LanceDB Agent Knowledge

Setup

pip install lancedb

​Example

agent_with_knowledge.py

import typer
from typing import Optional
from rich.prompt import Prompt

from bitca.agent import Agent
from bitca.knowledge.pdf import PDFUrlKnowledgeBase
from bitca.vectordb.lancedb import LanceDb
from bitca.vectordb.search import SearchType

# LanceDB Vector DB
vector_db = LanceDb(
    table_name="recipes",
    uri="/tmp/lancedb",
    search_type=SearchType.keyword,
)

# Knowledge Base
knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://bitca-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=vector_db,
)

# Comment out after first run
knowledge_base.load(recreate=True)


def lancedb_agent(user: str = "user"):
    run_id: Optional[str] = None

    agent = Agent(
        run_id=run_id,
        user_id=user,
        knowledge=knowledge_base,
        show_tool_calls=True,
        debug_mode=True,
    )

    if run_id is None:
        run_id = agent.run_id
        print(f"Started Run: {run_id}\n")
    else:
        print(f"Continuing Run: {run_id}\n")

    while True:
        message = Prompt.ask(f"[bold] :sunglasses: {user} [/bold]")
        if message in ("exit", "bye"):
            break
        agent.print_response(message)


if __name__ == "__main__":
    typer.run(lancedb_agent)

​LanceDb Params

Parameter	Type	Default	Description
uri	str	-	The URI to connect to.
table	LanceTable	-	The Lance table to use.
table_name	str	-	The name of the table to use.
connection	DBConnection	-	The database connection to use.
api_key	str	-	The API key to use.
embedder	Embedder	-	The embedder to use.
search_type	SearchType	vector	The search type to use.
distance	Distance	cosine	The distance to use.
nprobes	int	-	The number of probes to use. More Info
reranker	Reranker	-	The reranker to use. More Info
use_tantivy	bool	-	Whether to use tantivy.