Qdrant Vector Database

Use Qdrant as a vector database for your Knowledge Base.

Setup

Follow the instructions in the Qdrant Setup Guide to install Qdrant locally. Here is a guide to get API keys: Qdrant API Keys.

Example

agent_with_knowledge.py

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

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.qdrant import Qdrant

api_key = os.getenv("QDRANT_API_KEY")
qdrant_url = os.getenv("QDRANT_URL")
collection_name = "thai-recipe-index"

vector_db = Qdrant(
    collection=collection_name,
    url=qdrant_url,
    api_key=api_key,
)

knowledge_base = Knowledge(
    vector_db=vector_db,
)

def qdrant_agent(user: str = "user"):
    agent = Agent(
        knowledge=knowledge_base,
        debug_mode=True,
    )

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

if __name__ == "__main__":
    knowledge_base.insert(
        url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
    )

    typer.run(qdrant_agent)

Async Support ⚡

Qdrant also supports asynchronous operations, enabling concurrency and leading to better performance.

async_qdrant_db.py

import asyncio

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.qdrant import Qdrant

COLLECTION_NAME = "thai-recipes"

# Initialize Qdrant with local instance
vector_db = Qdrant(
    collection=COLLECTION_NAME,
    url="http://localhost:6333"
)

# Create knowledge base
knowledge_base = Knowledge(
    vector_db=vector_db,
)

agent = Agent(knowledge=knowledge_base)

if __name__ == "__main__":
    # Load knowledge base asynchronously
    asyncio.run(knowledge_base.ainsert(
            url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
        )
    )

    # Create and use the agent asynchronously
    asyncio.run(agent.aprint_response("How to make Tom Kha Gai", markdown=True))

Using aload() and aprint_response() with asyncio provides non-blocking operations, making your application more responsive under load.

Qdrant Params

Name	Type	Default	Description
collection	str	-	Name of the Qdrant collection
embedder	Embedder	OpenAIEmbedder()	Embedder for embedding the document contents
distance	Distance	Distance.cosine	Distance metric for similarity search
location	Optional[str]	None	Location of the Qdrant database
url	Optional[str]	None	URL of the Qdrant server
port	Optional[int]	6333	Port number for the Qdrant server
grpc_port	int	6334	gRPC port number for the Qdrant server
prefer_grpc	bool	False	Whether to prefer gRPC over HTTP
https	Optional[bool]	None	Whether to use HTTPS
api_key	Optional[str]	None	API key for authentication
prefix	Optional[str]	None	Prefix for the Qdrant API
timeout	Optional[float]	None	Timeout for Qdrant operations
host	Optional[str]	None	Host address for the Qdrant server
path	Optional[str]	None	Path to the Qdrant database