Mistral Embedder

The MistralEmbedder class is used to embed text data into vectors using the Mistral API. Get your key from here.

Usage

mistral_embedder.py

from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
from agno.knowledge.embedder.mistral import MistralEmbedder

# Embed sentence in database
embeddings = MistralEmbedder().get_embedding("The quick brown fox jumps over the lazy dog.")

# Print the embeddings and their dimensions
print(f"Embeddings: {embeddings[:5]}")
print(f"Dimensions: {len(embeddings)}")

# Use an embedder in a knowledge base
knowledge = Knowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="mistral_embeddings",
        embedder=MistralEmbedder(),
    ),
    max_results=2,
)

Params

Parameter	Type	Default	Description
model	str	"mistral-embed"	The name of the model used for generating embeddings.
dimensions	int	1024	The dimensionality of the embeddings generated by the model.
request_params	Optional[Dict[str, Any]]	-	Additional parameters to include in the API request. Optional.
api_key	str	-	The API key used for authenticating requests.
endpoint	str	-	The endpoint URL for the API requests.
max_retries	Optional[int]	-	The maximum number of retries for API requests. Optional.
timeout	Optional[int]	-	The timeout duration for API requests. Optional.
client_params	Optional[Dict[str, Any]]	-	Additional parameters for configuring the API client. Optional.
mistral_client	Optional[MistralClient]	-	An instance of the MistralClient to use for making API requests. Optional.
enable_batch	bool	False	Enable batch processing to reduce API calls and avoid rate limits
batch_size	int	100	Number of texts to process in each API call for batch operations.

Developer Resources

• View Cookbook