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Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.","zapabob\u002FSkills","zapabob","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F130830376?v=4","01M6DBMYZAGRTFQDSQYW3W764P","峯岸 亮","01M6DBMZ3GKDSJ01NZ9XTZD7JD",2,"MIT","https:\u002F\u002Fgithub.com\u002Fzapabob\u002FSkills\u002Fblob\u002Fmain\u002Farchive\u002Fskill-md-reviewed\u002Fsha256\u002F43\u002F430aea52052f7875a71c443b4568a4d2f6205d33ac0516a692c81f3f7b86c64e\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"vector database","systems","service","recommendations","latency","scaling","auto","use","filtering","research","applications","Production","metadata","vector","search","database","infrastructure","semantic search","rag","2026-11-10 10:12:30",false,"---\r\nname: pinecone\r\ndescription: Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (\u003C100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.\r\nversion: 1.0.0\r\nauthor: Orchestra Research\r\nlicense: MIT\r\ndependencies: [pinecone-client]\r\nmetadata:\r\n  hermes:\r\n    tags: [RAG, Pinecone, Vector Database, Managed Service, Serverless, Hybrid Search, Production, Auto-Scaling, Low Latency, Recommendations]\r\n\r\n---\r\n\r\n# Pinecone - Managed Vector Database\r\n\r\nThe vector database for production AI applications.\r\n\r\n## When to use Pinecone\r\n\r\n**Use when:**\r\n- Need managed, serverless vector database\r\n- Production RAG applications\r\n- Auto-scaling required\r\n- Low latency critical (\u003C100ms)\r\n- Don't want to manage infrastructure\r\n- Need hybrid search (dense + sparse vectors)\r\n\r\n**Metrics**:\r\n- Fully managed SaaS\r\n- Auto-scales to billions of vectors\r\n- **p95 latency \u003C100ms**\r\n- 99.9% uptime SLA\r\n\r\n**Use alternatives instead**:\r\n- **Chroma**: Self-hosted, open-source\r\n- **FAISS**: Offline, pure similarity search\r\n- **Weaviate**: Self-hosted with more features\r\n\r\n## Quick start\r\n\r\n### Installation\r\n\r\n```bash\r\npip install pinecone-client\r\n```\r\n\r\n### Basic usage\r\n\r\n```python\r\nfrom pinecone import Pinecone, ServerlessSpec\r\n\r\n# Initialize\r\npc = Pinecone(api_key=\"your-api-key\")\r\n\r\n# Create index\r\npc.create_index(\r\n    name=\"my-index\",\r\n    dimension=1536,  # Must match embedding dimension\r\n    metric=\"cosine\",  # or \"euclidean\", \"dotproduct\"\r\n    spec=ServerlessSpec(cloud=\"aws\", region=\"us-east-1\")\r\n)\r\n\r\n# Connect to index\r\nindex = pc.Index(\"my-index\")\r\n\r\n# Upsert vectors\r\nindex.upsert(vectors=[\r\n    {\"id\": \"vec1\", \"values\": [0.1, 0.2, ...], \"metadata\": {\"category\": \"A\"}},\r\n    {\"id\": \"vec2\", \"values\": [0.3, 0.4, ...], \"metadata\": {\"category\": \"B\"}}\r\n])\r\n\r\n# Query\r\nresults = index.query(\r\n    vector=[0.1, 0.2, ...],\r\n    top_k=5,\r\n    include_metadata=True\r\n)\r\n\r\nprint(results[\"matches\"])\r\n```\r\n\r\n## Core operations\r\n\r\n### Create index\r\n\r\n```python\r\n# Serverless (recommended)\r\npc.create_index(\r\n    name=\"my-index\",\r\n    dimension=1536,\r\n    metric=\"cosine\",\r\n    