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Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.","endman100\u002Fskill-router-nexus","endman100","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F31997019?v=4","01M6KPDX17BYDWW4RRB0TNGGMX","01M6KPDX64Y2QBEWVFG5W5PWPF",0,"MIT","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FLLM-and-Chatbot\u002Fqdrant\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"search","vector","Vector Search","Qdrant","qdrant vector search","filtering","search engine","when","hybrid","for","orchestra","Scalable","Hybrid Search","high-performance","use","performance","embeddings","semantic search","rag","distributed","storage","rust","2026-11-10 10:12:30",false,"---\nname: qdrant-vector-search\ndescription: 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.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [RAG, Vector Search, Qdrant, Semantic Search, Embeddings, Similarity Search, HNSW, Production, Distributed]\ndependencies: [qdrant-client>=1.12.0]\n---\n\n# Qdrant - Vector Similarity Search Engine\n\nHigh-performance vector database written in Rust for production RAG and semantic search.\n\n## When to use Qdrant\n\n**Use Qdrant when:**\n- Building production RAG systems requiring low latency\n- Need hybrid search (vectors + metadata filtering)\n- Require horizontal scaling with sharding\u002Freplication\n- Want on-premise deployment with full data control\n- Need multi-vector storage per record (dense + sparse)\n- Building real-time recommendation systems\n\n**Key features:**\n- **Rust-powered**: Memory-safe, high performance\n- **Rich filtering**: Filter by any payload field during search\n- **Multiple vectors**: Dense, sparse, multi-dense per point\n- **Quantization**: Scalar, product, binary for memory efficiency\n- **Distributed**: Raft consensus, sharding, replication\n- **REST + gRPC**: Both APIs with full feature parity\n\n**Use alternatives instead:**\n- **Chroma**: Simpler setup, embedded use cases\n- **FAISS**: Maximum raw speed, research\u002Fbatch processing\n- **Pinecone**: Fully managed, zero ops preferred\n- **Weaviate**: GraphQL preference, built-in vectorizers\n\n## Quick start\n\n### Installation\n\n```bash\n# Python client\npip install qdrant-client\n\n# Docker (recommended for development)\ndocker run -p 6333:6333 -p 6334:6334 qdrant\u002Fqdrant\n\n# Docker with persistent storage\ndocker run -p 6333:6333 -p 6334:6334 \\\n    -v $(pwd)\u002Fqdrant_storage:\u002Fqdrant\u002Fstorage \\\n    qdrant\u002Fqdrant\n```\n\n### Basic usage\n\n```python\nfrom qdrant_client import QdrantClient\nfrom qdrant_client.models import Distance, VectorParams, PointStruct\n\n# Connect to Qdrant\nclient = QdrantClient(host=\"localhost\", port=6333)\n\n# Create collection\nclient.create_collection(\n    collection_name=\"documents\",\n    vectors_config=VectorParams(size=384, distance=Distance.COSINE)\n)\n\n# Insert vectors with payload\nclient.upsert(\n    collection_name=\"documents\",\n    points=[\n        PointStruct(\n            id=1,\n            vector=[0.1, 0.2, ...],  # 384-dim vector\n            payload={\"title\": \"Doc 1\", \"category\": \"tech\"}\n        ),\n        PointStruct(\n            id=2,\n            vector=[0.3, 0.4, ...],\n            payload={\"title\": \"Doc 2\", \"category\": \"science\"}\n        )\n    ]\n)\n\n# Search with filtering\nresults = client.search(\n    collection_name=\"documents\",\n    query_vector=[0.15, 0.25, ...],\n    query_filter={\n        \"must\": [{\"key\": \"category\", \"match\": {\"value\": \"tech\"}}]\n    },\n    limit=10\n)\n\nfor point in results:\n    print(f\"ID: {point.id}, Score: {point.score}, Payload: {point.payload}\")\n```\n\n## Core concepts\n\n### Points - Basic data unit\n\n```python\nfrom qdrant_client.models import PointStruct\n\n# Point = ID + Vector(s) + Payload\npoint = PointStruct(\n    id=123,                              # Integer or UUID string\n    vector=[0.1, 0.2, 0.3, ...],        # Dense vector\n    payload={                            # Arbitrary JSON metadata\n        \"title\": \"Document title\",\n        \"category\": \"tech\",\n        \"timestamp\": 1699900000,\n        \"tags\": [\"python\", \"ml\"]\n    }\n)\n\n# Batch upsert (recommended)\nclient.upsert(\n    collection_name=\"documents\",\n    points=[point1, point2, point3],\n    wait=True  # Wait for indexing\n)\n```\n\n### Collections - Vector containers\n\n```python\nfrom qdrant_client.models import VectorParams, Distance, HnswConfigDiff\n\n# Create with HNSW configuration\nclient.create_collection(\n    collection_name=\"documents\",\n    vectors_config=VectorParams(\n        size=384,                        # Vector dimensions\n        distance=Distance.COSINE         # COSINE, EUCLID, DOT, MANHATTAN\n    ),\n    hnsw_config=HnswConfigDiff(\n        m=16,                            # Connections per node (default 16)\n        ef_construct=100,                # Build-time accuracy (default 100)\n        full_scan_threshold=10000        # Switch to brute force below this\n    ),\n    on_disk_payload=True                 # Store payload on disk\n)\n\n# Collection info\ninfo = client.get_collection(\"documents\")\nprint(f\"Points: {info.points_count}, Vectors: {info.vectors_count}\")\n```\n\n### Distance metrics\n\n| Metric | Use Case | Range |\n|--------|----------|-------|\n| `COSINE` | Text embeddings, normalized vectors | 0 to 2 |\n| `EUCLID` | Spatial data, image features | 0 to ∞ |\n| `DOT` | Recommendations, unnormalized | -∞ to ∞ |\n| `MANHATTAN` | Sparse features, discrete data | 0 to ∞ |\n\n## Search operations\n\n### Basic search\n\n```python\n# Simple nearest neighbor search\nresults = client.search(\n    collection_name=\"documents\",\n    query_vector=[0.1, 0.2, ...],\n    limit=10,\n    with_payload=True,\n    with_vectors=False  # Don't return vectors (faster)\n)\n```\n\n### Filtered search\n\n```python\nfrom qdrant_client.models import Filter, FieldCondition, MatchValue, Range\n\n# Complex filtering\nresults = client.search(\n    collection_name=\"documents\",\n    query_vector=query_embedding,\n    query_filter=Filter(\n        must=[\n            FieldCondition(key=\"category\", match=MatchValue(value=\"tech\")),\n            FieldCondition(key=\"timestamp\", range=Range(gte=1699000000))\n        ],\n        must_not=[\n            FieldCondition(key=\"status\", match=MatchValue(value=\"archived\"))\n        ]\n    ),\n    limit=10\n)\n\n# Shorthand filter syntax\nresults = client.search(\n    collection_name=\"documents\",\n    query_vector=query_embedding,\n    query_filter={\n        \"must\": [\n            {\"key\": \"category\", \"match\": {\"value\": \"tech\"}},\n            {\"key\": \"price\", \"range\": {\"gte\": 10, \"lte\": 100}}\n        ]\n    },\n    limit=10\n)\n```\n\n### Batch search\n\n```python\nfrom qdrant_client.models import SearchRequest\n\n# Multiple queries in one request\nresults = client.search_batch(\n    collection_name=\"documents\",\n    requests=[\n        SearchRequest(vector=[0.1, ...], limit=5),\n        SearchRequest(vector=[0.2, ...], limit=5, filter={\"must\": [...]}),\n        SearchRequest(vector=[0.3, ...], limit=10)\n    ]\n)\n```\n\n## RAG integration\n\n### With sentence-transformers\n\n```python\nfrom sentence_transformers import SentenceTransformer\nfrom qdrant_client import QdrantClient\nfrom qdrant_client.models import VectorParams, Distance, PointStruct\n\n# Initialize\nencoder = SentenceTransformer(\"all-MiniLM-L6-v2\")\nclient = QdrantClient(host=\"localhost\", port=6333)\n\n# Create collection\nclient.create_collection(\n    collection_name=\"knowledge_base\",\n    vectors_config=VectorParams(size=384, distance=Distance.COSINE)\n)\n\n# Index documents\ndocuments = [\n    {\"id\": 1, \"text\": \"Python is a programming language\", \"source\": \"wiki\"},\n    {\"id\": 2, \"text\": \"Machine learning uses algorithms\", \"source\": \"textbook\"},\n]\n\npoints = [\n    PointStruct(\n        id=doc[\"id\"],\n        vector=encoder.encode(doc[\"text\"]).tolist(),\n        payload={\"text\": doc[\"text\"], \"source\": doc[\"source\"]}\n    )\n    for doc in documents\n]\nclient.upsert(collection_name=\"knowledge_base\", points=points)\n\n# RAG retrieval\ndef retrieve(query: str, top_k: int = 5) -> list[dict]:\n    query_vector = encoder.encode(query).tolist()\n    results = client.search(\n        collection_name=\"knowledge_base\",\n        query_vector=query_vector,\n        limit=top_k\n    )\n    return [{\"text\": r.payload[\"text\"], \"score\": r.score} for r in results]\n\n# Use in RAG pipeline\ncontext = retrieve(\"What is Python?