The AzCognitiveSearchReader Loader returns a set of texts corresponding to documents retrieved from specific index of Azure Cognitive Search. The user initializes the loader with credentials (service name and key) and the index name.

Usage

Here's an example usage of the AzCognitiveSearchReader.

from llama_index import download_loader

AzCognitiveSearchReader = download_loader("AzCognitiveSearchReader")

reader = AzCognitiveSearchReader(
    "<Azure_Cognitive_Search_NAME>",
    "<Azure_Cognitive_Search_KEY>,
    "<Index_name>
)


query_sample = ""
documents = reader.load_data(
    query="<search_term>", content_field="<content_field_name>", filter="<azure_search_filter>"
)

Usage in combination with langchain


    from llama_index import GPTVectorStoreIndex, download_loader
    from langchain.chains.conversation.memory import ConversationBufferMemory
    from langchain.agents import Tool, AgentExecutor, load_tools, initialize_agent

    AzCognitiveSearchReader = download_loader("AzCognitiveSearchReader")

    az_loader = AzCognitiveSearchReader(
            COGNITIVE_SEARCH_SERVICE_NAME,
            COGNITIVE_SEARCH_KEY,
            INDEX_NAME)

    documents = az_loader.load_data(query, field_name)

    index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context)

    tools = [
        Tool(
            name="Azure cognitive search index",
            func=lambda q: index.query(q),
            description=f"Useful when you want answer questions about the text on azure cognitive search.",
        ),
    ]
    memory = ConversationBufferMemory(memory_key="chat_history")
    agent_chain = initialize_agent(
        tools, llm, agent="zero-shot-react-description", memory=memory
    )

    result = agent_chain.run(input="How can I contact with my health insurance?")

This loader is designed to be used as a way to load data into LlamaIndex and/or subsequently used as a Tool in a LangChain Agent. See here for examples.