From 4779767b6f782b9f0ce5f1906fefe3f225a7e501 Mon Sep 17 00:00:00 2001 From: ks6088ts Date: Tue, 8 Oct 2024 13:19:12 +0900 Subject: [PATCH] add slm based chat app --- apps/15_streamlit_chat_slm/README.md | 25 + apps/15_streamlit_chat_slm/chat.py | 43 + apps/15_streamlit_chat_slm/main.py | 67 + apps/15_streamlit_chat_slm/summarize.py | 162 + .../15_streamlit_chat_slm.summarize_graph.png | Bin 0 -> 20614 bytes poetry.lock | 3549 +++++++++-------- pyproject.toml | 7 +- 7 files changed, 2200 insertions(+), 1653 deletions(-) create mode 100644 apps/15_streamlit_chat_slm/README.md create mode 100644 apps/15_streamlit_chat_slm/chat.py create mode 100644 apps/15_streamlit_chat_slm/main.py create mode 100644 apps/15_streamlit_chat_slm/summarize.py create mode 100644 docs/images/15_streamlit_chat_slm.summarize_graph.png diff --git a/apps/15_streamlit_chat_slm/README.md b/apps/15_streamlit_chat_slm/README.md new file mode 100644 index 0000000..80bf7d5 --- /dev/null +++ b/apps/15_streamlit_chat_slm/README.md @@ -0,0 +1,25 @@ +# Streamlit Chat with SLM + +## Overview + +```shell +# Run Ollama server +$ ollama serve + +# Install dependencies +$ ollama pull phi3 + +# Run a simple chat with Ollama +$ poetry run python apps/15_streamlit_chat_slm/chat.py + +# Run summarization with SLM +$ poetry run python apps/15_streamlit_chat_slm/summarize.py + +# Run streamlit app +$ poetry run python -m streamlit run apps/15_streamlit_chat_slm/main.py +``` + +# References + +- [ChatOllama](https://python.langchain.com/docs/integrations/chat/ollama/) +- [Summarize Text](https://python.langchain.com/docs/tutorials/summarization/) diff --git a/apps/15_streamlit_chat_slm/chat.py b/apps/15_streamlit_chat_slm/chat.py new file mode 100644 index 0000000..6423411 --- /dev/null +++ b/apps/15_streamlit_chat_slm/chat.py @@ -0,0 +1,43 @@ +import argparse +import logging + +from dotenv import load_dotenv +from langchain_core.messages import AIMessage +from langchain_ollama import ChatOllama + + +def init_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + prog="slm_chat", + description="Chat with SLM model", + ) + parser.add_argument("-m", "--model", default="phi3") + parser.add_argument("-s", "--system", default="You are a helpful assistant.") + parser.add_argument("-p", "--prompt", default="What is the capital of France?") + parser.add_argument("-v", "--verbose", action="store_true") + return parser.parse_args() + + +if __name__ == "__main__": + args = init_args() + + # Set verbose mode + if args.verbose: + logging.basicConfig(level=logging.DEBUG) + + # Parse .env file and set environment variables + load_dotenv() + + llm = ChatOllama( + model=args.model, + temperature=0, + ) + + ai_msg: AIMessage = llm.invoke( + input=[ + ("system", args.system), + ("human", args.prompt), + ] + ) + print(ai_msg.model_dump_json(indent=2)) + # print(ai_msg.content) diff --git a/apps/15_streamlit_chat_slm/main.py b/apps/15_streamlit_chat_slm/main.py new file mode 100644 index 0000000..c3120bd --- /dev/null +++ b/apps/15_streamlit_chat_slm/main.py @@ -0,0 +1,67 @@ +import streamlit as st +from dotenv import load_dotenv +from