Embedding Models
Embedding models convert a LlamaCloud Index processed documents into vectors; choose from supported providers such as OpenAI, Azure, Bedrock, Cohere, Gemini, and HuggingFace.
Embedding Models
Section titled “Embedding Models”Once your input documents have been processed, they will go through a Embedding Model to convert them into vectors. We support a variety of embedding models that you can choose from:
Now that you’ve set up an Index end-to-end, you’re ready to start retrieving relevant context from your data ➡️
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/