| name | gigachat-sdk-files-embeddings |
| description | Uses the official GigaChat Python SDK for file storage, multimodal inputs, and text embeddings. Use when uploading documents or images, downloading generated assets, or building retrieval systems with embeddings directly on top of the gigachat package. |
GigaChat SDK Files And Embeddings
Use this skill for storage and vector workflows in the official SDK.
What this skill covers
- file upload, listing, download, delete
- supported file types and practical size limits
- multimodal workflows that depend on stored file IDs
- embeddings requests and model choice
- batching guidance for embeddings
Workflow
- If the task is pure text generation, use
gigachat-sdk-chat instead.
- For files, upload first and work with returned file IDs.
- In one model request, attach files of only one modality. Do not mix images, audio, and documents in the same request.
- For generated images or 3D assets, fetch content through the files API.
- For retrieval, pick the embeddings model first, then batch requests sensibly.
- If the user is actually building LangChain RAG, switch to
langchain-gigachat.
- Prefer the simplest smoke-covered single-file or embeddings pattern before expanding to more complex workflows.
Read these references as needed
- For file lifecycle and multimodal notes:
references/files.md
- For embeddings usage and retrieval notes:
references/embeddings.md
Default output
- treat one-modality-per-request as a hard constraint
- use smoke-covered single-file or multi-image patterns before suggesting broader multimodal behavior
- separate file lifecycle guidance from embeddings guidance
