Voyage AI Embedder
The VoyageAIEmbedder
class is used to embed text data into vectors using the Voyage AI API. Get your key from here.
Usage
cookbook/embedders/voyageai_embedder.py
from bitca.agent import AgentKnowledge
from bitca.vectordb.pgvector import PgVector
from bitca.embedder.voyageai import VoyageAIEmbedder
embeddings = VoyageAIEmbedder().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)}")
# Example usage:
knowledge_base = AgentKnowledge(
vector_db=PgVector(
db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
table_name="voyageai_embeddings",
embedder=VoyageAIEmbedder(),
),
num_documents=2,
)
Params
model
str
"voyage-2"
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.
base_url
str
"https://api.voyageai.com/v1/embeddings"
The base URL for the API endpoint.
max_retries
Optional[int]
-
The maximum number of retries for API requests. Optional.
timeout
Optional[float]
-
The timeout duration for API requests. Optional.
client_params
Optional[Dict[str, Any]]
-
Additional parameters for configuring the API client. Optional.
voyage_client
Optional[Client]
-
An instance of the Client to use for making API requests. Optional.
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