Documentation Index
Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
Use this file to discover all available pages before exploring further.
Overview
Qdrant is a vector similarity search engine. Use it for semantic search, recommendations, and RAG applications.
Installation
pip install "praisonai[tools]"
Environment Variables
export QDRANT_URL=http://localhost:6333
export QDRANT_API_KEY=your_api_key # Optional for cloud
Quick Start
from praisonai_tools import QdrantTool
# Initialize
qdrant = QdrantTool(url="http://localhost:6333")
# Search
results = qdrant.search("products", query_vector=[0.1, 0.2, ...], limit=5)
print(results)
Usage with Agent
from praisonaiagents import Agent
from praisonai_tools import QdrantTool
qdrant = QdrantTool(url="http://localhost:6333")
agent = Agent(
name="SearchAgent",
instructions="You perform semantic search using Qdrant.",
tools=[qdrant]
)
response = agent.chat("Find similar products to item 123")
print(response)
Available Methods
search(collection, query_vector, limit=10)
Search for similar vectors.
from praisonai_tools import QdrantTool
qdrant = QdrantTool(url="http://localhost:6333")
results = qdrant.search("documents", query_vector=[0.1, 0.2, 0.3], limit=5)
upsert(collection, points)
Insert or update points.
qdrant.upsert("documents", [
{"id": 1, "vector": [0.1, 0.2, 0.3], "payload": {"text": "Hello"}}
])
create_collection(name, vector_size)
Create a new collection.
qdrant.create_collection("my_collection", vector_size=384)
Docker Setup
docker run -d --name qdrant \
-p 6333:6333 \
qdrant/qdrant
Common Errors
| Error | Cause | Solution |
|---|
qdrant-client not installed | Missing dependency | Run pip install qdrant-client |
Connection refused | Qdrant not running | Start Qdrant server |
Collection not found | Collection doesn’t exist | Create collection first |
