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Covers creating indexes, upserting and querying vectors, metadata filtering, namespaces, hybrid dense and sparse search, index management, and deleting vectors. Use when building a production RAG system on a hosted vector store, adding semantic search or recommendations without running infrastructure, isolating per-user or per-tenant data with namespaces, combining dense and sparse vectors in one query, or filtering results by metadata. Do not use for self-hosted or local stores (Chroma, Weaviate) or offline similarity search (FAISS).","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1554,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fpinecone\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52],"query","managed","hybrid","tenant","Auto-Scaling","Hybrid Search","Managed Service","LlamaIndex","guides","recommendations","latency","scaling","local","self","weaviate","rag","do","self-hosted","chroma","auto","langchain","vector database","knowledge","offline","vector","search","database","infrastructure","semantic search","2026-11-10 10:12:30",false,"---\nname: pinecone\ndescription: Guides use of Pinecone, a managed serverless vector database, through its Python client and the LangChain and LlamaIndex integrations. Covers creating indexes, upserting and querying vectors, metadata filtering, namespaces, hybrid dense and sparse search, index management, and deleting vectors. Use when building a production RAG system on a hosted vector store, adding semantic search or recommendations without running infrastructure, isolating per-user or per-tenant data with namespaces, combining dense and sparse vectors in one query, or filtering results by metadata. Do not use for self-hosted or local stores (Chroma, Weaviate) or offline similarity search (FAISS).\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  category: knowledge-and-rag\n  maintainer: Kalaris Labs\n  tags: RAG, Pinecone, Vector Database, Managed Service, Serverless, Hybrid Search, Production, Auto-Scaling, Low Latency, Recommendations\n  dependencies: pinecone-client\n---\n\n# Pinecone - Managed Vector Database\n\nThe vector database for production AI applications.\n\n## When to use Pinecone\n\n**Use when:**\n- Need managed, serverless vector database\n- Production RAG applications\n- Auto-scaling required\n- Low latency critical (\u003C100ms)\n- Don't want to manage infrastructure\n- Need hybrid search (dense + sparse vectors)\n\n**Metrics**:\n- Fully managed SaaS\n- Auto-scales to billions of vectors\n- **p95 latency \u003C100ms**\n- 99.9% uptime SLA\n\n**Use alternatives instead**:\n- **Chroma**: Self-hosted, open-source\n- **FAISS**: Offline, pure similarity search\n- **Weaviate**: Self-hosted with more features\n\n## Quick start\n\n### Installation\n\n```bash\npip install pinecone-client\n```\n\n### Basic usage\n\n```python\nfrom pinecone import Pinecone, ServerlessSpec\n\n# Initialize\npc = Pinecone(api_key=\"your-api-key\")\n\n# Create index\npc.create_index(\n    name=\"my-index\",\n    dimension=1536,  # Must match embedding dimension\n    metric=\"cosine\",  # or \"euclidean\", \"dotproduct\"\n    spec=ServerlessSpec(cloud=\"aws\", region=\"us-east-1\")\n)\n\n# Connect to index\nindex = pc.Index(\"my-index\")\n\n# Upsert vectors\nindex.upsert(vectors=[\n    {\"id\": \"vec1\", \"values\": [0.1, 0.2, ...], \"metadata\": {\"category\": \"A\"}},\n    {\"id\": \"vec2\", \"values\": [0.3, 0.4, ...], \"metadata\": {\"category\": \"B\"}}\n])\n\n# Query\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    top_k=5,\n    include_metadata=True\n)\n\nprint(results[\"matches\"])\n```\n\n## Core operations\n\n### Create index\n\n```python\n# Serverless (recommended)\npc.create_index(\n    name=\"my-index\",\n    dimension=1536,\n    metric=\"cosine\",\n    spec=ServerlessSpec(\n        cloud=\"aws\",         # or \"gcp\", \"azure\"\n        region=\"us-east-1\"\n    )\n)\n\n# Pod-based (for consistent performance)\nfrom pinecone import PodSpec\n\npc.create_index(\n    name=\"my-index\",\n    