Search Results for "Vector Search" | AgentCC

agentdb-advanced-features

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

agentdb-semantic-vector-search

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hybrid-search-implementation

Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.

upstash-vector-db-skills

Upstash Vector DB setup, semantic search, namespaces, and embedding models (MixBread preferred). Use when building vector search features on Vercel.

vector-search-workflows

Vector search indexing and querying workflows using MCP Vector Search, including setup, reindexing, auto-index strategies, and MCP integration.

vector-searchembeddingsindexing+2

code-semantic-search

Semantic code search using Phase 1 vector embeddings and Phase 2 hybrid search.

rag-implementation

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

grepai-storage-qdrant

Configure Qdrant vector database for GrepAI. Use this skill for high-performance vector search.

postgres-semantic-search

PostgreSQL-based semantic and hybrid search with pgvector and ParadeDB. Use when implementing vector search, semantic search, hybrid search, or full-text search in PostgreSQL. Covers pgvector setup, indexing (HNSW, IVFFlat), hybrid search (FTS + BM25 + RRF), ParadeDB as Elasticsearch alternative, and re-ranking with Cohere/cross-encoders. Supports vector(1536) and halfvec(3072) types for OpenAI embeddings. Triggers: pgvector, vector search, semantic search, hybrid search, embedding search, PostgreSQL RAG, BM25, RRF, HNSW index, similarity search, ParadeDB, pg_search, reranking, Cohere rerank, pg_trgm, trigram, fuzzy search, LIKE, ILIKE, autocomplete, typo tolerance, fuzzystrmatch

tidb-sql

Write, review, and adapt SQL for TiDB with correct handling of TiDB-vs-MySQL differences (VECTOR type + vector indexes/functions, full-text search, AUTO_RANDOM, optimistic/pessimistic transactions, foreign keys, views, DDL limitations, and unsupported MySQL features like procedures/triggers/events/GEOMETRY/SPATIAL). Use when generating SQL that must run on TiDB, migrating MySQL SQL to TiDB, or debugging TiDB SQL compatibility errors.

pytidb

PyTiDB (pytidb) setup and usage for TiDB from Python. Covers connecting, table modeling (TableModel), CRUD, raw SQL, transactions, vector/full-text/hybrid search, auto-embedding, custom embedding functions, and reference templates/snippets (vector/hybrid/image) plus agent-oriented examples (RAG/memory/text2sql).

Doc-to-Vector Dataset Generator

patricio0312rev

Converts documents into clean, chunked datasets suitable for embeddings and vector search. Produces chunked JSONL files with metadata, deduplication logic, and quality checks. Use when preparing 'training data', 'vector datasets', 'document processing', or 'embedding data'.

typo3-solr

Expert guidance on Apache Solr search integration for TYPO3: installation, Index Queue, faceting, suggest, PSR-14 events, custom indexers, LLM/vector search (Solr native), DDEV/Docker/production setup, deep debugging & troubleshooting, and file indexing via SolrFAL. Use when working with solr, search, indexing, facets, suggest, autocomplete, vector search, solrfal, tika.

vector-db-setup

patricio0312rev

Sets up vector databases for semantic search including Pinecone, Chroma, pgvector, and Qdrant with embedding generation and similarity search. Use when users request "vector database", "semantic search", "embeddings storage", "Pinecone setup", or "similarity search".

surrealdb-python

ActiveInferenceInstitute

Master SurrealDB 2.3.x with Python for multi-model database operations including CRUD, graph relationships, vector search, and real-time queries. Use when working with SurrealDB databases, implementing graph traversal, semantic search with embeddings, or building RAG applications.

rag-infrastructure

Build and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment patterns.

knowledge-search

Query BK for library internals via vector search or direct Grep/Read

vector-database-ops

Deploy, manage, and optimize vector databases for AI applications. Covers Qdrant, Weaviate, pgvector, and Pinecone — collection management, indexing strategies, backup, and performance tuning for production RAG and semantic search workloads.

