AzureAISearchRetriever()Your Azure Active Directory token.
Automatically inferred from env var AZURE_AI_SEARCH_AD_TOKEN if not provided.
For more: https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
Azure AI Search service retriever.
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:
pip install -U 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:
from langchain_community.retrievers import AzureAISearchRetriever
retriever = AzureAISearchRetriever(
content_key="content", top_k=1, index_name="langchain-vector-demo"
)Usage:
retriever.invoke("here is my unstructured query string")Use within a chain:
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("...")