AzureAISearchRetriever

Setup:

See here for more detail: https://python.langchain.com/docs/integrations/retrievers/azure_ai_search/

We will need to install the below dependencies and set the required environment variables:

.. code-block:: bash

pip install -U langchain-community azure-identity azure-search-documents
export AZURE_AI_SEARCH_SERVICE_NAME="<YOUR_SEARCH_SERVICE_NAME>"
export AZURE_AI_SEARCH_INDEX_NAME="<YOUR_SEARCH_INDEX_NAME>"

export AZURE_AI_SEARCH_API_KEY="<YOUR_API_KEY>"
or
export AZURE_AI_SEARCH_BEARER_TOKEN="<YOUR_BEARER_TOKEN>"

Key init args:

content_key: str top_k: int index_name: str

Instantiate:

.. code-block:: python

from langchain_community.retrievers import AzureAISearchRetriever

retriever = AzureAISearchRetriever( content_key="content", top_k=1, index_name="langchain-vector-demo" )

Usage:

.. code-block:: python

retriever.invoke("here is my unstructured query string")

Use within a chain:

.. code-block:: python

from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_core.runnables import RunnablePassthrough from langchain_openai import AzureChatOpenAI

prompt = ChatPromptTemplate.from_template( """Answer the question based only on the context provided.

Context: {context}

Question: {question}""" )

llm = AzureChatOpenAI(azure_deployment="gpt-35-turbo")

def format_docs(docs): return "\n\n".join(doc.page_content for doc in docs)

chain = ( {"context": retriever | format_docs, "question": RunnablePassthrough()} | prompt | llm | StrOutputParser() )

chain.invoke("...")