cloudflare-vectorize

Complete knowledge domain for Cloudflare Vectorize - globally distributed vector database for building semantic search, RAG (Retrieval Augmented Generation), and AI-powered applications. Use when: creating vector indexes, inserting embeddings, querying vectors, implementing semantic search, building RAG systems, configuring metadata filtering, working with Workers AI embeddings, integrating with OpenAI embeddings, or encountering metadata index timing errors, dimension mismatches, filter syntax issues, or insert vs upsert confusion. Keywords: vectorize, vector database, vector index, vector search, similarity search, semantic search, nearest neighbor, knn search, ann search, RAG, retrieval augmented generation, chat with data, document search, semantic Q&A, context retrieval, bge-base, @cf/baai/bge-base-en-v1.5, text-embedding-3-small, text-embedding-3-large, Workers AI embeddings, openai embeddings, insert vectors, upsert vectors, query vectors, delete vectors, metadata filtering, namespace filtering, topK search, cosine similarity, euclidean distance, dot product, wrangler vectorize, metadata index, create vectorize index, vectorize dimensions, vectorize metric, vectorize binding

mongodb

Assists with designing document schemas, building aggregation pipelines, managing indexes, and operating MongoDB clusters. Use when working with flexible schemas, nested documents, horizontal scaling, Atlas Search, or vector search for AI applications. Trigger words: mongodb, mongo, document database, aggregation, atlas, nosql.

qdrant

Qdrant is an open-source vector similarity search engine with advanced filtering. Learn to create collections, upsert points with payloads, query with filters, and deploy with Docker for AI and semantic search applications.

openai-assistants

Complete guide for OpenAI's Assistants API v2: stateful conversational AI with built-in tools (Code Interpreter, File Search, Function Calling), vector stores for RAG (up to 10,000 files), thread/run lifecycle management, and streaming patterns. Both Node.js SDK and fetch approaches. ⚠️ DEPRECATION NOTICE: OpenAI plans to sunset Assistants API in H1 2026 in favor of Responses API. This skill remains valuable for existing apps and migration planning. Use when: building stateful chatbots with OpenAI, implementing RAG with vector stores, executing Python code with Code Interpreter, using file search for document Q&A, managing conversation threads, streaming assistant responses, or encountering errors like "thread already has active run", vector store indexing delays, run polling timeouts, or file upload issues. Keywords: openai assistants, assistants api, openai threads, openai runs, code interpreter assistant, file search openai, vector store openai, openai rag, assistant streaming, thread persistence, stateful chatbot, thread already has active run, run status polling, vector store error

mongodb-ai

MongoDB Atlas Vector Search and AI integration. Use when creating vector indexes, writing $vectorSearch queries, building RAG applications, implementing hybrid search, or storing AI agent memory. Triggers on "vector search", "vector index", "$vectorSearch", "embedding", "semantic search", "RAG", "retrieval augmented generation", "numCandidates", "similarity search", "cosine similarity", "hybrid search", "$rankFusion", "$scoreFusion", "rerank", "two-stage retrieval", "AI agent", "LLM memory", "quantization", "multi-tenant", "Search Nodes", "explain vectorsearch", "HNSW", "automated embedding", "autoEmbed", "Voyage AI", "voyage-4", "voyage-4-large", "voyage-code-3", "input_type", "asymmetric retrieval", "lexical prefilter", "fuzzy search vector", "phrase filter".

cloudflare-ai-search

Cloudflare AI Search for semantic search and vector embeddings in Workers

weaviate

Weaviate is an open-source vector database with built-in vectorization modules. Learn schema definition, GraphQL and REST APIs, hybrid search combining BM25 and vectors, and self-hosted deployment with Docker.

vector-mcp-search

Vector MCP search capabilities for A2A Agent.

Google Gemini Embeddings

This skill provides complete coverage of Google Gemini embeddings API (gemini-embedding-001) for building RAG systems, semantic search, document clustering, and similarity matching. Use when implementing vector search with Google's embedding models, integrating with Cloudflare Vectorize, or building retrieval-augmented generation systems. Covers SDK usage (@google/genai), fetch-based Workers implementation, batch processing, 8 task types (RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT, SEMANTIC_SIMILARITY, etc.), dimension optimization (128-3072), and cosine similarity calculations. Prevents 8+ embedding-specific errors including dimension mismatches, incorrect task types, rate limiting issues (100 RPM free tier), vector normalization mistakes, text truncation (2,048 token limit), and model version confusion. Includes production-ready RAG patterns with Cloudflare Vectorize integration, chunking strategies, and caching patterns. Token savings: ~60%. Production tested. Keywords: gemini embeddings, gemini-embedding-001, google embeddings, semantic search, RAG, vector search, document clustering, similarity search, retrieval augmented generation, vectorize integration, cloudflare vectorize embeddings, 768 dimensions, embed content gemini, batch embeddings, embeddings api, cosine similarity, vector normalization, retrieval query, retrieval document, task types, dimension mismatch, embeddings rate limit, text truncation, @google/genai

pgvector

Store and search vector embeddings in PostgreSQL with pgvector — no separate vector database needed. Use when someone asks to "vector search in Postgres", "store embeddings", "pgvector", "similarity search", "RAG with Postgres", "semantic search in existing database", or "add AI search to my app without a separate vector DB". Covers vector columns, indexing (IVFFlat, HNSW), similarity search, and integration with ORMs.

