MCP servers and skills: search, filter by where they run, and compare.
Databricks Vector Search endpoints and indexes for RAG and semantic search; covers index types, search modes, end-to-end RAG patterns
OfficialSkillLink only★ 347 in collection
Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector sear
OfficialSkillMIT★ 1.6k in collectionNo key
Redis Vector Library. The AI-native Python client for Redis.
in the readmeproduction-ready Python client for AI applications built on Redis. **Lightning-fast vector search meets enterprise-grade reliability.** Perfect for building **RAG pipelines** with real-time
OfficialPythonMIT★ 431No key
Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid sear
in the skill textAlloyDB hybrid search use cases in Google Cloud. Use when users need vector search combined with structured SQL filtering, faceted attributes, semantic reranking, in-database
OfficialSkillApache-2.0★ 21k in collectionNo key
Guides Qdrant search strategy selection. Use when someone asks 'should I use hybrid search?', 'BM25 or sparse vectors?', 'how to rerank?', 'results are not relevant', 'I don't get needed results from
in the skill textHow to Improve Search Results with Advanced Strategies These strategies complement basic vector search. Use them after confirming the embedding model is fitting the task and
OfficialSkillMIT★ 40k in collectionNo key
Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect o
in the skill textvalues, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter
OfficialSkillApache-2.0★ 21k in collectionNo key
Diagnoses Qdrant search quality issues. Use when someone reports 'results are bad', 'wrong results', 'not relevant results', 'missing matches', 'recall is low', 'approximate search worse than exact',
in the skill textchunked documents, use Grouping API to deduplicate Grouping Payload filtering and sparse vector search are different things. Metadata (dates, categories, tags) goes in payload for filtering
OfficialSkillMIT★ 40k in collectionNo key
Agent skills for Qdrant vector search: scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage across Python, TypeScript, Rust, Go,
OfficialSkillApache-2.0★ 256No key
Use Redis over HTTP from serverless and edge runtimes with @upstash/redis, and add rate limiting with @upstash/ratelimit. Use when the user mentions Upstash Redis, needs Redis from a Next.js route han
in the skill textrunning servers with a TCP Redis connection already in place: keep ioredis/node-redis. - Vector search or RAG: use a vector database skill instead. - Sub-millisecond, in-process
OfficialSkillMIT★ 40k in collectionNo key
Diagnoses and fixes slow Qdrant indexing and data ingestion. Use when someone reports 'uploads are slow', 'indexing takes forever', 'optimizer is stuck', 'HNSW build time too long', or 'data uploaded
in the skill textHNSW links for all payload indexes to ensure that quality of filtered vector search does not degrade. Some payload indexes (e.g. `text` fields with long texts
OfficialSkillMIT★ 40k in collectionNo key
An official Qdrant Model Context Protocol (MCP) server implementation
in the readmeexample of how to create a MCP server for Qdrant , a vector search engine. ## Overview An official Model Context Protocol server for keeping and retrieving memories
OfficialPythonApache-2.0★ 1.5kNo key
Expanding the candidate pool via relevance feedback, as an alternative to reranking when a dense retriever is too weak. Use when someone asks about 'Qdrant's Relevance Feedback API', 'improving dense
in the skill textAPI', 'improving dense search relevance/recall', 'how to discover/get more relevant results from vector search', 'cheaper/better alternative to reranking', 'using a more heavy/big embedding model for dense
OfficialSkillApache-2.0★ 256 in collectionNo key
Guides Qdrant search strategy selection. Use when someone asks 'should I use hybrid search?', 'how to rerank?', 'results are not relevant', 'I don't get needed results from my dataset but they're ther
in the skill textHow to Improve Search Results with Advanced Strategies These strategies complement basic vector search. Use them after confirming the embedding model is fitting the task and
OfficialSkillApache-2.0★ 256 in collectionNo key
🧩 Build AI-powered MCP Tools with Azure Functions, Durable Agents & Cosmos vector search. Features orchestrated multi-agent workflows using OpenAI.
