cohere's models and get started.\n", - "\n", - "However, if you prefer other options, you can access Cohere's models through other platforms such as Amazon Bedrock, Amazon SageMaker, Azure AI Studio, and Oracle Cloud Infrastructure (OCI) Generative AI Service.\n", - "\n", - "Read this documentation on [Cohere SDK cloud platform compatibility](https://docs.cohere.com/docs/cohere-works-everywhere)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Amazon Bedrock" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following is how you can create a Cohere client on Amazon Bedrock.\n", - "\n", - "For further information, read this documentation on [Cohere on Bedrock](https://docs.cohere.com/docs/cohere-on-aws#amazon-bedrock)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import cohere\n", - "\n", - "co = cohere.BedrockClient(\n", - " aws_region=\"...\",\n", - " aws_access_key=\"...\",\n", - " aws_secret_key=\"...\",\n", - " aws_session_token=\"...\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Amazon SageMaker" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following is how you can create a Cohere client on Amazon SageMaker.\n", - "\n", - "For further information, read this documentation on [Cohere on SageMaker](https://docs.cohere.com/docs/cohere-on-aws#amazon-sagemaker)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import cohere\n", - "\n", - "co = cohere.SagemakerClient(\n", - " aws_region=\"us-east-1\",\n", - " aws_access_key=\"...\",\n", - " aws_secret_key=\"...\",\n", - " aws_session_token=\"...\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Microsoft Azure" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following is how you can create a Cohere client on Microsoft Azure.\n", - "\n", - "For further information, read this documentation on [Cohere on Azure](https://docs.cohere.com/docs/cohere-on-microsoft-azure)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import cohere\n", - "\n", - "co = cohere.Client(\n", - " api_key=\"...\",\n", - " base_url=\"...\",\n", - ")" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/notebooks/guides/getting-started/v2/tutorial_pt2_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt2_v2.ipynb index 6355819..6f24a6e 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt2_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt2_v2.ipynb @@ -1,14 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \"Open\n", - "" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -47,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -56,7 +47,7 @@ "import cohere\n", "import json\n", "\n", - "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + "co = cohere.ClientV2(api_key=\"COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" ] }, { @@ -79,20 +70,18 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Sure! Here is a short introduction message: \n", + "Sure! Here is a draft of an introduction message: \n", "\n", - "\"Hi everyone! My name is [Your Name], and I am thrilled to be joining the Co1t team today. I am excited to get to know you all and contribute to the amazing work being done at this startup. I look forward to collaborating with you and being a part of Co1t's journey. Feel free to reach out, and I can't wait to meet everyone!\" \n", + "\"Hi everyone! My name is [Your Name], and I am thrilled to be joining the Co1t team today. I am excited to get to know you all and contribute to the amazing work being done at this startup. A little about me: [Brief description of your role, experience, and interests]. Outside of work, I enjoy [Hobbies and interests]. I look forward to collaborating with you all and being a part of Co1t's journey. Let's connect and make something great together!\" \n", "\n", - "This message expresses your enthusiasm for joining the team and serves as an introduction to your teammates. It also invites them to reach out and connect, fostering an open and friendly work environment. \n", - "\n", - "Is there anything else I can help with as you begin your journey at Co1t?\n" + "Feel free to edit and personalize the message to your liking. Good luck with your new role at Co1t!\n" ] } ], @@ -101,7 +90,7 @@ "message = \"I'm joining a new startup called Co1t today. Could you help me write a short introduction message to my teammates.\"\n", "\n", "# Generate the response\n", - "response = co.chat(model=\"command-r-plus\",\n", + "response = co.chat(model=\"command-r-plus-08-2024\",\n", " messages=[{\"role\": \"user\", \"content\": message}])\n", " # messages=[cohere.UserMessage(content=message)])\n", "\n", @@ -143,14 +132,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\"I'm thrilled to join the Co1t team and look forward to contributing my skills and ideas to drive innovation and success.\"\n" + "\"Hi everyone, my name is [Your Name], and I am thrilled to join the Co1t team today as a [Your Role], eager to contribute my skills and ideas to the company's growth and success!\"\n" ] } ], @@ -159,7 +148,7 @@ "message = \"I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.\"\n", "\n", "# Generate the response\n", - "response = co.chat(model=\"command-r-plus\",\n", + "response = co.chat(model=\"command-r-plus-08-2024\",\n", " messages=[{\"role\": \"user\", \"content\": message}])\n", " # messages=[cohere.UserMessage(content=message)])\n", "\n", @@ -179,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -210,7 +199,7 @@ "Ticket title:\"\"\"\n", "\n", "# Generate the response\n", - "response = co.chat(model=\"command-r-plus\",\n", + "response = co.chat(model=\"command-r-plus-08-2024\",\n", " messages=[{\"role\": \"user\", \"content\": message}])\n", "\n", "print(response.message.content[0].text)" @@ -243,14 +232,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\"Hi everyone, my name is [Your Name] and I am thrilled to join the Co1t team today as their newest member, bringing my skills and passion to the table, and I look forward to contributing to the team's success and being part of an innovative and dynamic startup environment.\"\n" + "\"Hi, I'm [Your Name] and I'm thrilled to join the Co1t team today as a [Your Role], eager to contribute my skills and ideas to help drive innovation and success for our startup!\"\n" ] } ], @@ -259,7 +248,7 @@ "message = \"I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.\"\n", "\n", "# Generate the response\n", - "response = co.chat(model=\"command-r-plus\",\n", + "response = co.chat(model=\"command-r-plus-08-2024\",\n", " messages=[{\"role\": \"user\", \"content\": message}])\n", "\n", "print(response.message.content[0].text)" @@ -280,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -302,7 +291,7 @@ "\n", "# Generate the response multiple times by specifying a low temperature value\n", "for idx in range(3):\n", - " response = co.chat(model=\"command-r-plus\",\n", + " response = co.chat(model=\"command-r-plus-08-2024\",\n", " messages=[{\"role\": \"user\", \"content\": message}],\n", " temperature=0)\n", "\n", @@ -318,20 +307,22 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1: \"Revolution enthusiast\"\n", + "1: Here is a suggestion: \n", + "\n", + "\"Revolution Enthusiast. History Fan.\" \n", "\n", - "2: Here is a suggestion for your request: \n", + "This introduction highlights your passion for the industrial revolution and its impact on history while keeping within the word limit.\n", "\n", - "\"IR Nerd, shaping modernity.\"\n", + "2: \"Revolution fan.\"\n", "\n", - "3: \"Revolution Enthusiast, Shaping Modernity.\"\n", + "3: \"IR enthusiast.\"\n", "\n" ] } @@ -342,7 +333,7 @@ "\n", "# Generate the response multiple times by specifying a low temperature value\n", "for idx in range(3):\n", - " response = co.chat(model=\"command-r-plus\",\n", + " response = co.chat(model=\"command-r-plus-08-2024\",\n", " messages=[{\"role\": \"user\", \"content\": message}],\n", " temperature=1)\n", "\n", @@ -379,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -406,7 +397,7 @@ "\n", "# Generate the response multiple times by adding the JSON schema\n", "response = co.chat(\n", - " model=\"command-r-plus\",\n", + " model=\"command-r-plus-08-2024\",\n", " messages=[{\"role\": \"user\", \"content\": message}],\n", " response_format={\n", " \"type\": \"json_object\",\n", @@ -457,14 +448,14 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\"Hi everyone, my name is [Your Name] and I am thrilled to join the Co1t team today as their newest member, bringing my skills and passion to the table, and I look forward to contributing to the team's success and being part of an innovative and dynamic startup environment.\"" + "\"Hi, I'm [Your Name] and I'm thrilled to join the Co1t team today as a [Your Role], passionate about [Your Expertise], and excited to contribute to our shared mission of [Startup's Mission]!\"" ] } ], @@ -473,7 +464,7 @@ "message = \"I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.\"\n", "\n", "# Generate the response by streaming it\n", - "response = co.chat_stream(model=\"command-r-plus\",\n", + "response = co.chat_stream(model=\"command-r-plus-08-2024\",\n", " messages=[{\"role\": \"user\", \"content\": message}])\n", "\n", "for event in response:\n", diff --git a/notebooks/guides/getting-started/v2/tutorial_pt3_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt3_v2.ipynb index 103bf4a..302bf87 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt3_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt3_v2.ipynb @@ -1,14 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \"Open\n", - "" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -43,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -51,7 +42,7 @@ "\n", "import cohere\n", "\n", - "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + "co = cohere.ClientV2(api_key=\"COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" ] }, { @@ -76,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -84,29 +75,22 @@ "output_type": "stream", "text": [ "Sure, here's a rhyme to break the ice,\n", - "A warm welcome, nice to greet,\n", + "A warm welcome to the team, so nice,\n", "\n", - "Hi, I'm [Your Name], bright and bold,\n", - "Stepping into this startup, a story to unfold,\n", - "At Co1t, a journey begins, a path unknown,\n", - "But together, we'll create a future that's ours to own. \n", + "Hi, I'm [Your Name], a new face,\n", + "Ready to join the Co1t space,\n", "\n", - "I bring [Your Skill Set] and a will to learn and grow,\n", - "Ready to take on challenges, and face the flow,\n", - "Excited to meet you all, a team united,\n", - "Let's innovate and collaborate, and make our mark, ignited! \n", + "A journey begins, a path unknown,\n", + "But together we'll make our mark, a foundation stone,\n", "\n", - "Cheers to the power of shared dreams,\n", - "Here's to a great start, so it seems! \n", + "Excited to learn and contribute my part,\n", + "Let's create, innovate, and leave a lasting art,\n", "\n", - "Looking forward to connecting with you all,\n", - "Let's turn ideas into reality's call! \n", + "Looking forward to our adventures yet untold,\n", + "With teamwork and passion, let's achieve our goals!\n", "\n", - "Regards, \n", - "[Your Name] \n", - "\n", - "A new colleague, eager and keen,\n", - "Ready to rock at Co1t's machine!\n" + "Cheers to a great start!\n", + "Your enthusiastic new mate.\n" ] } ], @@ -126,7 +110,7 @@ " {\"role\": \"user\", \"content\": message}]\n", "\n", "# Generate the response\n", - "response = co.chat(model=\"command-r-plus\",\n", + "response = co.chat(model=\"command-r-plus-08-2024\",\n", " messages=messages)\n", "\n", "print(response.message.content[0].text)" @@ -158,14 +142,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\"Hello, teammates! Thrilled to join the Co1t family today and looking forward to collaborating with this dynamic team to drive innovation and create impact.\"\n" + "\"Hello, teammates! I'm thrilled to join the Co1t family today and looking forward to getting to know you all and contributing to our shared success.\"\n" ] } ], @@ -182,7 +166,7 @@ " {\"role\": \"user\", \"content\": message}]\n", "\n", "# Generate the response\n", - "response = co.chat(model=\"command-r-plus\",\n", + "response = co.chat(model=\"command-r-plus-08-2024\",\n", " messages=messages)\n", "\n", "print(response.message.content[0].text)" @@ -210,14 +194,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\"Hey, future Co1t teammates! Stoked to join this awesome crew and can't wait to dive into some game-changing ideas and collaborations—let's make some magic happen!\"\n" + "\"Hey, future Co1t buddies! Stoked to join this awesome team, let's get to know each other and make some startup magic together!\"\n" ] } ], @@ -232,7 +216,7 @@ "messages.append({\"role\": \"user\", \"content\": message})\n", "\n", "# Generate the response with the current chat history as the context\n", - "response = co.chat(model=\"command-r-plus\",\n", + "response = co.chat(model=\"command-r-plus-08-2024\",\n", " messages=messages)\n", "\n", "print(response.message.content[0].text)" @@ -263,14 +247,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\"Super excited to be part of the Co1t family and can't wait to contribute my skills and ideas to the team's success; looking forward to an amazing journey ahead under your leadership and guidance!\"\n" + "\"Hi, boss! So excited to dive into my new role at Co1t and eager to learn from your mentorship and guidance. Let's crush it!\"\n" ] } ], @@ -285,7 +269,7 @@ "messages.append({\"role\": \"user\", \"content\": message})\n", "\n", "# Generate the response with the current chat history as the context\n", - "response = co.chat(model=\"command-r-plus\",\n", + "response = co.chat(model=\"command-r-plus-08-2024\",\n", " messages=messages)\n", "\n", "print(response.message.content[0].text)" @@ -307,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -318,15 +302,15 @@ "\n", "{'role': 'user', 'content': \"I'm joining a new startup called Co1t today. Could you help me write a short introduction message to my teammates.\"} \n", "\n", - "{'role': 'assistant', 'content': '\"Hello, teammates! Thrilled to join the Co1t family today and looking forward to collaborating with this dynamic team to drive innovation and create impact.\"'} \n", + "{'role': 'assistant', 'content': '\"Hello, teammates! I\\'m thrilled to join the Co1t family today and looking forward to getting to know you all and contributing to our shared success.\"'} \n", "\n", "{'role': 'user', 'content': 'Make it more upbeat and conversational.'} \n", "\n", - "{'role': 'assistant', 'content': '\"Hey, future Co1t teammates! Stoked to join this awesome crew and can\\'t wait to dive into some game-changing ideas and collaborations—let\\'s make some magic happen!\"'} \n", + "{'role': 'assistant', 'content': '\"Hey, future Co1t buddies! Stoked to join this awesome team, let\\'s get to know each other and make some startup magic together!\"'} \n", "\n", "{'role': 'user', 'content': 'Thanks. Could you create another one for my DM to my manager.'} \n", "\n", - "{'role': 'assistant', 'content': '\"Super excited to be part of the Co1t family and can\\'t wait to contribute my skills and ideas to the team\\'s success; looking forward to an amazing journey ahead under your leadership and guidance!\"'} \n", + "{'role': 'assistant', 'content': '\"Hi, boss! So excited to dive into my new role at Co1t and eager to learn from your mentorship and guidance. Let\\'s crush it!\"'} \n", "\n" ] } @@ -364,6 +348,11 @@ "\n", "In Part 4, you will learn how text embeddings can power an important use case for RAG, which is semantic search." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { diff --git a/notebooks/guides/getting-started/v2/tutorial_pt4_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt4_v2.ipynb index 1b08da0..fea6183 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt4_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt4_v2.ipynb @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ "import cohere\n", "import numpy as np\n", "\n", - "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + "co = cohere.ClientV2(api_key=\"COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" ] }, { @@ -79,12 +79,13 @@ "Right now, we are doing the former. We call the Embed endpoint using `co.embed()` and pass the following arguments:\n", "- `model`: Here we choose `embed-english-v3.0`, which generates embeddings of size 1024\n", "- `input_type`: We choose `search_document` to ensure the model treats these as the documents for search\n", - "- `texts`: The list of texts (the FAQs)" + "- `texts`: The list of texts (the FAQs)\n", + "- `embedding_types`: We choose `float` to get the float embeddings." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -106,7 +107,8 @@ "doc_emb = co.embed(\n", " model=\"embed-english-v3.0\",\n", " input_type=\"search_document\",\n", - " texts=[doc['text'] for doc in faqs_long]).embeddings" + " texts=[doc['text'] for doc in faqs_long],\n", + " embedding_types=[\"float\"]).embeddings.float" ] }, { @@ -138,18 +140,19 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "# Add the user query\n", - "query = \"How do I stay connected to what's happening at the company?\"\n", + "query = \"Ways to connect with my teammates\"\n", "\n", "# Embed the query\n", "query_emb = co.embed(\n", " model=\"embed-english-v3.0\",\n", " input_type=\"search_query\",\n", - " texts=[query]).embeddings" + " texts=[query],\n", + " embedding_types=[\"float\"]).embeddings.float" ] }, { @@ -174,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -182,11 +185,11 @@ "output_type": "stream", "text": [ "Rank: 1\n", - "Score: 0.44259421762799145\n", + "Score: 0.442758615743984\n", "Document: {'text': \"Finding Coffee Spots: For your caffeine fix, head to the break room's coffee machine or cross the street to the cafĂ© for artisan coffee.\"}\n", "\n", "Rank: 2\n", - "Score: 0.3277577200136209\n", + "Score: 0.32783563708365726\n", "Document: {'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}\n", "\n" ] @@ -195,10 +198,10 @@ "source": [ "# Compute dot product similarity and display results\n", "def return_results(query_emb, doc_emb, documents):\n", - " n = 2\n", + " n = 2 # customize your top N results\n", " scores = np.dot(query_emb, np.transpose(doc_emb))[0]\n", " max_idx = np.argsort(-scores)[:n]\n", - " \n", + " \n", " for rank, idx in enumerate(max_idx):\n", " print(f\"Rank: {rank+1}\")\n", " print(f\"Score: {scores[idx]}\")\n", @@ -231,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -262,7 +265,8 @@ "doc_emb = co.embed(\n", " model=\"embed-multilingual-v3.0\",\n", " input_type=\"search_document\",\n", - " texts=[doc['text'] for doc in faqs_short_fr]).embeddings\n", + " texts=[doc['text'] for doc in faqs_short_fr],\n", + " embedding_types=[\"float\"]).embeddings.float\n", "\n", "# Add the user query\n", "query = \"What's your remote-working policy?\"\n", @@ -271,7 +275,8 @@ "query_emb = co.embed(\n", " model=\"embed-multilingual-v3.0\",\n", " input_type=\"search_query\",\n", - " texts=[query]).embeddings\n", + " texts=[query],\n", + " embedding_types=[\"float\"]).embeddings.float\n", "\n", "# Compute dot product similarity and display results\n", "return_results(query_emb, doc_emb, faqs_short_fr)" @@ -309,38 +314,38 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "# Embed the documents with the given embedding types\n", "doc_emb = co.embed(\n", " model=\"embed-english-v3.0\",\n", - " embedding_types=[\"float\",\"int8\"],\n", " input_type=\"search_document\",\n", - " texts=[doc['text'] for doc in faqs_long]).embeddings\n", + " texts=[doc['text'] for doc in faqs_long],\n", + " embedding_types=[\"float\",\"int8\"]).embeddings\n", "\n", "# Add the user query\n", - "query = \"How do I stay connected to what's happening at the company?