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Qdrant Vector Search

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

技能元数据


来源	可选 — 通过 `hermes skills install official/mlops/qdrant`
路径	`optional-skills/mlops/qdrant`
版本	`1.0.0`
作者	Orchestra Research
许可证	MIT
依赖项	`qdrant-client>=1.12.0`
平台	linux, macos, windows
标签	`RAG`, `Vector Search`, `Qdrant`, `Semantic Search`, `Embeddings`, `Similarity Search`, `HNSW`, `Production`, `Distributed`

参考：完整 SKILL.md

:::info 以下是 Hermes 在触发此技能时加载的完整技能定义。这是技能激活时代理所看到的指令。 :::

Qdrant - Vector Similarity Search Engine

High-performance vector database written in Rust for production RAG and semantic search.

何时使用 Qdrant

Use Qdrant when:

Building production RAG systems requiring low latency
Need hybrid search (vectors + metadata filtering)
Require horizontal scaling with sharding/replication
Want on-premise deployment with full data control
Need multi-vector storage per record (dense + sparse)
Building real-time recommendation systems

主要功能：

Rust-powered: Memory-safe, high performance
Rich filtering: Filter by any payload field during search
Multiple vectors: Dense, sparse, multi-dense per point
Quantization: Scalar, product, binary for memory efficiency
Distributed: Raft consensus, sharding, replication
REST + gRPC: Both APIs with full feature parity

替代方案：

Chroma: Simpler setup, embedded use cases
FAISS: Maximum raw speed, research/batch processing
Pinecone: Fully managed, zero ops preferred
Weaviate: GraphQL preference, built-in vectorizers

快速开始

安装

# Python client
pip install qdrant-client
 
# Docker (recommended for development)
docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant
 
# Docker with persistent storage
docker run -p 6333:6333 -p 6334:6334 \
    -v $(pwd)/qdrant_storage:/qdrant/storage \
    qdrant/qdrant

基本使用

from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct
 
# Connect to Qdrant
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"}
        ),
        PointStruct(
            id=2,
            vector=[0.3, 0.4, ...],
            payload={"title": "Doc 2", "category": "science"}
        )
    ]
)
 
# 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
)
 
for point in results:
    print(f"ID: {point.id}, Score: {point.score}, Payload: {point.payload}")

核心概念

Points - Basic data unit

from qdrant_client.models import PointStruct
 
# Point = ID + Vector(s) + Payload
point = PointStruct(
    id=123,                              # Integer or UUID string
    vector=[0.1, 0.2, 0.3, ...],        # Dense vector
    payload={                            # Arbitrary JSON metadata
        "title": "Document title",
        "category": "tech",
        "timestamp": 1699900000,
        "tags": ["python", "ml"]
    }
)
 
# Batch upsert (recommended)
client.upsert(
    collection_name="documents",
    points=[point1, point2, point3],
    wait=True  # Wait for indexing
)

Collections - Vector containers

from qdrant_client.models import VectorParams, Distance, HnswConfigDiff
 
# Create with HNSW configuration
client.create_collection(
    collection_name="documents",
    vectors_config=VectorParams(
        size=384,                        # Vector dimensions
        distance=Distance.COSINE         # COSINE, EUCLID, DOT, MANHATTAN
    ),
    hnsw_config=HnswConfigDiff(
        m=16,                            # Connections per node (default 16)
        ef_construct=100,                # Build-time accuracy (default 100)
        full_scan_threshold=10000        # Switch to brute force below this
    ),
    on_disk_payload=True                 # Store payload on disk
)
 
# Collection info
info = client.get_collection("documents")
print(f"Points: {info.points_count}, Vectors: {info.vectors_count}")

Distance metrics

距离度量	用例	范围
`COSINE`	Text embeddings, normalized vectors	0 to 2
`EUCLID`	Spatial data, image features	0 to ∞
`DOT`	Recommendations, unnormalized	-∞ to ∞
`MANHATTAN`	Sparse features, discrete data	0 to ∞

Search operations

Basic search

# Simple nearest neighbor search
results = client.search(
    collection_name="documents",
    query_vector=[0.1, 0.2, ...],
    limit=10,
    with_payload=True,
    with_vectors=False  # Don't return vectors (faster)
)

Filtered search

from qdrant_client.models import Filter, FieldCondition, MatchValue, Range
 
# Complex filtering
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
)
 
# Shorthand filter syntax
results = client.search(
    collection_name="documents",
    query_vector=query_embedding,
    query_filter={
        "must": [
            {"key": "category", "match": {"value": "tech"}},
            {"key": "price", "range": {"gte": 10, "lte": 100}}
        ]
    },
    limit=10
)

Batch search

from qdrant_client.models import SearchRequest
 
# Multiple queries in one request
results = client.search_batch(
    collection_name="documents",
    requests=[
        SearchRequest(vector=[0.1, ...], limit=5),
        SearchRequest(vector=[0.2, ...], limit=5, filter={"must": [...]}),
        SearchRequest(vector=[0.3, ...], limit=10)
    ]
)

RAG integration

With sentence-transformers

from sentence_transformers import SentenceTransformer
from qdrant_client import QdrantClient
from qdrant_client.models import VectorParams, Distance, PointStruct
 