langchain_ollama import ChatOllama + +load_dotenv() + +SUPPORTED_MODELS = [ + "phi3", +] +with st.sidebar: + slm_model = st.selectbox( + label="Model", + options=SUPPORTED_MODELS, + index=0, + ) + "[Azure Portal](https://portal.azure.com/)" + "[Azure OpenAI Studio](https://oai.azure.com/resource/overview)" + "[View the source code](https://github.com/ks6088ts-labs/workshop-azure-openai/blob/main/apps/15_streamlit_chat_slm/main.py)" + + +def is_configured(): + return slm_model in SUPPORTED_MODELS + + +st.title("15_streamlit_chat_slm") + +if not is_configured(): + st.warning("Please fill in the required fields at the sidebar.") + +if "messages" not in st.session_state: + st.session_state["messages"] = [ + { + "role": "assistant", + "content": "Hello! I'm a helpful assistant.", + } + ] + +# Show chat messages +for msg in st.session_state.messages: + st.chat_message(msg["role"]).write(msg["content"]) + +# Receive user input +if prompt := st.chat_input(disabled=not is_configured()): + client = ChatOllama( + model=slm_model, + temperature=0, + ) + + st.session_state.messages.append( + { + "role": "user", + "content": prompt, + } + ) + st.chat_message("user").write(prompt) + with st.spinner("Thinking..."): + response = client.invoke( + input=st.session_state.messages, + ) + msg = response.content + st.session_state.messages.append( + { + "role": "assistant", + "content": msg, + } + ) + st.chat_message("assistant").write(msg) diff --git a/apps/15_streamlit_chat_slm/summarize.py b/apps/15_streamlit_chat_slm/summarize.py new file mode 100644 index 0000000..626b740 --- /dev/null +++ b/apps/15_streamlit_chat_slm/summarize.py @@ -0,0 +1,162 @@ +import asyncio +import operator +from os import getenv +from typing import Annotated, Literal, TypedDict + +from langchain.chains.combine_documents.reduce import acollapse_docs, split_list_of_docs +from langchain_community.document_loaders import WebBaseLoader +from langchain_core.documents import Document +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompts import ChatPromptTemplate +from langchain_ollama import ChatOllama +from langchain_openai import AzureChatOpenAI +from langchain_text_splitters import CharacterTextSplitter +from langgraph.constants import Send +from langgraph.graph import END, START, StateGraph + +token_max = 1000 +url = "https://lilianweng.github.io/posts/2023-06-23-agent/" + +llm_ollama = ChatOllama( + model="phi3", + temperature=0, +) +llm_azure_openai = AzureChatOpenAI( + temperature=0, + api_key=getenv("AZURE_OPENAI_API_KEY"), + api_version=getenv("AZURE_OPENAI_API_VERSION"), + azure_endpoint=getenv("AZURE_OPENAI_ENDPOINT"), + model=getenv("AZURE_OPENAI_GPT_MODEL"), +) +# Use the Ollama model +llm = llm_ollama + + +def length_function(documents: list[Document]) -> int: + """Get number of tokens for input contents.""" + return sum(llm.get_num_tokens(doc.page_content) for doc in documents) + + +# This will be the overall state of the main graph. +# It will contain the input document contents, corresponding +# summaries, and a final summary. +class OverallState(TypedDict): + # Notice here we use the operator.add + # This is