MistralAI Embeddings
MistralAI Embeddings turns text into a numeric fingerprint of its meaning using Mistral AI models. With that fingerprint, NappAI can find the documents that say something similar to what you are looking for, even when they use different words, for example in a knowledge base.
You connect the component to a vector database, which uses it both to store your documents and to search them. Use it if your company works with Mistral AI.
When to use it
- Give an agent the knowledge in your manuals, contracts or FAQs by storing them in a vector database.
- Build an internal search that finds documents by their meaning, not only by exact words.
- Your company already has a Mistral AI account.
Configuration
| Field | What it does |
|---|---|
| Credential (required) | The credential of the service Mistral AI API. Pick one with Choose an option or create one with Add new credential. See Credentials. |
| Model | The Mistral AI model that calculates the fingerprints. Default: mistral-embed, the only option in the list. |
Advanced options
| Field | What it does |
|---|---|
| Max Concurrent Requests | How many requests are sent to Mistral AI at the same time. Default: 64. |
| Max Retries | How many times a failed request is retried. Default: 5. |
| Request Timeout | Maximum waiting time for each request, in seconds. Default: 120. |
| API Endpoint | The address of the Mistral AI service. By default, Mistral AI’s public address; you do not need to change it. |
Batch processing
With the Batch Mode switch on, the component runs once for each item of the list you connect to Batch Input and returns all the results together as a list. Parallel Executions sets how many items are processed at the same time (1 to 5). Skip if Empty Batch Input skips the component when the list arrives empty, and Skip Branch if Empty Output stops the following components when the result is empty. More in Key concepts.
Output
- Embeddings: the model that calculates the fingerprints, ready to use. Connect it to the Embedding input of a vector database: Astra DB, Cassandra, Chroma DB, Couchbase, Elasticsearch, FAISS, Milvus, MongoDB Vector Store, PGVector, Pinecone, Qdrant, Redis, Supabase Vector Store, Upstash, Vectara, Weaviate Cloud (v4). Other components with an input of this type accept it too, such as Semantic Text Splitter or Text Embedder.
Example
A winery, Bodegas Valdeoro, wants its assistant to answer using its wine technical sheets.
- In a loading flow, split the documents into chunks with Recursive Character Text Splitter and pass them to Chroma DB with Operation set to
Add. - Add MistralAI Embeddings and connect its Embeddings output to the Embedding input of Chroma DB.
- In the assistant flow, use the same Chroma DB collection with the same MistralAI Embeddings component to find the chunks that answer each question.