LangChain Reference home pageLangChain ReferenceLangChain Reference
  • GitHub
  • Main Docs
Deep Agents
LangChain
LangGraph
Integrations
LangSmith
  • Overview
  • MCP Adapters
    • Overview
    • Agents
    • Callbacks
    • Chains
    • Chat models
    • Embeddings
    • Evaluation
    • Globals
    • Hub
    • Memory
    • Output parsers
    • Retrievers
    • Runnables
    • LangSmith
    • Storage
    Standard Tests
    Text Splitters
    ⌘I

    LangChain Assistant

    Ask a question to get started

    Enter to send•Shift+Enter new line

    Menu

    MCP Adapters
    OverviewAgentsCallbacksChainsChat modelsEmbeddingsEvaluationGlobalsHubMemoryOutput parsersRetrieversRunnablesLangSmithStorage
    Standard Tests
    Text Splitters
    Language
    Theme
    Pythonlangchain-classicchainsopenai_functionsqa_with_structure
    Module●Since v1.0

    qa_with_structure

    Functions

    function
    get_llm_kwargs

    Return the kwargs for the LLMChain constructor.

    deprecatedfunction
    create_qa_with_structure_chain

    Create a question answering chain with structure.

    Create a question answering chain that returns an answer with sources based on schema.

    deprecatedfunction
    create_qa_with_sources_chain

    Create a question answering chain that returns an answer with sources.

    Classes

    class
    AnswerWithSources

    An answer to the question, with sources.

    deprecatedclass
    LLMChain

    Chain to run queries against LLMs.

    This class is deprecated. See below for an example implementation using LangChain runnables:

    from langchain_core.output_parsers import StrOutputParser
    from langchain_core.prompts import PromptTemplate
    from langchain_openai import OpenAI
    
    prompt_template = "Tell me a {adjective} joke"
    prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
    model = OpenAI()
    chain = prompt | model | StrOutputParser()
    
    chain.invoke("your adjective here")
    View source on GitHub