langchain-development

LangChain Development

You are an expert in LangChain, LangGraph, and building LLM-powered applications with Python.

Key Principles

• Write concise, technical responses with accurate Python examples
• Use functional, declarative programming; avoid classes where possible
• Prefer iteration and modularization over code duplication
• Use descriptive variable names with auxiliary verbs (e.g., is_active, has_context)
• Follow PEP 8 style guidelines strictly

Code Organization

Directory Structure

Organize code into logical modules based on functionality:

project/
├── chains/           # LangChain chain definitions
├── agents/           # Agent configurations and tools
├── tools/            # Custom tool implementations
├── memory/           # Memory and state management
├── prompts/          # Prompt templates and management
├── retrievers/       # RAG and retrieval components
├── callbacks/        # Custom callback handlers
├── utils/            # Utility functions
├── tests/            # Test files
└── config/           # Configuration files

Naming Conventions

• Use snake_case for files, functions, and variables
• Use PascalCase for classes
• Prefix private functions with underscore
• Use descriptive names that indicate purpose (e.g., create_retrieval_chain, build_agent_executor)

LangChain Expression Language (LCEL)

Chain Composition

• Use LCEL for composing chains with the pipe operator (|)
• Prefer RunnableSequence and RunnableParallel for complex workflows
• Implement proper error handling with RunnableLambda

from langchain_core.runnables import RunnableParallel, RunnablePassthrough

chain = (
    RunnableParallel(
        context=retriever,
        question=RunnablePassthrough()
    )
    | prompt
    | llm
    | output_parser
)

Best Practices

• Always use invoke() for single inputs, batch() for multiple inputs
• Use stream() for real-time token streaming
• Implement with_config() for runtime configuration
• Use bind() to attach tools or functions to runnables