title: “Qdrant Vector Search — High-performance vector similarity search engine for RAG and semantic search” sidebar_label: “Qdrant Vector Search” description: “High-performance vector similarity search engine for RAG and semantic search”

{/* 本页面由 website/scripts/generate-skill-docs.py 根据技能对应的 SKILL.md 文件自动生成。请直接编辑源文件 SKILL.md，而非此页面。 */}

Qdrant 向量搜索引擎

专为 RAG 和语义搜索设计的高性能向量相似度搜索引擎。适用于构建需要快速最近邻搜索、混合搜索（向量与元数据过滤结合）或具备 Rust 强大性能支持的可扩展向量存储的实战级 RAG 系统。

技能元数据

参考：完整 SKILL.md 内容

:::info 以下是当触发该技能时 Hermes 会加载的完整技能定义。技能激活后，智能体将依据此内容执行操作。

::

Qdrant —— 向量相似度搜索引擎

基于 Rust 开发的高性能向量数据库，专为实战级 RAG 和语义搜索而设计。

何时使用 Qdrant

以下场景适合使用 Qdrant：

• 构建对低延迟有较高要求的实战级 RAG 系统
• 需要混合搜索功能（向量数据与元数据过滤结合）
• 需要通过分片/复制实现水平扩展
• 希望在本地部署以实现对数据的完全控制
• 每条记录需要存储多种向量格式（密集向量、稀疏向量）
• 开发实时推荐系统

核心特性：

• Rust 强力驱动：具备内存安全性与高性能
• 丰富过滤功能：搜索时可按任意字段进行过滤
• 多类型向量支持：单条数据可存储密集向量、稀疏向量及多种密集向量格式
• 量化技术：提供标量量化、乘积量化、二进制量化等多种方式以优化内存使用
• 分布式架构：支持 Raft 共识机制、分片与复制功能
• REST + gRPC 双接口：两种 API 具备完全一致的功能特性

可选替代方案：

• Chroma：部署更简单，适用于嵌入式场景
• FAISS：原始处理速度最快，适合研究用途及批量处理
• Pinecone：全托管服务，适合无需自行运维的场景
• Weaviate：优先支持 GraphQL 接口，内置向量化工具

快速入门

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

基本用法

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

积分——基本数据单位

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
)

集合——向量容器

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}")

距离度量标准

搜索操作

基本搜索

# 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)
)

筛选搜索

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
)

批量搜索

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集成

使用sentence-transformers实现

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?"

集成 LangChain 使用

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})

集成 LlamaIndex 使用

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

命名向量（不同的嵌入模型）

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
)

稀疏向量（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（内存优化）

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
)

载荷索引功能

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

生产环境部署

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"
)

性能调优

# 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

1. 批量操作——使用批量插入/查询以提高效率
2. 负载索引——对过滤条件中用到的字段进行索引
3. 量化处理——对于包含超过100万个向量的大型集合，建议启用该功能
4. 分片——当集合中的向量数量超过1000万时，应采用分片机制
5. 磁盘存储——对于较大的负载数据，建议启用on_disk_payload选项
6. 连接池——重复使用客户端实例

常见问题

带过滤条件的查询速度缓慢：

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

内存不足：

# 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
)

连接问题：

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

参考资料

• 高级用法 - 分布式模式、混合搜索、推荐功能
• 故障排查 - 常见问题、调试方法及性能优化技巧

资源链接

• GitHub仓库：https://github.com/qdrant/qdrant（拥有2.2万+星标）
• 官方文档：https://qdrant.tech/documentation/
• Python客户端：https://github.com/qdrant/qdrant-client
• 云服务平台：https://cloud.qdrant.io
• 当前版本：1.12.0及以上
• 许可证：Apache 2.0