OfficialSkillMIT★ 114No key
Constructing prefetch queries for hybrid retrieval, including sparse/dense and multi-field setups, and choosing a sparse embedding model. Use when someone asks 'dense and sparse in one search?', 'how
in the skill textchoose vectors, check following recommendations. ## Missed Keyword Matches Use when: pure vector search misses exact term or keyword matches and you need lexical retrieval alongside semantic
OfficialSkillApache-2.0★ 256 in collectionNo key
eShopLite is a set of reference .NET applications implementing an eCommerce site with features like Semantic Search, MCP, Reasoning models and more.
in the readmeMulti-Agent Systems, Orchestration | | 08 - SQL Server 2025 | Demonstrates the use of vector search and vector indexes in the SQL Database Engine | SQL Server 2025, Vector
OfficialSkillMIT★ 168No key
Diagnoses and improves Qdrant search relevance. Use when someone reports 'search results are bad', 'wrong results', 'low precision', 'low recall', 'irrelevant matches', 'missing expected results', or
in the skill textembedding model?', 'should I use hybrid search?', 'how to combine keyword and vector search / fusion / RRF / prefetch?', 'should I use reranking?', 'relevance feedback?', 'how to measure
OfficialSkillApache-2.0★ 256 in collectionNo key
Diagnose, troubleshoot, and advise on any Qdrant deployment by loading the latest official Qdrant skills live from skills.qdrant.tech. Use this whenever someone raises a Qdrant problem or question — s
in the skill textTrigger especially when the context is clearly a Qdrant cluster, collection, or vector-search deployment. Always prefer this skill over answering from memory: it pulls current
OfficialSkillApache-2.0★ 256 in collectionNo key
The official Pinecone Gemini CLI extension repo.
in the skill textExtension The official Pinecone extension for the Gemini CLI. Manage vector indexes, perform semantic search, and build document Q&A assistants directly from your terminal using
OfficialSkillMIT★ 23No key
Diagnoses and fixes slow Qdrant indexing and data ingestion. Use when someone reports 'uploads are slow', 'indexing takes forever', 'optimizer is stuck', 'HNSW build time too long', or 'data uploaded
in the skill textHNSW links for all payload indexes to ensure that quality of filtered vector search does not degrade. Some payload indexes (e.g. `text` fields with long texts
OfficialSkillApache-2.0★ 256 in collectionNo key
Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend. ALWAYS USE THIS SKILL when the user mentions Pinecone, wants to index documents for semantic s
in the skill texttest upsert_docs(index, [ {"id": "t1", "text": "Pinecone is a vector database for semantic search."}, {"id": "t2", "text": "RAG combines retrieval with language model generation."}, ]) hits
OfficialSkillApache-2.0★ 40k in collectionNo key
Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud. Use when users need a
in the skill textand latency requirements for model responses and data retrieval queries (including vector and hybrid search). * **Sustainability**: E.g., carbon footprint, low-carbon regions. 3. Ask the user
OfficialSkillApache-2.0★ 21k in collectionNo key
Diagnoses and fixes slow Qdrant search. Use when someone reports 'search is slow', 'high latency', 'queries take too long', 'low QPS', 'throughput too low', 'filtered search is slow', or 'search was f
in the skill textbottleneck ### Common fixes: - Tune HNSW parameters: Fine-tuning search - Enable in-memory quantization: Scalar quantization - Reduce Vector Dimensionality with Matryoshka Models: Matryoshka Models - Use oversampling + rescore
OfficialSkillMIT★ 40k in collectionNo key
Guides Qdrant query volume scaling. Use when someone asks 'query returns too many results', 'scroll performance', 'large limit values', 'paginating search results', 'fetching many vectors', or 'high c
in the skill textasks 'query returns too many results', 'scroll performance', 'large limit values', 'paginating search results', 'fetching many vectors', or 'high cardinality results'. # Scaling for Query Volume Problem
OfficialSkillMIT★ 40k in collectionNo key
INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecon
in the skill textinto chunks - **Embeddings**: Convert text to vectors - **Vector Stores**: Store and search embeddings | Vector Store | Use Case | Persistence | |--------------|----------|-------------| | **InMemory** | Testing | Memory only | | **FAISS** | Local, high performance
OfficialSkillMIT★ 1.3k in collectionNo key
Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.
