In continuation of the previous article1 to search the vector store for references to include in RAG, the following shows demonstrates the chat:

# Import libraries

from azure.search.documents import SearchClient

from openai import AzureOpenAI

# Set up the Azure OpenAI client

token_provider = get_bearer_token_provider(credential, "https://cognitiveservices.azure.com/.default")

openai_client = AzureOpenAI(

     api_version="2024-06-01",

     azure_endpoint=AZURE_OPENAI_ACCOUNT,

     azure_ad_token_provider=token_provider

 )

deployment_name = "gpt-4o"

# Set up the Azure Azure AI Search client

search_client = SearchClient(

     endpoint=AZURE_SEARCH_SERVICE,

     index_name=index_name,

     credential=credential

 )

# Provide instructions to the model

GROUNDED_PROMPT="""

You are an AI assistant that helps users learn from the information found in the source material.

Answer the query using only the sources provided below.

Use bullets if the answer has multiple points.

If the answer is longer than 3 sentences, provide a summary.

Answer ONLY with the facts listed in the list of sources below. Cite your source when you answer the question

If there isn't enough information below, say you don't know.

Do not generate answers that don't use the sources below.

Query: {query}

Sources:\n{sources}

"""

# Provide the search query.

# It's hybrid: a keyword search on "query", with text-to-vector conversion for "vector_query".

# The vector query finds 50 nearest neighbor matches in the search index

query="Do bicycles have a dedicated crossing at street intersections?"

vector_query = VectorizableTextQuery(text=query, k_nearest_neighbors=50, fields="text_vector")

# Set up the search results and the chat thread.

# Retrieve the selected fields from the search index related to the question.

# Search results are limited to the top 5 matches. Limiting top can help you stay under LLM quotas.

search_results = search_client.search(

    search_text=query,

    vector_queries= [vector_query],

    select=["id", "description"],

    top=5,

)

# Newlines could be in the OCR'd content or in PDFs, as is the case for the sample PDFs used for this tutorial.

# Use a unique separator to make the sources distinct.

# We chose repeated equal signs (=) followed by a newline because it's unlikely the source documents contain this sequence.

sources_formatted = "=================\n".join([f'TITLE: {document["title"]}, CONTENT: {document["chunk"]}, LOCATIONS: {document["locations"]}' for document in search_results])

response = openai_client.chat.completions.create(

    messages=[

        {

            "role": "user",

            "content": GROUNDED_PROMPT.format(query=query, sources=sources_formatted)

        }

    ],

    model=deployment_name

)

print(response.choices[0].message.content)