\"\n", + "query = \"Ways to connect with my teammates\"\n", "\n", "# Embed the query\n", "query_emb = co.embed(\n", " model=\"embed-english-v3.0\",\n", - " embedding_types=[\"float\",\"int8\"],\n", " input_type=\"search_query\",\n", - " texts=[query]).embeddings" + " texts=[query],\n", + " embedding_types=[\"float\",\"int8\"]).embeddings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Here are the search results of using the `float` embeddings." + "Here are the search results of using the `float` embeddings (same as the earlier example)." ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -348,19 +353,19 @@ "output_type": "stream", "text": [ "Rank: 1\n", - "Score: 0.352135965228231\n", - "Document: {'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}\n", + "Score: 0.3872984617627964\n", + "Document: {'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}\n", "\n", "Rank: 2\n", - "Score: 0.31995661889273097\n", - "Document: {'text': 'Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.'}\n", + "Score: 0.3272549670724577\n", + "Document: {'text': 'Proposing New Ideas: Innovation is welcomed! Share your brilliant ideas at our weekly team meetings or directly with your team lead.'}\n", "\n" ] } ], "source": [ "# Compute dot product similarity and display results\n", - "return_results(query_emb.float_, doc_emb.float_, faqs_long)" + "return_results(query_emb.float, doc_emb.float, faqs_long)" ] }, { @@ -372,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -380,12 +385,12 @@ "output_type": "stream", "text": [ "Rank: 1\n", - "Score: 563583\n", - "Document: {'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}\n", + "Score: 613377\n", + "Document: {'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}\n", "\n", "Rank: 2\n", - "Score: 508692\n", - "Document: {'text': 'Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.'}\n", + "Score: 515890\n", + "Document: {'text': 'Proposing New Ideas: Innovation is welcomed! Share your brilliant ideas at our weekly team meetings or directly with your team lead.'}\n", "\n" ] } diff --git a/notebooks/guides/getting-started/v2/tutorial_pt5_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt5_v2.ipynb index 190b49f..ae98708 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt5_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt5_v2.ipynb @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -52,7 +52,7 @@ "\n", "import cohere\n", "\n", - "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + "co = cohere.ClientV2(api_key=\"COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" ] }, { @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -94,14 +94,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "id='37015cd2-a6a7-465e-a1dc-a3994851848f' results=[RerankResponseResultsItem(document=None, index=2, relevance_score=0.01798621), RerankResponseResultsItem(document=None, index=3, relevance_score=8.463939e-06)] meta=ApiMeta(api_version=ApiMetaApiVersion(version='1', is_deprecated=None, is_experimental=None), billed_units=ApiMetaBilledUnits(input_tokens=None, output_tokens=None, search_units=1.0, classifications=None), tokens=None, warnings=None)\n" + "id='2fa5bc0d-28aa-4c99-8355-7de78dbf3c86' results=[RerankResponseResultsItem(document=None, index=2, relevance_score=0.01798621), RerankResponseResultsItem(document=None, index=3, relevance_score=8.463939e-06)] meta=ApiMeta(api_version=ApiMetaApiVersion(version='1', is_deprecated=None, is_experimental=None), billed_units=ApiMetaBilledUnits(input_tokens=None, output_tokens=None, search_units=1.0, classifications=None), tokens=None, warnings=None)\n" ] } ], @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -199,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -249,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -349,7 +349,7 @@ "4 Contractor " ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -374,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -424,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { diff --git a/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb index 1c656ef..424ae65 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -61,7 +61,7 @@ "import json\n", "from typing import List\n", "\n", - "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + "co = cohere.ClientV2(api_key=\"COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" ] }, { @@ -77,7 +77,7 @@ "source": [ "To see how RAG works, let's define the documents that the application has access to. We'll use a short list of documents consisting of internal FAQs about the fictitious company Co1t (in production, these documents are massive).\n", "\n", - "In this example, each document is a dictionary with one field, `text`. But we can define any number of fields we want, depending on the nature of the documents. For example, emails could contain `title` and `text` fields." + "In this example, each document is a `data` object with one field, `text`. But we can define any number of fields we want, depending on the nature of the documents. For example, emails could contain `title` and `text` fields." ] }, { @@ -87,9 +87,21 @@ "outputs": [], "source": [ "documents = [\n", - " {\"text\": \"Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward.\"},\n", - " {\"text\": \"Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.\"},\n", - " {\"text\": \"Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.\"}\n", + " {\n", + " \"data\": {\n", + " \"text\": \"Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward.\"\n", + " }\n", + " },\n", + " {\n", + " \"data\": {\n", + " \"text\": \"Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.\"\n", + " }\n", + " },\n", + " {\n", + " \"data\": {\n", + " \"text\": \"Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.\"\n", + " }\n", + " }\n", "]" ] }, @@ -97,13 +109,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To use these documents, we create a `user` message containing two content fields:\n", - "- `content` for the user's query.\n", - "- `documents` for storing all the documents.\n", - "\n", - "The `documents` field consists of a list of dictionaries where each dictionary represents a document. A document can have different fields depending on its nature, such as `title`, `text`, `url`, etc. Optionally, you can also pass your own IDs for each document using the `id` field. Otherwise, the API will automatically generate the IDs based on the documents position in the list.\n", + "To call the Chat API with RAG, pass the following parameters at a minimum. This tells the model to run in RAG-mode and use these documents in its response.\n", "\n", - "We then pass this to the `messages` parameter in the Chat endpoint call. This tells the model to run in RAG-mode and use these documents in its response.\n", + "- `model` for the model ID\n", + "- `messages` for the user's query.\n", + "- `documents` for defining the documents.\n", "\n", "Let's create a query asking about the company's support for personal well-being, which is not going to be available to the model based on the data its trained on. It will need to use external documents.\n", "\n", @@ -114,39 +124,17 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 37, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Yes, we offer gym memberships, on-site yoga classes, and comprehensive health insurance.\n", - "\n", - "CITATIONS:\n", - "start=14 end=29 text='gym memberships' sources=[Source_Document(id='doc:0:2', document={'id': 'doc:0:2', 'text': 'Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.'}, type='document')] \n", - "\n", - "start=31 end=51 text='on-site yoga classes' sources=[Source_Document(id='doc:0:2', document={'id': 'doc:0:2', 'text': 'Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.'}, type='document')] \n", - "\n", - "start=57 end=88 text='comprehensive health insurance.' sources=[Source_Document(id='doc:0:2', document={'id': 'doc:0:2', 'text': 'Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.'}, type='document')] \n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Add the user query\n", "query = \"Are there health benefits?\"\n", "\n", - "## OPTIONAL: Define document IDs\n", - "# for i, doc in enumerate(documents):\n", - "# doc['id'] = str(i)\n", - "\n", - "# Add the user message containing the query and documents\n", - "messages = [{'role': 'user', 'content': query, \"documents\": documents}]\n", - "\n", "# Generate the response\n", - "response = co.chat(model=\"command-r-plus\",\n", - " messages=messages)\n", + "response = co.chat(model=\"command-r-plus-08-2024\",\n", + " messages=[{'role': 'user', 'content': query}],\n", + " documents=documents)\n", "\n", "# Display the response\n", "print(response.message.content[0].text)\n", @@ -179,51 +167,56 @@ "\n", "Let's now look at the first step—search query generation. The chatbot needs to generate an optimal set of search queries to use for retrieval. \n", "\n", - "There are different possible approaches to this. In this example, we'll take a tool use approach.\n", + "There are different possible approaches to this. In this example, we'll take a [tool use](v2/docs/tool-use) approach.\n", + "\n", + "Here, we build a tool that takes a user query and returns a list of relevant document snippets for that query. The tool can generate zero, one or multiple search queries depending on the user query.\n", "\n", - "Here, we build a tool that takes a user query and returns a list of relevant document snippets for that query. The tool can generate one or multiple search queries depending on the user query.\n", + "We'll not do a detailed walkthrough of tool use here as we'll do that in the next tutorial, which is a dedicated tutorial on tool use.\n", "\n", - "We'll not do a detailed walkthrough of tool use here as we'll do that in the next tutorial, which is a dedicated tutorial on tool use." + "We recommend using the v1 API for this functionality in order to leverage the `force_single_step` feature, as follows:" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ + "co_v1 = cohere.Client(api_key=\"COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys\n", + "\n", "def generate_search_queries(message: str) -> List[str]:\n", " \n", - " tools = [{\n", - " \"type\": \"function\",\n", - " \"function\": {\n", - " \"name\": \"document_search\",\n", - " \"description\" : \"Searches for textual documents based on a user query.\",\n", - " \"parameters\": {\n", - " \"type\": \"object\",\n", - " \"properties\": {\n", - " \"query\": {\"type\": \"string\", \"description\": \"The search query\"}\n", - " },\n", - " \"required\": [\"query\"]\n", + " # Define the query generation tool\n", + " query_gen_tool = [\n", + " {\n", + " \"name\": \"internet_search\",\n", + " \"description\": \"Returns a list of relevant document snippets for a textual query retrieved from the internet\",\n", + " \"parameter_definitions\": {\n", + " \"queries\": {\n", + " \"description\": \"a list of queries to search the internet with.\",\n", + " \"type\": \"List[str]\",\n", + " \"required\": True\n", " }\n", " }\n", " }]\n", - " \n", - " response = co.chat(\n", - " model=\"command-r\",\n", - " messages=[{\"role\": \"user\", \"content\": message}],\n", - " tools=tools\n", - " )\n", - " \n", + "\n", + " # Define a preamble to optimize search query generation\n", + " instructions = \"Write a search query that will find helpful information for answering the user's question accurately. If you need more than one search query, write a list of search queries. If you decide that a search is very unlikely to find information that would be useful in constructing a response to the user, you should instead directly answer.\"\n", + "\n", + " # Generate search queries (if any)\n", " search_queries = []\n", - " \n", - " if response.message.tool_calls:\n", - " res = response.message\n", - " documents = []\n", - " for tc in res.tool_calls:\n", - " query = json.loads(tc.function.arguments)[\"query\"]\n", - " search_queries.append(query)\n", - " \n", + "\n", + " response = co_v1.chat(\n", + " preamble=instructions,\n", + " model=\"command-r-08-2024\",\n", + " message=message,\n", + " force_single_step=True,\n", + " tools=query_gen_tool\n", + " )\n", + "\n", + " if response.tool_calls:\n", + " search_queries = response.tool_calls[0].parameters[\"queries\"]\n", + "\n", " return search_queries" ] }, @@ -231,24 +224,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In the example below, the tool breaks down the user message into two separate queries." + "In the example above, the tool breaks down the user message into two separate queries." ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['stay connected with the company', 'team events']\n" + "['how to stay connected with the company', 'does the company organize team events']\n" ] } ], "source": [ - "query = \"How to stay connected with the company and do you organize team events?\"\n", + "query = \"How to stay connected with the company, and do you organize team events?\"\n", "queries_for_search = generate_search_queries(query)\n", "print(queries_for_search)" ] @@ -262,14 +255,14 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['working hours flexibility']\n" + "['how flexible are the working hours at the company']\n" ] } ], @@ -279,6 +272,32 @@ "print(queries_for_search)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And in the example below, the tool decides that no retrieval is needed to answer the query." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[]\n" + ] + } + ], + "source": [ + "query = \"What is 2 + 2\"\n", + "queries_for_search = generate_search_queries(query)\n", + "print(queries_for_search)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -305,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -327,7 +346,8 @@ "doc_emb = co.embed(\n", " model=\"embed-english-v3.0\",\n", " input_type=\"search_document\",\n", - " texts=[doc['text'] for doc in faqs_long]).embeddings\n" + " texts=[doc['text'] for doc in faqs_long],\n", + " embedding_types=[\"float\"]).embeddings.float" ] }, { @@ -341,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -365,7 +385,8 @@ "query_emb = co.embed(\n", " model=\"embed-english-v3.0\",\n", " input_type=\"search_query\",\n", - " texts=[queries_for_search]).embeddings\n" + " texts=[queries_for_search],\n", + " embedding_types=[\"float\"]).embeddings.float" ] }, { @@ -381,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -445,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -485,11 +506,11 @@ "source": [ "Finally we reach the step that we saw in the earlier `Basic RAG` section.\n", "\n", - "To use these documents, we create a `user` message containing two content types:\n", - "- `text` for the user's query.\n", - "- `document` for storing all the documents.\n", + "To call the Chat API with RAG, we pass the following parameters. This tells the model to run in RAG-mode and use these documents in its response.\n", "\n", - "We then pass this to the `messages` parameter in the Chat endpoint call. This tells the model to run in RAG-mode and use these documents in its response.\n", + "- `model` for the model ID\n", + "- `messages` for the user's query.\n", + "- `documents` for defining the documents.\n", "\n", "The response is then generated based on the the query and the documents retrieved.\n", "\n", @@ -522,12 +543,10 @@ } ], "source": [ - "# Add the user message containing the query and documents\n", - "messages = [{'role': 'user', 'content': query, \"documents\": reranked_documents}]\n", - "\n", "# Generate the response\n", - "response = co.chat(model=\"command-r-plus\",\n", - " messages=messages)\n", + "response = co.chat(model=\"command-r-plus-08-2024\",\n", + " messages=[{'role': 'user', 'content': query}],\n", + " documents=reranked_documents)\n", "\n", "# Display the response\n", "print(response.message.content[0].text)\n", diff --git a/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb index 43ecfa6..aa7779c 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb @@ -56,9 +56,8 @@ "\n", "import cohere\n", "import json\n", - "import os\n", "\n", - "co = cohere.ClientV2(api_key=os.environ[\"COHERE_API_KEY\"]) # Get your free API key: https://dashboard.cohere.com/api-keys" + "co = cohere.ClientV2(api_key=\"COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" ] }, { @@ -82,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -226,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -256,7 +255,7 @@ "\n", "# Step 2: Tool planning and calling\n", "response = co.chat(\n", - " model=\"command-r-plus\",\n", + " model=\"command-r-plus-08-2024\",\n", " messages=messages,\n", " tools=tools\n", " )\n", @@ -290,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -325,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -333,15 +332,13 @@ "output_type": "stream", "text": [ "Response:\n", - "Yes, there is an email from IT with a comprehensive guide attached to help you set up your work accounts.\n", + "Yes, there is an email from IT with a comprehensive guide attached.\n", "==================================================\n", "\n", "CITATIONS:\n", - "start=17 end=30 text='email from IT' sources=[Source_Tool(type='tool', id='search_emails_vrv2xt48qcgn:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'})] \n", + "start=17 end=30 text='email from IT' sources=[Source_Tool(type='tool', id='search_emails_dy73yjrx50xq:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'})] \n", "\n", - "start=38 end=66 text='comprehensive guide attached' sources=[Source_Tool(type='tool', id='search_emails_vrv2xt48qcgn:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'})] \n", - "\n", - "start=79 end=104 text='set up your work accounts' sources=[Source_Tool(type='tool', id='search_emails_vrv2xt48qcgn:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'})] \n", + "start=38 end=66 text='comprehensive guide attached' sources=[Source_Tool(type='tool', id='search_emails_dy73yjrx50xq:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'})] \n", "\n" ] } @@ -349,7 +346,7 @@ "source": [ "# Step 4: Response and citation generation\n", "response = co.chat(\n", - " model=\"command-r-plus\",\n", + " model=\"command-r-plus-08-2024\",\n", " messages=messages,\n", " tools=tools\n", ")\n", @@ -387,11 +384,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "model = \"command-r-plus\"\n", + "model = \"command-r-plus-08-2024\"\n", "\n", "system_message=\"\"\"## Task and Context\n", "You are an assistant who assists new employees of Co1t with their first week. You respond to their questions and assist them with their needs. Today is Monday, June 24, 2024\"\"\"\n", @@ -486,14 +483,24 @@ "I will search the user's emails for lunch invites and then create a calendar event for each day they are invited to lunch. \n", "\n", "Tool calls:\n", - "Tool name: search_emails | Parameters: {\"query\":\"lunch invites\"}\n", + "Tool name: search_emails | Parameters: {\"query\":\"lunch invite\"}\n", + "==================================================\n", + "Tool plan:\n", + "I have found an email inviting the user to a welcoming lunch on Thursday at noon. I will now create a calendar event for this. \n", + "\n", + "Tool calls:\n", + "Tool name: create_calendar_event | Parameters: {\"date\":\"06/27/24\",\"duration\":1,\"time\":\"12:00\"}\n", "==================================================\n", "Response:\n", - "Sure, I can see that you have a welcoming lunch this Thursday at noon. I've created a one-hour calendar event for you at this time.