Integrations
LanceDB Integration
Example
import typer
from typing import Optional
from rich.prompt import Prompt
from phi.agent import Agent
from phi.knowledge.pdf import PDFUrlKnowledgeBase
from phi.vectordb.lancedb import LanceDb
from phi.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://phi-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 | - | The search type to use. |
distance | Distance | - | The distance to use. |
nprobes | int | - | The number of probes to use. |
reranker | Reranker | - | The reranker to use. |
use_tantivy | bool | - | Whether to use tantivy. |
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