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HeidiSteenHeidiSteen
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replaced query with simplified version
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‎Quickstart-RAG/Quickstart-rag.ipynb‎

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@@ -79,18 +79,22 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Run the code\n",
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"## Create a virtual environment\n",
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"\n",
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"1. Create a virtual environment. In Visual Studio Code, press Ctrl-shift-P to open the command palette, search for \"Python: Create Environment\", and then select `Venv` to create a virtual environment in the current workspace.\n",
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"Create a virtual environment so that you can install the dependencies in isolation.\n",
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"\n",
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"1. In Visual Studio Code, press Ctrl-shift-P to open the command palette, search for \"Python: Create Environment\", and then select `Venv` to create a virtual environment in the current workspace.\n",
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"\n",
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"1. Select Quickstart-RAG\\requirements.txt for the dependencies.\n",
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"\n",
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"It takes several minutes to create the environment. When the environment is ready, continue to the next step."
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"It takes several minutes to create the environment. When the environment is ready, continue to the next step.\n",
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"\n",
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"## Run the code"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -100,7 +104,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -112,7 +116,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -124,139 +128,85 @@
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Set up the clients, define a chat instance, create a search function\n",
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"from azure.core.credentials_async import AsyncTokenCredential\n",
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"from azure.identity.aio import get_bearer_token_provider\n",
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"from azure.search.documents.aio import SearchClient\n",
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"from openai import AsyncAzureOpenAI\n",
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"from enum import Enum\n",
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"from typing import List, Optional\n",
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"\n",
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"def create_openai_client(credential: AsyncTokenCredential) -> AsyncAzureOpenAI:\n",
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" token_provider = get_bearer_token_provider(credential, \"https://cognitiveservices.azure.com/.default\")\n",
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" return AsyncAzureOpenAI(\n",
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" api_version=\"2024-04-01-preview\",\n",
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" azure_endpoint=AZURE_OPENAI_ACCOUNT,\n",
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" azure_ad_token_provider=token_provider\n",
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" )\n",
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"\n",
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"def create_search_client(credential: AsyncTokenCredential) -> SearchClient:\n",
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" return SearchClient(\n",
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" endpoint=AZURE_SEARCH_SERVICE,\n",
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" index_name=\"hotels-sample-index\",\n",
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" credential=credential\n",
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" )\n",
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"\n",
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"# This quickstart is only using text at this time\n",
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"class SearchType(Enum):\n",
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" TEXT = \"text\"\n",
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" VECTOR = \"vector\"\n",
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" HYBRID = \"hybrid\"\n",
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"\n",
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"# This function retrieves the sselected fields from the search index\n",
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"async def get_sources(search_client: SearchClient, query: str, search_type: SearchType, use_semantic_reranker: bool = True, sources_to_include: int = 5) -> List[str]:\n",
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" search_type == SearchType.TEXT,\n",
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" response = await search_client.search(\n",
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" search_text=query,\n",
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" query_type=\"semantic\" if use_semantic_reranker else \"simple\",\n",
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" top=sources_to_include,\n",
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" select=\"Description,HotelName,Tags\"\n",
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" )\n",
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"\n",
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" return [ document async for document in response ]\n",
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"\n",
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"GROUNDED_PROMPT=\"\"\"\n",
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"You are a friendly assistant that recommends hotels based on activities and amenities.\n",
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"Answer the query using only the sources provided below in a friendly and concise bulleted manner.\n",
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"Answer ONLY with the facts listed in the list of sources below.\n",
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"If there isn't enough information below, say you don't know.\n",
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"Do not generate answers that don't use the sources below.\n",
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"Query: {query}\n",
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"Sources:\\n{sources}\n",
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"\"\"\"\n",
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"class ChatThread:\n",
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" def __init__(self):\n",
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" self.messages = []\n",
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" self.search_results = []\n",
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" \n",
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" def append_message(self, role: str, message: str):\n",
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" self.messages.append({\n",
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" \"role\": role,\n",
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" \"content\": message\n",
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" })\n",
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"\n",
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" async def append_grounded_message(self, search_client: SearchClient, query: str, search_type: SearchType, use_semantic_reranker: bool = True, sources_to_include: int = 5):\n",
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" sources = await get_sources(search_client, query, search_type, use_semantic_reranker, sources_to_include)\n",
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" sources_formatted = \"\\n\".join([f'{document[\"HotelName\"]}:{document[\"Description\"]}:{document[\"Tags\"]}' for document in sources])\n",
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" self.append_message(role=\"user\", message=GROUNDED_PROMPT.format(query=query, sources=sources_formatted))\n",
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" self.search_results.append(\n",
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" {\n",
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" \"message_index\": len(self.messages) - 1,\n",
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" \"query\": query,\n",
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" \"sources\": sources\n",
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" }\n",
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" )\n",
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"\n",
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" async def get_openai_response(self, openai_client: AsyncAzureOpenAI, model: str):\n",
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" response = await openai_client.chat.completions.create(\n",
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" messages=self.messages,\n",
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" model=model\n",
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" )\n",
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" self.append_message(role=\"assistant\", message=response.choices[0].message.content)\n",
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"\n",
