PDF Knowledge Base

The PDFKnowledgeBase reads local PDF 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 pypdf

knowledge_base.py

from bitca.knowledge.pdf import PDFKnowledgeBase, PDFReader
from bitca.vectordb.pgvector import PgVector

pdf_knowledge_base = PDFKnowledgeBase(
    path="data/pdfs",
    # Table name: ai.pdf_documents
    vector_db=PgVector(
        table_name="pdf_documents",
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    ),
    reader=PDFReader(chunk=True),
)

Then use the knowledge_base with an Agent:

agent.py

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
path	Union[str, Path]	-	Path to PDF files. Can point to a single PDF file or a directory of PDF files.
vector_db	VectorDb	-	Vector Database for the Knowledge Base. Example: PgVector
reader	Union[PDFReader, PDFImageReader]	PDFReader()	A PDFReader that converts the PDFs 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. For Example: Create an index for PgVector.
chunking_strategy	ChunkingStrategy	FixedSizeChunking	The chunking strategy to use.