OpenAI Embedder

Bitcadata uses OpenAIEmbedder as the default embeder for the vector database. The OpenAIEmbedder class is used to embed text data into vectors using the OpenAI API. Get your key from here.

​Usage

cookbook/embedders/openai_embedder.py

from bitca.agent import AgentKnowledge
from bitca.vectordb.pgvector import PgVector
from bitca.embedder.openai import OpenAIEmbedder

embeddings = OpenAIEmbedder().get_embedding("Embed me")

# 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="openai_embeddings",
        embedder=OpenAIEmbedder(),
    ),
    num_documents=2,
)

​Params

Parameter	Type	Default	Description
model	str	"text-embedding-ada-002"	The name of the model used for generating embeddings.
dimensions	int	1536	The dimensionality of the embeddings generated by the model.
encoding_format	Literal['float', 'base64']	"float"	The format in which the embeddings are encoded. Options are “float” or “base64”.
user	str	-	The user associated with the API request.
api_key	str	-	The API key used for authenticating requests.
organization	str	-	The organization associated with the API request.
base_url	str	-	The base URL for the API endpoint.
request_params	Optional[Dict[str, Any]]	-	Additional parameters to include in the API request.
client_params	Optional[Dict[str, Any]]	-	Additional parameters for configuring the API client.
openai_client	Optional[OpenAIClient]	-	An instance of the OpenAIClient to use for making API requests.