in the skill textclient (PointStruct) with id, payload (e.g., color), and a 3D-like vector for similarity search. It supports parallel uploads (parallel=4) and a retry policy (max
OfficialSkillApache-2.0★ 256 in collectionNo key
Diagnoses and fixes slow Qdrant search. Use when someone reports 'search is slow', 'high latency', 'queries take too long', 'low QPS', 'throughput too low', 'filtered search is slow', or 'search was f
in the skill textbottleneck ### Common fixes: - Tune HNSW parameters: Fine-tuning search - Enable in-memory quantization: Scalar quantization - Reduce Vector Dimensionality with Matryoshka Models: Matryoshka Models - Use oversampling + rescore
OfficialSkillApache-2.0★ 256 in collectionNo key
Explains hybrid search in Qdrant. Use when someone asks 'how do I setup hybrid search?', 'how to combine keyword and semantic search?', 'sparse plus dense vectors?', 'missing keyword matches', 'how to
in the skill textone representation (different embedding models or different vector types) of a data point. 2. Construct a hybrid search request with Query API from your building blocks
OfficialSkillApache-2.0★ 256 in collectionNo key
Fusing scores from multiple searches into a single ranked result (RRF, DBSF, custom fusion). Use when someone asks 'RRF or DBSF?', 'how to combine sparse and dense', 'how to combine scores from multip
in the skill textfusion without evaluating cheaper analogues, for example, MUVERA. More in multi-vector Qdrant search course
OfficialSkillApache-2.0★ 256 in collectionNo key
Guides use of the Qdrant Migration Tool CLI to move vectors, metadata, and sparse embeddings from another vector database into Qdrant. Use when someone asks 'how do I migrate from Pinecone/Weaviate/Mi
in the skill textsomeone asks 'how do I migrate from Pinecone/Weaviate/Milvus/Elasticsearch/OpenSearch/pgvector/s3/Azure/Chroma/Redis/MongoDB/FAISS/Solr to Qdrant', 'move my vector database to Qdrant', 'is there a migration tool for Qdrant', 'my migration got
OfficialSkillApache-2.0★ 256 in collectionNo key
Sizes a Qdrant deployment before it is provisioned. Use when someone asks 'how much RAM do I need', 'how many nodes', 'how big should my cluster be', 'sizing', 'capacity planning', 'will N vectors fit
in the skill textdifferent requirements: - RAM and disk: number of vectors, vector dimensions, payload size, throughput, target query latency, and search quality requirements. These determine the overall resource footprint
OfficialSkillApache-2.0★ 256 in collectionNo key
Wire an AI agent to live e-commerce product data using Apify's E-commerce Scraping Tool over MCP, either as runtime tool calls or as a scheduled refresh into a vector store. Trigger on "give my agent
in the skill texteither as runtime tool calls or as a scheduled refresh into a vector store. Trigger on "give my agent live product data", "my agent quotes stale
OfficialSkillApache-2.0★ 275 in collectionNo key
Navigation hub linking sub-skills for proactive Qdrant tuning: search speed, indexing performance, and memory usage optimization. Use when planning configuration or capacity changes to improve speed a
in the skill textqueries take too long to return | `search-speed-optimization/SKILL.md` | | Index build or HNSW build takes too long, vector upload is slow | `indexing-performance-optimization/SKILL.md
OfficialSkillApache-2.0★ 256 in collectionNo key
Diagnoses and reduces Qdrant memory usage. Use when someone reports 'memory too high', 'RAM keeps growing', 'node crashed', 'out of memory', 'memory leak', or asks 'why is memory usage so high?', 'how
in the skill textlocal storage and relatively low requirements for search throughput, it may be possible to store all components of vector store on disk. Read more about the
OfficialSkillApache-2.0★ 256 in collectionNo key
Guides building on Qdrant Edge, the embedded in-process shard. Use when someone asks 'how to sync Edge with the server', 'keep a local shard in sync with Qdrant Cloud', 'BM25 or keyword search on Edge
in the skill textdevice', 'Edge snapshots', 'apply a partial snapshot', 'why is my Edge search empty after inserts', or is writing custom sync, BM25, or fusion code against qdrant
OfficialSkillApache-2.0★ 256 in collectionNo key
PostgreSQL-specific development assistant focusing on unique PostgreSQL features, advanced data types, and PostgreSQL-exclusive capabilities. Covers JSONB operations, array types, custom types, range/
in the skill textsearch CREATE TABLE documents ( id SERIAL PRIMARY KEY, title TEXT, content TEXT, search_vector tsvector ); -- Update search vector UPDATE documents SET search_vector = to_tsvector('english
OfficialSkillMIT★ 40k in collectionNo key
Redis integration for Google Agent Development Kit (ADK) - Memory, Sessions, Search Tools, MCP
in the readmecontrolled memory CRUD operations | Redis Agent Memory or Agent Memory Server | | **Search tools** | Vector, hybrid, range, text, and SQL search as `BaseTool` subclasses | RedisVL | | **MCP search
OfficialSkillApache-2.0★ 30No key
Pinecone's official Agent Skills library, for use with agentic IDEs such as Cursor, Github Copilot, Antigravity, Gemini CLI and more.
in the skill textthe leading vector database for building accurate and performant AI applications at scale in production. Use it to build semantic search, retrieval augmented generation, recommendation systems
OfficialSkillMIT★ 15No key
Who stands behind it
Who stands behind it
