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Qdrant

Qdrant is a high-performance vector database for semantic search and RAG.

Installation

pip install "praisonai[tools]"

Docker Setup

docker run -d --name praison-qdrant -p 6333:6333 qdrant/qdrant

Usage

from praisonai.persistence.factory import create_knowledge_store
from praisonai.persistence.knowledge.base import KnowledgeDocument

store = create_knowledge_store("qdrant", url="http://localhost:6333")

# Create collection
store.create_collection("my_knowledge", dimension=384)

# Insert documents
docs = [
    KnowledgeDocument(
        id="doc-1",
        content="Python is great for AI",
        embedding=[0.1] * 384,
        metadata={"category": "programming"}
    )
]
store.insert("my_knowledge", docs)

# Search
results = store.search("my_knowledge", query_embedding=[0.1] * 384, limit=5)

store.close()

CLI

praisonai persistence doctor --knowledge-url "http://localhost:6333"

Qdrant Cloud

store = create_knowledge_store(
    "qdrant",
    url="https://xxx.qdrant.io",
    api_key="your-api-key"
)

Troubleshooting

Connection refused:
docker ps | grep qdrant
docker logs praison-qdrant