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" def get_last_message(self) -> Optional[object]:\n",
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" return self.messages[-1] if len(self.messages) > 0 else None\n",
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"\n",
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" def get_last_message_sources(self) -> Optional[List[object]]:\n",
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" return self.search_results[-1][\"sources\"] if len(self.search_results) > 0 else None"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Based on your preferences, I recommend the following hotels:\n",
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"Sure, here are a few hotels that match your criteria: \n",
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"\n",
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"- Ocean Air Motel: This oceanfront hotel overlooks the beach and features rooms with private balconies, 2 indoor and outdoor pools, and it's located steps away from various shops and entertainment on the boardwalk.\n",
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"- Marquis Plaza & Suites: Offering views of the ocean and amenities like free Wi-Fi, a full kitchen, and a free breakfast buffet, this hotel is a great option for those who want to spend a day at the aquarium and then come home to relax.\n",
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"- Trails End Motel: Although not directly on the ocean, this non-smoking hotel offers great views, an on-site bar/restaurant, a hot breakfast buffet, and free wireless internet. Only 8 miles from downtown and 15 miles from the airport, it's a convenient choice.\n"
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"- Ocean Air Motel, which is an oceanfront hotel with beach access, private balconies, indoor and outdoor pools, shops, and art entertainment nearby.\n",
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"- Marquis Plaza & Suites, which offers ocean views, free Wi-Fi, a full kitchen, and free breakfast buffet, along with beach access and a pool.\n",
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"- Oceanside Resort is a new luxury hotel with bay views from every room, a rooftop pool, waterfront dining, and laundry service. \n",
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"\n",
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"Unfortunately, the Double Sanctuary Resort and Triple Landscape Hotel don't have beach access or mention views.\n"
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]
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}
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],
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"source": [
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"# Instantiate the chat thread and run the conversation\n",
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"import azure.identity.aio\n",
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"\n",
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"chat_thread = ChatThread()\n",
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"chat_deployment = AZURE_DEPLOYMENT_MODEL\n",
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"\n",
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"async with azure.identity.aio.DefaultAzureCredential() as credential, create_search_client(credential) as search_client, create_openai_client(credential) as openai_client:\n",
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" await chat_thread.append_grounded_message(\n",
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" search_client=search_client,\n",
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" query=\"Can you recommend a few hotels near the ocean with beach access and good views\",\n",
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" search_type=SearchType(search_type),\n",
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" use_semantic_reranker=use_semantic_reranker,\n",
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" sources_to_include=sources_to_include\n",
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" )\n",
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" await chat_thread.get_openai_response(openai_client=openai_client, model=chat_deployment)\n",
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"# Set up the query for generating responses\n",
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"from azure.identity import DefaultAzureCredential\n",
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"from azure.identity import get_bearer_token_provider\n",
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"from azure.search.documents import SearchClient\n",
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"from openai import AzureOpenAI\n",
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"\n",
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"credential = DefaultAzureCredential()\n",
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"token_provider = get_bearer_token_provider(credential, \"https://cognitiveservices.azure.com/.default\")\n",
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"openai_client = AzureOpenAI(\n",
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" api_version=\"2024-06-01\",\n",
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" azure_endpoint=AZURE_OPENAI_ACCOUNT,\n",
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" azure_ad_token_provider=token_provider\n",
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")\n",
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"\n",
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"search_client = SearchClient(\n",
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" endpoint=AZURE_SEARCH_SERVICE,\n",
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" index_name=\"hotels-sample-index\",\n",
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" credential=credential\n",
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")\n",
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"\n",
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"# This prompt provides instructions to the model\n",
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"GROUNDED_PROMPT=\"\"\"\n",
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"You are a friendly assistant that recommends hotels based on activities and amenities.\n",
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"Answer the query using only the sources provided below in a friendly and concise bulleted manner.\n",
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"Answer ONLY with the facts listed in the list of sources below.\n",
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"If there isn't enough information below, say you don't know.\n",
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"Do not generate answers that don't use the sources below.\n",
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"Query: {query}\n",
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"Sources:\\n{sources}\n",
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"\"\"\"\n",
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"\n",
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"print(chat_thread.get_last_message()[\"content\"])"
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"# Query is the question being asked. It's sent to the search engine and the LLM.\n",
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"query=\"Can you recommend a few hotels near the ocean with beach access and good views\"\n",
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"\n",
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"# Set up the search results and the chat thread.\n",
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"# Retrieve the selected fields from the search index related to the question.\n",
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"search_results = search_client.search(\n",
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" search_text=query,\n",
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" top=5,\n",
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" select=\"Description,HotelName,Tags\"\n",
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")\n",
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"sources_formatted = \"\\n\".join([f'{document[\"HotelName\"]}:{document[\"Description\"]}:{document[\"Tags\"]}' for document in search_results])\n",
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"\n",
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"response = openai_client.chat.completions.create(\n",
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" messages=[\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": GROUNDED_PROMPT.format(query=query, sources=sources_formatted)\n",
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" }\n",
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" ],\n",
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" model=AZURE_DEPLOYMENT_MODEL\n",
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")\n",
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"\n",
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"print(response.choices[0].message.content)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"If you get an authorization error message, wait a few minutes and try again. It can take several minutes for role assignments to become operational."
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"If you get an authorization error instead of results, wait a few minutes and try again. It can take several minutes for role assignments to become operational."
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]
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}
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],

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