| name | langchain-gigachat |
| description | Uses GigaChat through LangChain primitives: chat models, embeddings, message conversion, streaming, tool binding, and RAG workflows. Use when building LangChain chains, agents, or retrieval pipelines on top of GigaChat instead of calling the raw SDK directly. |
LangChain GigaChat
Use this skill when the user is building with LangChain abstractions, not raw gigachat.
What this skill covers
langchain-gigachat installation and initialization
- chat model usage through LangChain messages and runnables
- embeddings through
GigaChatEmbeddings
- tool binding and structured outputs where supported
- RAG setup using LangChain vector stores
- practical wrapper behavior around the underlying SDK
Workflow
- If credentials and TLS are not configured, use
gigachat-setup.
- If the task does not need LangChain primitives, prefer
gigachat-sdk-* skills.
- Initialize
GigaChat or GigaChatEmbeddings with the same auth inputs as the SDK.
- Work with LangChain message objects, not raw GigaChat payloads, unless debugging conversion.
- For RAG, pair
GigaChatEmbeddings with a vector store and keep retrieval logic in LangChain.
- Avoid double retries when LangChain and the SDK both retry.
- Prefer the simplest smoke-covered LangChain pattern before adding wrapper-specific complexity.
Read these references as needed
- For chat models and message/tool behavior:
references/chat-models.md
- For embeddings and RAG patterns:
references/embeddings-rag.md
Default output
- keep the user inside LangChain abstractions unless there is a wrapper boundary bug
- do not mix raw SDK code into normal LangChain starter examples
- call out wrapper caveats instead of assuming full parity with generic LangChain behavior
Boundaries
- Do not explain OpenAI-compatible base URLs here; use
gpt2giga.