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LlamaIndex Framework
Integrations
Embeddings

Anyscale Embeddings

This guide shows you how to use Anyscale Embeddings through Anyscale Endpoints.

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex πŸ¦™.

%pip install llama-index-embeddings-anyscale
!pip install llama-index
from llama_index.embeddings.anyscale import AnyscaleEmbedding
embed_model = AnyscaleEmbedding(
api_key=ANYSCALE_ENDPOINT_TOKEN, embed_batch_size=10
)
# Basic embedding example
embeddings = 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/