Qdrant Hybrid Search

Code

1import typer
2from kern.agent import Agent
3from kern.knowledge.knowledge import Knowledge
4from kern.vectordb.qdrant import Qdrant
5from kern.vectordb.search import SearchType
6from rich.prompt import Prompt
7
8COLLECTION_NAME = "thai-recipes"
9
10vector_db = Qdrant(
11    collection=COLLECTION_NAME,
12    url="http://localhost:6333",
13    search_type=SearchType.hybrid,
14)
15
16knowledge = Knowledge(
17    name="My Qdrant Vector Knowledge Base",
18    vector_db=vector_db,
19)
20
21knowledge.insert(
22    name="Recipes",
23    url="https://kern-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
24    metadata={"doc_type": "recipe_book"},
25)
26
27def qdrantdb_agent(user: str = "user"):
28    agent = Agent(
29        user_id=user,
30        knowledge=knowledge,
31        search_knowledge=True,
32    )
33
34    while True:
35        message = Prompt.ask(f"[bold] :sunglasses: {user} [/bold]")
36        if message in ("exit", "bye"):
37            break
38        agent.print_response(message)
39
40if __name__ == "__main__":
41    typer.run(qdrantdb_agent)

Note

To use hybrid search without internet access, pre-cache the model and pass fastembed_kwargs={"local_files_only": True, "cache_dir": "/path/to/local/model/cache"} to load models from the cache and disable downloads:

1Qdrant(
2    collection="my_collection",
3    search_type=SearchType.hybrid,
4    fastembed_kwargs={
5        "local_files_only": True,
6        "cache_dir": "/path/to/cached/models",
7    },
8)

Usage

Set up your virtual environment

1uv venv --python 3.12
2source .venv/bin/activate

1uv venv --python 3.12
2.venv\Scripts\activate

Install dependencies

1uv pip install -U qdrant-client typer rich pypdf openai kern-ai

Run Qdrant

1docker run -d --name qdrant -p 6333:6333 qdrant/qdrant:latest

Run Agent

1python cookbook/08_knowledge/vector_db/qdrant_db/qdrant_db_hybrid_search.py