custom_github_copilot_agent_builder: Instructions file for GitHub Copilot

LangChain Python Instructions

These instructions guide GitHub Copilot in generating code and documentation for LangChain applications in Python. Focus on LangChain-specific patterns, APIs, and best practices.

Runnable Interface (LangChain-specific)

LangChain's Runnable interface is the foundation for composing and executing chains, chat models, output parsers, retrievers, and LangGraph graphs. It provides a unified API for invoking, batching, streaming, inspecting, and composing components.

Key LangChain-specific features:

• All major LangChain components (chat models, output parsers, retrievers, graphs) implement the Runnable interface.
• Supports synchronous (invoke, batch, stream) and asynchronous (ainvoke, abatch, astream) execution.
• Batching (batch, batch_as_completed) is optimized for parallel API calls; set max_concurrency in RunnableConfig to control parallelism.
• Streaming APIs (stream, astream, astream_events) yield outputs as they are produced, critical for responsive LLM apps.
• Input/output types are component-specific (e.g., chat models accept messages, retrievers accept strings, output parsers accept model outputs).
• Inspect schemas with get_input_schema, get_output_schema, and their JSONSchema variants for validation and OpenAPI generation.
• Use with_types to override inferred input/output types for complex LCEL chains.
• Compose Runnables declaratively with LCEL: chain = prompt | chat_model | output_parser.
• Propagate RunnableConfig (tags, metadata, callbacks, concurrency) automatically in Python 3.11+; manually in async code for Python 3.9/3.10.
• Create custom runnables with RunnableLambda (simple transforms) or RunnableGenerator (streaming transforms); avoid subclassing directly.
• Configure runtime attributes and alternatives with configurable_fields and configurable_alternatives for dynamic chains and LangServe deployments.

LangChain best practices:

• Use batching for parallel API calls to LLMs or retrievers; set max_concurrency to avoid rate limits.
• Prefer streaming APIs for chat UIs and long outputs.
• Always validate input/output schemas for custom chains and deployed endpoints.
• Use tags and metadata in RunnableConfig for tracing in LangSmith and debugging complex chains.
• For custom logic, wrap functions with RunnableLambda or RunnableGenerator instead of subclassing.
• For advanced configuration, expose fields and alternatives via configurable_fields and configurable_alternatives.