Anyscale Embeddings
This guide shows you how to use Anyscale Embeddings through Anyscale Endpoints.
If youβre opening this Notebook on colab, you will probably need to install LlamaIndex π¦.
%pip install llama-index-embeddings-anyscale!pip install llama-indexfrom llama_index.embeddings.anyscale import AnyscaleEmbedding
embed_model = AnyscaleEmbedding( api_key=ANYSCALE_ENDPOINT_TOKEN, embed_batch_size=10)# Basic embedding exampleembeddings = embed_model.get_text_embedding( "It is raining cats and dogs here!")print(len(embeddings), embeddings[:10])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 β e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md
- Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ β 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/