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-classicevaluationqaeval_chain
    Module●Since v1.0

    eval_chain

    LLM Chains for evaluating question answering.

    Attributes

    attribute
    CONTEXT_PROMPT
    attribute
    COT_PROMPT
    attribute
    PROMPT
    attribute
    RUN_KEY: str

    Classes

    class
    LLMEvalChain

    A base class for evaluators that use an LLM.

    class
    StringEvaluator

    String evaluator interface.

    Grade, tag, or otherwise evaluate predictions relative to their inputs and/or reference labels.

    class
    QAEvalChain

    LLM Chain for evaluating question answering.

    class
    ContextQAEvalChain

    LLM Chain for evaluating QA w/o GT based on context.

    class
    CotQAEvalChain

    LLM Chain for evaluating QA using chain of thought reasoning.

    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