dimension=1536,\n    metric=\"cosine\",\n    spec=PodSpec(\n        environment=\"us-east1-gcp\",\n        pod_type=\"p1.x1\"\n    )\n)\n```\n\n### Upsert vectors\n\n```python\n# Single upsert\nindex.upsert(vectors=[\n    {\n        \"id\": \"doc1\",\n        \"values\": [0.1, 0.2, ...],  # 1536 dimensions\n        \"metadata\": {\n            \"text\": \"Document content\",\n            \"category\": \"tutorial\",\n            \"timestamp\": \"2025-01-01\"\n        }\n    }\n])\n\n# Batch upsert (recommended)\nvectors = [\n    {\"id\": f\"vec{i}\", \"values\": embedding, \"metadata\": metadata}\n    for i, (embedding, metadata) in enumerate(zip(embeddings, metadatas))\n]\n\nindex.upsert(vectors=vectors, batch_size=100)\n```\n\n### Query vectors\n\n```python\n# Basic query\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    top_k=10,\n    include_metadata=True,\n    include_values=False\n)\n\n# With metadata filtering\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    top_k=5,\n    filter={\"category\": {\"$eq\": \"tutorial\"}}\n)\n\n# Namespace query\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    top_k=5,\n    namespace=\"production\"\n)\n\n# Access results\nfor match in results[\"matches\"]:\n    print(f\"ID: {match['id']}\")\n    print(f\"Score: {match['score']}\")\n    print(f\"Metadata: {match['metadata']}\")\n```\n\n### Metadata filtering\n\n```python\n# Exact match\nfilter = {\"category\": \"tutorial\"}\n\n# Comparison\nfilter = {\"price\": {\"$gte\": 100}}  # $gt, $gte, $lt, $lte, $ne\n\n# Logical operators\nfilter = {\n    \"$and\": [\n        {\"category\": \"tutorial\"},\n        {\"difficulty\": {\"$lte\": 3}}\n    ]\n}  # Also: $or\n\n# In operator\nfilter = {\"tags\": {\"$in\": [\"python\", \"ml\"]}}\n```\n\n## Namespaces\n\n```python\n# Partition data by namespace\nindex.upsert(\n    vectors=[{\"id\": \"vec1\", \"values\": [...]}],\n    namespace=\"user-123\"\n)\n\n# Query specific namespace\nresults = index.query(\n    vector=[...],\n    namespace=\"user-123\",\n    top_k=5\n)\n\n# List namespaces\nstats = index.describe_index_stats()\nprint(stats['namespaces'])\n```\n\n## Hybrid search (dense + sparse)\n\n```python\n# Upsert with sparse vectors\nindex.upsert(vectors=[\n    {\n        \"id\": \"doc1\",\n        \"values\": [0.1, 0.2, ...],  # Dense vector\n        \"sparse_values\": {\n            \"indices\": [10, 45, 123],  # Token IDs\n            \"values\": [0.5, 0.3, 0.8]   # TF-IDF scores\n        },\n        \"metadata\": {\"text\": \"...\"}\n    }\n])\n\n# Hybrid query\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    sparse_vector={\n        \"indices\": [10, 45],\n        \"values\": [0.5, 0.3]\n    },\n    top_k=5,\n    alpha=0.5  # 0=sparse, 1=dense, 0.5=hybrid\n)\n```\n\n## LangChain integration\n\n```python\nfrom langchain_pinecone import PineconeVectorStore\nfrom langchain_openai import OpenAIEmbeddings\n\n# Create vector store\nvectorstore = PineconeVectorStore.from_documents(\n    documents=docs,\n    embedding=OpenAIEmbeddings(),\n    index_name=\"my-index\"\n)\n\n# Query\nresults = vectorstore.similarity_search(\"query\", k=5)\n\n# With metadata filter\nresults = vectorstore.similarity_search(\n    \"query\",\n    k=5,\n    filter={\"category\": \"tutorial\"}\n)\n\n# As retriever\nretriever = vectorstore.as_retriever(search_kwargs={\"k\": 10})\n```\n\n## LlamaIndex integration\n\n```python\nfrom llama_index.vector_stores.pinecone import PineconeVectorStore\n\n# Connect to Pinecone\npc = Pinecone(api_key=\"your-key\")\npinecone_index = pc.Index(\"my-index\")\n\n# Create vector store\nvector_store = PineconeVectorStore(pinecone_index=pinecone_index)\n\n# Use in LlamaIndex\nfrom llama_index.core import StorageContext, VectorStoreIndex\n\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\nindex = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\n```\n\n## Index management\n\n```python\n# List indices\nindexes = pc.list_indexes()\n\n# Describe index\nindex_info = pc.describe_index(\"my-index\")\nprint(index_info)\n\n# Get index stats\nstats = index.describe_index_stats()\nprint(f\"Total