\n", + "Sure, I found an email from John inviting you to a welcoming lunch this Thursday at noon. I've created a one-hour event on your calendar for this Thursday at 12 pm.\n", "==================================================\n", "\n", "CITATIONS:\n", - "start=32 end=69 text='welcoming lunch this Thursday at noon' sources=[Source_Tool(type='tool', id='search_emails_92q6pjvjhehe:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'})] \n", + "start=17 end=32 text='email from John' sources=[Source_Tool(type='tool', id='search_emails_j72zv2xhq0sj:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'})] \n", + "\n", + "start=51 end=88 text='welcoming lunch this Thursday at noon' sources=[Source_Tool(type='tool', id='search_emails_j72zv2xhq0sj:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'})] \n", + "\n", + "start=105 end=163 text='one-hour event on your calendar for this Thursday at 12 pm' sources=[Source_Tool(type='tool', id='create_calendar_event_vs7mxjzk9jzs:0', tool_output={'is_success': 'true', 'message': 'Created a 1 hour long event at 12:00 on 06/27/24'})] \n", "\n" ] } From 0523b2dfbfd4a45760bde5a6101a37393531a142 Mon Sep 17 00:00:00 2001 From: mrmer1 Date: Thu, 19 Sep 2024 13:09:54 +0800 Subject: [PATCH 07/11] update tutorials --- .../guides/getting-started/v2/tutorial_pt2_v2.ipynb | 11 ++++++++++- .../guides/getting-started/v2/tutorial_pt3_v2.ipynb | 11 ++++++++++- 2 files changed, 20 insertions(+), 2 deletions(-) diff --git a/notebooks/guides/getting-started/v2/tutorial_pt2_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt2_v2.ipynb index 6f24a6e..473566f 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt2_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt2_v2.ipynb @@ -1,5 +1,14 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -47,7 +56,7 @@ "import cohere\n", "import json\n", "\n", - "co = cohere.ClientV2(api_key=\"COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + "co = cohere.ClientV2(api_key\"COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" ] }, { diff --git a/notebooks/guides/getting-started/v2/tutorial_pt3_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt3_v2.ipynb index 302bf87..197acf3 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt3_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt3_v2.ipynb @@ -1,5 +1,14 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -42,7 +51,7 @@ "\n", "import cohere\n", "\n", - "co = cohere.ClientV2(api_key=\"COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + "co = cohere.ClientV2(api_key\"COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" ] }, { From c020ff40f7a498db7e25d0b8b5501032469143fb Mon Sep 17 00:00:00 2001 From: mrmer1 Date: Fri, 20 Sep 2024 15:34:39 +0800 Subject: [PATCH 08/11] update post review --- notebooks/Vanilla_RAG_v2.ipynb | 158 +++++++----------- notebooks/agents/Vanilla_Tool_Use_v2.ipynb | 33 +--- .../getting-started/v2/tutorial_pt6_v2.ipynb | 2 +- 3 files changed, 68 insertions(+), 125 deletions(-) diff --git a/notebooks/Vanilla_RAG_v2.ipynb b/notebooks/Vanilla_RAG_v2.ipynb index 48de1f4..e480cb3 100644 --- a/notebooks/Vanilla_RAG_v2.ipynb +++ b/notebooks/Vanilla_RAG_v2.ipynb @@ -41,18 +41,7 @@ "metadata": { "id": "rACbepFGgbOo" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/meor/anaconda3/lib/python3.11/site-packages/pydantic/_internal/_config.py:341: UserWarning: Valid config keys have changed in V2:\n", - "* 'allow_population_by_field_name' has been renamed to 'populate_by_name'\n", - "* 'smart_union' has been removed\n", - " warnings.warn(message, UserWarning)\n" - ] - } - ], + "outputs": [], "source": [ "# pip install cohere\n", "\n", @@ -74,7 +63,7 @@ "outputs": [], "source": [ "# we'll get some wikipedia data\n", - "! pip install wikipedia -qq\n", + "# ! pip install wikipedia -qq\n", "\n", "import wikipedia" ] @@ -94,7 +83,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The text has roughly 5812 words.\n" + "The text has roughly 5896 words.\n" ] } ], @@ -120,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -138,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -151,7 +140,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The text has been broken down in 107 chunks.\n" + "The text has been broken down in 108 chunks.\n" ] } ], @@ -183,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -196,20 +185,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "We just computed 107 embeddings.\n" + "We just computed 108 embeddings.\n" ] } ], "source": [ "# Because the texts being embedded are the chunks we are searching over, we set the input type as search_doc\n", - "model=\"embed-english-v3.0\"\n", - "response = co.embed(\n", - " texts= chunks,\n", - " model=model,\n", - " input_type=\"search_document\",\n", - " embedding_types=['float']\n", - ")\n", - "embeddings = response.embeddings.float\n", + "model = \"embed-english-v3.0\"\n", + "\n", + "def batch_embed(texts, batch_size=96):\n", + " all_embeddings = []\n", + " for i in range(0, len(texts), batch_size):\n", + " batch = texts[i:i+batch_size]\n", + " response = co.embed(\n", + " texts=batch,\n", + " model=model,\n", + " input_type=\"search_document\",\n", + " embedding_types=['float']\n", + " )\n", + " all_embeddings.extend(response.embeddings.float)\n", + " return all_embeddings\n", + "\n", + "embeddings = batch_embed(chunks)\n", "print(f\"We just computed {len(embeddings)} embeddings.\")" ] }, @@ -238,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": { "id": "H2srFH-IgbOs" }, @@ -269,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": { "id": "Y2HTxspKgbOs" }, @@ -294,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -336,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -349,7 +346,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "similarity scores: [0.6880419226352857, 0.3923392545434105, 0.6386815248269733, 0.43505965112768996, 0.35780784367307705, 0.3923210354894715, 0.3406860631010847, 0.2957357005793907, 0.4255159555931599, 0.14503223489330883, 0.3997846789722547, 0.3743933002525113, 0.4034607365399171, 0.3244752223188998, 0.3206445944107157, 0.3409994933473336, 0.23203650717727414, 0.497148799385201, 0.34487158221487835, 0.28824423308146263, 0.5782290303802001, 0.5489319961327446, 0.778415400224538, 0.5236541350938922, 0.5481962732642285, 0.7135549552247115, 0.5205999648021981, 0.5872268968480292, 0.26489409375108064, 0.6410858426784221, 0.5367759890829636, 0.6816158875713925, 0.39144361423311, 0.4827300300834518, 0.4502725966061075, 0.2462873309174519, 0.443300705486635, 0.39120172781317253, 0.20620678706300988, 0.4371599199261209, 0.37572638119299867, 0.4614621126654143, 0.29906070488176123, 0.36477353501504023, 0.36024747243912564, 0.3929914083901088, 0.23114430973151348, 0.4646991530089856, 0.1187422018303958, 0.4217084543208151, 0.36510789830148477, 0.26769253214700306, 0.36598547319281477, 0.3792997473359336, 0.4596196436425069, 0.43869634271638114, 0.20089603894036867, 0.42191807980549356, 0.4387291730859993, 0.4488941445207048, 0.1299006327023146, 0.3832393275291005, 0.14269299802866453, 0.5386575762368317, 0.29891892602821285, 0.4128023121085502, 0.15622874884471297, 0.5037411178038066, 0.3403791753246366, 0.4518658626088099, 0.3588444212378783, 0.39024780342329135, 0.48874362388129355, 0.2970250944589432, 0.4890213355379532, 0.2601457789171118, 0.5098062623890619, 0.41745989801233, 0.18695460607524733, 0.5309005848922221, 0.5226886534429271, 0.5478914338259878, 0.5004863890173412, 0.1968363843203233, 0.4210806632601615, 0.5351498045642535, 0.28640413417722466, 0.31654174678868613, 0.21754347066657245, 0.46012838286736324, 0.14089744261122422, 0.20637564356989352, 0.5713764499847603, 0.3808494424653835, 0.5832486106225127, 0.41007417198464485, 0.2611055925841932, 0.3805828196439077, 0.28780942000368676, 0.3138126314582887, 0.3471942628542812, 0.21954342602333704, 0.14159076456040556, 0.4685623412304875, 0.4516605901453241, 0.5117576674528891, 0.4176107168986097]\n", + "similarity scores: [0.6880419226352857, 0.3923392545434105, 0.6386815248269733, 0.43618145115797535, 0.35780784367307705, 0.3923210354894715, 0.3406860631010847, 0.2957357005793907, 0.4255159555931599, 0.14503223489330883, 0.3997846789722547, 0.3743933002525113, 0.4083288113267294, 0.26194266616864986, 0.31982912150076953, 0.3531164824914523, 0.23203650717727414, 0.497148799385201, 0.34487158221487835, 0.28824423308146263, 0.5782290303802001, 0.5489319961327446, 0.778415400224538, 0.5236541350938922, 0.5481962732642285, 0.7135549552247115, 0.5205999648021981, 0.5872268968480292, 0.26489409375108064, 0.6410858426784221, 0.5367759890829636, 0.6816158875713925, 0.39144361423311, 0.4827300300834518, 0.4502725966061075, 0.2462873309174519, 0.443300705486635, 0.39120172781317253, 0.20620678706300988, 0.4371599199261209, 0.37572638119299867, 0.4614621126654143, 0.29906070488176123, 0.36477353501504023, 0.36024747243912564, 0.3929914083901088, 0.23114430973151348, 0.4646991530089856, 0.1187422018303958, 0.4217084543208151, 0.36510789830148477, 0.26769253214700306, 0.36598547319281477, 0.3792997473359336, 0.4596196436425069, 0.43869634271638114, 0.20089603894036867, 0.42191807980549356, 0.4387291730859993, 0.4488941445207048, 0.1299006327023146, 0.3832393275291005, 0.14269299802866453, 0.5386575762368317, 0.29891892602821285, 0.4128023121085502, 0.15622874884471297, 0.5037411178038066, 0.3403791753246366, 0.4518658626088099, 0.3588444212378783, 0.39024780342329135, 0.48874362388129355, 0.2970250944589432, 0.4890213355379532, 0.2601457789171118, 0.5098062623890619, 0.41745989801233, 0.18695460607524733, 0.5309005848922221, 0.5226886534429271, 0.5478914338259878, 0.5004863890173412, 0.1968363843203233, 0.4210806632601615, 0.5341083271502703, 0.28640413417722466, 0.31654174678868613, 0.21754347066657245, 0.46012838286736324, 0.14089744261122422, 0.20637564356989352, 0.5713764499847603, 0.3808494424653835, 0.5832486106225127, 0.41007417198464485, 0.2604390796614135, 0.3801380463864371, 0.2867894923131921, 0.3135170641699137, 0.3470364583530195, 0.21980436849993726, 0.1414052619528618, 0.468511284019416, 0.4496650446352944, 0.5239361429406763, 0.19838970836663622, 0.42025656379414894]\n", "Here are the indices of the top 10 chunks after retrieval: [22 25 0 31 29 2 27 94 20 92]\n", "Here are the top 10 chunks after retrieval: \n", "== Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there's a logical place to stop the [first] movie before the book is over\".\n", @@ -402,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -450,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 14, "metadata": { "id": "oCNXWH8GgbOt" }, @@ -468,7 +465,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -482,34 +479,24 @@ "output_type": "stream", "text": [ "Final answer:\n", - "*Dune: Part Two* is a 2024 American epic science fiction film directed and co-produced by Denis Villeneuve, who also co-wrote the screenplay with Jon Spaihts.\n", - "\n", - "The film was produced by Villeneuve, Mary Parent, and Cale Boyter, with the following serving as executive producers:\n", - "- Tanya Lapointe\n", - "- Brian Herbert\n", - "- Byron Merritt\n", - "- Kim Herbert\n", - "- Thomas Tull\n", - "- Richard P. Rubinstein\n", - "- John Harrison\n", - "- Herbert W. Gain\n", - "- Kevin J. Anderson (creative consultant)\n" + "*Dune: Part Two* is a 2024 American epic science fiction film directed and co-produced by Denis Villeneuve, who co-wrote the screenplay with Jon Spaihts. The film was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers. Kevin J. Anderson was the creative consultant.\n" ] } ], "source": [ "# retrieved documents\n", "documents = [\n", - " {\"title\": \"chunk 0\", \"snippet\": top_chunks_after_rerank[0]},\n", - " {\"title\": \"chunk 1\", \"snippet\": top_chunks_after_rerank[1]},\n", - " {\"title\": \"chunk 2\", \"snippet\": top_chunks_after_rerank[2]},\n", + " {\"data\": {\"title\": \"chunk 0\", \"snippet\": top_chunks_after_rerank[0]}},\n", + " {\"data\": {\"title\": \"chunk 1\", \"snippet\": top_chunks_after_rerank[1]}},\n", + " {\"data\": {\"title\": \"chunk 2\", \"snippet\": top_chunks_after_rerank[2]}},\n", " ]\n", "\n", "# get model response\n", "response = co.chat(\n", " model=\"command-r-08-2024\",\n", " messages=[{\"role\" : \"system\", \"content\" : preamble},\n", - " {\"role\" : \"user\", \"content\" : query, \"documents\" : documents}],\n", + " {\"role\" : \"user\", \"content\" : query}],\n", + " documents=documents, \n", " temperature=0.3\n", ")\n", "\n", @@ -545,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -559,20 +546,23 @@ "output_type": "stream", "text": [ "Citations that support the final answer:\n", - "start=22 end=61 text='2024 American epic science fiction film' sources=[Source_Document(id='doc:1:1', document={'id': 'doc:1:1', 'snippet': 'Dune: Part Two is a 2024 American epic science fiction film directed and co-produced by Denis Villeneuve, who co-wrote the screenplay with Jon Spaihts. The sequel to Dune (2021), it is the second of a two-part adaptation of the 1965 novel Dune by Frank Herbert. It follows Paul Atreides as he unites with the Fremen people of the desert planet Arrakis to wage war against House Harkonnen. TimothĂ©e Chalamet, Rebecca Ferguson, Josh Brolin, Stellan SkarsgĂ„rd, Dave Bautista, Zendaya, Charlotte Rampling, and Javier', 'title': 'chunk 1'}, type='document')]\n", - "start=62 end=106 text='directed and co-produced by Denis Villeneuve' sources=[Source_Document(id='doc:1:1', document={'id': 'doc:1:1', 'snippet': 'Dune: Part Two is a 2024 American epic science fiction film directed and co-produced by Denis Villeneuve, who co-wrote the screenplay with Jon Spaihts. The sequel to Dune (2021), it is the second of a two-part adaptation of the 1965 novel Dune by Frank Herbert. It follows Paul Atreides as he unites with the Fremen people of the desert planet Arrakis to wage war against House Harkonnen. TimothĂ©e Chalamet, Rebecca Ferguson, Josh Brolin, Stellan SkarsgĂ„rd, Dave Bautista, Zendaya, Charlotte Rampling, and Javier', 'title': 'chunk 1'}, type='document')]\n", - "start=117 end=158 text='co-wrote the screenplay with Jon Spaihts.' sources=[Source_Document(id='doc:1:1', document={'id': 'doc:1:1', 'snippet': 'Dune: Part Two is a 2024 American epic science fiction film directed and co-produced by Denis Villeneuve, who co-wrote the screenplay with Jon Spaihts. The sequel to Dune (2021), it is the second of a two-part adaptation of the 1965 novel Dune by Frank Herbert. It follows Paul Atreides as he unites with the Fremen people of the desert planet Arrakis to wage war against House Harkonnen. TimothĂ©e Chalamet, Rebecca Ferguson, Josh Brolin, Stellan SkarsgĂ„rd, Dave Bautista, Zendaya, Charlotte Rampling, and Javier', 'title': 'chunk 1'}, type='document')]\n", - "start=173 end=225 text='produced by Villeneuve, Mary Parent, and Cale Boyter' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", - "start=257 end=276 text='executive producers' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", - "start=280 end=294 text='Tanya Lapointe' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", - "start=297 end=310 text='Brian Herbert' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", - "start=313 end=326 text='Byron Merritt' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", - "start=329 end=340 text='Kim Herbert' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", - "start=343 end=354 text='Thomas Tull' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", - "start=357 end=378 text='Richard P. Rubinstein' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", - "start=381 end=394 text='John Harrison' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", - "start=397 end=412 text='Herbert W. Gain' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n", - "start=415 end=454 text='Kevin J. Anderson (creative consultant)' sources=[Source_Document(id='doc:1:0', document={'id': 'doc:1:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'}, type='document')]\n" + "start=22 end=61 text='2024 American epic science fiction film' sources=[DocumentSource(type='document', id='doc:1', document={'id': 'doc:1', 'snippet': 'Dune: Part Two is a 2024 American epic science fiction film directed and co-produced by Denis Villeneuve, who co-wrote the screenplay with Jon Spaihts. The sequel to Dune (2021), it is the second of a two-part adaptation of the 1965 novel Dune by Frank Herbert. It follows Paul Atreides as he unites with the Fremen people of the desert planet Arrakis to wage war against House Harkonnen. TimothĂ©e Chalamet, Rebecca Ferguson, Josh Brolin, Stellan SkarsgĂ„rd, Dave Bautista, Zendaya, Charlotte Rampling, and Javier', 'title': 'chunk 1'})]\n", + "start=62 end=106 text='directed and co-produced by Denis Villeneuve' sources=[DocumentSource(type='document', id='doc:1', document={'id': 'doc:1', 'snippet': 'Dune: Part Two is a 2024 American epic science fiction film directed and co-produced by Denis Villeneuve, who co-wrote the screenplay with Jon Spaihts. The sequel to Dune (2021), it is the second of a two-part adaptation of the 1965 novel Dune by Frank Herbert. It follows Paul Atreides as he unites with the Fremen people of the desert planet Arrakis to wage war against House Harkonnen. TimothĂ©e Chalamet, Rebecca Ferguson, Josh Brolin, Stellan SkarsgĂ„rd, Dave Bautista, Zendaya, Charlotte Rampling, and Javier', 'title': 'chunk 1'})]\n", + "start=112 end=153 text='co-wrote the screenplay with Jon Spaihts.' sources=[DocumentSource(type='document', id='doc:1', document={'id': 'doc:1', 'snippet': 'Dune: Part Two is a 2024 American epic science fiction film directed and co-produced by Denis Villeneuve, who co-wrote the screenplay with Jon Spaihts. The sequel to Dune (2021), it is the second of a two-part adaptation of the 1965 novel Dune by Frank Herbert. It follows Paul Atreides as he unites with the Fremen people of the desert planet Arrakis to wage war against House Harkonnen. TimothĂ©e Chalamet, Rebecca Ferguson, Josh Brolin, Stellan SkarsgĂ„rd, Dave Bautista, Zendaya, Charlotte Rampling, and Javier', 'title': 'chunk 1'})]\n", + "start=167 end=189 text='produced by Villeneuve' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'})]\n", + "start=191 end=202 text='Mary Parent' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'})]\n", + "start=208 end=219 text='Cale Boyter' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'})]\n", + "start=226 end=240 text='Tanya Lapointe' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'})]\n", + "start=242 end=255 text='Brian Herbert' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'})]\n", + "start=257 end=270 text='Byron Merritt' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'})]\n", + "start=272 end=283 text='Kim Herbert' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'})]\n", + "start=285 end=296 text='Thomas Tull' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'})]\n", + "start=298 end=319 text='Richard P. Rubinstein' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'})]\n", + "start=321 end=334 text='John Harrison' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'})]\n", + "start=340 end=355 text='Herbert W. Gain' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'})]\n", + "start=367 end=387 text='executive producers.' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'})]\n", + "start=388 end=405 text='Kevin J. Anderson' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'})]\n", + "start=414 end=434 text='creative consultant.' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'snippet': 'Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there\\'s a logical place to stop the [first] movie before the book is over\".', 'title': 'chunk 0'})]\n" ] } ], @@ -584,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -597,22 +587,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "*Dune: Part Two* is a 2024 American epic science fiction film[1] directed and co-produced by Denis Villeneuve[1], who also co-wrote the screenplay with Jon Spaihts.[1]\n", - "\n", - "The film was produced by Villeneuve, Mary Parent, and Cale Boyter[0], with the following serving as executive producers[0]:\n", - "- Tanya Lapointe[0]\n", - "- Brian Herbert[0]\n", - "- Byron Merritt[0]\n", - "- Kim Herbert[0]\n", - "- Thomas Tull[0]\n", - "- Richard P. Rubinstein[0]\n", - "- John Harrison[0]\n", - "- Herbert W. Gain[0]\n", - "- Kevin J. Anderson (creative consultant)[0]\n", + "*Dune: Part Two* is a 2024 American epic science fiction film[1] directed and co-produced by Denis Villeneuve[1], who co-wrote the screenplay with Jon Spaihts.[1] The film was produced by Villeneuve[0], Mary Parent[0], and Cale Boyter[0], with Tanya Lapointe[0], Brian Herbert[0], Byron Merritt[0], Kim Herbert[0], Thomas Tull[0], Richard P. Rubinstein[0], John Harrison[0], and Herbert W. Gain[0] serving as executive producers.[0] Kevin J. Anderson[0] was the creative consultant.[0]\n", "\n", "Source documents:\n", - "[0] \n", - "[1] \n" + "[0] title: chunk 0, snippet: Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there's a logical place to stop the [first] movie before the book is over\".\n", + "[1] title: chunk 1, snippet: Dune: Part Two is a 2024 American epic science fiction film directed and co-produced by Denis Villeneuve, who co-wrote the screenplay with Jon Spaihts. The sequel to Dune (2021), it is the second of a two-part adaptation of the 1965 novel Dune by Frank Herbert. It follows Paul Atreides as he unites with the Fremen people of the desert planet Arrakis to wage war against House Harkonnen. TimothĂ©e Chalamet, Rebecca Ferguson, Josh Brolin, Stellan SkarsgĂ„rd, Dave Bautista, Zendaya, Charlotte Rampling, and Javier\n" ] } ], @@ -650,12 +629,12 @@ "# Print footnotes\n", "if response.message.citations:\n", " print(\"\\nSource documents:\")\n", - " print(list_sources(response.message.citations, fields=['text', 'another_field'])) # Specify the fields you want to include" + " print(list_sources(response.message.citations, fields=['title','snippet']))" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 24, "metadata": { "id": "Kp4c_HkYIEn_" }, @@ -664,18 +643,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "*Dune: Part Two* is a 2024 American epic science fiction film[1] directed and co-produced by Denis Villeneuve[1], who also co-wrote the screenplay with Jon Spaihts.[1]\n", - "\n", - "The film was produced by Villeneuve, Mary Parent, and Cale Boyter[0], with the following serving as executive producers[0]:\n", - "- Tanya Lapointe[0]\n", - "- Brian Herbert[0]\n", - "- Byron Merritt[0]\n", - "- Kim Herbert[0]\n", - "- Thomas Tull[0]\n", - "- Richard P. Rubinstein[0]\n", - "- John Harrison[0]\n", - "- Herbert W. Gain[0]\n", - "- Kevin J. Anderson (creative consultant)[0]\n", + "*Dune: Part Two* is a 2024 American epic science fiction film[1] directed and co-produced by Denis Villeneuve[1], who co-wrote the screenplay with Jon Spaihts.[1] The film was produced by Villeneuve[0], Mary Parent[0], and Cale Boyter[0], with Tanya Lapointe[0], Brian Herbert[0], Byron Merritt[0], Kim Herbert[0], Thomas Tull[0], Richard P. Rubinstein[0], John Harrison[0], and Herbert W. Gain[0] serving as executive producers.[0] Kevin J. Anderson[0] was the creative consultant.[0]\n", "\n", "Source documents:\n", "[0] snippet: Dune: Part Two was produced by Villeneuve, Mary Parent, and Cale Boyter, with Tanya Lapointe, Brian Herbert, Byron Merritt, Kim Herbert, Thomas Tull, Jon Spaihts, Richard P. Rubinstein, John Harrison, and Herbert W. Gain serving as executive producers and Kevin J. Anderson as creative consultant. Legendary CEO Joshua Grode confirmed in April 2019 that they plan to make a sequel, adding that \"there's a logical place to stop the [first] movie before the book is over\"., title: chunk 0\n", diff --git a/notebooks/agents/Vanilla_Tool_Use_v2.ipynb b/notebooks/agents/Vanilla_Tool_Use_v2.ipynb index dcae7ff..8af952b 100644 --- a/notebooks/agents/Vanilla_Tool_Use_v2.ipynb +++ b/notebooks/agents/Vanilla_Tool_Use_v2.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -40,34 +40,9 @@ "id": "us5dkKrLCbXW", "outputId": "94c97f62-77fb-4492-a4e4-d9eeee4e438c" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.8/52.8 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m28.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h" - ] - } - ], - "source": [ - "# we'll use Cohere to do Tool Use\n", - "# TODO: upgrade to \"cohere>5\"\n", - "# ! pip install cohere --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "j0DC3iPftLGo" - }, "outputs": [], "source": [ - "import cohere, json\n", - "API_KEY = \"...\" # fill in your Cohere API key here\n", - "co = cohere.Client(API_KEY)" + "# ! pip install cohere --quiet" ] }, { @@ -79,7 +54,7 @@ "import cohere\n", "import os\n", "import json\n", - "co = cohere.ClientV2(os.getenv(\"COHERE_API_KEY\"))" + "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" ] }, { @@ -631,7 +606,7 @@ "kernelspec": { "display_name": "base", "language": "python", - "name": "base" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb index 424ae65..f818553 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb @@ -459,7 +459,7 @@ "- `top_n`: The top reranked documents to select\n", "- `model`: We choose Rerank English 3\n", "\n", - "Looking at the results, we see that the given a query about getting to know the team, the document that talks about joining Slack channels is now ranked higher (1st) compared to earlier (3rd).\n", + "Looking at the results, we see that since the query is about getting to know the team, the document that talks about joining Slack channels is now ranked higher (1st) compared to earlier (3rd).\n", "\n", "Here we select `top_n` to be 2, which will be the documents we will pass next for response generation." ] From a9a703f468b0e017f6f1958ac9967bb838806dca Mon Sep 17 00:00:00 2001 From: mrmer1 Date: Thu, 26 Sep 2024 21:13:21 +0800 Subject: [PATCH 09/11] update v2 nbs --- .../getting-started/v2/tutorial_pt4_v2.ipynb | 24 +- .../getting-started/v2/tutorial_pt6_v2.ipynb | 219 +++++++++++------- 2 files changed, 151 insertions(+), 92 deletions(-) diff --git a/notebooks/guides/getting-started/v2/tutorial_pt4_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt4_v2.ipynb index fea6183..cef6055 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt4_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt4_v2.ipynb @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -185,12 +185,12 @@ "output_type": "stream", "text": [ "Rank: 1\n", - "Score: 0.442758615743984\n", - "Document: {'text': \"Finding Coffee Spots: For your caffeine fix, head to the break room's coffee machine or cross the street to the cafĂ© for artisan coffee.\"}\n", + "Score: 0.3872984617627964\n", + "Document: {'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}\n", "\n", "Rank: 2\n", - "Score: 0.32783563708365726\n", - "Document: {'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}\n", + "Score: 0.3272549670724577\n", + "Document: {'text': 'Proposing New Ideas: Innovation is welcomed! Share your brilliant ideas at our weekly team meetings or directly with your team lead.'}\n", "\n" ] } @@ -234,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -314,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -345,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -377,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -436,7 +436,7 @@ "kernelspec": { "display_name": "base", "language": "python", - "name": "base" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb index f818553..2ebc192 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt6_v2.ipynb @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -124,9 +124,21 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Yes, there are health benefits. We offer gym memberships, on-site yoga classes, and comprehensive health insurance.\n", + "\n", + "CITATIONS:\n", + "start=41 end=115 text='gym memberships, on-site yoga classes, and comprehensive health insurance.' sources=[DocumentSource(type='document', id='doc:2', document={'id': 'doc:2', 'text': 'Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.'})] \n", + "\n" + ] + } + ], "source": [ "# Add the user query\n", "query = \"Are there health benefits?\"\n", @@ -169,53 +181,59 @@ "\n", "There are different possible approaches to this. In this example, we'll take a [tool use](v2/docs/tool-use) approach.\n", "\n", - "Here, we build a tool that takes a user query and returns a list of relevant document snippets for that query. The tool can generate zero, one or multiple search queries depending on the user query.\n", - "\n", - "We'll not do a detailed walkthrough of tool use here as we'll do that in the next tutorial, which is a dedicated tutorial on tool use.\n", - "\n", - "We recommend using the v1 API for this functionality in order to leverage the `force_single_step` feature, as follows:" + "Here, we build a tool that takes a user query and returns a list of relevant document snippets for that query. The tool can generate zero, one or multiple search queries depending on the user query." ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ - "co_v1 = cohere.Client(api_key=\"COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys\n", - "\n", "def generate_search_queries(message: str) -> List[str]:\n", " \n", " # Define the query generation tool\n", " query_gen_tool = [\n", - " {\n", - " \"name\": \"internet_search\",\n", - " \"description\": \"Returns a list of relevant document snippets for a textual query retrieved from the internet\",\n", - " \"parameter_definitions\": {\n", - " \"queries\": {\n", - " \"description\": \"a list of queries to search the internet with.\",\n", - " \"type\": \"List[str]\",\n", - " \"required\": True\n", - " }\n", + " {\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"internet_search\",\n", + " \"description\": \"Returns a list of relevant document snippets for a textual query retrieved from the internet\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"queries\": {\n", + " \"type\": \"array\",\n", + " \"items\": {\"type\": \"string\"},\n", + " \"description\": \"a list of queries to search the internet with.\",\n", + " }\n", + " },\n", + " \"required\": [\"queries\"],\n", + " },\n", + " },\n", " }\n", - " }]\n", + " ]\n", + "\n", "\n", " # Define a preamble to optimize search query generation\n", " instructions = \"Write a search query that will find helpful information for answering the user's question accurately. If you need more than one search query, write a list of search queries. If you decide that a search is very unlikely to find information that would be useful in constructing a response to the user, you should instead directly answer.\"\n", "\n", " # Generate search queries (if any)\n", " search_queries = []\n", - "\n", - " response = co_v1.chat(\n", - " preamble=instructions,\n", + " \n", + " res = co.chat(\n", " model=\"command-r-08-2024\",\n", - " message=message,\n", - " force_single_step=True,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": instructions},\n", + " {\"role\": \"user\", \"content\": message},\n", + " ],\n", " tools=query_gen_tool\n", " )\n", - "\n", - " if response.tool_calls:\n", - " search_queries = response.tool_calls[0].parameters[\"queries\"]\n", + " \n", + " if res.message.tool_calls:\n", + " for tc in res.message.tool_calls:\n", + " queries = json.loads(tc.function.arguments)[\"queries\"]\n", + " search_queries.extend(queries)\n", "\n", " return search_queries" ] @@ -229,14 +247,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['how to stay connected with the company', 'does the company organize team events']\n" + "['how to stay connected with the company', 'do companies organise team events']\n" ] } ], @@ -255,14 +273,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['how flexible are the working hours at the company']\n" + "['How flexible are the working hours?']\n" ] } ], @@ -281,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -324,29 +342,69 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "# Define the documents\n", "faqs_long = [\n", - " {\"text\": \"Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.\"},\n", - " {\"text\": \"Finding Coffee Spots: For your caffeine fix, head to the break room's coffee machine or cross the street to the cafĂ© for artisan coffee.\"},\n", - " {\"text\": \"Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!\"},\n", - " {\"text\": \"Working Hours Flexibility: We prioritize work-life balance. While our core hours are 9 AM to 5 PM, we offer flexibility to adjust as needed.\"},\n", - " {\"text\": \"Side Projects Policy: We encourage you to pursue your passions. Just be mindful of any potential conflicts of interest with our business.\"},\n", - " {\"text\": \"Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward.\"},\n", - " {\"text\": \"Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.\"},\n", - " {\"text\": \"Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.\"},\n", - " {\"text\": \"Performance Reviews Frequency: We conduct informal check-ins every quarter and formal performance reviews twice a year.\"},\n", - " {\"text\": \"Proposing New Ideas: Innovation is welcomed! Share your brilliant ideas at our weekly team meetings or directly with your team lead.\"},\n", + " {\n", + " \"data\": {\n", + " \"text\": \"Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.\"\n", + " }\n", + " },\n", + " {\n", + " \"data\": {\n", + " \"text\": \"Finding Coffee Spots: For your caffeine fix, head to the break room's coffee machine or cross the street to the cafĂ© for artisan coffee.\"\n", + " }\n", + " },\n", + " {\n", + " \"data\": {\n", + " \"text\": \"Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!\"\n", + " }\n", + " },\n", + " {\n", + " \"data\": {\n", + " \"text\": \"Working Hours Flexibility: We prioritize work-life balance. While our core hours are 9 AM to 5 PM, we offer flexibility to adjust as needed.\"\n", + " }\n", + " },\n", + " {\n", + " \"data\": {\n", + " \"text\": \"Side Projects Policy: We encourage you to pursue your passions. Just be mindful of any potential conflicts of interest with our business.\"\n", + " }\n", + " },\n", + " {\n", + " \"data\": {\n", + " \"text\": \"Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward.\"\n", + " }\n", + " },\n", + " {\n", + " \"data\": {\n", + " \"text\": \"Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.\"\n", + " }\n", + " },\n", + " {\n", + " \"data\": {\n", + " \"text\": \"Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.\"\n", + " }\n", + " },\n", + " {\n", + " \"data\": {\n", + " \"text\": \"Performance Reviews Frequency: We conduct informal check-ins every quarter and formal performance reviews twice a year.\"\n", + " }\n", + " },\n", + " {\n", + " \"data\": {\n", + " \"text\": \"Proposing New Ideas: Innovation is welcomed! Share your brilliant ideas at our weekly team meetings or directly with your team lead.\"\n", + " }\n", + " },\n", "]\n", "\n", "# Embed the documents\n", "doc_emb = co.embed(\n", " model=\"embed-english-v3.0\",\n", " input_type=\"search_document\",\n", - " texts=[doc['text'] for doc in faqs_long],\n", + " texts=[doc['data']['text'] for doc in faqs_long],\n", " embedding_types=[\"float\"]).embeddings.float" ] }, @@ -361,14 +419,14 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Search query: how to get to know teammates\n" + "Search query: how to get to know your teammates\n" ] } ], @@ -402,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -410,24 +468,24 @@ "output_type": "stream", "text": [ "Rank: 1\n", - "Score: 0.34212792245283796\n", - "Document: {'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}\n", + "Score: 0.32653470360872655\n", + "Document: {'data': {'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}}\n", "\n", "Rank: 2\n", - "Score: 0.2883222063024371\n", - "Document: {'text': 'Proposing New Ideas: Innovation is welcomed! Share your brilliant ideas at our weekly team meetings or directly with your team lead.'}\n", + "Score: 0.26851855352264786\n", + "Document: {'data': {'text': 'Proposing New Ideas: Innovation is welcomed! Share your brilliant ideas at our weekly team meetings or directly with your team lead.'}}\n", "\n", "Rank: 3\n", - "Score: 0.278128283997032\n", - "Document: {'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}\n", + "Score: 0.2581341975304149\n", + "Document: {'data': {'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}}\n", "\n", "Rank: 4\n", - "Score: 0.19474858706643985\n", - "Document: {'text': \"Finding Coffee Spots: For your caffeine fix, head to the break room's coffee machine or cross the street to the cafĂ© for artisan coffee.\"}\n", + "Score: 0.18633336738178463\n", + "Document: {'data': {'text': \"Finding Coffee Spots: For your caffeine fix, head to the break room's coffee machine or cross the street to the cafĂ© for artisan coffee.\"}}\n", "\n", "Rank: 5\n", - "Score: 0.13713692506528824\n", - "Document: {'text': 'Side Projects Policy: We encourage you to pursue your passions. Just be mindful of any potential conflicts of interest with our business.'}\n", + "Score: 0.13022396595682814\n", + "Document: {'data': {'text': 'Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.'}}\n", "\n" ] } @@ -443,7 +501,7 @@ "for rank, idx in enumerate(max_idx):\n", " print(f\"Rank: {rank+1}\")\n", " print(f\"Score: {scores[idx]}\")\n", - " print(f\"Document: {retrieved_documents[rank]}\\n\")\n" + " print(f\"Document: {retrieved_documents[rank]}\\n\")" ] }, { @@ -466,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -474,12 +532,12 @@ "output_type": "stream", "text": [ "Rank: 1\n", - "Score: 0.0020507434\n", - "Document: {'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}\n", + "Score: 0.0040072887\n", + "Document: {'data': {'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}}\n", "\n", "Rank: 2\n", - "Score: 0.0014158706\n", - "Document: {'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}\n", + "Score: 0.0020829707\n", + "Document: {'data': {'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}}\n", "\n" ] } @@ -487,7 +545,7 @@ "source": [ "# Rerank the documents\n", "results = co.rerank(query=queries_for_search,\n", - " documents=retrieved_documents,\n", + " documents=[doc['data']['text'] for doc in retrieved_documents],\n", " top_n=2,\n", " model='rerank-english-v3.0')\n", "\n", @@ -521,23 +579,31 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "You can get to know your teammates by joining your company's Slack channels and taking part in team-building activities. You will receive an invite to join relevant Slack channels via email. Your company also organises monthly outings and weekly game nights to foster team spirit.\n", + "You can get to know your teammates by joining Slack channels and participating in team-building activities. You will receive an invite via email to join relevant channels to stay informed and engaged. There are monthly outings and weekly game nights to foster team spirit. You can suggest new activity ideas at any time.