\")\nprompt = f\"Context: {context}\\n\\nQuestion: What is Python?\"\n```\n\n### With LangChain\n\n```python\nfrom langchain_community.vectorstores import Qdrant\nfrom langchain_community.embeddings import HuggingFaceEmbeddings\n\nembeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\nvectorstore = Qdrant.from_documents(documents, embeddings, url=\"http:\u002F\u002Flocalhost:6333\", collection_name=\"docs\")\nretriever = vectorstore.as_retriever(search_kwargs={\"k\": 5})\n```\n\n### With LlamaIndex\n\n```python\nfrom llama_index.vector_stores.qdrant import QdrantVectorStore\nfrom llama_index.core import VectorStoreIndex, StorageContext\n\nvector_store = QdrantVectorStore(client=client, collection_name=\"llama_docs\")\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\nindex = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\nquery_engine = index.as_query_engine()\n```\n\n## Multi-vector support\n\n### Named vectors (different embedding models)\n\n```python\nfrom qdrant_client.models import VectorParams, Distance\n\n# Collection with multiple vector types\nclient.create_collection(\n    collection_name=\"hybrid_search\",\n    vectors_config={\n        \"dense\": VectorParams(size=384, distance=Distance.COSINE),\n        \"sparse\": VectorParams(size=30000, distance=Distance.DOT)\n    }\n)\n\n# Insert with named vectors\nclient.upsert(\n    collection_name=\"hybrid_search\",\n    points=[\n        PointStruct(\n            id=1,\n            vector={\n                \"dense\": dense_embedding,\n                \"sparse\": sparse_embedding\n            },\n            payload={\"text\": \"document text\"}\n        )\n    ]\n)\n\n# Search specific vector\nresults = client.search(\n    collection_name=\"hybrid_search\",\n    query_vector=(\"dense\", query_dense),  # Specify which vector\n    limit=10\n)\n```\n\n### Sparse vectors (BM25, SPLADE)\n\n```python\nfrom qdrant_client.models import SparseVectorParams, SparseIndexParams, SparseVector\n\n# Collection with sparse vectors\nclient.create_collection(\n    collection_name=\"sparse_search\",\n    vectors_config={},\n    sparse_vectors_config={\"text\": SparseVectorParams(index=SparseIndexParams(on_disk=False))}\n)\n\n# Insert sparse vector\nclient.upsert(\n    collection_name=\"sparse_search\",\n    points=[PointStruct(id=1, vector={\"text\": SparseVector(indices=[1, 5, 100], values=[0.5, 0.8, 0.2])}, payload={\"text\": \"document\"})]\n)\n```\n\n## Quantization (memory optimization)\n\n```python\nfrom qdrant_client.models import ScalarQuantization, ScalarQuantizationConfig, ScalarType\n\n# Scalar quantization (4x memory reduction)\nclient.create_collection(\n    collection_name=\"quantized\",\n    vectors_config=VectorParams(size=384, distance=Distance.COSINE),\n    quantization_config=ScalarQuantization(\n        scalar=ScalarQuantizationConfig(\n            type=ScalarType.INT8,\n            quantile=0.99,        # Clip outliers\n            always_ram=True      # Keep quantized in RAM\n        )\n    )\n)\n\n# Search with rescoring\nresults = client.search(\n    collection_name=\"quantized\",\n    query_vector=query,\n    search_params={\"quantization\": {\"rescore\": True}},  # Rescore top results\n    limit=10\n)\n```\n\n## Payload indexing\n\n```python\nfrom qdrant_client.models import PayloadSchemaType\n\n# Create payload index for faster filtering\nclient.create_payload_index(\n    collection_name=\"documents\",\n    field_name=\"category\",\n    