Index and Query GigaChat Files

GigaChat SDK Files and Embeddings covers the two storage-adjacent capabilities of the official gigachat Python SDK: the file API - upload, listing, download, and delete, including supported file types and practical size limits - and text embeddings, including model choice and batching guidance. It also documents multimodal workflows, which depend on referencing files by the ID returned from an earlier upload rather than sending raw content inline.

Use it when a task uploads documents or images, downloads generated assets (like images or 3D content) back out of GigaChat, or needs embeddings for a retrieval system built directly on the SDK. If the task is pure text generation with no files or embeddings involved, use gigachat-sdk-chat instead. If retrieval is actually being built as a LangChain RAG pipeline rather than directly on the SDK, switch to langchain-gigachat, which wraps these same capabilities in LangChain's abstractions.

The file workflow is upload-first: a file is uploaded, GigaChat returns a file ID, and that ID - not the raw file - is what gets referenced in later requests. A hard constraint applies to multimodal requests: only one modality's worth of files can be attached to a single model request, so images, audio, and documents must never be mixed together in the same call. For generated outputs like images or 3D assets, content is retrieved back through the same files API rather than returned inline. For retrieval workloads, the recommended order is to pick an embeddings model first, then batch embedding requests sensibly rather than sending them one at a time. Two reference files hold the deeper detail: references/files.md for file lifecycle and multimodal notes, and references/embeddings.md for embeddings usage and retrieval notes.

This skill is one part of a small GigaChat skill family: gigachat-sdk-chat handles pure chat/text generation, langchain-gigachat wraps files and embeddings (among other things) in LangChain abstractions for chain- or agent-based retrieval, and gigachat-setup covers the credentials and TLS configuration all of them assume is already working. Keeping file-lifecycle guidance and embeddings guidance separate is a deliberate design choice here, since the two have different constraints (one-modality-per-request vs. batching).

Developers working directly against the gigachat SDK (not through LangChain) who need to upload or fetch files, work with multimodal inputs correctly, or build a retrieval system on GigaChat embeddings without guessing at file ID handling or batching behavior.