Instruction file imported from hans5549/hans-threejs-example (.github/instructions/langchain-python.instructions.md). Copyright stays with the author.

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.

• Use LangChain's chat model integrations for conversational AI:

• Import from langchain.chat_models or langchain_openai (e.g., ChatOpenAI).

• Compose messages using SystemMessage, HumanMessage, AIMessage.

• For tool calling, use bind_tools(tools) method.

• For structured outputs, use with_structured_output(schema).

Example:

from langchain_openai import ChatOpenAI
from langchain.schema import HumanMessage, SystemMessage

chat = ChatOpenAI(model="gpt-4", temperature=0)
messages = [
    SystemMessage(content="You are a helpful assistant."),
    HumanMessage(content="What is LangChain?")
]
response = chat.invoke(messages)
print(response.content)

• Compose messages as a list of SystemMessage, HumanMessage, and optionally AIMessage objects.
• For RAG, combine chat models with retrievers/vectorstores for context injection.
• Use streaming=True for real-time token streaming (if supported).
• Use tools argument for function/tool calling (OpenAI, Anthropic, etc.).
• Use response_format="json" for structured outputs (OpenAI models).

Best practices:

• Always validate model outputs before using them in downstream tasks.

• Prefer explicit message types for clarity and reliability.

• For Copilot, provide clear, actionable prompts and document expected outputs.

• LLM client factory: centralize provider configs (API keys), timeouts, retries, and telemetry. Provide a single place to switch providers or client settings.

• Prompt templates: store templates under prompts/ and load via a safe helper. Keep templates small and testable.

• Chains vs Agents: prefer Chains for deterministic pipelines (RAG, summarization). Use Agents when you require planning or dynamic tool selection.

• Tools: implement typed adapter interfaces for tools; validate inputs and outputs strictly.

• Memory: default to stateless design. When memory is needed, store minimal context and document retention/erasure policies.

• Retrievers: build retrieval + rerank pipelines. Keep vectorstore schema stable (id, text, metadata).

Patterns

• Callbacks & tracing: use LangChain callbacks and integrate with LangSmith or your tracing system to capture request/response lifecycle.
• Separation of concerns: keep prompt construction, LLM wiring, and business logic separate to simplify testing and reduce accidental prompt changes.

Embeddings & vectorstores

• Use consistent chunking and metadata fields (source, page, chunk_index).
• Cache embeddings to avoid repeated cost for unchanged documents.
• Local/dev: Chroma or FAISS. Production: managed vector DBs (Pinecone, Qdrant, Milvus, Weaviate) depending on scale and SLAs.

Vector stores (LangChain-specific)

• Use LangChain's vectorstore integrations for semantic search, retrieval-augmented generation (RAG), and document similarity workflows.
• Always initialize vectorstores with a supported embedding model (e.g., OpenAIEmbeddings, HuggingFaceEmbeddings).
• Prefer official integrations (e.g., Chroma, FAISS, Pinecone, Qdrant, Weaviate) for production; use InMemoryVectorStore for tests and demos.
• Store documents as LangChain Document objects with page_content and metadata.
• Use add_documents(documents, ids=...) to add/update documents. Always provide unique IDs for upserts.
• Use delete(ids=...) to remove documents by ID.
• Use similarity_search(query, k=4, filter={...}) to retrieve top-k similar documents. Use metadata filters for scoped search.
• For RAG, connect your vectorstore to a retriever and chain with an LLM (see LangChain Retriever and RAGChain docs).
• For advanced search, use vectorstore-specific options: Pinecone supports hybrid search and metadata filtering; Chroma supports filtering and custom distance metrics.
• Always validate the vectorstore integration and API version in your environment; breaking changes are common between LangChain releases.
• Example (InMemoryVectorStore):

from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import OpenAIEmbeddings
from langchain_core.documents import Document

embedding_model = OpenAIEmbeddings()
vector_store = InMemoryVectorStore(embedding=embedding_model)

documents = [Document(page_content="LangChain content", metadata={"source": "doc1"})]
vector_store.add_documents(documents=documents, ids=["doc1"])

results = vector_store.similarity_search("What is RAG?", k=2)
for doc in results:
    print(doc.page_content, doc.metadata)

• For production, prefer persistent vectorstores (Chroma, Pinecone, Qdrant, Weaviate) and configure authentication, scaling, and backup as per provider docs.
• Reference: https://python.langchain.com/docs/integrations/vectorstores/

Prompt engineering & governance

• Store canonical prompts under prompts/ and reference them by filename from code.
• Write unit tests that assert required placeholders exist and that rendered prompts fit expected patterns (length, variables present).
• Maintain a CHANGELOG for prompt and schema changes that affect behavior.