vectors: {stats['total_vector_count']}\")\nprint(f\"Namespaces: {stats['namespaces']}\")\n\n# Delete index\npc.delete_index(\"my-index\")\n```\n\n## Delete vectors\n\n```python\n# Delete by ID\nindex.delete(ids=[\"vec1\", \"vec2\"])\n\n# Delete by filter\nindex.delete(filter={\"category\": \"old\"})\n\n# Delete all in namespace\nindex.delete(delete_all=True, namespace=\"test\")\n\n# Delete entire index\nindex.delete(delete_all=True)\n```\n\n## Best practices\n\n1. **Use serverless** - Auto-scaling, cost-effective\n2. **Batch upserts** - More efficient (100-200 per batch)\n3. **Add metadata** - Enable filtering\n4. **Use namespaces** - Isolate data by user\u002Ftenant\n5. **Monitor usage** - Check Pinecone dashboard\n6. **Optimize filters** - Index frequently filtered fields\n7. **Test with free tier** - 1 index, 100K vectors free\n8. **Use hybrid search** - Better quality\n9. **Set appropriate dimensions** - Match embedding model\n10. **Regular backups** - Export important data\n\n## Performance\n\n| Operation | Latency | Notes |\n|-----------|---------|-------|\n| Upsert | ~50-100ms | Per batch |\n| Query (p50) | ~50ms | Depends on index size |\n| Query (p95) | ~100ms | SLA target |\n| Metadata filter | ~+10-20ms | Additional overhead |\n\n## Pricing (as of 2025)\n\n**Serverless**:\n- $0.096 per million read units\n- $0.06 per million write units\n- $0.06 per GB storage\u002Fmonth\n\n**Free tier**:\n- 1 serverless index\n- 100K vectors (1536 dimensions)\n- Great for prototyping\n\n## Resources\n\n- **Website**: https:\u002F\u002Fwww.pinecone.io\n- **Docs**: https:\u002F\u002Fdocs.pinecone.io\n- **Console**: https:\u002F\u002Fapp.pinecone.io\n- **Pricing**: https:\u002F\u002Fwww.pinecone.io\u002Fpricing\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm the corpus, embedding model and vector store versions, and where the index will live.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Index a small subset and test retrieval on known question-passage pairs.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Measure recall@k on a labeled set; answers cite retrieved passages only.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| Retrieval returns irrelevant chunks | Revisit chunking, add BM25\u002Fhybrid search, or re-rank before changing the generator. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Answers must come from retrieved text; say when the corpus does not contain the answer.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `faiss`: Facebook's library for efficient similarity search and clustering of dense vectors.\n- `qdrant-vector-search`: High-performance vector similarity search engine for RAG and semantic search.\n- `chroma`: Open-source embedding database for AI applications.\n","# Pinecone - Managed Vector Database\n\nThe vector database for production AI applications.\n\n## When to use Pinecone\n\n**Use when:**\n- Need managed, serverless vector database\n- Production RAG applications\n- Auto-scaling required\n- Low latency critical (\u003C100ms)\n- Don't want to manage infrastructure\n- Need hybrid search (dense + sparse vectors)\n\n**Metrics**:\n- Fully managed SaaS\n- Auto-scales to billions of vectors\n- **p95 latency \u003C100ms**\n- 99.9% uptime SLA\n\n**Use alternatives instead**:\n- **Chroma**: Self-hosted, open-source\n- **FAISS**: Offline, pure similarity search\n- **Weaviate**: Self-hosted with more features\n\n## Quick start\n\n### Installation\n\n```bash\npip install pinecone-client\n```\n\n### Basic usage\n\n```python\nfrom pinecone import Pinecone, ServerlessSpec\n\n# Initialize\npc = Pinecone(api_key=\"your-api-key\")\n\n# Create index\npc.create_index(\n    name=\"my-index\",\n    dimension=1536,  # Must match embedding dimension\n    metric=\"cosine\",  # or \"euclidean\", \"dotproduct\"\n    spec=ServerlessSpec(cloud=\"aws\", region=\"us-east-1\")\n)\n\n# Connect to index\nindex = pc.Index(\"my-index\")\n\n# Upsert vectors\nindex.upsert(vectors=[\n    {\"id\": \"vec1\", \"values\": [0.1, 0.2, ...], \"metadata\": {\"category\": \"A\"}},\n    {\"id\": \"vec2\", \"values\": [0.3, 0.4, ...], \"metadata\": {\"category\": \"B\"}}\n])\n\n# Query\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    top_k=5,\n    include_metadata=True\n)\n\nprint(results[\"matches\"])\n```\n\n## Core operations\n\n### Create index\n\n```python\n# Serverless (recommended)\npc.create_index(\n    name=\"my-index\",\n    dimension=1536,\n    metric=\"cosine\",\n    spec=ServerlessSpec(\n        cloud=\"aws\",         # or \"gcp\", \"azure\"\n        region=\"us-east-1\"\n    )\n)\n\n# Pod-based (for consistent performance)\nfrom pinecone import PodSpec\n\npc.create_index(\n    name=\"my-index\",\n    dimension=1536,\n    metric=\"cosine\",\n    spec=PodSpec(\n        environment=\"us-east1-gcp\",\n        pod_type=\"p1.x1\"\n    )\n)\n```\n\n### Upsert vectors\n\n```python\n# Single upsert\nindex.upsert(vectors=[\n    {\n        \"id\": \"doc1\",\n        \"values\": [0.1, 0.2, ...],  # 1536 dimensions\n        \"metadata\": {\n            \"text\": \"Document content\",\n            \"category\": \"tutorial\",\n            \"timestamp\": \"2025-01-01\"\n        }\n    }\n])\n\n# Batch upsert (recommended)\nvectors = [\n    {\"id\": f\"vec{i}\", \"values\": embedding, \"metadata\": metadata}\n    for i, (embedding, metadata) in enumerate(zip(embeddings, metadatas))\n]\n\nindex.upsert(vectors=vectors, batch_size=100)\n```\n\n### Query vectors\n\n```python\n# Basic query\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    top_k=10,\n    include_metadata=True,\n    include_values=False\n)\n\n# With metadata filtering\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    top_k=5,\n    filter={\"category\": {\"$eq\": \"tutorial\"}}\n)\n\n# Namespace query\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    top_k=5,\n    namespace=\"production\"\n)\n\n# Access results\nfor match in results[\"matches\"]:\n    print(f\"ID: {match['id']}\")\n    print(f\"Score: {match['score']}\")\n    print(f\"Metadata: {match['metadata']}\")\n```\n\n### Metadata filtering\n\n```python\n# Exact match\nfilter = {\"category\": \"tutorial\"}\n\n# Comparison\nfilter = {\"price\": {\"$gte\": 100}}  # $gt, $gte, $lt, $lte, $ne\n\n# Logical operators\nfilter = {\n    \"$and\": [\n        {\"category\": \"tutorial\"},\n        {\"difficulty\": {\"$lte\": 3}}\n    ]\n}  # Also: $or\n\n# In operator\nfilter = {\"tags\": {\"$in\": [\"python\", \"ml\"]}}\n```\n\n## Namespaces\n\n```python\n# Partition data by namespace\nindex.upsert(\n    vectors=[{\"id\": \"vec1\", \"values\": [...]}],\n    namespace=\"user-123\"\n)\n\n# Query specific namespace\nresults = index.query(\n    vector=[...],\n    namespace=\"user-123\",\n    top_k=5\n)\n\n# List namespaces\nstats = index.describe_index_stats()\nprint(stats['namespaces'])\n```\n\n## Hybrid search (dense + sparse)\n\n```python\n# Upsert with sparse vectors\nindex.upsert(vectors=[\n    {\n        \"id\": \"doc1\",\n        \"values\": [0.1, 0.2, ...],  # Dense vector\n        \"sparse_values\": {\n            \"indices\": [10, 45, 123],  # Token IDs\n            \"values\": [0.5, 0.3, 0.8]   # TF-IDF scores\n        },\n        \"metadata\": {\"text\": \"...\"}\n    }\n])\n\n# Hybrid query\nresults = index.query(\n    vector=[0.1, 0.2, ...],\n    sparse_vector={\n        \"indices\": [10, 45],\n        \"values\": [0.5, 0.3]\n    },\n    top_k=5,\n    alpha=0.5  # 0=sparse, 1=dense, 0.5=hybrid\n)\n```\n\n## LangChain integration\n\n```python\nfrom langchain_pinecone import PineconeVectorStore\nfrom langchain_openai import OpenAIEmbeddings\n\n# Create vector store\nvectorstore = PineconeVectorStore.from_documents(\n    documents=docs,\n    embedding=OpenAIEmbeddings(),\n    index_name=\"my-index\"\n)\n\n# Query\nresults = vectorstore.similarity_search(\"query\", k=5)\n\n# With metadata filter\nresults = vectorstore.similarity_search(\n    \"query\",\n    k=5,\n    filter={\"category\": \"tutorial\"}\n)\n\n# As retriever\nretriever = vectorstore.as_retriever(search_kwargs={\"k\": 10})\n```\n\n## LlamaIndex integration\n\n```python\nfrom llama_index.vector_stores.pinecone