- 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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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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