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Embedding Models

Cohere Embedding

Cohere provides the embedding model that converts a LlamaCloud Index documents into vectors, configured with a Cohere API key.

Embed data using Cohere’s API.

  1. Select Cohere Embedding from the Embedding Model dropdown.
  2. Enter your Cohere API key.

cohere

pipeline = client.pipelines.upsert(
name="test-pipeline",
project_id="my-project-id",
data_sink_id=None, # optional
embedding_config={
'type': 'COHERE_EMBEDDING',
'component': {
'api_key': '<YOUR_COHERE_API_KEY>', # editable
},
},
llama_parse_parameters={},
transform_config={"mode": "auto", "chunk_overlap": 128, "chunk_size": 1028},
)
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/