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LlamaIndex Framework
Integrations
Embeddings

MistralAI Embeddings

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

%pip install llama-index-embeddings-mistralai
!pip install llama-index
# imports
from llama_index.embeddings.mistralai import MistralAIEmbedding
# get API key and create embeddings
api_key = "YOUR API KEY"
model_name = "mistral-embed"
embed_model = MistralAIEmbedding(model_name=model_name, api_key=api_key)
embeddings = embed_model.get_text_embedding("La Plateforme - The Platform")
print(f"Dimension of embeddings: {len(embeddings)}")
Dimension of embeddings: 1024
embeddings[:5]
[-0.0299224853515625,
-0.0028362274169921875,
0.0282745361328125,
-0.034759521484375,
-0.0017366409301757812]
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/