field_schema=PayloadSchemaType.KEYWORD\n)\n\nclient.create_payload_index(\n    collection_name=\"documents\",\n    field_name=\"timestamp\",\n    field_schema=PayloadSchemaType.INTEGER\n)\n\n# Index types: KEYWORD, INTEGER, FLOAT, GEO, TEXT (full-text), BOOL\n```\n\n## Production deployment\n\n### Qdrant Cloud\n\n```python\nfrom qdrant_client import QdrantClient\n\n# Connect to Qdrant Cloud\nclient = QdrantClient(\n    url=\"https:\u002F\u002Fyour-cluster.cloud.qdrant.io\",\n    api_key=\"your-api-key\"\n)\n```\n\n### Performance tuning\n\n```python\n# Optimize for search speed (higher recall)\nclient.update_collection(\n    collection_name=\"documents\",\n    hnsw_config=HnswConfigDiff(ef_construct=200, m=32)\n)\n\n# Optimize for indexing speed (bulk loads)\nclient.update_collection(\n    collection_name=\"documents\",\n    optimizer_config={\"indexing_threshold\": 20000}\n)\n```\n\n## Best practices\n\n1. **Batch operations** - Use batch upsert\u002Fsearch for efficiency\n2. **Payload indexing** - Index fields used in filters\n3. **Quantization** - Enable for large collections (>1M vectors)\n4. **Sharding** - Use for collections >10M vectors\n5. **On-disk storage** - Enable `on_disk_payload` for large payloads\n6. **Connection pooling** - Reuse client instances\n\n## Common issues\n\n**Slow search with filters:**\n```python\n# Create payload index for filtered fields\nclient.create_payload_index(\n    collection_name=\"docs\",\n    field_name=\"category\",\n    field_schema=PayloadSchemaType.KEYWORD\n)\n```\n\n**Out of memory:**\n```python\n# Enable quantization and on-disk storage\nclient.create_collection(\n    collection_name=\"large_collection\",\n    vectors_config=VectorParams(size=384, distance=Distance.COSINE),\n    quantization_config=ScalarQuantization(...),\n    on_disk_payload=True\n)\n```\n\n**Connection issues:**\n```python\n# Use timeout and retry\nclient = QdrantClient(\n    host=\"localhost\",\n    port=6333,\n    timeout=30,\n    prefer_grpc=True  # gRPC for better performance\n)\n```\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Distributed mode, hybrid search, recommendations\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues, debugging, performance tuning\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fqdrant\u002Fqdrant (22k+ stars)\n- **Docs**: https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002F\n- **Python Client**: https:\u002F\u002Fgithub.com\u002Fqdrant\u002Fqdrant-client\n- **Cloud**: https:\u002F\u002Fcloud.qdrant.io\n- **Version**: 1.12.0+\n- **License**: Apache 2.0\n","# Qdrant - Vector Similarity Search Engine\n\nHigh-performance vector database written in Rust for production RAG and semantic search.\n\n## When to use Qdrant\n\n**Use Qdrant when:**\n- Building production RAG systems requiring low latency\n- Need hybrid search (vectors + metadata filtering)\n- Require horizontal scaling with sharding\u002Freplication\n- Want on-premise deployment with full data control\n- Need multi-vector storage per record (dense + sparse)\n- Building real-time recommendation systems\n\n**Key features:**\n- **Rust-powered**: Memory-safe, high performance\n- **Rich filtering**: Filter by any payload field during search\n- **Multiple vectors**: Dense, sparse, multi-dense per point\n- **Quantization**: Scalar, product, binary for memory efficiency\n- **Distributed**: Raft consensus, sharding, replication\n- **REST + gRPC**: Both APIs with full feature parity\n\n**Use alternatives instead:**\n- **Chroma**: Simpler setup, embedded use cases\n- **FAISS**: Maximum raw speed, research\u002Fbatch processing\n- **Pinecone**: Fully managed, zero ops preferred\n- **Weaviate**: GraphQL preference, built-in vectorizers\n\n## Quick start\n\n### Installation\n\n```bash\n# Python client\npip install qdrant-client\n\n# Docker (recommended for development)\ndocker run -p 6333:6333 -p 6334:6334 qdrant\u002Fqdrant\n\n# Docker with persistent storage\ndocker run -p 6333:6333 -p 6334:6334 \\\n    -v $(pwd)\u002Fqdrant_storage:\u002Fqdrant\u002Fstorage \\\n    qdrant\u002Fqdrant\n```\n\n### Basic usage\n\n```python\nfrom qdrant_client import QdrantClient\nfrom qdrant_client.models import Distance, VectorParams, PointStruct\n\n# Connect to Qdrant\nclient = QdrantClient(host=\"localhost\", port=6333)\n\n# Create collection\nclient.create_collection(\n    collection_name=\"documents\",\n    vectors_config=VectorParams(size=384, distance=Distance.COSINE)\n)\n\n# Insert vectors with payload\nclient.upsert(\n    collection_name=\"documents\",\n    points=[\n        PointStruct(\n            id=1,\n            vector=[0.1, 0.2, ...],  # 384-dim vector\n            payload={\"title\": \"Doc 1\", \"category\": \"tech\"}\n        ),\n        PointStruct(\n            id=2,\n            vector=[0.3, 0.4, ...],\n            payload={\"title\": \"Doc 2\", \"category\": \"science\"}\n        )\n    ]\n)\n\n# Search with filtering\nresults = client.search(\n    collection_name=\"documents\",\n    query_vector=[0.15, 0.25, ...],\n    query_filter={\n        \"must\": [{\"key\": \"category\", \"match\": {\"value\": \"tech\"}}]\n    },\n    limit=10\n)\n\nfor point in results:\n    print(f\"ID: {point.id}, Score: {point.score}, Payload: {point.payload}\")\n```\n\n## Core concepts\n\n### Points - Basic data unit\n\n```python\nfrom qdrant_client.models import PointStruct\n\n# Point = ID + Vector(s) + Payload\npoint = PointStruct(\n    id=123,                              # Integer or UUID string\n    vector=[0.1, 0.2, 0.3, ...],        # Dense vector\n    payload={                            # Arbitrary JSON metadata\n        \"title\": \"Document title\",\n        \"category\": \"tech\",\n        \"timestamp\": 1699900000,\n        \"tags\": [\"python\", \"ml\"]\n    }\n)\n\n# Batch upsert (recommended)\nclient.upsert(\n    collection_name=\"documents\",\n    points=[point1, point2, point3],\n    wait=True  # Wait for indexing\n)\n```\n\n### Collections - Vector containers\n\n```python\nfrom qdrant_client.models import VectorParams, Distance, HnswConfigDiff\n\n# Create with HNSW configuration\nclient.create_collection(\n    collection_name=\"documents\",\n    vectors_config=VectorParams(\n        size=384,                        # Vector dimensions\n        distance=Distance.COSINE         # COSINE, EUCLID, DOT, MANHATTAN\n    ),\n    hnsw_config=HnswConfigDiff(\n        m=16,                            # Connections per node (default 16)\n        ef_construct=100,                # Build-time accuracy (default 100)\n        full_scan_threshold=10000        # Switch to brute force below this\n    ),\n    on_disk_payload=True                 # Store payload on disk\n)\n\n# Collection info\ninfo = client.get_collection(\"documents\")\nprint(f\"Points: {info.points_count}, Vectors: {info.vectors_count}\")\n```\n\n### Distance metrics\n\n| Metric | Use Case | Range |\n|--------|----------|-------|\n| `COSINE` | Text embeddings, normalized vectors | 0 to 2 |\n| `EUCLID` | Spatial data, image features | 0 to ∞ |\n| `DOT` | Recommendations, unnormalized | -∞ to ∞ |\n| `MANHATTAN` | Sparse features, discrete data | 0 to ∞ |\n\n## Search operations\n\n### Basic search\n\n```python\n# Simple nearest neighbor search\nresults = client.search(\n    collection_name=\"documents\",\n    query_vector=[0.1, 0.2, ...],\n    limit=10,\n    with_payload=True,\n    with_vectors=False  # Don't return vectors (faster)\n)\n```\n\n### Filtered search\n\n```python\nfrom qdrant_client.models import Filter, FieldCondition, MatchValue, Range\n\n# Complex filtering\nresults = client.search(\n    collection_name=\"documents\",\n    query_vector=query_embedding,\n    query_filter=Filter(\n        must=[\n            FieldCondition(key=\"category\", match=MatchValue(value=\"tech\")),\n            FieldCondition(key=\"timestamp\", range=Range(gte=1699000000))\n        ],\n        must_not=[\n            FieldCondition(key=\"status\", match=MatchValue(value=\"archived\"))\n        ]\n    ),\n    limit=10\n)\n\n# Shorthand filter syntax\nresults = client.search(\n    