spec=ServerlessSpec(\r\n        cloud=\"aws\",         # or \"gcp\", \"azure\"\r\n        region=\"us-east-1\"\r\n    )\r\n)\r\n\r\n# Pod-based (for consistent performance)\r\nfrom pinecone import PodSpec\r\n\r\npc.create_index(\r\n    name=\"my-index\",\r\n    dimension=1536,\r\n    metric=\"cosine\",\r\n    spec=PodSpec(\r\n        environment=\"us-east1-gcp\",\r\n        pod_type=\"p1.x1\"\r\n    )\r\n)\r\n```\r\n\r\n### Upsert vectors\r\n\r\n```python\r\n# Single upsert\r\nindex.upsert(vectors=[\r\n    {\r\n        \"id\": \"doc1\",\r\n        \"values\": [0.1, 0.2, ...],  # 1536 dimensions\r\n        \"metadata\": {\r\n            \"text\": \"Document content\",\r\n            \"category\": \"tutorial\",\r\n            \"timestamp\": \"2025-01-01\"\r\n        }\r\n    }\r\n])\r\n\r\n# Batch upsert (recommended)\r\nvectors = [\r\n    {\"id\": f\"vec{i}\", \"values\": embedding, \"metadata\": metadata}\r\n    for i, (embedding, metadata) in enumerate(zip(embeddings, metadatas))\r\n]\r\n\r\nindex.upsert(vectors=vectors, batch_size=100)\r\n```\r\n\r\n### Query vectors\r\n\r\n```python\r\n# Basic query\r\nresults = index.query(\r\n    vector=[0.1, 0.2, ...],\r\n    top_k=10,\r\n    include_metadata=True,\r\n    include_values=False\r\n)\r\n\r\n# With metadata filtering\r\nresults = index.query(\r\n    vector=[0.1, 0.2, ...],\r\n    top_k=5,\r\n    filter={\"category\": {\"$eq\": \"tutorial\"}}\r\n)\r\n\r\n# Namespace query\r\nresults = index.query(\r\n    vector=[0.1, 0.2, ...],\r\n    top_k=5,\r\n    namespace=\"production\"\r\n)\r\n\r\n# Access results\r\nfor match in results[\"matches\"]:\r\n    print(f\"ID: {match['id']}\")\r\n    print(f\"Score: {match['score']}\")\r\n    print(f\"Metadata: {match['metadata']}\")\r\n```\r\n\r\n### Metadata filtering\r\n\r\n```python\r\n# Exact match\r\nfilter = {\"category\": \"tutorial\"}\r\n\r\n# Comparison\r\nfilter = {\"price\": {\"$gte\": 100}}  # $gt, $gte, $lt, $lte, $ne\r\n\r\n# Logical operators\r\nfilter = {\r\n    \"$and\": [\r\n        {\"category\": \"tutorial\"},\r\n        {\"difficulty\": {\"$lte\": 3}}\r\n    ]\r\n}  # Also: $or\r\n\r\n# In operator\r\nfilter = {\"tags\": {\"$in\": [\"python\", \"ml\"]}}\r\n```\r\n\r\n## Namespaces\r\n\r\n```python\r\n# Partition data by namespace\r\nindex.upsert(\r\n    vectors=[{\"id\": \"vec1\", \"values\": [...]}],\r\n    namespace=\"user-123\"\r\n)\r\n\r\n# Query specific namespace\r\nresults = index.query(\r\n    vector=[...],\r\n    namespace=\"user-123\",\r\n    top_k=5\r\n)\r\n\r\n# List namespaces\r\nstats = index.describe_index_stats()\r\nprint(stats['namespaces'])\r\n```\r\n\r\n## Hybrid search (dense + sparse)\r\n\r\n```python\r\n# Upsert with sparse vectors\r\nindex.upsert(vectors=[\r\n    {\r\n        \"id\": \"doc1\",\r\n        \"values\": [0.1, 0.2, ...],  # Dense vector\r\n        \"sparse_values\": {\r\n            \"indices\": [10, 45, 123],  # Token IDs\r\n            \"values\": [0.5, 0.3, 0.8]   # TF-IDF scores\r\n        },\r\n        \"metadata\": {\"text\": \"...