\n", "\n", "CITATIONS:\n", - "start=38 end=75 text=\"joining your company's Slack channels\" sources=[Source_Document(id='doc:0:0', document={'id': 'doc:0:0', 'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}, type='document')] \n", + "start=38 end=60 text='joining Slack channels' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'})] \n", + "\n", + "start=82 end=107 text='team-building activities.' sources=[DocumentSource(type='document', id='doc:1', document={'id': 'doc:1', 'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'})] \n", + "\n", + "start=117 end=144 text='receive an invite via email' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'})] \n", "\n", - "start=95 end=120 text='team-building activities.' sources=[Source_Document(id='doc:0:1', document={'id': 'doc:0:1', 'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}, type='document')] \n", + "start=148 end=170 text='join relevant channels' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'})] \n", "\n", - "start=130 end=190 text='receive an invite to join relevant Slack channels via email.' sources=[Source_Document(id='doc:0:0', document={'id': 'doc:0:0', 'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}, type='document')] \n", + "start=174 end=200 text='stay informed and engaged.' sources=[DocumentSource(type='document', id='doc:0', document={'id': 'doc:0', 'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'})] \n", "\n", - "start=219 end=280 text='monthly outings and weekly game nights to foster team spirit.' sources=[Source_Document(id='doc:0:1', document={'id': 'doc:0:1', 'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}, type='document')] \n", + "start=211 end=249 text='monthly outings and weekly game nights' sources=[DocumentSource(type='document', id='doc:1', document={'id': 'doc:1', 'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'})] \n", + "\n", + "start=253 end=272 text='foster team spirit.' sources=[DocumentSource(type='document', id='doc:1', document={'id': 'doc:1', 'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'})] \n", + "\n", + "start=281 end=320 text='suggest new activity ideas at any time.' sources=[DocumentSource(type='document', id='doc:1', document={'id': 'doc:1', 'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'})] \n", "\n" ] } @@ -557,20 +623,13 @@ " for citation in response.message.citations:\n", " print(citation, \"\\n\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { "display_name": "base", "language": "python", - "name": "base" + "name": "python3" }, "language_info": { "codemirror_mode": { From ba52a465a6b834fbbe3401b22fce7ce0d377f829 Mon Sep 17 00:00:00 2001 From: mrmer1 Date: Wed, 9 Oct 2024 16:32:44 +0800 Subject: [PATCH 10/11] update v2 tool use code --- .../Multi_Step_Tool_Use_Spotify_v2.ipynb | 393 ++++++++++++++---- notebooks/agents/Vanilla_Tool_Use_v2.ipynb | 210 ++++------ .../getting-started/v2/tutorial_pt7_v2.ipynb | 158 +++---- 3 files changed, 486 insertions(+), 275 deletions(-) diff --git a/notebooks/agents/Multi_Step_Tool_Use_Spotify_v2.ipynb b/notebooks/agents/Multi_Step_Tool_Use_Spotify_v2.ipynb index bb1960b..b0bcb5f 100644 --- a/notebooks/agents/Multi_Step_Tool_Use_Spotify_v2.ipynb +++ b/notebooks/agents/Multi_Step_Tool_Use_Spotify_v2.ipynb @@ -46,12 +46,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "! pip install tavily-python --q\n", - "\n", + "! pip install tavily-python --q --disable-pip-version-check\n", "from tavily import TavilyClient\n", "tavily_client = TavilyClient(api_key=\"TAVILY_API_KEY\")" ] @@ -73,19 +72,19 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# here's a web search engine\n", "def web_search(query: str) -> list[dict]:\n", - " response = tavily_client.search(query, max_results=3)['results']\n", - " return {\"results\": response}" + " results = tavily_client.search(query, max_results=3)[\"results\"]\n", + " return results" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -100,13 +99,13 @@ " \"properties\": {\n", " \"query\": {\n", " \"type\": \"string\",\n", - " \"description\": \"Query to search the internet with\"\n", + " \"description\": \"Query to search the internet with\",\n", " }\n", " },\n", - " \"required\": [\"query\"]\n", - " }\n", - " }\n", - "}\n" + " \"required\": [\"query\"],\n", + " },\n", + " },\n", + "}" ] }, { @@ -118,12 +117,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# here's a python console, which can be used to access the spreadsheet, but also more generally to code and plot stuff\n", "import io, contextlib\n", + "\n", + "\n", "def python_interpreter(code: str) -> list[dict]:\n", " output = io.StringIO()\n", " try:\n", @@ -131,21 +132,14 @@ " with contextlib.redirect_stdout(output):\n", " exec(code, globals())\n", " except Exception as e:\n", - " return {\n", - " \"error\": str(e),\n", - " \"executed_code\": code\n", - " }\n", + " return {\"error\": str(e), \"executed_code\": code}\n", " # Get stdout\n", - " return {\n", - " \t\t\"console_output\": output.getvalue(),\n", - " \"executed_code\": code\n", - " \t}\n", - "\n" + " return [{\"console_output\": output.getvalue(), \"executed_code\": code}]" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -171,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -190,17 +184,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/meor/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n", - " from pandas.core import (\n" - ] - }, { "data": { "text/html": [ @@ -352,7 +338,7 @@ "[3 rows x 25 columns]" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -374,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -409,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -427,87 +413,347 @@ "==================================================\n", "\n", "TOOL PLAN:\n", - "I will first inspect the dataset to understand its structure and the columns it contains. Then, I will write and execute Python code to find the top 3 most streamed songs on Spotify in 2023 and their respective artists. Finally, I will search for the age and citizenship of each artist online.\n", + "I will first inspect the dataset to understand its structure. Then, I will use Python to find the top 3 most streamed songs on Spotify in 2023. I will then search for the age and citizenship of the artists of these songs.\n", "\n", "TOOL CALLS:\n", "Tool name: python_interpreter\n", " import pandas as pd\n", " \n", - " df = pd.read_csv('spotify_dataset.csv')\n", + " df = pd.read_csv(\"spotify_dataset.csv\")\n", " \n", - " print(df.columns)\n", + " # Inspect the dataset\n", + " print(\"Columns:\", df.columns.tolist())\n", + " print(\"Data types:\", df.dtypes)\n", + " print(\"First 5 rows:\")\n", + " print(df.head())\n", "None\n", "\n", "TOOL RESULTS:\n", - "{'console_output': \"Index(['track_name', 'artist(s)_name', 'artist_count', 'released_year',\\n 'released_month', 'released_day', 'in_spotify_playlists',\\n 'in_spotify_charts', 'streams', 'in_apple_playlists', 'in_apple_charts',\\n 'in_deezer_playlists', 'in_deezer_charts', 'in_shazam_charts', 'bpm',\\n 'key', 'mode', 'danceability', 'valence', 'energy', 'acousticness',\\n 'instrumentalness', 'liveness', 'speechiness', 'release_date'],\\n dtype='object')\\n\", 'executed_code': \"import pandas as pd\\n\\ndf = pd.read_csv('spotify_dataset.csv')\\n\\nprint(df.columns)\"} \n", + "[{'console_output': \"Columns: ['track_name', 'artist(s)_name', 'artist_count', 'released_year', 'released_month', 'released_day', 'in_spotify_playlists', 'in_spotify_charts', 'streams', 'in_apple_playlists', 'in_apple_charts', 'in_deezer_playlists', 'in_deezer_charts', 'in_shazam_charts', 'bpm', 'key', 'mode', 'danceability', 'valence', 'energy', 'acousticness', 'instrumentalness', 'liveness', 'speechiness', 'release_date']\\nData types: track_name object\\nartist(s)_name object\\nartist_count int64\\nreleased_year int64\\nreleased_month int64\\nreleased_day int64\\nin_spotify_playlists int64\\nin_spotify_charts int64\\nstreams float64\\nin_apple_playlists int64\\nin_apple_charts int64\\nin_deezer_playlists object\\nin_deezer_charts int64\\nin_shazam_charts object\\nbpm int64\\nkey object\\nmode object\\ndanceability int64\\nvalence int64\\nenergy int64\\nacousticness int64\\ninstrumentalness int64\\nliveness int64\\nspeechiness int64\\nrelease_date object\\ndtype: object\\nFirst 5 rows:\\n track_name artist(s)_name artist_count \\\\\\n0 Seven (feat. Latto) (Explicit Ver.) Latto, Jung Kook 2 \\n1 LALA Myke Towers 1 \\n2 vampire Olivia Rodrigo 1 \\n3 Cruel Summer Taylor Swift 1 \\n4 WHERE SHE GOES Bad Bunny 1 \\n\\n released_year released_month released_day in_spotify_playlists \\\\\\n0 2023 7 14 553 \\n1 2023 3 23 1474 \\n2 2023 6 30 1397 \\n3 2019 8 23 7858 \\n4 2023 5 18 3133 \\n\\n in_spotify_charts streams in_apple_playlists ... key mode \\\\\\n0 147 141381703.0 43 ... B Major \\n1 48 133716286.0 48 ... C# Major \\n2 113 140003974.0 94 ... F Major \\n3 100 800840817.0 116 ... A Major \\n4 50 303236322.0 84 ... A Minor \\n\\n danceability valence energy acousticness instrumentalness liveness \\\\\\n0 80 89 83 31 0 8 \\n1 71 61 74 7 0 10 \\n2 51 32 53 17 0 31 \\n3 55 58 72 11 0 11 \\n4 65 23 80 14 63 11 \\n\\n speechiness release_date \\n0 4 2023-07-14 \\n1 4 2023-03-23 \\n2 6 2023-06-30 \\n3 15 2019-08-23 \\n4 6 2023-05-18 \\n\\n[5 rows x 25 columns]\\n\", 'executed_code': 'import pandas as pd\\r\\n\\r\\ndf = pd.read_csv(\"spotify_dataset.csv\")\\r\\n\\r\\n# Inspect the dataset\\r\\nprint(\"Columns:\", df.columns.tolist())\\r\\nprint(\"Data types:\", df.dtypes)\\r\\nprint(\"First 5 rows:\")\\r\\nprint(df.head())'}] \n", "\n", "\n", "TOOL PLAN:\n", - "I now know the column names of the dataset. I will now find the top 3 most streamed songs on Spotify in 2023 and then find the age and citizenship of the artists of those songs.\n", + "The dataset contains information about the top streamed songs on Spotify, including the artist's name, the year the song was released, and the number of streams.\n", + "\n", + "I will now write and execute Python code to find the top 3 most streamed songs on Spotify in 2023.\n", "\n", "TOOL CALLS:\n", "Tool name: python_interpreter\n", " import pandas as pd\n", " \n", - " df = pd.read_csv('spotify_dataset.csv')\n", + " df = pd.read_csv(\"spotify_dataset.csv\")\n", " \n", - " # Filter for songs released in 2023\n", - " df_2023 = df[df['released_year'] == 2023]\n", + " # Filter the dataset to only include songs released in 2023\n", + " df_2023 = df[df[\"released_year\"] == 2023]\n", " \n", - " # Sort by streams in descending order\n", - " df_2023 = df_2023.sort_values(by='streams', ascending=False)\n", + " # Sort the songs by the number of streams in descending order\n", + " df_2023_sorted = df_2023.sort_values(by=\"streams\", ascending=False)\n", " \n", - " # Get the top 3 songs\n", - " top_3_songs = df_2023.head(3)\n", + " # Get the top 3 most streamed songs\n", + " top_3_songs = df_2023_sorted.head(3)\n", " \n", - " print(\"Top 3 most streamed songs on Spotify in 2023:\")\n", - " print(top_3_songs[['track_name', 'artist(s)_name']])\n", + " # Print the top 3 most streamed songs\n", + " print(\"Top 3 most streamed songs:\")\n", + " print(top_3_songs[['track_name', 'artist(s)_name', 'streams']])\n", "None\n", "\n", "TOOL RESULTS:\n", - "{'console_output': 'Top 3 most streamed songs on Spotify in 2023:\\n track_name artist(s)_name\\n12 Flowers Miley Cyrus\\n6 Ella Baila Sola Eslabon Armado, Peso Pluma\\n133 Shakira: Bzrp Music Sessions, Vol. 53 Shakira, Bizarrap\\n', 'executed_code': 'import pandas as pd\\n\\ndf = pd.read_csv(\\'spotify_dataset.csv\\')\\n\\n# Filter for songs released in 2023\\ndf_2023 = df[df[\\'released_year\\'] == 2023]\\n\\n# Sort by streams in descending order\\ndf_2023 = df_2023.sort_values(by=\\'streams\\', ascending=False)\\n\\n# Get the top 3 songs\\ntop_3_songs = df_2023.head(3)\\n\\nprint(\"Top 3 most streamed songs on Spotify in 2023:\")\\nprint(top_3_songs[[\\'track_name\\', \\'artist(s)_name\\']])'} \n", + "[{'console_output': 'Top 3 most streamed songs:\\n track_name artist(s)_name \\\\\\n12 Flowers Miley Cyrus \\n6 Ella Baila Sola Eslabon Armado, Peso Pluma \\n133 Shakira: Bzrp Music Sessions, Vol. 53 Shakira, Bizarrap \\n\\n streams \\n12 1.316856e+09 \\n6 7.259801e+08 \\n133 7.219756e+08 \\n', 'executed_code': 'import pandas as pd\\r\\n\\r\\ndf = pd.read_csv(\"spotify_dataset.csv\")\\r\\n\\r\\n# Filter the dataset to only include songs released in 2023\\r\\ndf_2023 = df[df[\"released_year\"] == 2023]\\r\\n\\r\\n# Sort the songs by the number of streams in descending order\\r\\ndf_2023_sorted = df_2023.sort_values(by=\"streams\", ascending=False)\\r\\n\\r\\n# Get the top 3 most streamed songs\\r\\ntop_3_songs = df_2023_sorted.head(3)\\r\\n\\r\\n# Print the top 3 most streamed songs\\r\\nprint(\"Top 3 most streamed songs:\")\\r\\nprint(top_3_songs[[\\'track_name\\', \\'artist(s)_name\\', \\'streams\\']])'}] \n", + "\n", + "\n", + "TOOL PLAN:\n", + "I have found that the top 3 most streamed songs on Spotify in 2023 were:\n", + "1. Flowers by Miley Cyrus\n", + "2. Ella Baila Sola by Eslabon Armado, Peso Pluma\n", + "3. Shakira: Bzrp Music Sessions, Vol. 53 by Shakira, Bizarrap\n", + "\n", + "I will now search for the age and citizenship of each of these artists.\n", + "\n", + "TOOL CALLS:\n", + "Tool name: web_search | Parameters: {\"query\":\"Miley Cyrus age\"}\n", + "Tool name: web_search | Parameters: {\"query\":\"Miley Cyrus citizenship\"}\n", + "Tool name: web_search | Parameters: {\"query\":\"Eslabon Armado age\"}\n", + "Tool name: web_search | Parameters: {\"query\":\"Eslabon Armado citizenship\"}\n", + "Tool name: web_search | Parameters: {\"query\":\"Peso Pluma age\"}\n", + "Tool name: web_search | Parameters: {\"query\":\"Peso Pluma citizenship\"}\n", + "Tool name: web_search | Parameters: {\"query\":\"Shakira age\"}\n", + "Tool name: web_search | Parameters: {\"query\":\"Shakira citizenship\"}\n", + "Tool name: web_search | Parameters: {\"query\":\"Bizarrap age\"}\n", + "Tool name: web_search | Parameters: {\"query\":\"Bizarrap citizenship\"}\n", + "\n", + "TOOL RESULTS:\n", + "[{'title': 'Miley Cyrus Height, Weight, Age, Husband, Bio, Net Worth, Facts', 'url': 'https://www.celebsline.com/miley-cyrus/', 'content': 'Miley Cyrus Height, Weight, Age, Husband, Bio, Net Worth, Facts Miley Cyrus Biography Miley Cyrus Husband and Love Life Miley Cyrus released her first studio album “Meet Miley Cyrus” On June 26, 2007, under the Walt Disney label, which received several Gold and Platinum certificates. Miley Cyrus Net Worth Miley Cyrus’s net worth is estimated at $160 million US dollars as of 2024. Miley Cyrus\\xa0Facts Miley Cyrus was named no. Adam Levine Height, Biography, Age, Wiki, Wife, Net Worth, Facts Anitta Biography, Age, Height, Family, Education, Husband, Albums Gafoor Height, Wiki, Biography, Age, Family, Movies, Facts', 'score': 0.94699025, 'raw_content': None}, {'title': 'Miley Cyrus - Biography - IMDb', 'url': 'https://www.imdb.com/name/nm1415323/bio/', 'content': 'Miley Cyrus is an actress and singer born on November 23, 1992 in Tennessee. She rose to fame as the star of Hannah Montana (2006) and has since appeared in movies, TV shows and music videos.', 'score': 0.9436599, 'raw_content': None}, {'title': 'Miley Cyrus | Biography, TV Shows, Songs, & Facts | Britannica', 'url': 'https://www.britannica.com/biography/Miley-Cyrus', 'content': 'Meanwhile, Cyrus continued to record under her own name for the EP The Time of Our Lives (2009), which featured the hit “Party in the U.S.A.,” and the full-length album Can’t Be Tamed (2010). She then veered away from the mainstream with Miley Cyrus & Her Dead Petz (2015), a collection of loosely psychedelic songs recorded in large part with the rock band the Flaming Lips and made available for free on the Internet. In 2005 Cyrus auditioned for the lead role in Hannah Montana, a Disney Channel series about a girl who leads a double life as a normal middle-school student and, with the help of a blonde wig, as a glamorous pop singer. Cyrus was a coach on the television singing competition The Voice in 2016–17, and she costarred with Woody Allen and Elaine May in Allen’s six-episode TV series Crisis in Six Scenes (2016). The show debuted the following year (featuring Billy Ray as her TV dad) and became an immediate sensation, scoring record ratings for cable television and turning the 13-year-old into a bona fide star.', 'score': 0.8539127, 'raw_content': None}] \n", + "\n", + "[{'title': \"Miley Cyrus Talks Global Citizen's Fight to End Inequality, COVID-19\", 'url': 'https://www.rollingstone.com/music/music-news/miley-cyrus-global-citizen-global-goal-interview-1021734/', 'content': \"Miley Cyrus Talks Fighting Inequality, COVID-19 at Global Citizen's 'Global Goal' Special. Advocacy org's latest event raised $6.9 billion in pledges to help develop, distribute a vaccine to ...\", 'score': 0.98184055, 'raw_content': None}, {'title': 'Miley Cyrus Performs Heartfelt Beatles Cover for ... - Global Citizen', 'url': 'https://www.globalcitizen.org/en/content/miley-cyrus-sings-help-beatles-global-goal-unite/', 'content': \"Miley Cyrus Performs Heartfelt Beatles Cover for 'Global Goal: Unite for Our Future' to Help Beat COVID-19 You can join our Global Goal: Unite for the Future campaign to make sure everyone, everywhere can access the tools needed to beat COVID-19and take action here. The pledging summit prior to the concert, mobilized over $6.9 billion to help the world’s most vulnerable in response to the campaign launched in May. Hosted by Dwayne Johnson, the globally televised and digitally streamed concert event highlighted the disproportionate impact COVID-19 has on marginalized communities. Countries responded to the nearly 800,000 actions Global Citizens, Cyrus, and other celebrities took to fight COVID-19\\xa0and protect the most marginalized people from its impact.\", 'score': 0.9801293, 'raw_content': None}, {'title': 'Miley Cyrus - Ethnicity of Celebs | EthniCelebs.com', 'url': 'https://ethnicelebs.com/miley-cyrus', 'content': 'Date of Birth: November 23, 1992. Ethnicity: English, Scottish, Irish, Welsh, possibly other. Miley Cyrus is an American actress, singer, and songwriter. She has starred on the series Hannah Montana, Crisis in Six Scenes, and Black Mirror: Rachel, Jack and Ashley Too, in the films Bolt, Hannah Montana: The Movie, The Last Song, LOL, and So ...', 'score': 0.7594039, 'raw_content': None}] \n", + "\n", + "[{'title': 'Eslabon Armado - Wikipedia', 'url': 'https://en.wikipedia.org/wiki/Eslabon_Armado', 'content': 'Eslabon Armado is an American regional Mexican group from Patterson, California, formed in 2017.The group consists of Pedro Tovar (lead vocals), Brian Tovar (bass), Ulises GonzĂĄlez (acoustic guitar), and DamiĂĄn Pacheco (twelve-string guitar) . For most of their career, the group had a three-piece lineup, consisting of brothers Pedro and Brian Tovar, and their friend Gabriel Hidalgo.', 'score': 0.9983834, 'raw_content': None}, {'title': 'Pedro Tovar - Age, Family, Bio | Famous Birthdays', 'url': 'https://www.famousbirthdays.com/people/pedro-tovar.html', 'content': \"Age 22 years old #7,224 Most Popular. Boost. About . Singer and songwriter who has earned over 950,000 fans on his pedro.tovar007 TikTok account. He provides the lead vocals for the popular trio Eslabon Armado and he wrote and coproduced all nine songs on the band's EP Vibras de Noche. ...\", 'score': 0.9967397, 'raw_content': None}, {'title': 'EslabĂłn Armado - Members, Ages, Trivia | Famous Birthdays', 'url': 'https://www.famousbirthdays.com/bands/eslab--n-armado.html', 'content': 'EslabĂłn Armado Members. Pedro Tovar, 22 1. Brian Tovar, 20 2. Gabriel Hidalgo, 23 Former. 3. Damian Pacheco, 22 4. Popularity Band #149 California Band #25 Regional Mexican Band #2 EslabĂłn Armado Fans Also Viewed Fuerza Regida. Los Tigres del Norte ...', 'score': 0.994089, 'raw_content': None}] \n", + "\n", + "[{'title': 'Eslabon Armado - Wikipedia', 'url': 'https://en.wikipedia.org/wiki/Eslabon_Armado', 'content': 'Eslabon Armado is an American regional Mexican group from Patterson, California, formed in 2017.The group consists of Pedro Tovar (lead vocals), Brian Tovar (bass), Ulises GonzĂĄlez (acoustic guitar), and DamiĂĄn Pacheco (twelve-string guitar) . For most of their career, the group had a three-piece lineup, consisting of brothers Pedro and Brian Tovar, and their friend Gabriel Hidalgo.', 'score': 0.99731654, 'raw_content': None}, {'title': 'Eslabon Armado: A New Era in Regional Mexican Music - Lalo', 'url': 'https://www.lalo.app/story/eslabon-armado-new-era-in-regional-mexican-music', 'content': 'đŸ‡ČđŸ‡œđŸ‡Ș🇾 Leer versiĂłn en español. Eslabon Armado is an American regional Mexican group that breaks boundaries and puts a new spin on traditional Latin music. They started as teenagers in 2017. 