collection_name=\"documents\",\n    query_vector=query_embedding,\n    query_filter={\n        \"must\": [\n            {\"key\": \"category\", \"match\": {\"value\": \"tech\"}},\n            {\"key\": \"price\", \"range\": {\"gte\": 10, \"lte\": 100}}\n        ]\n    },\n    limit=10\n)\n```\n\n### Batch search\n\n```python\nfrom qdrant_client.models import SearchRequest\n\n# Multiple queries in one request\nresults = client.search_batch(\n    collection_name=\"documents\",\n    requests=[\n        SearchRequest(vector=[0.1, ...], limit=5),\n        SearchRequest(vector=[0.2, ...], limit=5, filter={\"must\": [...]}),\n        SearchRequest(vector=[0.3, ...], limit=10)\n    ]\n)\n```\n\n## RAG integration\n\n### With sentence-transformers\n\n```python\nfrom sentence_transformers import SentenceTransformer\nfrom qdrant_client import QdrantClient\nfrom qdrant_client.models import VectorParams, Distance, PointStruct\n\n# Initialize\nencoder = SentenceTransformer(\"all-MiniLM-L6-v2\")\nclient = QdrantClient(host=\"localhost\", port=6333)\n\n# Create collection\nclient.create_collection(\n    collection_name=\"knowledge_base\",\n    vectors_config=VectorParams(size=384, distance=Distance.COSINE)\n)\n\n# Index documents\ndocuments = [\n    {\"id\": 1, \"text\": \"Python is a programming language\", \"source\": \"wiki\"},\n    {\"id\": 2, \"text\": \"Machine learning uses algorithms\", \"source\": \"textbook\"},\n]\n\npoints = [\n    PointStruct(\n        id=doc[\"id\"],\n        vector=encoder.encode(doc[\"text\"]).tolist(),\n        payload={\"text\": doc[\"text\"], \"source\": doc[\"source\"]}\n    )\n    for doc in documents\n]\nclient.upsert(collection_name=\"knowledge_base\", points=points)\n\n# RAG retrieval\ndef retrieve(query: str, top_k: int = 5) -> list[dict]:\n    query_vector = encoder.encode(query).tolist()\n    results = client.search(\n        collection_name=\"knowledge_base\",\n        query_vector=query_vector,\n        limit=top_k\n    )\n    return [{\"text\": r.payload[\"text\"], \"score\": r.score} for r in results]\n\n# Use in RAG pipeline\ncontext = retrieve(\"What is Python?\")\nprompt = f\"Context: {context}\\n\\nQuestion: What is Python?\"\n```\n\n### With LangChain\n\n```python\nfrom langchain_community.vectorstores import Qdrant\nfrom langchain_community.embeddings import HuggingFaceEmbeddings\n\nembeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\nvectorstore = Qdrant.from_documents(documents, embeddings, url=\"http:\u002F\u002Flocalhost:6333\", collection_name=\"docs\")\nretriever = vectorstore.as_retriever(search_kwargs={\"k\": 5})\n```\n\n### With LlamaIndex\n\n```python\nfrom llama_index.vector_stores.qdrant import QdrantVectorStore\nfrom llama_index.core import VectorStoreIndex, StorageContext\n\nvector_store = QdrantVectorStore(client=client, collection_name=\"llama_docs\")\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\nindex = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\nquery_engine = index.as_query_engine()\n```\n\n## Multi-vector support\n\n### Named vectors (different embedding models)\n\n```python\nfrom qdrant_client.models import VectorParams, Distance\n\n# Collection with multiple vector types\nclient.create_collection(\n    collection_name=\"hybrid_search\",\n    vectors_config={\n        \"dense\": VectorParams(size=384, distance=Distance.COSINE),\n        \"sparse\": VectorParams(size=30000, distance=Distance.DOT)\n    }\n)\n\n# Insert with named vectors\nclient.upsert(\n    collection_name=\"hybrid_search\",\n    points=[\n        PointStruct(\n            id=1,\n            vector={\n                \"dense\": dense_embedding,\n                \"sparse\": sparse_embedding\n            },\n            payload={\"text\": \"document text\"}\n        )\n    ]\n)\n\n# Search specific vector\nresults = client.search(\n    collection_name=\"hybrid_search\",\n    query_vector=(\"dense\", query_dense),  # Specify which vector\n    limit=10\n)\n```\n\n### Sparse vectors (BM25, SPLADE)\n\n```python\nfrom qdrant_client.models