Cohere Embedder

The CohereEmbedder class is used to embed text data into vectors using the Cohere API. You can get started with Cohere from here

Get your key from here.

Usage

cohere_embedder.py

from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
from agno.knowledge.embedder.cohere import CohereEmbedder

# Add embedding to database
embeddings = CohereEmbedder(id="embed-english-v3.0").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="cohere_embeddings",
        embedder=CohereEmbedder(id="embed-english-v3.0"),
    ),
    max_results=2,
)

Params

Parameter	Type	Default	Description
model	str	"embed-english-v3.0"	The name of the model used for generating embeddings.
input_type	str	search_query	The type of input to embed. You can find more details here
embedding_types	Optional[List[str]]	-	The type of embeddings to generate. Optional.
api_key	str	-	The Cohere API key used for authenticating requests.
request_params	Optional[Dict[str, Any]]	-	Additional parameters to include in the API request. Optional.
client_params	Optional[Dict[str, Any]]	-	Additional parameters for configuring the API client. Optional.
cohere_client	Optional[CohereClient]	-	An instance of the CohereClient 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