PgVector Agent Knowledge

Setup

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql/data/pgdata \
  -v pgvolume:/var/lib/postgresql/data \
  -p 5532:5432 \
  --name pgvector \
  bitcadata/pgvector:16

​Example

agent_with_knowledge.py

from bitca.agent import Agent
from bitca.model.openai import OpenAIChat
from bitca.knowledge.pdf import PDFUrlKnowledgeBase
from bitca.vectordb.pgvector import PgVector, SearchType

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://bitca-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=PgVector(table_name="recipes", db_url=db_url, search_type=SearchType.hybrid),
)
# Load the knowledge base: Comment out after first run
knowledge_base.load(recreate=True, upsert=True)

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    knowledge=knowledge_base,
    # Add a tool to read chat history.
    read_chat_history=True,
    show_tool_calls=True,
    markdown=True,
    # debug_mode=True,
)
agent.print_response("How do I make chicken and galangal in coconut milk soup", stream=True)
agent.print_response("What was my last question?", stream=True)

​PgVector Params

Parameter	Type	Default	Description
table_name	str	-	The name of the table to use.
schema	str	-	The schema to use.
db_url	str	-	The database URL to connect to.
db_engine	Engine	-	The database engine to use.
embedder	Embedder	-	The embedder to use.
search_type	SearchType	vector	The search type to use.
vector_index	Union[Ivfflat, HNSW]	-	The vector index to use.
distance	Distance	cosine	The distance to use.
prefix_match	bool	-	Whether to use prefix matching.
vector_score_weight	float	0.5	Weight for vector similarity in hybrid search. Must be between 0 and 1.
content_language	str	-	The content language to use.
schema_version	int	-	The schema version to use.
auto_upgrade_schema	bool	-	Whether to auto upgrade the schema.