import SparseVectorParams, SparseIndexParams, SparseVector\n\n# Collection with sparse vectors\nclient.create_collection(\n    collection_name=\"sparse_search\",\n    vectors_config={},\n    sparse_vectors_config={\"text\": SparseVectorParams(index=SparseIndexParams(on_disk=False))}\n)\n\n# Insert sparse vector\nclient.upsert(\n    collection_name=\"sparse_search\",\n    points=[PointStruct(id=1, vector={\"text\": SparseVector(indices=[1, 5, 100], values=[0.5, 0.8, 0.2])}, payload={\"text\": \"document\"})]\n)\n```\n\n## Quantization (memory optimization)\n\n```python\nfrom qdrant_client.models import ScalarQuantization, ScalarQuantizationConfig, ScalarType\n\n# Scalar quantization (4x memory reduction)\nclient.create_collection(\n    collection_name=\"quantized\",\n    vectors_config=VectorParams(size=384, distance=Distance.COSINE),\n    quantization_config=ScalarQuantization(\n        scalar=ScalarQuantizationConfig(\n            type=ScalarType.INT8,\n            quantile=0.99,        # Clip outliers\n            always_ram=True      # Keep quantized in RAM\n        )\n    )\n)\n\n# Search with rescoring\nresults = client.search(\n    collection_name=\"quantized\",\n    query_vector=query,\n    search_params={\"quantization\": {\"rescore\": True}},  # Rescore top results\n    limit=10\n)\n```\n\n## Payload indexing\n\n```python\nfrom qdrant_client.models import PayloadSchemaType\n\n# Create payload index for faster filtering\nclient.create_payload_index(\n    collection_name=\"documents\",\n    field_name=\"category\",\n    field_schema=PayloadSchemaType.KEYWORD\n)\n\nclient.create_payload_index(\n    collection_name=\"documents\",\n    field_name=\"timestamp\",\n    field_schema=PayloadSchemaType.INTEGER\n)\n\n# Index types: KEYWORD, INTEGER, FLOAT, GEO, TEXT (full-text), BOOL\n```\n\n## Production deployment\n\n### Qdrant Cloud\n\n```python\nfrom qdrant_client import QdrantClient\n\n# Connect to Qdrant Cloud\nclient = QdrantClient(\n    url=\"https:\u002F\u002Fyour-cluster.cloud.qdrant.io\",\n    api_key=\"your-api-key\"\n)\n```\n\n### Performance tuning\n\n```python\n# Optimize for search speed (higher recall)\nclient.update_collection(\n    collection_name=\"documents\",\n    hnsw_config=HnswConfigDiff(ef_construct=200, m=32)\n)\n\n# Optimize for indexing speed (bulk loads)\nclient.update_collection(\n    collection_name=\"documents\",\n    optimizer_config={\"indexing_threshold\": 20000}\n)\n```\n\n## Best practices\n\n1. **Batch operations** - Use batch upsert\u002Fsearch for efficiency\n2. **Payload indexing** - Index fields used in filters\n3. **Quantization** - Enable for large collections (>1M vectors)\n4. **Sharding** - Use for collections >10M vectors\n5. **On-disk storage** - Enable `on_disk_payload` for large payloads\n6. **Connection pooling** - Reuse client instances\n\n## Common issues\n\n**Slow search with filters:**\n```python\n# Create payload index for filtered fields\nclient.create_payload_index(\n    collection_name=\"docs\",\n    field_name=\"category\",\n    field_schema=PayloadSchemaType.KEYWORD\n)\n```\n\n**Out of memory:**\n```python\n# Enable quantization and on-disk storage\nclient.create_collection(\n    collection_name=\"large_collection\",\n    vectors_config=VectorParams(size=384, distance=Distance.COSINE),\n    quantization_config=ScalarQuantization(...),\n    on_disk_payload=True\n)\n```\n\n**Connection issues:**\n```python\n# Use timeout and retry\nclient = QdrantClient(\n    host=\"localhost\",\n    port=6333,\n    timeout=30,\n    prefer_grpc=True  # gRPC for better performance\n)\n```\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Distributed mode, hybrid search, recommendations\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues, debugging, performance tuning\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fqdrant\u002Fqdrant (22k+ stars)\n- **Docs**: https:\u002F\u002Fqdrant.tech\u002Fdocumentation\u002F\n- **Python Client**: https:\u002F\u002Fgithub.com\u002Fqdrant\u002Fqdrant-client\n- **Cloud**: https:\u002F\u002Fcloud.qdrant.io\n- **Version**: 1.12.0+\n- **License**: Apache 2.0\n",{"name":5,"tags":45,"author":53,"license":14,"version":54,"description":7,"dependencies":55},[46,20,21,47,48,49,50,51,52],"RAG","Semantic Search","Embeddings","Similarity Search","HNSW","Production","Distributed","Orchestra Research","1.0.0",[56],"qdrant-client>=1.12.0","main","f19b254e8975d0bfddb394686706106930d7964d","LLM-and-Chatbot\u002Fqdrant","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus","https:\u002F\u002Fgithub.com\u002Fendman100","2026-11-11 18:19:10",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add endman100\u002Fskill-router-nexus --skill qdrant-vector-search",{"tool":69,"command":70},"bunx","bunx skills add endman100\u002Fskill-router-nexus --skill qdrant-vector-search",{"tool":72,"command":73},"pnpm","pnpm dlx 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AI 教学角色","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FSKILL.md",[95],{"categoryNo":96,"categoryName":97,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000039","教师与教育","PROFESSION","适用职业",[101,102,103,104,105,106,107,108,109,110,111],"create master","master","create","生成","true","webfetch","edit","write","read","grep","Bash",{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":116,"sourceHtmlUrl":117,"categories":118,"keywords":130,"publishedAt":40},"01M6M43AKE95Y64E1JRXEF5QQ3","writing-dna-skill","从至少 20 篇完整文章中蒸馏可复用的写作 DNA，分析语言、文章结构、选题逻辑、素材策略、认知框架和视觉风格，并生成 Writing-DNA.md；按该 DNA 写作时会读取全部蒸馏产物和 5 篇相关原文。用于中英文作者、账号、品牌或出版物的风格分析与一致性写作。 Distill reusable Writing DNA from at least 20 complete articles for Chinese or English authors, publications, brands, and accounts; use for language, structure, topic logic, source strategy, cognitive-frame, and visual-style 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Use when drafting, editing, or reviewing text to eliminate predictable AI tells.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FSKILL.md",[155,156],{"categoryNo":120,"categoryName":121,"taxonomyType":122,"taxonomyTypeDesc":123},{"categoryNo":128,"categoryName":129,"taxonomyType":98,"taxonomyTypeDesc":99},[158,159,160,161,25,162,163,164,165,32,166,167,168,27,169,133],"slop","stop slop","stop","drafting","drafts","https","patterns","prose","remove","editing","text","content",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":174,"sourceHtmlUrl":175,"categories":176,"keywords":177,"publishedAt":40},"01M6M41X0B7EBX42TK435DMGGR","shuorenhua","检查和清理中英文文本里的 AI 套路，适用于“去 AI 味”“说人话”“自然一点”“别像模板”“先标问题”这类改写和审稿需求；按场景控制力度，同时保留事实、术语、语域和责任主体。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FSKILL.md",[],[172,178],"文本",{"skillNo":180,"name":181,"nameZh":6,"description":182,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":183,"sourceHtmlUrl":184,"categories":185,"keywords":186,"publishedAt":40},"01M6M3ZD12WBEW5KXKDM4N6DFG","naming","Name products, SaaS, brands, open source projects, bots, and apps. Use when the user needs to name something, find a brand name, or pick a product name. Metaphor-driven process that produces memorable, meaningful names and avoids AI slop.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FSKILL.md",[],[181,187,188,189,190,191,32,192,109,145,193,194,195,196,111,197,198,199,200,25,201,202,158],"repo","websearch","product name","apps","bots","view","brand","pick","gh","projects","open","saas","open source","product","describe","ai slop",{"skillNo":204,"name":205,"nameZh":6,"description":206,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":207,"sourceHtmlUrl":208,"categories":209,"keywords":211,"publishedAt":40},"01M6M3YVXAFAAWJ7F6JMPFREYE","humanizer-zh","去除文本中的 AI 生成痕迹。适用于编辑或审阅文本，使其听起来更自然、更像人类书写。\n基于维基百科的\"AI 写作特征\"综合指南。检测并修复以下模式：夸大的象征意义、\n宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、\nAI 词汇、否定式排比、过多的连接性短语。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FSKILL.md",[210],{"categoryNo":120,"categoryName":121,"taxonomyType":122,"taxonomyTypeDesc":123},[212,213,214,109,108,107,133,104,178,215,158,160],"humanizer","zh","humanizer zh","翻译"]