\"}\r\n    }\r\n])\r\n\r\n# Hybrid query\r\nresults = index.query(\r\n    vector=[0.1, 0.2, ...],\r\n    sparse_vector={\r\n        \"indices\": [10, 45],\r\n        \"values\": [0.5, 0.3]\r\n    },\r\n    top_k=5,\r\n    alpha=0.5  # 0=sparse, 1=dense, 0.5=hybrid\r\n)\r\n```\r\n\r\n## LangChain integration\r\n\r\n```python\r\nfrom langchain_pinecone import PineconeVectorStore\r\nfrom langchain_openai import OpenAIEmbeddings\r\n\r\n# Create vector store\r\nvectorstore = PineconeVectorStore.from_documents(\r\n    documents=docs,\r\n    embedding=OpenAIEmbeddings(),\r\n    index_name=\"my-index\"\r\n)\r\n\r\n# Query\r\nresults = vectorstore.similarity_search(\"query\", k=5)\r\n\r\n# With metadata filter\r\nresults = vectorstore.similarity_search(\r\n    \"query\",\r\n    k=5,\r\n    filter={\"category\": \"tutorial\"}\r\n)\r\n\r\n# As retriever\r\nretriever = vectorstore.as_retriever(search_kwargs={\"k\": 10})\r\n```\r\n\r\n## LlamaIndex integration\r\n\r\n```python\r\nfrom llama_index.vector_stores.pinecone import PineconeVectorStore\r\n\r\n# Connect to Pinecone\r\npc = Pinecone(api_key=\"your-key\")\r\npinecone_index = pc.Index(\"my-index\")\r\n\r\n# Create vector store\r\nvector_store = PineconeVectorStore(pinecone_index=pinecone_index)\r\n\r\n# Use in LlamaIndex\r\nfrom llama_index.core import StorageContext, VectorStoreIndex\r\n\r\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\r\nindex = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\r\n```\r\n\r\n## Index management\r\n\r\n```python\r\n# List indices\r\nindexes = pc.list_indexes()\r\n\r\n# Describe index\r\nindex_info = pc.describe_index(\"my-index\")\r\nprint(index_info)\r\n\r\n# Get index stats\r\nstats = index.describe_index_stats()\r\nprint(f\"Total vectors: {stats['total_vector_count']}\")\r\nprint(f\"Namespaces: {stats['namespaces']}\")\r\n\r\n# Delete index\r\npc.delete_index(\"my-index\")\r\n```\r\n\r\n## Delete vectors\r\n\r\n```python\r\n# Delete by ID\r\nindex.delete(ids=[\"vec1\", \"vec2\"])\r\n\r\n# Delete by filter\r\nindex.delete(filter={\"category\": \"old\"})\r\n\r\n# Delete all in namespace\r\nindex.delete(delete_all=True, namespace=\"test\")\r\n\r\n# Delete entire index\r\nindex.delete(delete_all=True)\r\n```\r\n\r\n## Best practices\r\n\r\n1. **Use serverless** - Auto-scaling, cost-effective\r\n2. **Batch upserts** - More efficient (100-200 per batch)\r\n3. **Add metadata** - Enable filtering\r\n4. **Use namespaces** - Isolate data by user\u002Ftenant\r\n5. **Monitor usage** - Check Pinecone dashboard\r\n6. **Optimize filters** - Index frequently filtered fields\r\n7. **Test with free tier** - 1 index, 100K vectors free\r\n8. **Use hybrid search** - Better quality\r\n9. **Set appropriate dimensions** - Match embedding model\r\n10. **Regular backups** - Export important data\r\n\r\n## Performance\r\n\r\n| Operation | Latency | Notes |\r\n|-----------|---------|-------|\r\n| Upsert | ~50-100ms | Per batch |\r\n| Query (p50) | ~50ms | Depends on index size |\r\n| Query (p95) | ~100ms | SLA target |\r\n| Metadata filter | ~+10-20ms | Additional overhead |\r\n\r\n## Pricing (as of 