- Developers & Engineers: Build efficient retrieval systems, search features, and RAG pipelines for text-heavy datasets.
- Data Scientists & Analysts: Cluster and classify large corpora of documents or logs semantically.
- AI Researchers: Use it for semantic search benchmarking or powering LLM retrieval pipelines.
- Enterprises & Product Teams: Integrate document similarity and search features into applications and platforms.
- Open-Source Advocates: Access via LangChain, Pinecone, Meilisearch, Zilliz, and other developer-friendly ecosystems.
How to Use Mistral Embed?
- Call the API: Use endpoint `mistral-embed` via Mistral’s API with appropriate API key.
- Process Text Inputs: Submit batches up to 8,192 tokens to generate 1,024-dimensional embeddings.
- Integrate With Vector DBs: Push embeddings to stores like Pinecone, Meilisearch, or Zilliz; perform semantic search and RAG.
- Optimize Retrieval: Use cosine or dot-product similarity on normalized vectors; adjust hybrid search configurations as needed.
- Scale via SDKs: Utilize LangChain’s `MistralAIEmbeddings` for seamless embedding integration in Python workflows.
- High Capacity: Supports embedding long documents (up to 8K tokens)—ideal for summaries and larger texts.
- Modern Architecture: Outperforms or matches other top embedding models despite its relatively smaller size.
- Developer-Friendly: Easily integrates with major vector databases via open SDKs and ecosystem tools.
- Optimized for Retrieval: Designed to deliver accurate similarity results for RAG, classification, and clustering.
- Handles longer inputs (up to 8K tokens) better than many competitors
- Embeds down to 1,024 dimensions—efficient embedding size
- Broad integration via Pinecone, Zilliz, Meilisearch, LangChain, etc.
- Suitable for semantic search, classification, and clustering
- Developer ecosystem and SDK support smooth adoption
- Proprietary model—not open-source, restricted to Mistral API use
- Larger vector size (1,024 dims) may raise storage costs
- No official code embedding support in mistral-embed—there’s separate model for that
API only
$0.1 per 1M input tokens
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