because we want combine all the summaries we generate + # from individual nodes back into one list - this is essentially + # the "reduce" part + contents: list[str] + summaries: Annotated[list, operator.add] + collapsed_summaries: list[Document] + final_summary: str + + +# This will be the state of the node that we will "map" all +# documents to in order to generate summaries +class SummaryState(TypedDict): + content: str + + +map_prompt = ChatPromptTemplate.from_messages([("system", "Write a concise summary of the following:\\n\\n{context}")]) + +map_chain = map_prompt | llm | StrOutputParser() + + +# Here we generate a summary, given a document +async def generate_summary(state: SummaryState): + response = await map_chain.ainvoke(state["content"]) + return {"summaries": [response]} + + +# Here we define the logic to map out over the documents +# We will use this an edge in the graph +def map_summaries(state: OverallState): + # We will return a list of `Send` objects + # Each `Send` object consists of the name of a node in the graph + # as well as the state to send to that node + return [Send("generate_summary", {"content": content}) for content in state["contents"]] + + +def collect_summaries(state: OverallState): + return {"collapsed_summaries": [Document(summary) for summary in state["summaries"]]} + + +# Also available via the hub: `hub.pull("rlm/reduce-prompt")` +reduce_template = """ +The following is a set of summaries: +{docs} +Take these and distill it into a final, consolidated summary +of the main themes. +""" + +reduce_prompt = ChatPromptTemplate([("human", reduce_template)]) + +reduce_chain = reduce_prompt | llm | StrOutputParser() + + +# Add node to collapse summaries +async def collapse_summaries(state: OverallState): + doc_lists = split_list_of_docs(state["collapsed_summaries"], length_function, token_max) + results = [] + for doc_list in doc_lists: + results.append(await acollapse_docs(doc_list, reduce_chain.ainvoke)) + + return {"collapsed_summaries": results} + + +# This represents a conditional edge in the graph that determines +# if we should collapse the summaries or not +def should_collapse( + state: OverallState, +) -> Literal["collapse_summaries", "generate_final_summary"]: + num_tokens = length_function(state["collapsed_summaries"]) + if num_tokens > token_max: + return "collapse_summaries" + else: + return "generate_final_summary" + + +# Here we will generate the final summary +async def generate_final_summary(state: OverallState): + response = await reduce_chain.ainvoke(state["collapsed_summaries"]) + return {"final_summary": response} + + +async def main(): + # Construct the graph + # Nodes: + graph = StateGraph(OverallState) + graph.add_node("generate_summary", generate_summary) # same as before + graph.add_node("collect_summaries", collect_summaries) + graph.add_node("collapse_summaries", collapse_summaries) + graph.add_node("generate_final_summary", generate_final_summary) + + # Edges: + graph.add_conditional_edges(START, map_summaries, ["generate_summary"]) + graph.add_edge("generate_summary", "collect_summaries") + graph.add_conditional_edges("collect_summaries", should_collapse) + graph.add_conditional_edges("collapse_summaries", should_collapse) + graph.add_edge("generate_final_summary", END) + + app = graph.compile() + + # create graph image + app.get_graph().draw_mermaid_png(output_file_path="docs/images/15_streamlit_chat_slm.summarize_graph.png") + + text_splitter = CharacterTextSplitter.from_tiktoken_encoder(chunk_size=1000, chunk_overlap=0) + + loader = WebBaseLoader(web_path=url) + docs = loader.load() + + split_docs = text_splitter.split_documents(docs) + print(f"Generated {len(split_docs)} documents.") + + async for step in app.astream( + {"contents": [doc.page_content for doc in split_docs]}, + {"recursion_limit": 10}, + ): + print(list(step.keys())) + print(step) + + +asyncio.run(main()) diff --git a/docs/images/15_streamlit_chat_slm.summarize_graph.png b/docs/images/15_streamlit_chat_slm.summarize_graph.png new file mode 100644 index 0000000000000000000000000000000000000000..7bb5b90f92ce8b7b0c33789d7b956ea079a1b00d GIT binary patch literal 20614 zcmcG$1z1~4yFVI=m0|^oYtgn?aVTEASWAmrA$WpAa48fk?hd6`ae}*Rakm5uQrz7x zdw*xQ`~1)M?f<#YbMGWsd9!Bb_sYzg_0Bu9ayNaq2zdNPT22~(gaiN}-M;{L%Sh95 z5)%4vRg|UW6r}!@(F3?oC@%p3ODhLE6`9vG8k$-(=!^fV@kgEk(B9^c@BfhQaZe}z zKnDQESpP$re>#m}WNZ(-FR*|Ap|`tdelP5kd-{p#zvyRw(1!n_h5n$O9Bdr!%e?)A zw)>zWaZelF)6Y!*MjQT(2HM#DQ9k6pjF2VR@ef{qq(3CaGPY8EfB*gP{zCz<2dDsK z0I&bh|Ni%Vvi=4D@Sgwx$PvHK872Y%)qVf~$?WfQ^j`r0+|K|&)zI&Azinb;U}x|r z9LoLifr$wKaF`1KU~2*Z1j7KpBke!)?hpS#Hk$h^ihH@N?_Z_>Fu)i<1CRq)0e}Fu zdx{hA62JlAy_*9_0FWO%_~U(_Q10KT4^dH3P*5MCqoX~?R#7!@h$J4Am4r!BN?JzUFzX(f>`$J^zuDe5{DXVrpxx*3^2Yo8hW&8 z$zy-D^c3M_elJ_)+O(PD+h`RRLW4LqbBv5HJBjz>00o`pOb^!wTM^k9Z-Fki0MRz{ zxp*4+WK$i-k$8O_xtFxG2ywi}cz8(vx5E>){C-N|oU+81!)N0~^aJKc0U{jVd*=iZRfU8K5i@|CatwyT1XxMa~<=8&|QdfdW35E*d=0@qfWY_;bjH?w(N9~W55Vpb)+^e(E=7n0qVbDb$JC*PRsk@WgIu1h}R>=P=`a~~+Gd&qG?3$qxbUsU(gD7oZ%S z6}`SJX;69PRrm^1ZPlQ2@uVrGe zDmk;I5jrAI(6!@6!D}P>-RHIcaJ5NwZX#i#!epKz52-PI)X|f$5dOUl8#AY~f(L~T z7mYbY6C=%-XFFQqwp9mp>NcYD!(%sFP^E?!B~_xa+nOVIj>D3YD3W6T_k;L{A}(%< z@B)xo#c{a(mbU08#X*L_FYhl{U>CU8k8zQ^P3+4=Dnaf>?(Y<9cHNRFYi=htPkE)| zYS6hu1LgzHZz;K5J2#HHH3;7U{Bj71G-fo~OD4WW|LRpa(y{!N=Y2 z9&gIb)*}w+Mp1A+5-0@Z?{x&-m=Ae{6W8fWh6$ z5ur03_^o6sg>5N#vI5&1c@4?)Lmuj*k*Ojq$E}XxNhtzkjn*T8ptoKh;S>R>`KGs| zREN*MyCwa{b0vDc36;0R)Chku79hLwvafb@;i=uYwH3qlCt^@X`C$VMb&ndMZlinm zI0XYcnFOXumwv)B9PMy=iB)`#SGgDLk&D0ZgRA29H75RlWb@A%#q)LNm!JH;bE+DK z=@@V5hPi7G2^duZV#AL)MU0E7qPa~ERfE*o=Q&OY(3QJXmkA8_`j*$l8^-qI5_Ys1 z=jDOxodfqmygb|A#U%S@X8$esMeL1cRJBzH{|g?lgr1B9Lw$K2?glB_UmRQu|MSQHx}FZa7cCu#M*gU%k#Hs~a$cNOwd<$fZ!k z*Y`B9an|`r!vh_C=e=9S2xyMtW7+PPNW_4_F$Hy{;_lcljGSP-X#{G#!-sGx5boE- zn|y0Xn$RS*cyqTsiz-^ThSyZN`ND*jeu_}>g(QniKEMdK`OPc2uob_Y;2@e61i#vQV!Xst)j z@~xq~0%N?F>e#NhE!A|8lp6mh7lEi@IM+kC_9!{7_r|o%SQ{HOfN@?Y3aY+cQ(gFZ z2f!&IxmAaLl$7fEugj^`S0KDHzWR&*HvUS!6vgBVPBA0DDB_2(wTl)f^quDPAn}yI zz=wg}|8;motqE}Ow|V?DqhFfEL19gt^@>107>PVYd~>?473F)rW&E! zt)#_*GH+yUoggA0)VY`Jhu=Miwz*|0VtvJ7#;kq%z8--CL3p78t*J?{Q=)RH% z^HMtAWo8ZN9qTG<>=zN3{=yz6<#c7DO+7VtWGcQgTZoBtlHEpDy(sDxc8hqKU zYL+~kqBat@Ia&$X&2(@;83`1WC6$HFKgQIXw~nZY7w#+;1XmG7jySTov(^PidP=

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