2025)\r\n\r\n**Serverless**:\r\n- $0.096 per million read units\r\n- $0.06 per million write units\r\n- $0.06 per GB storage\u002Fmonth\r\n\r\n**Free tier**:\r\n- 1 serverless index\r\n- 100K vectors (1536 dimensions)\r\n- Great for prototyping\r\n\r\n## Resources\r\n\r\n- **Website**: https:\u002F\u002Fwww.pinecone.io\r\n- **Docs**: https:\u002F\u002Fdocs.pinecone.io\r\n- **Console**: https:\u002F\u002Fapp.pinecone.io\r\n- **Pricing**: https:\u002F\u002Fwww.pinecone.io\u002Fpricing\r\n","# Pinecone - Managed Vector Database\r\n\r\nThe vector database for production AI applications.\r\n\r\n## When to use Pinecone\r\n\r\n**Use when:**\r\n- Need managed, serverless vector database\r\n- Production RAG applications\r\n- Auto-scaling required\r\n- Low latency critical (\u003C100ms)\r\n- Don't want to manage infrastructure\r\n- Need hybrid search (dense + sparse vectors)\r\n\r\n**Metrics**:\r\n- Fully managed SaaS\r\n- Auto-scales to billions of vectors\r\n- **p95 latency \u003C100ms**\r\n- 99.9% uptime SLA\r\n\r\n**Use alternatives instead**:\r\n- **Chroma**: Self-hosted, open-source\r\n- **FAISS**: Offline, pure similarity search\r\n- **Weaviate**: Self-hosted with more features\r\n\r\n## Quick start\r\n\r\n### Installation\r\n\r\n```bash\r\npip install pinecone-client\r\n```\r\n\r\n### Basic usage\r\n\r\n```python\r\nfrom pinecone import Pinecone, ServerlessSpec\r\n\r\n# Initialize\r\npc = Pinecone(api_key=\"your-api-key\")\r\n\r\n# Create index\r\npc.create_index(\r\n    name=\"my-index\",\r\n    dimension=1536,  # Must match embedding dimension\r\n    metric=\"cosine\",  # or \"euclidean\", \"dotproduct\"\r\n    spec=ServerlessSpec(cloud=\"aws\", region=\"us-east-1\")\r\n)\r\n\r\n# Connect to index\r\nindex = pc.Index(\"my-index\")\r\n\r\n# Upsert vectors\r\nindex.upsert(vectors=[\r\n    {\"id\": \"vec1\", \"values\": [0.1, 0.2, ...], \"metadata\": {\"category\": \"A\"}},\r\n    {\"id\": \"vec2\", \"values\": [0.3, 0.4, ...], \"metadata\": {\"category\": \"B\"}}\r\n])\r\n\r\n# Query\r\nresults = index.query(\r\n    vector=[0.1, 0.2, ...],\r\n    top_k=5,\r\n    include_metadata=True\r\n)\r\n\r\nprint(results[\"matches\"])\r\n```\r\n\r\n## Core operations\r\n\r\n### Create index\r\n\r\n```python\r\n# Serverless (recommended)\r\npc.create_index(\r\n    name=\"my-index\",\r\n    dimension=1536,\r\n    metric=\"cosine\",\r\n    spec=ServerlessSpec(\r\n        cloud=\"aws\",         # or \"gcp\", \"azure\"\r\n        region=\"us-east-1\"\r\n    )\r\n)\r\n\r\n# Pod-based (for consistent performance)\r\nfrom pinecone import PodSpec\r\n\r\npc.create_index(\r\n    name=\"my-index\",\r\n    dimension=1536,\r\n    metric=\"cosine\",\r\n    spec=PodSpec(\r\n        environment=\"us-east1-gcp\",\r\n        pod_type=\"p1.x1\"\r\n    )\r\n)\r\n```\r\n\r\n### Upsert vectors\r\n\r\n```python\r\n# Single upsert\r\nindex.upsert(vectors=[\r\n    {\r\n        \"id\": \"doc1\",\r\n        \"values\": [0.1, 0.2, ...],  # 1536 dimensions\r\n        \"metadata\": {\r\n            \"text\": \"Document content\",\r\n            \"category\": \"tutorial\",\r\n            \"timestamp\": \"2025-01-01\"\r\n        }\r\n    }\r\n])\r\n\r\n# Batch upsert (recommended)\r\nvectors = [\r\n    {\"id\": f\"vec{i}\", \"values\": embedding, \"metadata\": metadata}\r\n    for i, (embedding, metadata) in enumerate(zip(embeddings, metadatas))\r\n]\r\n\r\nindex.upsert(vectors=vectors, batch_size=100)\r\n```\r\n\r\n### Query vectors\r\n\r\n```python\r\n# Basic query\r\nresults = index.query(\r\n    vector=[0.1, 0.2, ...],\r\n    top_k=10,\r\n    include_metadata=True,\r\n    include_values=False\r\n)\r\n\r\n# With metadata filtering\r\nresults = index.query(\r\n    vector=[0.1, 0.2, ...],\r\n    top_k=5,\r\n    filter={\"category\": {\"$eq\": \"tutorial\"}}\r\n)\r\n\r\n# Namespace query\r\nresults = index.query(\r\n    vector=[0.1, 0.2, ...],\r\n    top_k=5,\r\n    namespace=\"production\"\r\n)\r\n\r\n# Access results\r\nfor match in results[\"matches\"]:\r\n    print(f\"ID: {match['id']}\")\r\n    print(f\"Score: {match['score']}\")\r\n    print(f\"Metadata: {match['metadata']}\")\r\n```\r\n\r\n### Metadata filtering\r\n\r\n```python\r\n# Exact match\r\nfilter = {\"category\": \"tutorial\"}\r\n\r\n# Comparison\r\nfilter = {\"price\": {\"$gte\": 100}}  # $gt, $gte, $lt, $lte, $ne\r\n\r\n# Logical operators\r\nfilter = {\r\n    \"$and\": [\r\n        {\"category\": \"tutorial\"},\r\n        {\"difficulty\": {\"$lte\": 3}}\r\n    ]\r\n}  # Also: $or\r\n\r\n# In operator\r\nfilter = {\"tags\": {\"$in\": [\"python\", \"ml\"]}}\r\n```\r\n\r\n## Namespaces\r\n\r\n```python\r\n# Partition data by namespace\r\nindex.upsert(\r\n    vectors=[{\"id\": \"vec1\", \"values\": [...]}],\r\n    namespace=\"user-123\"\r\n)\r\n\r\n# Query specific namespace\r\nresults = index.query(\r\n    vector=[...],\r\n    namespace=\"user-123\",\r\n    top_k=5\r\n)\r\n\r\n# List namespaces\r\nstats = index.describe_index_stats()\r\nprint(stats['namespaces'])\r\n```\r\n\r\n## Hybrid search (dense + sparse)\r\n\r\n```python\r\n# Upsert with sparse vectors\r\nindex.upsert(vectors=[\r\n    {\r\n        \"id\": \"doc1\",\r\n        \"values\": [0.1, 0.2, ...],  # Dense vector\r\n        \"sparse_values\": {\r\n            \"indices\": [10, 45, 123],  # Token IDs\r\n            \"values\": [0.5, 0.3, 0.8]   # TF-IDF scores\r\n        },\r\n        \"metadata\": {\"text\": \"...\"}\r\n    }\r\n])\r\n\r\n# Hybrid query\r\nresults = index.query(\r\n    vector=[0.1, 0.2, ...],\r\n    sparse_vector={\r\n        \"indices\": [10, 45],\r\n        \"values\": [0.5, 0.3]\r\n    },\r\n    top_k=5,\r\n    alpha=0.5  # 0=sparse, 1=dense, 0.5=hybrid\r\n)\r\n```\r\n\r\n## LangChain integration\r\n\r\n```python\r\nfrom langchain_pinecone import PineconeVectorStore\r\nfrom langchain_openai import OpenAIEmbeddings\r\n\r\n# Create vector store\r\nvectorstore = PineconeVectorStore.from_documents(\r\n    documents=docs,\r\n    embedding=OpenAIEmbeddings(),\r\n    index_name=\"my-index\"\r\n)\r\n\r\n# Query\r\nresults = vectorstore.similarity_search(\"query\", k=5)\r\n\r\n# With metadata filter\r\nresults = vectorstore.similarity_search(\r\n    \"query\",\r\n    k=5,\r\n    