 And they have since released six studio albums and received numerous awards including two Billboard Music Awards, two Billboard Latin Music Awards, and two Latin American Music Awards.', 'score': 0.97313875, 'raw_content': None}, {'title': 'Eslabon Armado Musician - All About Jazz', 'url': 'https://www.allaboutjazz.com/musicians/eslabon-armado', 'content': \"The record-breaking group Eslabon Armado has defied cultural barriers to surpass all expectations in the most unconventional way possible. Becoming the first ever Mexican act to reach No. 1 on Billboard's Global 200, they are conquering the world and overcoming adversities in the most unconventional way. Born and raised in California to ...\", 'score': 0.96485513, 'raw_content': None}] \n", + "\n", + "[{'title': 'Who is Peso Pluma? His real name, age, height, nationality, and more', 'url': 'https://wegotthiscovered.com/celebrities/who-is-peso-pluma-his-real-name-age-height-nationality-and-more/', 'content': 'Peso Pluma is a 24-year-old rapper, singer, and songwriter from Mexico. He was born on June 15, 1999, and his name means \"heavy feather\" in English.', 'score': 0.9997818, 'raw_content': None}, {'title': 'Peso Pluma - Age, Family, Bio | Famous Birthdays', 'url': 'https://www.famousbirthdays.com/people/peso-pluma.html', 'content': 'Peso Pluma is a 25-year-old rapper from Guadalajara, Mexico, born on June 15, 1999. He has over 10 million Spotify listeners and 13 million Instagram followers, and has collaborated with Becky G and Tony Aguirre.', 'score': 0.9997677, 'raw_content': None}, {'title': 'Peso Pluma talks global rise of Mexican music, performs at Coachella', 'url': 'https://www.usatoday.com/story/entertainment/music/2023/10/03/peso-pluma-interview-genesis-mexican-music-making-history/71029635007/', 'content': 'Peso Pluma is a 24-year-old singer of corridos tumbados and corridos bĂ©licos, a mix of electronic beats and narcoculture lyrics. He has 21 Billboard Latin Music Award nominations, a Coachella performance, and a global fan base.', 'score': 0.99972624, 'raw_content': None}] \n", + "\n", + "[{'title': 'Peso Pluma: The Mexican singer making history and first US tour - NBC ...', 'url': 'https://www.nbcnewyork.com/entertainment/entertainment-news/whos-peso-pluma-what-to-know-about-the-mexican-singer-making-history-and-his-first-us-tour/4515079/', 'content': 'Hassan Emilio Kabande Laija, better known as Peso Pluma, is a 24-year-old Mexican musician who has achieved huge recognition on social media for viral songs -- one of them being \"El Belicon\" which ...', 'score': 0.96458924, 'raw_content': None}, {'title': 'Peso Pluma - Wikipedia', 'url': 'https://en.wikipedia.org/wiki/Peso_Pluma', 'content': 'In this Spanish name, the first or paternal surname is Kabande and the second or maternal family name is Laija. Hassan Emilio Kabande Laija (born 15 June 1999), known professionally as Peso Pluma, is a Mexican rapper and singer. Learning to play the guitar during his early teens, he developed his craft by writing songs inspired by regional ...', 'score': 0.9457011, 'raw_content': None}, {'title': 'How Peso Pluma Brought Mexican Music to the World Stage', 'url': 'https://info.xposuremusic.com/article/peso-pluma-mexican-music', 'content': 'Peso Pluma has thus far racked up 11 top 10 hits in Mexico, while also cracking the U.S. top 40 on six occasions — the highest being his collab with compatriots Eslabon Armado, \"Ella Baila Sola\", which peaked at #4 on the Billboard Hot 100. He\\'s also featured on the remix of Yng Lucas\\' \"La Bebe\", which Pitchfork named among its 20 ...', 'score': 0.9323302, 'raw_content': None}] \n", + "\n", + "[{'title': 'Shakira - Biography - IMDb', 'url': 'https://m.imdb.com/name/nm0787680/bio/', 'content': 'Shakira is a Colombian singer, songwriter, and philanthropist born on February 2, 1977. She is known for her fusion of Latin, rock, and Middle Eastern music styles, her belly dancing skills, and her foundation for education and nutrition.', 'score': 0.9219218, 'raw_content': None}, {'title': 'Shakira | Biography, Music & News - Billboard', 'url': 'https://www.billboard.com/artist/shakira/', 'content': 'Shakira is a Colombian singer born on Feb. 2, 1977. She has released several albums in English and Spanish, and has won multiple Grammys and Billboard awards.', 'score': 0.9129032, 'raw_content': None}, {'title': 'Shakira Biography - Facts, Childhood, Family Life & Achievements', 'url': 'https://www.thefamouspeople.com/profiles/shakira-4125.php', 'content': \"Shakira Biography Shakira is a highly successful Columbian pop artist, best known for her scintillating belly dance moves in the video of the song ‘Hips Don’t Lie.’ Having a keen interest in music from childhood, Shakira wrote her first poem when she was just 4. Also Known As: Shakira Isabel Mebarak Ripoll How did Shakira start her music career? What is Shakira's most popular song? Where is Shakira from? What languages does Shakira speak? Recommended Lists: Shakira was born Shakira Isabel Mebarak Ripoll on February 2, 1977, in Barranquilla, Colombia, to Nidia Ripoll and William Mebarak Chadid. Shakira's tenth studio album released on March 25, 2014. Shakira and Gerard were blessed with a son named Milan Pique on January 22, 2013.\", 'score': 0.89715594, 'raw_content': None}] \n", + "\n", + "[{'title': '\"Silence Is Dangerous\": Shakira Pens Powerful Essay on Children ...', 'url': 'https://www.hollywoodreporter.com/news/music-news/silence-is-dangerous-shakira-pens-powerful-essay-on-children-separated-at-us-southern-border-4085691/', 'content': 'Shakira acknowledges that she isn\\'t an American citizen, and could be \"perceived as an outsider commenting on domestic policies. However, the United States\\' decisions affect us all, even ...', 'score': 0.9600627, 'raw_content': None}, {'title': \"Discover Shakira's Nationality: Colombian Citizenship And Lebanese ...\", 'url': 'https://astroinsightz.com/what-is-the-nationality-of-shakira/', 'content': 'Shakira often speaks fondly of her Colombian roots, incorporating elements of Colombian culture into her songs and performances. One of the key aspects of Colombian citizenship that has influenced Shakira is the rich musical heritage of the country. Colombia is known for its diverse range of musical styles, including cumbia, vallenato, and salsa.', 'score': 0.95606345, 'raw_content': None}, {'title': 'Shakira - Wikipedia', 'url': 'https://en.wikipedia.org/wiki/Shakira', 'content': 'She was marked as \"one of the most influential artists of the 21st century\" by ET.[167]\\nThroughout her career, Shakira has earned several titles, including \"The Crossover Queen\" by The Economist,[168] \"The Queen of World Cup\" by Billboard,[169] and \"Latin America\\'s Pop Queen\" by Pitchfork.[170] Billboard has also noted that Shakira\\'s music videos have \"redefined the role of dancing in music videos\", while listing her as the best Latin female music video artist of all time.[171] The Middle Eastern newspaper El Correo del Golfo credits Shakira with \"having opened the way\" for several Hispanic singers today.[172]\\nThe authors of Reggaeton, published by Duke University Press, credited Shakira for popularizing the genre (reggaeton) in North America, Europe, and Asia,[173] while the Public Broadcasting Service called her one of the three \"most successful artists of the so-called Golden Age of Latin Music which reshaped America\\'s cultural landscape for the twenty-first century\".[174]\\nAlongside her impact on Latin and mainstream pop culture, Shakira has also impacted popular culture in the Arab World due to her popularity in the region. One of the most influential female artists of the 21st century, Shakira is credited with opening the doors of the international market for other Latin artists.[7][8] With a catalog of 145 songs, Shakira has sold over 95 million records, making her one of the best-selling music artists of all time.[9] Billboard reported that as of 2023, she is the best-selling female Latin artist of all time.[10] Noted to be a \"global phenom\" whose impact has \"reached every corner of the world\", Shakira has been described as an \"artistic link between the west and the east\" for popularizing Middle Eastern sounds in the West, and western sounds in the East (mainly the Middle East).[11][12] \"[140]\\nInfluences\\nAs a child, Shakira was influenced by rock music, listening heavily to rock bands like Led Zeppelin,[141] the Beatles, Nirvana,[142] the Police and U2,[143] while her other influences included Gloria Estefan, Madonna, Sheryl Crow, Alanis Morissette, Marc Anthony, Meredith Brooks, and the Cure.[144][145] She cited Depeche Mode\\'s \"Enjoy the Silence\" as the song that first sparked her passion for pop music.[146]\\nDance\\nShakira is well known for her dancing in her music videos and concerts.[147] Her distinctive dancing style is said to combine Latin dancing with Middle Eastern belly dancing,[147][148] which is derived from her Lebanese heritage,[147] and her hip shaking is mentioned in songs, such as Fifth Harmony\\'s \"Brave Honest Beautiful\".[149] She is noted for usually employing minimal production, usually taking the stage with minimal makeup and natural hair, and without background dancers in her performances, preferring to focus on her vocals, dance moves, and stage presence.[150] Lola Indigo,[247] Young Miko,[248] Nora Fatehi,[249] Kris Kross Ámsterdam,[250] Bad Gyal,[251] Jackson Wang,[252] and Ayra Starr.[253]\\nShakira has even received the admiration of intellectuals and writers such as Nobel Prize winner Gabriel GarcĂ­a MĂĄrquez who has said \"No one of any age can sing or dance with the innocent sensuality Shakira seems to have invented\".[25] MĂĄrquez wrote an essay exalting Shakira\\'s \"phenomenal musical talent\", and \"extraordinary maturity\".[159]\\nThe newspaper El Correo del Golfo wrote that Shakira was the greatest exponent of Middle Eastern music in the West, citing her interest in Arabic music and dance.[172]\\nHer influence has transcended the boundaries of pop culture, that she has become a socio-political influencer, and was named as one of the \"World\\'s Greatest Leaders\" of 2017 by Fortune.[254] His lawsuit was dismissed by a Los Angeles County Superior Court judge in August\\xa02013.[317]\\nShakira entered a relationship with the Spanish football player Gerard PiquĂ© in 2011.[318][319] PiquĂ©, who is exactly ten years her junior, and Shakira met in spring 2010 after he appeared in the music video for Shakira\\'s song \"Waka Waka (This Time for Africa)\", the official song of the 2010 FIFA World Cup.[315] Shakira gave birth to the couple\\'s first son, Milan, on 22\\xa0January 2013 in Barcelona, where the family took residence.[320] Shakira gave birth to their second son, Sasha, on 29\\xa0January 2015.[321] Forbes listed Shakira and PiquĂ© in their list of \"World\\'s Most Powerful Couples\".[322]', 'score': 0.19436783, 'raw_content': None}] \n", + "\n", + "[{'title': 'Bizarrap - Age, Family, Bio | Famous Birthdays', 'url': 'https://www.famousbirthdays.com/people/bizarrap.html', 'content': 'Bizarrap: his birthday, what he did before fame, his family life, fun trivia facts, popularity rankings, and more. ... Birthplace Argentina. Age 26 years old #10,025 Most Popular. Boost. About . Trap music producer who first became known for his remixes of popular hit songs by Argentinian trappers.', 'score': 0.99818975, 'raw_content': None}, {'title': 'Bizarrap - Wikipedia', 'url': 'https://en.wikipedia.org/wiki/Bizarrap', 'content': 'Gonzalo JuliĂĄn Conde (born 29 August 1998), known professionally as Bizarrap, is an Argentine DJ and producer. He specializes in EDM, Latin trap and Latin hip hop. He is known for his Bzrp Music Sessions, a series of independent tracks which he records with a wide variety of artists. [1] He was nominated for a Latin Grammy Award for Producer ...', 'score': 0.98184055, 'raw_content': None}, {'title': 'Bizarrap Lyrics, Songs, and Albums - Genius', 'url': 'https://genius.com/artists/Bizarrap', 'content': 'Gonzalo JuliĂĄn Conde, tambiĂ©n conocido como Bizarrap o BZRP, es un productor musical y creador de contenido audiovisual argentino. En 2017, comenzĂł en YouTube como un canal de', 'score': 0.98166555, 'raw_content': None}] \n", + "\n", + "[{'title': 'Bizarrap - Wikipedia', 'url': 'https://en.wikipedia.org/wiki/Bizarrap', 'content': 'Gonzalo JuliĂĄn Conde (born 29 August 1998), known professionally as Bizarrap, is an Argentine DJ and producer.He specializes in EDM, Latin trap and Latin hip hop.He is known for his Bzrp Music Sessions, a series of independent tracks which he records with a wide variety of artists. [1] He was nominated for a Latin Grammy Award for Producer of the Year in 2021 and 2023.', 'score': 0.93913656, 'raw_content': None}, {'title': \"Bizarrap: Billboard's Latin Artist on the Rise\", 'url': 'https://www.billboard.com/music/latin/bizarrap-interview-latin-artist-on-the-rise-9574988/', 'content': \"Argentine producer Bizarrap's music sessions featuring artists such as Nathy Peluso and Nicki Nicole have not only gone viral on YouTube, raking in millions of views in a matter of days, they ...\", 'score': 0.8071568, 'raw_content': None}, {'title': 'How Bizarrap Became the Hottest Name in Latin Music - Miami New Times', 'url': 'https://www.miaminewtimes.com/music/who-is-bizarrap-how-the-argentinian-producer-conquered-latin-music-18964117', 'content': 'Bizarrap won \"Song of the Year,\" \"Best Pop Song,\" and \"Best Urban Song\" at the 24th annual Latin Grammy Awards in Sevilla, Spain, last year. Photo by Borja B. Hojas/Getty Images for Latin ...', 'score': 0.7190878, 'raw_content': None}] \n", "\n", "\n", "RESPONSE:\n", "The top 3 most streamed songs on Spotify in 2023 were:\n", - "1. *Flowers* by Miley Cyrus\n", - "2. *Ella Baila Sola* by Eslabon Armado and Peso Pluma\n", - "3. *Shakira: Bzrp Music Sessions, Vol. 53* by Shakira and Bizarrap\n", + "1. 'Flowers' by Miley Cyrus\n", + "2. 'Ella Baila Sola' by Eslabon Armado and Peso Pluma\n", + "3. 'Shakira: Bzrp Music Sessions, Vol. 53' by Shakira and Bizarrap\n", "\n", - "Miley Cyrus is 30 years old and American. Eslabon Armado is a Mexican group, and Peso Pluma is a Mexican singer. Shakira is 46 years old and Colombian.\n", + "Miley Cyrus is an American actress, singer, and songwriter. She was born on November 23, 1992, making her 31 years old.\n", + "\n", + "Eslabon Armado is an American regional Mexican group from Patterson, California, formed in 2017. The group consists of Pedro Tovar (lead vocals), Brian Tovar (bass), Ulises GonzĂĄlez (acoustic guitar), and DamiĂĄn Pacheco (twelve-string guitar). The members of the group are in their early 20s.\n", + "\n", + "Peso Pluma is a 24-25-year-old rapper, singer, and songwriter from Mexico. He was born on June 15, 1999.\n", + "\n", + "Shakira is a Colombian singer, songwriter, and philanthropist born on February 2, 1977. She is known for her fusion of Latin, rock, and Middle Eastern music styles, her belly dancing skills, and her foundation for education and nutrition.\n", + "\n", + "Bizarrap is an Argentine DJ and producer. He was born on August 29, 1998, making him 25 years old.\n", "\n", "CITATIONS:\n", - "Start: 59 | End: 66 | Text: 'Flowers'\n", + "Start: 58 | End: 67 | Text: ''Flowers''\n", "Sources:\n", - "python_interpreter_ryqfeye95gay:0\n", + "python_interpreter_zvrr4ac8hhps:0\n", "--------------------------------------------------\n", "Start: 71 | End: 82 | Text: 'Miley Cyrus'\n", "Sources:\n", - "python_interpreter_ryqfeye95gay:0\n", + "python_interpreter_zvrr4ac8hhps:0\n", "--------------------------------------------------\n", - "Start: 87 | End: 102 | Text: 'Ella Baila Sola'\n", + "Start: 86 | End: 103 | Text: ''Ella Baila Sola''\n", "Sources:\n", - "python_interpreter_ryqfeye95gay:0\n", + "python_interpreter_zvrr4ac8hhps:0\n", "--------------------------------------------------\n", "Start: 107 | End: 121 | Text: 'Eslabon Armado'\n", "Sources:\n", - "python_interpreter_ryqfeye95gay:0\n", + "python_interpreter_zvrr4ac8hhps:0\n", "--------------------------------------------------\n", "Start: 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"--------------------------------------------------\n", + "Start: 232 | End: 239 | Text: 'actress'\n", + "Sources:\n", + "web_search_hrz3bzcs7a8n:2\n", + "--------------------------------------------------\n", + "Start: 241 | End: 247 | Text: 'singer'\n", + "Sources:\n", + "web_search_hrz3bzcs7a8n:2\n", + "--------------------------------------------------\n", + "Start: 253 | End: 264 | Text: 'songwriter.'\n", + "Sources:\n", + "web_search_hrz3bzcs7a8n:2\n", + "--------------------------------------------------\n", + "Start: 281 | End: 298 | Text: 'November 23, 1992'\n", + "Sources:\n", + "web_search_0me46ed9cxp9:1\n", + "web_search_hrz3bzcs7a8n:2\n", + "--------------------------------------------------\n", + "Start: 347 | End: 355 | Text: 'American'\n", + "Sources:\n", + "web_search_ra443ajyz6xj:0\n", + "web_search_ta7g2cd67jrx:0\n", + "web_search_ta7g2cd67jrx:1\n", + "web_search_ta7g2cd67jrx:2\n", + "--------------------------------------------------\n", + "Start: 356 | End: 378 | Text: 'regional Mexican group'\n", + "Sources:\n", + "web_search_ra443ajyz6xj:0\n", + "web_search_ta7g2cd67jrx:0\n", + "web_search_ta7g2cd67jrx:1\n", + "web_search_ta7g2cd67jrx:2\n", + "--------------------------------------------------\n", + "Start: 384 | End: 405 | Text: 'Patterson, California'\n", + "Sources:\n", + "web_search_ra443ajyz6xj:0\n", + "web_search_ta7g2cd67jrx:0\n", + "web_search_ta7g2cd67jrx:2\n", + "--------------------------------------------------\n", + "Start: 417 | End: 422 | Text: '2017.'\n", + "Sources:\n", + "web_search_ra443ajyz6xj:0\n", + "web_search_ta7g2cd67jrx:0\n", + "web_search_ta7g2cd67jrx:1\n", + "--------------------------------------------------\n", + "Start: 445 | End: 470 | Text: 'Pedro Tovar (lead vocals)'\n", + "Sources:\n", + "web_search_ra443ajyz6xj:0\n", + "web_search_ra443ajyz6xj:1\n", + "web_search_ra443ajyz6xj:2\n", + "web_search_ta7g2cd67jrx:0\n", + "--------------------------------------------------\n", + "Start: 472 | End: 490 | Text: 'Brian Tovar (bass)'\n", + "Sources:\n", + "web_search_ra443ajyz6xj:0\n", + "web_search_ra443ajyz6xj:2\n", + "web_search_ta7g2cd67jrx:0\n", + "--------------------------------------------------\n", + "Start: 492 | End: 525 | Text: 'Ulises GonzĂĄlez (acoustic guitar)'\n", + "Sources:\n", + "web_search_ra443ajyz6xj:0\n", + "web_search_ta7g2cd67jrx:0\n", + "--------------------------------------------------\n", + "Start: 531 | End: 568 | Text: 'DamiĂĄn Pacheco (twelve-string guitar)'\n", + "Sources:\n", + "web_search_ra443ajyz6xj:0\n", + "web_search_ra443ajyz6xj:2\n", + "web_search_ta7g2cd67jrx:0\n", + "--------------------------------------------------\n", + "Start: 608 | End: 618 | Text: 'early 20s.'