Fireworks Embedder

The FireworksEmbedder can be used to embed text data into vectors using the Fireworks API. Fireworks uses the OpenAI API specification, so the FireworksEmbedder class is similar to the OpenAIEmbedder class, incorporating adjustments to ensure compatibility with the Fireworks platform. Get your key from here.

Usage

fireworks_embedder.py

from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
from agno.knowledge.embedder.fireworks import FireworksEmbedder

# Embed sentence in database
embeddings = FireworksEmbedder().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="fireworks_embeddings",
        embedder=FireworksEmbedder(),
    ),
    max_results=2,
)

Params

Parameter	Type	Default	Description
model	str	"nomic-ai/nomic-embed-text-v1.5"	The name of the model used for generating embeddings.
dimensions	int	768	The dimensionality of the embeddings generated by the model.
api_key	str	-	The API key used for authenticating requests.
base_url	str	"https://api.fireworks.ai/inference/v1"	The base URL for the API endpoint.
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