import PineconeVectorStore\n\n# Connect to Pinecone\npc = Pinecone(api_key=\"your-key\")\npinecone_index = pc.Index(\"my-index\")\n\n# Create vector store\nvector_store = PineconeVectorStore(pinecone_index=pinecone_index)\n\n# Use in LlamaIndex\nfrom llama_index.core import StorageContext, VectorStoreIndex\n\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\nindex = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\n```\n\n## Index management\n\n```python\n# List indices\nindexes = pc.list_indexes()\n\n# Describe index\nindex_info = pc.describe_index(\"my-index\")\nprint(index_info)\n\n# Get index stats\nstats = index.describe_index_stats()\nprint(f\"Total vectors: {stats['total_vector_count']}\")\nprint(f\"Namespaces: {stats['namespaces']}\")\n\n# Delete index\npc.delete_index(\"my-index\")\n```\n\n## Delete vectors\n\n```python\n# Delete by ID\nindex.delete(ids=[\"vec1\", \"vec2\"])\n\n# Delete by filter\nindex.delete(filter={\"category\": \"old\"})\n\n# Delete all in namespace\nindex.delete(delete_all=True, namespace=\"test\")\n\n# Delete entire index\nindex.delete(delete_all=True)\n```\n\n## Best practices\n\n1. **Use serverless** - Auto-scaling, cost-effective\n2. **Batch upserts** - More efficient (100-200 per batch)\n3. **Add metadata** - Enable filtering\n4. **Use namespaces** - Isolate data by user\u002Ftenant\n5. **Monitor usage** - Check Pinecone dashboard\n6. **Optimize filters** - Index frequently filtered fields\n7. **Test with free tier** - 1 index, 100K vectors free\n8. **Use hybrid search** - Better quality\n9. **Set appropriate dimensions** - Match embedding model\n10. **Regular backups** - Export important data\n\n## Performance\n\n| Operation | Latency | Notes |\n|-----------|---------|-------|\n| Upsert | ~50-100ms | Per batch |\n| Query (p50) | ~50ms | Depends on index size |\n| Query (p95) | ~100ms | SLA target |\n| Metadata filter | ~+10-20ms | Additional overhead |\n\n## Pricing (as of 2025)\n\n**Serverless**:\n- $0.096 per million read units\n- $0.06 per million write units\n- $0.06 per GB storage\u002Fmonth\n\n**Free tier**:\n- 1 serverless index\n- 100K vectors (1536 dimensions)\n- Great for prototyping\n\n## Resources\n\n- **Website**: https:\u002F\u002Fwww.pinecone.io\n- **Docs**: https:\u002F\u002Fdocs.pinecone.io\n- **Console**: https:\u002F\u002Fapp.pinecone.io\n- **Pricing**: https:\u002F\u002Fwww.pinecone.io\u002Fpricing\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm the corpus, embedding model and vector store versions, and where the index will live.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Index a small subset and test retrieval on known question-passage pairs.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Measure recall@k on a labeled set; answers cite retrieved passages only.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| Retrieval returns irrelevant chunks | Revisit chunking, add BM25\u002Fhybrid search, or re-rank before changing the generator. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Answers must come from retrieved text; say when the corpus does not contain the answer.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `faiss`: Facebook's library for efficient similarity search and clustering of dense vectors.\n- `qdrant-vector-search`: High-performance vector similarity search engine for RAG and semantic search.\n- `chroma`: Open-source embedding database for AI applications.\n",{"name":5,"license":15,"metadata":58,"description":7},{"tags":59,"version":60,"category":61,"maintainer":62,"dependencies":63},"RAG, Pinecone, Vector Database, Managed Service, Serverless, Hybrid Search, Production, Auto-Scaling, Low Latency, Recommendations","1.0.0","knowledge-and-rag","Kalaris Labs","pinecone-client","main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fpinecone",468,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-11 02:22:54",{"source":8,"skillName":5,"commands":75,"prompt":85,"zipAvailable":86},[76,79,82],{"tool":77,"command":78},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill