Qdrant vector database for knowledge storage
pip install "praisonai[tools]"
docker run -d --name praison-qdrant -p 6333:6333 qdrant/qdrant
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()
praisonai persistence doctor --knowledge-url "http://localhost:6333"
store = create_knowledge_store(
"qdrant",
url="https://xxx.qdrant.io",
api_key="your-api-key"
)
docker ps | grep qdrant
docker logs praison-qdrant