Chat models

LangChain offers a consistent interface for chat models with additional features for monitoring, debugging, and optimization.

Integrations

Integrations are either:

1. Official: packaged langchain-<provider> integrations maintained by the LangChain team or provider.
2. Community: contributed integrations (in langchain-community).

Chat models typically follow a naming convention with a Chat prefix (e.g., ChatOpenAI, ChatAnthropic, ChatOllama). Models without the Chat prefix (or with an LLM suffix) often implement the older string-in/string-out interface and are less preferred for modern chat workflows.

Interface

Chat models implement BaseChatModel and support the Runnable interface: streaming, async, batching, and more. Many operations accept and return LangChain messages (roles like system, user, assistant). See the BaseChatModel API reference for details.

Key methods include:

• invoke(messages, ...) — send a list of messages and receive a response.
• stream(messages, ...) — stream partial outputs as tokens arrive.
• batch(inputs, ...) — batch multiple requests.
• bind_tools(tools) — attach tool adapters for tool calling.
• with_structured_output(schema) — helper to request structured responses.

Inputs and outputs

• LangChain supports its own message format and OpenAI's message format; pick one consistently in your codebase.
• Messages include a role and content blocks; content can include structured or multimodal payloads where supported.

Standard parameters

Commonly supported parameters (provider-dependent):

• model: model identifier (eg. gpt-4o, gpt-3.5-turbo).
• temperature: randomness control (0.0 deterministic — 1.0 creative).
• timeout: seconds to wait before canceling.
• max_tokens: response token limit.
• stop: stop sequences.
• max_retries: retry attempts for network/limit failures.
• api_key, base_url: provider auth and endpoint configuration.
• rate_limiter: optional BaseRateLimiter to space requests and avoid provider quota errors.

Note: Not all parameters are implemented by every provider. Always consult the provider integration docs.

Tool calling

Chat models can call tools (APIs, DBs, system adapters). Use LangChain's tool-calling APIs to:

• Register tools with strict input/output typing.
• Observe and log tool call requests and results.
• Validate tool outputs before passing them back to the model or executing side effects.

See the tool-calling guide in the LangChain docs for examples and safe patterns.

Structured outputs

Use with_structured_output or schema-enforced methods to request JSON or typed outputs from the model. Structured outputs are essential for reliable extraction and downstream processing (parsers, DB writes, analytics).

Multimodality

Some models support multimodal inputs (images, audio). Check provider docs for supported input types and limitations. Multimodal outputs are rare — treat them as experimental and validate rigorously.

Context window

Models have a finite context window measured in tokens. When designing conversational flows:

• Keep messages concise and prioritize important context.
• Trim old context (summarize or archive) outside the model when it exceeds the window.
• Use a retriever + RAG pattern to surface relevant long-form context instead of pasting large documents into the chat.

Advanced topics

Rate-limiting

• Use rate_limiter when initializing chat models to space calls.
• Implement retry with exponential backoff and consider fallback models or degraded modes when throttled.

Caching

• Exact-input caching for conversations is often ineffective. Consider semantic caching (embedding-based) for repeated meaning-level queries.
• Semantic caching introduces dependency on embeddings and is not universally suitable.
• Cache only where it reduces cost and meets correctness requirements (e.g., FAQ bots).

Best practices

• Use type hints and dataclasses for public APIs.
• Validate inputs before calling LLMs or tools.
• Load secrets from secret managers; never log secrets or unredacted model outputs.
• Deterministic tests: mock LLMs and embedding calls.
• Cache embeddings and frequent retrieval results.
• Observability: log request_id, model name, latency, and sanitized token counts.
• Implement exponential backoff and idempotency for external calls.

Security & privacy

• Treat model outputs as untrusted. Sanitize before executing generated code or system commands.
• Validate any user-supplied URLs and inputs to avoid SSRF and injection attacks.
• Document data retention and add an API to erase user data on request.
• Limit stored PII and encrypt sensitive fields at rest.