Document Knowledge Base

The DocumentKnowledgeBase reads local docs files, converts them into vector embeddings and loads them to a vector databse.

​Usage

We are using a local PgVector database for this example. Make sure it’s running

pip install textract

from bitca.knowledge.document import DocumentKnowledgeBase
from bitca.vectordb.pgvector import PgVector

knowledge_base = DocumentKnowledgeBase(
    path="data/docs",
    # Table name: ai.documents
    vector_db=PgVector(
        table_name="documents",
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    ),
)

Then use the knowledge_base with an Agent:

from bitca.agent import Agent
from knowledge_base import knowledge_base

agent = Agent(
    knowledge=knowledge_base,
    search_knowledge=True,
)
agent.knowledge.load(recreate=False)

agent.print_response("Ask me about something from the knowledge base")

​Params

Parameter	Type	Default	Description
documents	List[Document]	-	List of documents to load into the vector database.
vector_db	VectorDb	-	Vector Database for the Knowledge Base.
reader	Reader	-	A Reader that converts the content of the documents into Documents for the vector database.
num_documents	int	5	Number of documents to return on search.
optimize_on	int	-	Number of documents to optimize the vector db on.
chunking_strategy	ChunkingStrategy	FixedSizeChunking	The chunking strategy to use.