GigaChat
%pip install llama-index-embeddings-gigachat!pip install llama-indexfrom llama_index.embeddings.gigachat import GigaChatEmbedding
gigachat_embedding = GigaChatEmbedding( auth_data="your-auth-data", scope="your-scope", # Set scope 'GIGACHAT_API_PERS' for personal use or 'GIGACHAT_API_CORP' for corporate use.)
queries_embedding = gigachat_embedding._get_query_embeddings( ["This is a passage!", "This is another passage"])print(queries_embedding)
text_embedding = gigachat_embedding._get_text_embedding("Where is blue?")print(text_embedding)Note for AI agents: this documentation is built for programmatic access.
- Overview of all docs: https://developers.llamaindex.ai/llms.txt
- Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md
- Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters.
- A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/python/shared/mcp/