Workflow:Mistralai Client python Text Embeddings
| Knowledge Sources | |
|---|---|
| Domains | LLMs, Embeddings, NLP, Python_SDK |
| Last Updated | 2026-02-15 14:00 GMT |
Overview
End-to-end process for generating text embeddings from the Mistral AI Embeddings API for use in similarity search, clustering, and retrieval-augmented generation.
Description
This workflow covers how to generate dense vector representations (embeddings) of text using the Mistral AI Embeddings API. The embeddings model converts input text into fixed-dimensional numerical vectors that capture semantic meaning, enabling downstream tasks such as semantic similarity computation, document clustering, nearest-neighbor search, and retrieval-augmented generation (RAG) pipelines. The API supports batch processing of multiple text inputs in a single request.
Usage
Execute this workflow when you need to convert text into vector representations for semantic search, document retrieval, clustering, classification, or building RAG pipelines. This is appropriate whenever you need to measure similarity between texts or store text representations in a vector database.
Execution Steps
Step 1: Install SDK and Configure Authentication
Install the mistralai Python package and configure the MISTRAL_API_KEY environment variable. The Embeddings API uses the same authentication as chat completion.
Key considerations:
- Same installation and authentication as other Mistral API endpoints
- No additional dependencies required for embeddings
Step 2: Initialize the Mistral Client
Create a Mistral client instance with the API key. The embeddings resource is available as client.embeddings.
Key considerations:
- Use context manager pattern for resource management
- The same client instance can be used for both chat and embeddings
Step 3: Prepare Input Texts
Assemble the list of text strings to embed. The API supports batch processing, so multiple texts can be embedded in a single request for efficiency.
Key considerations:
- Input is a list of strings
- Batch size affects API call efficiency
- Texts should be preprocessed (cleaned, trimmed) as needed for your use case
- Very long texts may need to be chunked to fit within model context limits
Step 4: Generate Embeddings
Call the embeddings.create() method (sync) or embeddings.create_async() method (async) with the embedding model identifier and the list of input texts.
Key considerations:
- Use model="mistral-embed" for the Mistral embeddings model
- The inputs parameter accepts a list of strings
- Each input text produces one embedding vector
- Token usage is tracked in the response
Step 5: Process Embedding Vectors
Extract the embedding vectors from the response for downstream use. Each embedding is a list of floating-point numbers representing the text in the model's vector space.
Key considerations:
- Response contains a list of embedding objects, each with an embedding vector
- Vectors can be stored in vector databases (FAISS, Pinecone, Weaviate, etc.)
- Cosine similarity is the standard metric for comparing Mistral embeddings
- Embedding dimensionality is fixed per model