Use when "Qdrant", "vector database", "vector search", "semantic search", "embedding storage", or asking about "HNSW index", "similarity search", "RAG retrieval", "vector filtering", "hybrid search"
Manages Qdrant vector database for semantic search and RAG systems.
/plugin marketplace add eyadsibai/ltk/plugin install ltk@ltk-marketplaceThis skill inherits all available tools. When active, it can use any tool Claude has access to.
High-performance vector similarity search for RAG and semantic search.
# Python client
pip install qdrant-client
# Docker (recommended for development)
docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct
client = QdrantClient(host="localhost", port=6333)
# Create collection
client.create_collection(
collection_name="documents",
vectors_config=VectorParams(size=384, distance=Distance.COSINE)
)
# Insert vectors with payload
client.upsert(
collection_name="documents",
points=[
PointStruct(
id=1,
vector=[0.1, 0.2, ...], # 384-dim vector
payload={"title": "Doc 1", "category": "tech"}
)
]
)
# Search with filtering
results = client.search(
collection_name="documents",
query_vector=[0.15, 0.25, ...],
query_filter={
"must": [{"key": "category", "match": {"value": "tech"}}]
},
limit=10
)
| Metric | Use Case | Range |
|---|---|---|
| COSINE | Text embeddings, normalized vectors | 0 to 2 |
| EUCLID | Spatial data, image features | 0 to infinity |
| DOT | Recommendations, unnormalized | -infinity to infinity |
from sentence_transformers import SentenceTransformer
from qdrant_client import QdrantClient
from qdrant_client.models import VectorParams, Distance, PointStruct
encoder = SentenceTransformer("all-MiniLM-L6-v2")
client = QdrantClient(host="localhost", port=6333)
client.create_collection(
collection_name="knowledge_base",
vectors_config=VectorParams(size=384, distance=Distance.COSINE)
)
# Index documents
documents = [
{"id": 1, "text": "Python is a programming language"},
{"id": 2, "text": "Machine learning uses algorithms"},
]
points = [
PointStruct(
id=doc["id"],
vector=encoder.encode(doc["text"]).tolist(),
payload={"text": doc["text"]}
)
for doc in documents
]
client.upsert(collection_name="knowledge_base", points=points)
# RAG retrieval
def retrieve(query: str, top_k: int = 5):
query_vector = encoder.encode(query).tolist()
results = client.search(
collection_name="knowledge_base",
query_vector=query_vector,
limit=top_k
)
return [{"text": r.payload["text"], "score": r.score} for r in results]
from langchain_community.vectorstores import Qdrant
from langchain_community.embeddings import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vectorstore = Qdrant.from_documents(
documents, embeddings,
url="http://localhost:6333",
collection_name="docs"
)
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
from qdrant_client.models import Filter, FieldCondition, MatchValue, Range
results = client.search(
collection_name="documents",
query_vector=query_embedding,
query_filter=Filter(
must=[
FieldCondition(key="category", match=MatchValue(value="tech")),
FieldCondition(key="timestamp", range=Range(gte=1699000000))
],
must_not=[
FieldCondition(key="status", match=MatchValue(value="archived"))
]
),
limit=10
)
from qdrant_client.models import ScalarQuantization, ScalarQuantizationConfig, ScalarType
# 4x memory reduction
client.create_collection(
collection_name="quantized",
vectors_config=VectorParams(size=384, distance=Distance.COSINE),
quantization_config=ScalarQuantization(
scalar=ScalarQuantizationConfig(
type=ScalarType.INT8,
quantile=0.99,
always_ram=True
)
)
)
from qdrant_client.models import PayloadSchemaType
# Create index for faster filtering
client.create_payload_index(
collection_name="documents",
field_name="category",
field_schema=PayloadSchemaType.KEYWORD
)
Slow search with filters: Create payload index for filtered fields.
Out of memory: Enable quantization and on-disk storage.
Connection issues:
client = QdrantClient(
host="localhost",
port=6333,
timeout=30,
prefer_grpc=True # gRPC for better performance
)
| Tool | Best For |
|---|---|
| Qdrant | Production RAG, filtering, on-premise |
| Chroma | Simpler setup, embedded use cases |
| FAISS | Maximum raw speed, batch processing |
| Pinecone | Fully managed, zero ops |
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