filter={\"category\": \"tutorial\"}\r\n)\r\n\r\n# As retriever\r\nretriever = vectorstore.as_retriever(search_kwargs={\"k\": 10})\r\n```\r\n\r\n## LlamaIndex integration\r\n\r\n```python\r\nfrom llama_index.vector_stores.pinecone import PineconeVectorStore\r\n\r\n# Connect to Pinecone\r\npc = Pinecone(api_key=\"your-key\")\r\npinecone_index = pc.Index(\"my-index\")\r\n\r\n# Create vector store\r\nvector_store = PineconeVectorStore(pinecone_index=pinecone_index)\r\n\r\n# Use in LlamaIndex\r\nfrom llama_index.core import StorageContext, VectorStoreIndex\r\n\r\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\r\nindex = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\r\n```\r\n\r\n## Index management\r\n\r\n```python\r\n# List indices\r\nindexes = pc.list_indexes()\r\n\r\n# Describe index\r\nindex_info = pc.describe_index(\"my-index\")\r\nprint(index_info)\r\n\r\n# Get index stats\r\nstats = index.describe_index_stats()\r\nprint(f\"Total vectors: {stats['total_vector_count']}\")\r\nprint(f\"Namespaces: {stats['namespaces']}\")\r\n\r\n# Delete index\r\npc.delete_index(\"my-index\")\r\n```\r\n\r\n## Delete vectors\r\n\r\n```python\r\n# Delete by ID\r\nindex.delete(ids=[\"vec1\", \"vec2\"])\r\n\r\n# Delete by filter\r\nindex.delete(filter={\"category\": \"old\"})\r\n\r\n# Delete all in namespace\r\nindex.delete(delete_all=True, namespace=\"test\")\r\n\r\n# Delete entire index\r\nindex.delete(delete_all=True)\r\n```\r\n\r\n## Best practices\r\n\r\n1. **Use serverless** - Auto-scaling, cost-effective\r\n2. **Batch upserts** - More efficient (100-200 per batch)\r\n3. **Add metadata** - Enable filtering\r\n4. **Use namespaces** - Isolate data by user\u002Ftenant\r\n5. **Monitor usage** - Check Pinecone dashboard\r\n6. **Optimize filters** - Index frequently filtered fields\r\n7. **Test with free tier** - 1 index, 100K vectors free\r\n8. **Use hybrid search** - Better quality\r\n9. **Set appropriate dimensions** - Match embedding model\r\n10. **Regular backups** - Export important data\r\n\r\n## Performance\r\n\r\n| Operation | Latency | Notes |\r\n|-----------|---------|-------|\r\n| Upsert | ~50-100ms | Per batch |\r\n| Query (p50) | ~50ms | Depends on index size |\r\n| Query (p95) | ~100ms | SLA target |\r\n| Metadata filter | ~+10-20ms | Additional overhead |\r\n\r\n## Pricing (as of 2025)\r\n\r\n**Serverless**:\r\n- $0.096 per million read units\r\n- $0.06 per million write units\r\n- $0.06 per GB storage\u002Fmonth\r\n\r\n**Free tier**:\r\n- 1 serverless index\r\n- 100K vectors (1536 dimensions)\r\n- Great for prototyping\r\n\r\n## Resources\r\n\r\n- **Website**: https:\u002F\u002Fwww.pinecone.io\r\n- **Docs**: https:\u002F\u002Fdocs.pinecone.io\r\n- **Console**: https:\u002F\u002Fapp.pinecone.io\r\n- **Pricing**: https:\u002F\u002Fwww.pinecone.io\u002Fpricing\r\n",{"name":5,"author":48,"license":15,"version":49,"metadata":50,"description":7,"dependencies":62},"Orchestra Research","1.0.0",{"hermes":51},{"tags":52},[53,54,55,56,57,58,35,59,60,61],"RAG","Pinecone","Vector Database","Managed 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