# Initialize
encoder = SentenceTransformer("all-MiniLM-L6-v2")
client = QdrantClient(host="localhost", port=6333)
 
# Create collection
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", "source": "wiki"},
    {"id": 2, "text": "Machine learning uses algorithms", "source": "textbook"},
]
 
points = [
    PointStruct(
        id=doc["id"],
        vector=encoder.encode(doc["text"]).tolist(),
        payload={"text": doc["text"], "source": doc["source"]}
    )
    for doc in documents
]
client.upsert(collection_name="knowledge_base", points=points)
 
# RAG retrieval
def retrieve(query: str, top_k: int = 5) -> list[dict]:
    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]
 
# Use in RAG pipeline
context = retrieve("What is Python?")
prompt = f"Context: {context}\n\nQuestion: What is Python?"

With LangChain

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})

With LlamaIndex

from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
 
vector_store = QdrantVectorStore(client=client, collection_name="llama_docs")
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
query_engine = index.as_query_engine()

多向量支持

Named vectors (different embedding models)

from qdrant_client.models import VectorParams, Distance
 
# Collection with multiple vector types
client.create_collection(
    collection_name="hybrid_search",
    vectors_config={
        "dense": VectorParams(size=384, distance=Distance.COSINE),
        "sparse": VectorParams(size=30000, distance=Distance.DOT)
    }
)
 
# Insert with named vectors
client.upsert(
    collection_name="hybrid_search",
    points=[
        PointStruct(
            id=1,
            vector={
                "dense": dense_embedding,
                "sparse": sparse_embedding
            },
            payload={"text": "document text"}
        )
    ]
)
 
# Search specific vector
results = client.search(
    collection_name="hybrid_search",
    query_vector=("dense", query_dense),  # Specify which vector
    limit=10
)

Sparse vectors (BM25, SPLADE)

from qdrant_client.models import SparseVectorParams, SparseIndexParams, SparseVector
 
# Collection with sparse vectors
client.create_collection(
    collection_name="sparse_search",
    vectors_config={},
    sparse_vectors_config={"text": SparseVectorParams(index=SparseIndexParams(on_disk=False))}
)
 
# Insert sparse vector
client.upsert(
    collection_name="sparse_search",
    points=[PointStruct(id=1, vector={"text": SparseVector(indices=[1, 5, 100], values=[0.5, 0.8, 0.2])}, payload={"text": "document"})]
)

量化 (memory optimization)

from qdrant_client.models import ScalarQuantization, ScalarQuantizationConfig, ScalarType
 
# Scalar quantization (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,        # Clip outliers
            always_ram=True      # Keep quantized in RAM
        )
    )
)
 
# Search with rescoring
results = client.search(
    collection_name="quantized",
    query_vector=query,
    search_params={"quantization": {"rescore": True}},  # Rescore top results
    limit=10
)

负载索引

from qdrant_client.models import PayloadSchemaType
 
# Create payload index for faster filtering
client.create_payload_index(
    collection_name="documents",
    field_name="category",
    field_schema=PayloadSchemaType.KEYWORD
)
 
client.create_payload_index(
    collection_name="documents",
    field_name="timestamp",
    field_schema=PayloadSchemaType.INTEGER
)
 
# Index types: KEYWORD, INTEGER, FLOAT, GEO, TEXT (full-text), BOOL

生产部署

Qdrant Cloud

from qdrant_client import QdrantClient
 
# Connect to Qdrant Cloud
client = QdrantClient(
    url="https://your-cluster.cloud.qdrant.io",
    api_key="your-api-key"
)

性能 tuning

# Optimize for search speed (higher recall)
client.update_collection(
    collection_name="documents",
    hnsw_config=HnswConfigDiff(ef_construct=200, m=32)
)
 
# Optimize for indexing speed (bulk loads)
client.update_collection(
    collection_name="documents",
    optimizer_config={"indexing_threshold": 20000}
)

最佳实践

Batch operations - Use batch upsert/search for efficiency
Payload indexing - Index fields used in filters
Quantization - Enable for large collections (>1M vectors)
Sharding - Use for collections >10M vectors
On-disk storage - Enable on_disk_payload for large payloads
Connection pooling - Reuse client instances

常见问题

Slow search with filters:

# Create payload index for filtered fields
client.create_payload_index(
    collection_name="docs",
    field_name="category",
    field_schema=PayloadSchemaType.KEYWORD
)

Out of memory:

# Enable quantization and on-disk storage
client.create_collection(
    collection_name="large_collection",
    vectors_config=VectorParams(size=384, distance=Distance.COSINE),
    quantization_config=ScalarQuantization(...),
    on_disk_payload=True
)

Connection issues:

# Use timeout and retry
client = QdrantClient(
    host="localhost",
    port=6333,
    timeout=30,
    prefer_grpc=True  # gRPC for better performance
)

References

Advanced Usage - Distributed mode, hybrid search, recommendations
Troubleshooting - Common issues, debugging, performance tuning

资源

GitHub： https://github.com/qdrant/qdrant (22k+ stars)
文档： https://qdrant.tech/documentation/
Python Client: https://github.com/qdrant/qdrant-client
Cloud: https://cloud.qdrant.io
版本： 1.12.0+
许可证： Apache 2.0

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