pinecone",{"tool":80,"command":81},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill pinecone",{"tool":83,"command":84},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill pinecone","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fpinecone\u002FSKILL.md",true,[88],{"path":89,"resourceType":90,"resourceTypeDesc":91,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Fdeployment.md","REFERENCE","参考文档",3557,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fpinecone\u002Freferences\u002Fdeployment.md",[95,122,147,177,198,212],{"skillNo":96,"name":97,"nameZh":6,"description":98,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":99,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":53},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[49,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121],"deja search","error","ready","never","memory","re","decide","fix","design","debugging","when","you","done","reviewer","io","change","cli","merge","past",{"skillNo":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":99,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":128,"publishedAt":53},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[129,130,131,132,107,133,134,135,136,137,138,139,140,117,141,142,143,144,145,146],"reflect","deja reflect","brew","remember","project rules","project","session","end","reflection","Rules","decisions","review","with","record","github","next","https","node",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":99,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":160,"publishedAt":53},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[153,154,157],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":155,"categoryName":156,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":158,"categoryName":159,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[149,161,162,163,164,165,134,166,108,167,168,169,170,113,171,172,173,174,175,176],"Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","URL","image","mcp server","schedule","api","publish",{"skillNo":178,"name":179,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":99,"licenseFileUrl":6,"sourceHtmlUrl":181,"categories":182,"keywords":183,"publishedAt":53},"01M71569ZM2X3H5RBFK8PVKMK2","review-study","Review a QuestionPunk survey before launch for wording, answer choices, branching, and missing settings. Use for check my survey or review this draft before publishing.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fquestionpunk\u002Fcodex-plugin\u002Fskills\u002Freview-study\u002FSKILL.md",[],[184,185,140,186,187,188,189,190,191,192,193,194,195,196,197],"study","review study","for","answer","missing","survey","publishing","use","check","draft","settings","launch","my","branching",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":99,"licenseFileUrl":6,"sourceHtmlUrl":202,"categories":203,"keywords":204,"publishedAt":53},"01M71567GV0C35YQBY0DPGR86B","manage-study","Find QuestionPunk studies, check status, publish an explicitly requested draft, or retrieve its participant link. Use for find my survey, publish this study, or how many people responded.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fquestionpunk\u002Fcodex-plugin\u002Fskills\u002Fmanage-study\u002FSKILL.md",[],[205,184,206,186,189,191,207,192,193,176,208,209,210,211,196],"manage study","manage","people","how","find","link","status",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":99,"licenseFileUrl":6,"sourceHtmlUrl":216,"categories":217,"keywords":218,"publishedAt":53},"01M71565A8TCA01AZQZRGRYMFZ","create-study","Create a saved QuestionPunk survey draft from a research brief or edit specified questions. Use for requests such as make a customer feedback survey or shorten my onboarding study.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fquestionpunk\u002Fcodex-plugin\u002Fskills\u002Fcreate-study\u002FSKILL.md",[],[219,184,220,221,222,223,186,224,189,225,191,226,227,193,228,229,230,231,232,196],"create study","create","brief","as","feedback","customer feedback","research brief","shorten","saved","research","requests","customer","questions","edit"]