\n", + "Sources:\n", + "web_search_ra443ajyz6xj:1\n", + "web_search_ra443ajyz6xj:2\n", + "--------------------------------------------------\n", + "Start: 636 | End: 638 | Text: '24'\n", + "Sources:\n", + "web_search_q7bna7km1re0:0\n", + "web_search_q7bna7km1re0:1\n", + "web_search_q7bna7km1re0:2\n", + "web_search_6bma458t4fxh:0\n", + "--------------------------------------------------\n", + "Start: 638 | End: 641 | Text: '-25'\n", + "Sources:\n", + "web_search_q7bna7km1re0:1\n", + "--------------------------------------------------\n", + "Start: 641 | End: 650 | Text: '-year-old'\n", + "Sources:\n", + "web_search_q7bna7km1re0:0\n", + "web_search_q7bna7km1re0:1\n", + "web_search_q7bna7km1re0:2\n", + "web_search_6bma458t4fxh:0\n", + "--------------------------------------------------\n", + "Start: 651 | End: 657 | Text: 'rapper'\n", + "Sources:\n", + "web_search_q7bna7km1re0:0\n", + "web_search_q7bna7km1re0:1\n", + "web_search_6bma458t4fxh:0\n", + "web_search_6bma458t4fxh:1\n", + "--------------------------------------------------\n", + "Start: 659 | End: 665 | Text: 'singer'\n", + "Sources:\n", + "web_search_q7bna7km1re0:0\n", + "web_search_q7bna7km1re0:1\n", + "web_search_q7bna7km1re0:2\n", + "web_search_6bma458t4fxh:0\n", + "web_search_6bma458t4fxh:1\n", + "--------------------------------------------------\n", + "Start: 671 | End: 681 | Text: 'songwriter'\n", + "Sources:\n", + "web_search_q7bna7km1re0:0\n", + "web_search_6bma458t4fxh:1\n", + "--------------------------------------------------\n", + "Start: 687 | End: 694 | Text: 'Mexico.'\n", + "Sources:\n", + "web_search_q7bna7km1re0:0\n", + "web_search_q7bna7km1re0:1\n", + "web_search_6bma458t4fxh:0\n", + "web_search_6bma458t4fxh:1\n", + "--------------------------------------------------\n", + "Start: 710 | End: 724 | Text: 'June 15, 1999.'\n", + "Sources:\n", + "web_search_q7bna7km1re0:0\n", + "web_search_q7bna7km1re0:1\n", + "web_search_6bma458t4fxh:0\n", + "web_search_6bma458t4fxh:1\n", + "--------------------------------------------------\n", + "Start: 739 | End: 748 | Text: 'Colombian'\n", + "Sources:\n", + "web_search_7hw36333m853:0\n", + "web_search_7hw36333m853:1\n", + "web_search_7hw36333m853:2\n", + "web_search_2xr2d7ntz6zj:1\n", + "web_search_2xr2d7ntz6zj:2\n", + "--------------------------------------------------\n", + "Start: 749 | End: 755 | Text: 'singer'\n", + "Sources:\n", + "web_search_7hw36333m853:0\n", + "web_search_7hw36333m853:1\n", + "web_search_7hw36333m853:2\n", + "web_search_2xr2d7ntz6zj:2\n", + "--------------------------------------------------\n", + "Start: 757 | End: 767 | Text: 'songwriter'\n", + "Sources:\n", + "web_search_7hw36333m853:0\n", + "web_search_7hw36333m853:1\n", + "web_search_7hw36333m853:2\n", + "--------------------------------------------------\n", + "Start: 773 | End: 787 | Text: 'philanthropist'\n", + "Sources:\n", + "web_search_7hw36333m853:0\n", + "--------------------------------------------------\n", + "Start: 796 | End: 813 | Text: 'February 2, 1977.'\n", + "Sources:\n", + "web_search_7hw36333m853:0\n", + "web_search_7hw36333m853:1\n", + "web_search_7hw36333m853:2\n", + "--------------------------------------------------\n", + "Start: 835 | End: 889 | Text: 'fusion of Latin, rock, and Middle Eastern music styles'\n", + "Sources:\n", + "web_search_7hw36333m853:0\n", + "web_search_7hw36333m853:1\n", + "web_search_7hw36333m853:2\n", + "web_search_2xr2d7ntz6zj:2\n", + "--------------------------------------------------\n", + "Start: 895 | End: 915 | Text: 'belly dancing skills'\n", + "Sources:\n", + "web_search_7hw36333m853:0\n", + "web_search_7hw36333m853:1\n", + "web_search_7hw36333m853:2\n", + "web_search_2xr2d7ntz6zj:2\n", + "--------------------------------------------------\n", + "Start: 925 | End: 964 | Text: 'foundation for education and nutrition.'\n", + "Sources:\n", + "web_search_7hw36333m853:0\n", + "--------------------------------------------------\n", + "Start: 981 | End: 990 | Text: 'Argentine'\n", + "Sources:\n", + "web_search_py92r6q4y3jq:0\n", + "web_search_py92r6q4y3jq:1\n", + "web_search_py92r6q4y3jq:2\n", + "web_search_wrc3j3warw8z:0\n", + "web_search_wrc3j3warw8z:1\n", + "web_search_wrc3j3warw8z:2\n", + "--------------------------------------------------\n", + "Start: 991 | End: 993 | Text: 'DJ'\n", + "Sources:\n", + "web_search_py92r6q4y3jq:1\n", + "web_search_wrc3j3warw8z:0\n", + "--------------------------------------------------\n", + "Start: 998 | End: 1007 | Text: 'producer.'\n", + "Sources:\n", + "web_search_py92r6q4y3jq:1\n", + "web_search_wrc3j3warw8z:0\n", + "web_search_wrc3j3warw8z:1\n", + "web_search_wrc3j3warw8z:2\n", + "--------------------------------------------------\n", + "Start: 1023 | End: 1038 | Text: 'August 29, 1998'\n", + "Sources:\n", + "web_search_py92r6q4y3jq:1\n", + "web_search_wrc3j3warw8z:0\n", "--------------------------------------------------\n" ] } @@ -526,7 +772,7 @@ "res = co.chat(model=model,\n", " messages=messages,\n", " tools=tools,\n", - " temperature=0)\n", + " temperature=0.1)\n", "\n", "# Keep invoking tools as long as the model generates tool calls\n", "while res.message.tool_calls:\n", @@ -551,18 +797,19 @@ " print(\"\\nTOOL RESULTS:\")\n", " for tc in res.message.tool_calls:\n", " tool_result = functions_map[tc.function.name](**json.loads(tc.function.arguments))\n", - " tool_content = [json.dumps(tool_result)]\n", " print(tool_result, \"\\n\")\n", - " \n", - " messages.append({'role': 'tool',\n", - " 'tool_call_id': tc.id,\n", - " 'tool_content': tool_content}) \n", + " tool_content = []\n", + " for data in tool_result:\n", + " tool_content.append({\"type\": \"document\", \"document\": {\"data\": json.dumps(data)}}) # You can add your own \"id\" field in the \"document\" object, otherwise IDs will be auto-generated\n", + " messages.append(\n", + " {\"role\": \"tool\", \"tool_call_id\": tc.id, \"content\": tool_content}\n", + " )\n", "\n", " # 4 - Model either generates more tool calls or returns a response\n", " res = co.chat(model=model,\n", " messages=messages,\n", " tools=tools,\n", - " temperature=0)\n", + " temperature=0.1)\n", " \n", "messages.append({\"role\": \"assistant\", \"content\": res.message.content[0].text})\n", "\n", diff --git a/notebooks/agents/Vanilla_Tool_Use_v2.ipynb b/notebooks/agents/Vanilla_Tool_Use_v2.ipynb index 8af952b..0b73121 100644 --- a/notebooks/agents/Vanilla_Tool_Use_v2.ipynb +++ b/notebooks/agents/Vanilla_Tool_Use_v2.ipynb @@ -32,29 +32,14 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "us5dkKrLCbXW", - "outputId": "94c97f62-77fb-4492-a4e4-d9eeee4e438c" - }, - "outputs": [], - "source": [ - "# ! pip install cohere --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import cohere\n", - "import os\n", "import json\n", - "co = cohere.ClientV2(api_key=\"YOUR_COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + "\n", + "co = cohere.ClientV2(\"COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" ] }, { @@ -69,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "id": "yZffY8xItLGp" }, @@ -119,40 +104,39 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "id": "YuIH4us8tLGp" }, "outputs": [], "source": [ + "# Function definitions\n", "def query_daily_sales_report(day: str) -> dict:\n", " \"\"\"\n", " Function to retrieve the sales report for the given day\n", " \"\"\"\n", " report = sales_database.get(day, {})\n", " if report:\n", - " return {\n", - " 'date': day,\n", - " 'summary': f\"Total Sales Amount: {report['total_sales_amount']}, Total Units Sold: {report['total_units_sold']}\"\n", - " }\n", + " return [{\n", + " \"date\": day,\n", + " \"summary\": f\"Total Sales Amount: {report['total_sales_amount']}, Total Units Sold: {report['total_units_sold']}\",\n", + " }]\n", " else:\n", - " return {'date': day, 'summary': 'No sales data available for this day.'}\n", - "\n", + " return [{\"date\": day, \"summary\": \"No sales data available for this day.\"}] \n", + " # You can return a list of objects e.g. [{\"url\": \"abc.com\", \"text\": \"...\"}, {\"url\": \"xyz.com\", \"text\": \"...\"}]\n", "\n", "def query_product_catalog(category: str) -> dict:\n", " \"\"\"\n", " Function to retrieve products for the given category\n", " \"\"\"\n", " products = product_catalog.get(category, [])\n", - " return {\n", - " 'category': category,\n", - " 'products': products\n", - " }\n", + " return [{\"category\": category, \"products\": products}]\n", + " # You can return a list of objects e.g. [{\"url\": \"abc.com\", \"text\": \"...\"}, {\"url\": \"xyz.com\", \"text\": \"...\"}]\n", "\n", "\n", "functions_map = {\n", " \"query_daily_sales_report\": query_daily_sales_report,\n", - " \"query_product_catalog\": query_product_catalog\n", + " \"query_product_catalog\": query_product_catalog,\n", "}" ] }, @@ -178,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "id": "aIk-of_OtLGp" }, @@ -239,14 +223,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "id": "JuDgJ7fjtLGq" }, "outputs": [], "source": [ - "# preamble containing instructions about the task and the desired style for the output.\n", - "preamble = \"\"\"\n", + "# System message containing instructions about the task and the desired style for the output.\n", + "system_message = \"\"\"\n", "## Task and Context\n", "You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.\n", "\n", @@ -254,7 +238,7 @@ "Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.\n", "\"\"\"\n", "\n", - "# user request\n", + "# User request\n", "message = \"Can you provide a sales summary for 29th September 2023, and also give me some details about the products in the 'Electronics' category, for example their prices and stock levels?\"" ] }, @@ -270,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -286,7 +270,7 @@ "The model recommends doing the following tool calls:\n", "\n", "Tool plan:\n", - "I will search for the sales summary for 29th September 2023 and the details of products in the 'Electronics' category. \n", + "I will first search for the sales summary for 29th September 2023. Then, I will search for the products in the 'Electronics' category. \n", "\n", "Tool calls:\n", "Tool name: query_daily_sales_report | Parameters: {\"day\":\"2023-09-29\"}\n", @@ -295,24 +279,29 @@ } ], "source": [ - "messages=[{\"role\": \"system\", \"content\": preamble},\n", + "messages=[{\"role\": \"system\", \"content\": system_message},\n", " {\"role\": \"user\", \"content\": message}]\n", "\n", - "response = co.chat(\n", - " model=\"command-r-plus\",\n", - " messages=messages,\n", - " tools=tools\n", - ")\n", + "\n", + "response = co.chat(model=\"command-r-plus-08-2024\",\n", + " messages=messages,\n", + " tools=tools)\n", "\n", "print(\"The model recommends doing the following tool calls:\\n\")\n", "print(\"Tool plan:\")\n", - "print(response.message.tool_plan,\"\\n\")\n", + "print(response.message.tool_plan, \"\\n\")\n", "print(\"Tool calls:\")\n", "for tc in response.message.tool_calls:\n", " print(f\"Tool name: {tc.function.name} | Parameters: {tc.function.arguments}\")\n", - " \n", + "\n", "# append the chat history\n", - "messages.append({'role': 'assistant', 'tool_calls': response.message.tool_calls, 'tool_plan': response.message.tool_plan})" + "messages.append(\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"tool_calls\": response.message.tool_calls,\n", + " \"tool_plan\": response.message.tool_plan,\n", + " }\n", + ")" ] }, { @@ -329,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -343,50 +332,30 @@ "output_type": "stream", "text": [ "Tool results that will be fed back to the model in step 4:\n", - "{\n", - " \"date\": \"2023-09-29\",\n", - " \"summary\": \"Total Sales Amount: 10000, Total Units Sold: 250\"\n", - "}\n", - "{\n", - " \"category\": \"Electronics\",\n", - " \"products\": [\n", - " {\n", - " \"product_id\": \"E1001\",\n", - " \"name\": \"Smartphone\",\n", - " \"price\": 500,\n", - " \"stock_level\": 20\n", - " },\n", - " {\n", - " \"product_id\": \"E1002\",\n", - " \"name\": \"Laptop\",\n", - " \"price\": 1000,\n", - " \"stock_level\": 15\n", - " },\n", - " {\n", - " \"product_id\": \"E1003\",\n", - " \"name\": \"Tablet\",\n", - " \"price\": 300,\n", - " \"stock_level\": 25\n", - " }\n", - " ]\n", - "}\n" + "{'type': 'document', 'document': {'data': '{\"date\": \"2023-09-29\", \"summary\": \"Total Sales Amount: 10000, Total Units Sold: 250\"}'}}\n", + "Tool results that will be fed back to the model in step 4:\n", + "{'type': 'document', 'document': {'data': '{\"category\": \"Electronics\", \"products\": [{\"product_id\": \"E1001\", \"name\": \"Smartphone\", \"price\": 500, \"stock_level\": 20}, {\"product_id\": \"E1002\", \"name\": \"Laptop\", \"price\": 1000, \"stock_level\": 15}, {\"product_id\": \"E1003\", \"name\": \"Tablet\", \"price\": 300, \"stock_level\": 25}]}'}}\n" ] } ], "source": [ - "tool_content = []\n", "# Iterate over the tool calls generated by the model\n", "for tc in response.message.tool_calls:\n", " # here is where you would call the tool recommended by the model, using the parameters recommended by the model\n", - " tool_result= functions_map[tc.function.name](**json.loads(tc.function.arguments))\n", + " tool_result = functions_map[tc.function.name](**json.loads(tc.function.arguments))\n", " # store the output in a list\n", - " tool_content.append(json.dumps(tool_result))\n", + " tool_content = []\n", + " for data in tool_result:\n", + " tool_content.append({\"type\": \"document\", \"document\": {\"data\": json.dumps(data)}})\n", + " # Optional: add an \"id\" field in the \"document\" object, otherwise IDs are auto-generated\n", " # append the chat history\n", - " messages.append({'role': 'tool', 'tool_call_id': tc.id, 'tool_content': tool_content}) \n", - "\n", - "print(\"Tool results that will be fed back to the model in step 4:\")\n", - "for result in tool_content:\n", - " print(json.dumps(json.loads(result), indent=2))\n" + " messages.append(\n", + " {\"role\": \"tool\", \"tool_call_id\": tc.id, \"content\": tool_content}\n", + " )\n", + " \n", + " print(\"Tool results that will be fed back to the model in step 4:\")\n", + " for result in tool_content:\n", + " print(result)" ] }, { @@ -408,45 +377,32 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 15, "metadata": { "id": "MKnjXVfXtLGr" }, - "outputs": [], - "source": [ - "response = co.chat(\n", - " model=\"command-r-plus\",\n", - " messages=messages,\n", - " tools=tools\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "jlxKTsaztLGr", - "outputId": "b2cd8667-bca9-4928-c423-61930b4b49fa" - }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Final answer:\n", - "On 29 September 2023, the total sales amount was $10,000, and 250 units were sold. \n", + "On 29th September 2023, the total sales amount was 10,000, and the total units sold were 250.\n", "\n", - "Here are the details for the products in the 'Electronics' category:\n", - "- Smartphone: $500, 20 in stock\n", - "- Laptop: $1,000, 15 in stock\n", - "- Tablet: $300, 25 in stock\n" + "The following products are available in the Electronics category:\n", + "- Smartphone (E1001) - ÂŁ500, 20 in stock\n", + "- Laptop (E1002) - ÂŁ1,000, 15 in stock\n", + "- Tablet (E1003) - ÂŁ300, 25 in stock\n" ] } ], "source": [ + "response = co.chat(\n", + " model=\"command-r-plus-08-2024\",\n", + " messages=messages,\n", + " tools=tools\n", + ")\n", + "\n", "print(\"Final answer:\")\n", "print(response.message.content[0].text)" ] @@ -468,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -482,47 +438,39 @@ "output_type": "stream", "text": [ "Citations that support the final answer:\n", - "start=26 end=56 text='total sales amount was $10,000' sources=[Source_Tool(id='query_daily_sales_report_6c5v4phf8d4f:0', tool_output={'date': '2023-09-29', 'summary': 'Total Sales Amount: 10000, Total Units Sold: 250'}, type='tool'), Source_Tool(id='query_product_catalog_7395zjrh8m8w:0', tool_output={'date': '2023-09-29', 'summary': 'Total Sales Amount: 10000, Total Units Sold: 250'}, type='tool')]\n", - "Start: 26 | End: 56 | Text: 'total sales amount was $10,000'\n", - "start=62 end=81 text='250 units were sold' sources=[Source_Tool(id='query_daily_sales_report_6c5v4phf8d4f:0', tool_output={'date': '2023-09-29', 'summary': 'Total Sales Amount: 10000, Total Units Sold: 250'}, type='tool'), Source_Tool(id='query_product_catalog_7395zjrh8m8w:0', tool_output={'date': '2023-09-29', 'summary': 'Total Sales Amount: 10000, Total Units Sold: 250'}, type='tool')]\n", - "Start: 62 | End: 81 | Text: '250 units were sold'\n", - "start=156 end=185 text='Smartphone: $500, 20 in stock' sources=[Source_Tool(id='query_daily_sales_report_6c5v4phf8d4f:1', tool_output={'category': 'Electronics', 'products': '[{\"name\":\"Smartphone\",\"price\":500,\"product_id\":\"E1001\",\"stock_level\":20},{\"name\":\"Laptop\",\"price\":1000,\"product_id\":\"E1002\",\"stock_level\":15},{\"name\":\"Tablet\",\"price\":300,\"product_id\":\"E1003\",\"stock_level\":25}]'}, type='tool'), Source_Tool(id='query_product_catalog_7395zjrh8m8w:1', tool_output={'category': 'Electronics', 'products': '[{\"name\":\"Smartphone\",\"price\":500,\"product_id\":\"E1001\",\"stock_level\":20},{\"name\":\"Laptop\",\"price\":1000,\"product_id\":\"E1002\",\"stock_level\":15},{\"name\":\"Tablet\",\"price\":300,\"product_id\":\"E1003\",\"stock_level\":25}]'}, type='tool')]\n", - "Start: 156 | End: 185 | Text: 'Smartphone: $500, 20 in stock'\n", - "start=188 end=215 text='Laptop: $1,000, 15 in stock' sources=[Source_Tool(id='query_daily_sales_report_6c5v4phf8d4f:1', tool_output={'category': 'Electronics', 'products': '[{\"name\":\"Smartphone\",\"price\":500,\"product_id\":\"E1001\",\"stock_level\":20},{\"name\":\"Laptop\",\"price\":1000,\"product_id\":\"E1002\",\"stock_level\":15},{\"name\":\"Tablet\",\"price\":300,\"product_id\":\"E1003\",\"stock_level\":25}]'}, type='tool'), Source_Tool(id='query_product_catalog_7395zjrh8m8w:1', tool_output={'category': 'Electronics', 'products': '[{\"name\":\"Smartphone\",\"price\":500,\"product_id\":\"E1001\",\"stock_level\":20},{\"name\":\"Laptop\",\"price\":1000,\"product_id\":\"E1002\",\"stock_level\":15},{\"name\":\"Tablet\",\"price\":300,\"product_id\":\"E1003\",\"stock_level\":25}]'}, type='tool')]\n", - "Start: 188 | End: 215 | Text: 'Laptop: $1,000, 15 in stock'\n", - "start=218 end=243 text='Tablet: $300, 25 in stock' sources=[Source_Tool(id='query_daily_sales_report_6c5v4phf8d4f:1', tool_output={'category': 'Electronics', 'products': '[{\"name\":\"Smartphone\",\"price\":500,\"product_id\":\"E1001\",\"stock_level\":20},{\"name\":\"Laptop\",\"price\":1000,\"product_id\":\"E1002\",\"stock_level\":15},{\"name\":\"Tablet\",\"price\":300,\"product_id\":\"E1003\",\"stock_level\":25}]'}, type='tool'), Source_Tool(id='query_product_catalog_7395zjrh8m8w:1', tool_output={'category': 'Electronics', 'products': '[{\"name\":\"Smartphone\",\"price\":500,\"product_id\":\"E1001\",\"stock_level\":20},{\"name\":\"Laptop\",\"price\":1000,\"product_id\":\"E1002\",\"stock_level\":15},{\"name\":\"Tablet\",\"price\":300,\"product_id\":\"E1003\",\"stock_level\":25}]'}, type='tool')]\n", - "Start: 218 | End: 243 | Text: 'Tablet: $300, 25 in stock'\n" + "Start: 28 | End: 57 | Text: 'total sales amount was 10,000'\n", + "Start: 67 | End: 93 | Text: 'total units sold were 250.'\n", + "Start: 163 | End: 201 | Text: 'Smartphone (E1001) - ÂŁ500, 20 in stock'\n", + "Start: 204 | End: 240 | Text: 'Laptop (E1002) - ÂŁ1,000, 15 in stock'\n", + "Start: 243 | End: 277 | Text: 'Tablet (E1003) - ÂŁ300, 25 in stock'\n" ] } ], "source": [ "print(\"Citations that support the final answer:\")\n", "for citation in response.message.citations:\n", - " print(citation)\n", " print(f\"Start: {citation.start} | End: {citation.end} | Text: '{citation.text}'\")" ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "On 29 September 2023, the total sales amount was $10,000[3,4], and 250 units were sold[3,4]. \n", + "On 29th September 2023, the total sales amount was 10,000[2], and the total units sold were 250.[2]\n", "\n", - "Here are the details for the products in the 'Electronics' category:\n", - "- Smartphone: $500, 20 in stock[1,2]\n", - "- Laptop: $1,000, 15 in stock[1,2]\n", - "- Tablet: $300, 25 in stock[1,2]\n", + "The following products are available in the Electronics category:\n", + "- Smartphone (E1001) - ÂŁ500, 20 in stock[1]\n", + "- Laptop (E1002) - ÂŁ1,000, 15 in stock[1]\n", + "- Tablet (E1003) - ÂŁ300, 25 in stock[1]\n", "\n", "Source tools:\n", - "[1]: query_daily_sales_report_6c5v4phf8d4f:1 | {'category': 'Electronics', 'products': '[{\"name\":\"Smartphone\",\"price\":500,\"product_id\":\"E1001\",\"stock_level\":20},{\"name\":\"Laptop\",\"price\":1000,\"product_id\":\"E1002\",\"stock_level\":15},{\"name\":\"Tablet\",\"price\":300,\"product_id\":\"E1003\",\"stock_level\":25}]'}\n", - "[2]: query_product_catalog_7395zjrh8m8w:1 | {'category': 'Electronics', 'products': '[{\"name\":\"Smartphone\",\"price\":500,\"product_id\":\"E1001\",\"stock_level\":20},{\"name\":\"Laptop\",\"price\":1000,\"product_id\":\"E1002\",\"stock_level\":15},{\"name\":\"Tablet\",\"price\":300,\"product_id\":\"E1003\",\"stock_level\":25}]'}\n", - "[3]: query_daily_sales_report_6c5v4phf8d4f:0 | {'date': '2023-09-29', 'summary': 'Total Sales Amount: 10000, Total Units Sold: 250'}\n", - "[4]: query_product_catalog_7395zjrh8m8w:0 | {'date': '2023-09-29', 'summary': 'Total Sales Amount: 10000, Total Units Sold: 250'}\n" + "[1]: query_product_catalog_zeq4jk768e19:0 | {'content': '{\"category\": \"Electronics\", \"products\": [{\"product_id\": \"E1001\", \"name\": \"Smartphone\", \"price\": 500, \"stock_level\": 20}, {\"product_id\": \"E1002\", \"name\": \"Laptop\", \"price\": 1000, \"stock_level\": 15}, {\"product_id\": \"E1003\", \"name\": \"Tablet\", \"price\": 300, \"stock_level\": 25}]}'}\n", + "[2]: query_daily_sales_report_7me1rzpgww1x:0 | {'date': '2023-09-29', 'summary': 'Total Sales Amount: 10000, Total Units Sold: 250'}\n" ] } ], @@ -606,7 +554,7 @@ "kernelspec": { "display_name": "base", "language": "python", - "name": "python3" + "name": "base" }, "language_info": { "codemirror_mode": { diff --git a/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb index aa7779c..84c0a08 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb @@ -56,8 +56,9 @@ "\n", "import cohere\n", "import json\n", + "import os\n", "\n", - "co = cohere.ClientV2(api_key=\"COHERE_API_KEY\") # Get your free API key: https://dashboard.cohere.com/api-keys" + "co = cohere.ClientV2(api_key=os.environ[\"COHERE_API_KEY\"]) # Get your free API key: https://dashboard.cohere.com/api-keys" ] }, { @@ -81,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -91,14 +92,14 @@ " {\"text\": \"Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward.\"},\n", " {\"text\": \"Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.\"}\n", " ]\n", - " return {\"faqs\" : faqs}\n", + " return faqs\n", "\n", "def search_emails(query):\n", " emails = [\n", " {\"from\": \"it@co1t.com\", \"to\": \"david@co1t.com\", \"date\": \"2024-06-24\", \"subject\": \"Setting Up Your IT Needs\", \"text\": \"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\"},\n", " {\"from\": \"john@co1t.com\", \"to\": \"david@co1t.com\", \"date\": \"2024-06-24\", \"subject\": \"First Week Check-In\", \"text\": \"Hello! I hope you're settling in well. Let's connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it's a great opportunity to get to know your colleagues!\"}\n", " ]\n", - " return {\"emails\" : emails}\n", + " return emails\n", " \n", "def create_calendar_event(date: str, time: str, duration: int):\n", " # You can implement any logic here\n", @@ -131,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -225,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -233,16 +234,16 @@ "output_type": "stream", "text": [ "Tool plan:\n", - "I will search the user's emails for any messages about getting set up with IT. \n", + "I will search the emails for any messages about getting set up with IT. \n", "\n", "Tool calls:\n", - "Tool name: search_emails | Parameters: {\"query\":\"IT setup\"}\n" + "Tool name: search_emails | Parameters: {\"query\":\"getting setup with IT\"}\n" ] } ], "source": [ "# Create custom system message\n", - "system_message=\"\"\"## Task and Context\n", + "system_message = \"\"\"## Task and Context\n", "You are an assistant who assist new employees of Co1t with their first week. You respond to their questions and assist them with their needs. Today is Monday, June 24, 2024\"\"\"\n", "\n", "\n", @@ -250,25 +251,29 @@ "message = \"Is there any message about getting setup with IT?\"\n", "\n", "# Add the system and user messages to the chat history\n", - "messages = [{\"role\": \"system\", \"content\": system_message},\n", - " {\"role\": \"user\", \"content\": message}]\n", + "messages = [\n", + " {\"role\": \"system\", \"content\": system_message},\n", + " {\"role\": \"user\", \"content\": message},\n", + "]\n", "\n", "# Step 2: Tool planning and calling\n", - "response = co.chat(\n", - " model=\"command-r-plus-08-2024\",\n", - " messages=messages,\n", - " tools=tools\n", - " )\n", + "response = co.chat(model=\"command-r-plus-08-2024\", messages=messages, tools=tools)\n", "\n", - "if response.message.tool_calls: \n", + "if response.message.tool_calls:\n", " print(\"Tool plan:\")\n", - " print(response.message.tool_plan,\"\\n\")\n", + " print(response.message.tool_plan, \"\\n\")\n", " print(\"Tool calls:\")\n", " for tc in response.message.tool_calls:\n", " print(f\"Tool name: {tc.function.name} | Parameters: {tc.function.arguments}\")\n", - " \n", + "\n", " # Append tool calling details to the chat history\n", - " messages.append({\"role\": \"assistant\", \"tool_calls\": response.message.tool_calls, \"tool_plan\": response.message.tool_plan})" + " messages.append(\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"tool_calls\": response.message.tool_calls,\n", + " \"tool_plan\": response.message.tool_plan,\n", + " }\n", + " )" ] }, { @@ -289,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -297,20 +302,23 @@ "output_type": "stream", "text": [ "Tool results:\n", - "{\"emails\": [{\"from\": \"it@co1t.com\", \"to\": \"david@co1t.com\", \"date\": \"2024-06-24\", \"subject\": \"Setting Up Your IT Needs\", \"text\": \"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\"}, {\"from\": \"john@co1t.com\", \"to\": \"david@co1t.com\", \"date\": \"2024-06-24\", \"subject\": \"First Week Check-In\", \"text\": \"Hello! I hope you're settling in well. Let's connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon\\u2014it's a great opportunity to get to know your colleagues!\"}]}\n" + "{'type': 'document', 'document': {'id': '0', 'data': {'from': 'it@co1t.com', 'to': 'david@co1t.com', 'date': '2024-06-24', 'subject': 'Setting Up Your IT Needs', 'text': 'Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.'}}}\n", + "{'type': 'document', 'document': {'id': '1', 'data': {'from': 'john@co1t.com', 'to': 'david@co1t.com', 'date': '2024-06-24', 'subject': 'First Week Check-In', 'text': \"Hello! I hope you're settling in well. Let's connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it's a great opportunity to get to know your colleagues!\"}}}\n" ] } ], "source": [ "# Step 3: Tool execution\n", - "tool_content = []\n", "for tc in response.message.tool_calls:\n", " tool_result = functions_map[tc.function.name](**json.loads(tc.function.arguments))\n", - " tool_content.append(json.dumps(tool_result))\n", + " tool_content = []\n", + " for data in tool_result:\n", + " tool_content.append({\"type\": \"document\", \"document\": {\"data\": json.dumps(data)}})\n", + " # Optional: add an \"id\" field in the \"document\" object, otherwise IDs are auto-generated\n", " # Append tool results to the chat history\n", - " messages.append({\"role\": \"tool\", \"tool_call_id\": tc.id, \"tool_content\": tool_content}) \n", + " messages.append({\"role\": \"tool\", \"tool_call_id\": tc.id, \"content\": tool_content})\n", "\n", - "print(\"Tool results:\") \n", + "print(\"Tool results:\")\n", "for result in tool_content:\n", " print(result)" ] @@ -324,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -332,13 +340,16 @@ "output_type": "stream", "text": [ "Response:\n", - "Yes, there is an email from IT with a comprehensive guide attached.\n", + "Yes, there is an email from it@co1t.com with the subject 'Setting Up Your IT Needs'. It says:\n", + "> Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\n", "==================================================\n", "\n", "CITATIONS:\n", - "start=17 end=30 text='email from IT' sources=[Source_Tool(type='tool', id='search_emails_dy73yjrx50xq:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'})] \n", + "start=28 end=39 text='it@co1t.com' sources=[ToolSource(type='tool', id='0', tool_output={'date': '2024-06-24', 'from': 'it@co1t.com', 'subject': 'Setting Up Your IT Needs', 'text': 'Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.', 'to': 'david@co1t.com'})] \n", "\n", - "start=38 end=66 text='comprehensive guide attached' sources=[Source_Tool(type='tool', id='search_emails_dy73yjrx50xq:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'})] \n", + "start=57 end=83 text=\"'Setting Up Your IT Needs'\" sources=[ToolSource(type='tool', id='0', tool_output={'date': '2024-06-24', 'from': 'it@co1t.com', 'subject': 'Setting Up Your IT Needs', 'text': 'Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.', 'to': 'david@co1t.com'})] \n", + "\n", + "start=96 end=244 text='Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.' sources=[ToolSource(type='tool', id='0', tool_output={'date': '2024-06-24', 'from': 'it@co1t.com', 'subject': 'Setting Up Your IT Needs', 'text': 'Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.', 'to': 'david@co1t.com'})] \n", "\n" ] } @@ -384,73 +395,80 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "model = \"command-r-plus-08-2024\"\n", "\n", - "system_message=\"\"\"## Task and Context\n", + "system_message = \"\"\"## Task and Context\n", "You are an assistant who assists new employees of Co1t with their first week. You respond to their questions and assist them with their needs. Today is Monday, June 24, 2024\"\"\"\n", "\n", + "\n", "def run_assistant(query, messages=None):\n", " if messages is None:\n", " messages = []\n", - " \n", + "\n", " if \"system\" not in {m.get(\"role\") for m in messages}:\n", " messages.append({\"role\": \"system\", \"content\": system_message})\n", - " \n", + "\n", " # Step 1: get user message\n", " print(f\"Question:\\n{query}\")\n", - " print(\"=\"*50)\n", - " \n", + " print(\"=\" * 50)\n", + "\n", " messages.append({\"role\": \"user\", \"content\": query})\n", "\n", " # Step 2: Generate tool calls (if any)\n", - " response = co.chat(\n", - " model=model,\n", - " messages=messages,\n", - " tools=tools\n", - " )\n", + " response = co.chat(model=model, messages=messages, tools=tools)\n", "\n", " while response.message.tool_calls:\n", - " \n", + "\n", " print(\"Tool plan:\")\n", - " print(response.message.tool_plan,\"\\n\")\n", + " print(response.message.tool_plan, \"\\n\")\n", " print(\"Tool calls:\")\n", " for tc in response.message.tool_calls:\n", - " print(f\"Tool name: {tc.function.name} | Parameters: {tc.function.arguments}\")\n", - " print(\"=\"*50)\n", + " print(\n", + " f\"Tool name: {tc.function.name} | Parameters: {tc.function.arguments}\"\n", + " )\n", + " print(\"=\" * 50)\n", + "\n", + " messages.append(\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"tool_calls\": response.message.tool_calls,\n", + " \"tool_plan\": response.message.tool_plan,\n", + " }\n", + " )\n", "\n", - " messages.append({\"role\": \"assistant\", \"tool_calls\": response.message.tool_calls, \"tool_plan\": response.message.tool_plan}) \n", - " \n", " # Step 3: Get tool results\n", - " tool_content = []\n", " for idx, tc in enumerate(response.message.tool_calls):\n", - " tool_result = functions_map[tc.function.name](**json.loads(tc.function.arguments))\n", - " tool_content.append(json.dumps(tool_result))\n", - " messages.append({\"role\": \"tool\", \"tool_call_id\": tc.id, \"tool_content\": tool_content})\n", - " \n", - " # Step 4: Generate response and citations \n", - " response = co.chat(\n", - " model=model,\n", - " messages=messages,\n", - " tools=tools\n", - " )\n", - " \n", + " tool_result = functions_map[tc.function.name](\n", + " **json.loads(tc.function.arguments)\n", + " )\n", + " tool_content = []\n", + " for data in tool_result:\n", + " tool_content.append({\"type\": \"document\", \"document\": {\"data\": json.dumps(data)}})\n", + " # Optional: add an \"id\" field in the \"document\" object, otherwise IDs are auto-generated\n", + " messages.append(\n", + " {\"role\": \"tool\", \"tool_call_id\": tc.id, \"content\": tool_content}\n", + " )\n", + "\n", + " # Step 4: Generate response and citations\n", + " response = co.chat(model=model, messages=messages, tools=tools)\n", + "\n", " messages.append({\"role\": \"assistant\", \"content\": response.message.content[0].text})\n", - " \n", + "\n", " # Print final response\n", " print(\"Response:\")\n", " print(response.message.content[0].text)\n", - " print(\"=\"*50)\n", - " \n", + " print(\"=\" * 50)\n", + "\n", " # Print citations (if any)\n", " if response.message.citations:\n", " print(\"\\nCITATIONS:\")\n", " for citation in response.message.citations:\n", " print(citation, \"\\n\")\n", - " \n", + "\n", " return messages" ] }, @@ -469,7 +487,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -480,27 +498,25 @@ "Can you check if there are any lunch invites, and for those days, create a one-hour event on my calendar at 12PM.\n", "==================================================\n", "Tool plan:\n", - "I will search the user's emails for lunch invites and then create a calendar event for each day they are invited to lunch. \n", + "I will first search the user's emails for lunch invites. Then, I will create a one-hour event on the user's calendar at 12PM for each day that has a lunch invite. \n", "\n", "Tool calls:\n", "Tool name: search_emails | Parameters: {\"query\":\"lunch invite\"}\n", "==================================================\n", "Tool plan:\n", - "I have found an email inviting the user to a welcoming lunch on Thursday at noon. I will now create a calendar event for this. \n", + "I found one email containing a lunch invite for Thursday at noon. I will now create a one-hour event on the user's calendar for that day. \n", "\n", "Tool calls:\n", "Tool name: create_calendar_event | Parameters: {\"date\":\"06/27/24\",\"duration\":1,\"time\":\"12:00\"}\n", "==================================================\n", "Response:\n", - "Sure, I found an email from John inviting you to a welcoming lunch this Thursday at noon. I've created a one-hour event on your calendar for this Thursday at 12 pm.\n", + "I found one lunch invite for Thursday at noon. I have created a one-hour event on your calendar for that day.\n", "==================================================\n", "\n", "CITATIONS:\n", - "start=17 end=32 text='email from John' sources=[Source_Tool(type='tool', id='search_emails_j72zv2xhq0sj:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'})] \n", - "\n", - "start=51 end=88 text='welcoming lunch this Thursday at noon' sources=[Source_Tool(type='tool', id='search_emails_j72zv2xhq0sj:0', tool_output={'emails': '[{\"date\":\"2024-06-24\",\"from\":\"it@co1t.com\",\"subject\":\"Setting Up Your IT Needs\",\"text\":\"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.\",\"to\":\"david@co1t.com\"},{\"date\":\"2024-06-24\",\"from\":\"john@co1t.com\",\"subject\":\"First Week Check-In\",\"text\":\"Hello! I hope you\\'re settling in well. Let\\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\\'s a great opportunity to get to know your colleagues!\",\"to\":\"david@co1t.com\"}]'})] \n", + "start=29 end=46 text='Thursday at noon.' sources=[ToolSource(type='tool', id='1', tool_output={'date': '2024-06-24', 'from': 'john@co1t.com', 'subject': 'First Week Check-In', 'text': \"Hello! I hope you're settling in well. Let's connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it's a great opportunity to get to know your colleagues!\", 'to': 'david@co1t.com'})] \n", "\n", - "start=105 end=163 text='one-hour event on your calendar for this Thursday at 12 pm' sources=[Source_Tool(type='tool', id='create_calendar_event_vs7mxjzk9jzs:0', tool_output={'is_success': 'true', 'message': 'Created a 1 hour long event at 12:00 on 06/27/24'})] \n", + "start=64 end=109 text='one-hour event on your calendar for that day.' sources=[ToolSource(type='tool', id='0', tool_output={'content': 'is_success'}), ToolSource(type='tool', id='1', tool_output={'date': '2024-06-24', 'from': 'john@co1t.com', 'subject': 'First Week Check-In', 'text': \"Hello! I hope you're settling in well. Let's connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it's a great opportunity to get to know your colleagues!\", 'to': 'david@co1t.com'})] \n", "\n" ] } From 434539b73c9047ca93bace9592188226e42a4c0b Mon Sep 17 00:00:00 2001 From: mrmer1 Date: Mon, 11 Nov 2024 17:49:55 +0800 Subject: [PATCH 11/11] pip installs --- notebooks/agents/Multi_Step_Tool_Use_Spotify_v2.ipynb | 9 +++++++++ notebooks/agents/Vanilla_Tool_Use_v2.ipynb | 11 ++++++++++- .../guides/getting-started/v2/tutorial_pt7_v2.ipynb | 11 ++++++++++- 3 files changed, 29 insertions(+), 2 deletions(-) diff --git a/notebooks/agents/Multi_Step_Tool_Use_Spotify_v2.ipynb b/notebooks/agents/Multi_Step_Tool_Use_Spotify_v2.ipynb index b0bcb5f..577ab9f 100644 --- a/notebooks/agents/Multi_Step_Tool_Use_Spotify_v2.ipynb +++ b/notebooks/agents/Multi_Step_Tool_Use_Spotify_v2.ipynb @@ -30,6 +30,15 @@ "## Setup" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! pip install cohere==5.11.3 pandas==2.2.2" + ] + }, { "cell_type": "code", "execution_count": 1, diff --git a/notebooks/agents/Vanilla_Tool_Use_v2.ipynb b/notebooks/agents/Vanilla_Tool_Use_v2.ipynb index 0b73121..f788632 100644 --- a/notebooks/agents/Vanilla_Tool_Use_v2.ipynb +++ b/notebooks/agents/Vanilla_Tool_Use_v2.ipynb @@ -30,6 +30,15 @@ "- Step 4: the **model generates a final answer with precise citations based on the tool results**" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! pip install cohere==5.11.3" + ] + }, { "cell_type": "code", "execution_count": 13, @@ -554,7 +563,7 @@ "kernelspec": { "display_name": "base", "language": "python", - "name": "base" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb b/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb index 84c0a08..18bbf50 100644 --- a/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb +++ b/notebooks/guides/getting-started/v2/tutorial_pt7_v2.ipynb @@ -45,6 +45,15 @@ "To get started, first we need to install the `cohere` library and create a Cohere client." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! pip install cohere==5.11.3" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -52,7 +61,7 @@ "outputs": [], "source": [ "\n", - "# pip install cohere\n", + "\n", "\n", "import cohere\n", "import json\n",