chromadb

Assists with storing, searching, and managing vector embeddings using ChromaDB. Use when building RAG pipelines, semantic search engines, or recommendation systems. Trigger words: chromadb, chroma, vector database, embeddings, semantic search, similarity search, vector store, rag.

agent-memory

Add persistent memory to AI coding agents — file-based, vector, and semantic search memory systems that survive between sessions. Use when a user asks to "remember this", "add memory to my agent", "persist context between sessions", "build a knowledge base for my agent", "set up agent memory", or "make my AI remember things". Covers file-based memory (MEMORY.md), SQLite with embeddings, vector databases (ChromaDB, Pinecone), semantic search, memory consolidation, and automatic context injection.

convex

Assists with building real-time reactive backends using Convex. Use when creating databases with automatic client sync, reactive queries, file storage, scheduled functions, or full-text and vector search. Trigger words: convex, reactive backend, real-time database, useQuery, useMutation, convex functions, convex schema.

lancedb

Embedded vector database with LanceDB — serverless, zero-config vector search for AI applications. Use when someone asks to "vector search without a server", "embedded vector database", "LanceDB", "local vector search", "serverless vector DB", "vector search in a file", or "lightweight RAG storage". Covers table creation, vector search, full-text search, hybrid search, and multimodal embeddings.

pinecone

Pinecone is a managed vector database for AI and machine learning applications. Learn to create indexes, upsert embeddings, query by similarity, use namespaces and metadata filtering for semantic search and RAG pipelines.

upstash

Assists with building serverless applications using Upstash Redis, QStash, Workflow, and Vector. Use when adding caching, rate limiting, message queues, durable workflows, or vector search to edge and serverless applications. Trigger words: upstash, serverless redis, rate limiting, qstash, upstash workflow, upstash vector.

Vector Database Expert

Vector databases - Pinecone, Weaviate, Chroma, Qdrant for RAG and semantic search

basic-usage

Use when getting started with llmemory document storage and search - covers installation, initialization, adding documents, vector search, hybrid search, semantic search, BM25 full-text search, document management, and building RAG systems with multi-tenant support

hybrid-search

Use when building search systems that need both semantic similarity and keyword matching - covers combining vector and BM25 search with Reciprocal Rank Fusion, alpha tuning for search weight control, and optimizing retrieval quality

context-graph

Use when storing decision traces, querying past precedents, or implementing learning loops. Load in COMPLETE state or when needing to learn from history. Covers semantic search with Voyage AI embeddings, ChromaDB for cross-platform vector storage, and pattern extraction from history.

memory-recall

Retrieve coding patterns from GLOBAL vector database (cross-project learning). Auto-invokes when TodoWrite has >3 tasks or when user says "--recall". Searches relevant role collections based on task context.

vector-index-tuning

Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.

websearch-service

A real-time online search service based on SearXNG and VLM (Visual-Language Model). Designed specifically to fetch the latest news, live events, and specific factual information. Built-in intelligent two-layer cache (vector + database) and automatic webpage content extraction and analysis.

embedding-service

Basic text vectorization (embedding) service. Converts natural language into high-dimensional dense vectors, providing core data support for downstream tasks such as semantic search, clustering analysis, and recommendation systems.

similarity-search-patterns

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

hybrid-search-implementation

agent-skills-hub

Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.

ai-engineer

agent-skills-hub

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.

vector-database-engineer

agent-skills-hub

Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar

surrealdb

Expert SurrealDB 3 skill. Use when working with SurrealDB, SurrealQL queries, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, graph traversal, vector search, security and permissions, deployment and operations, performance tuning, SDK integration (JavaScript, Python, Go, Rust, Java, .NET), Surrealism WASM extensions, Surrealist IDE, Surreal-Sync migrations, or SurrealFS.

rag-engineer

agent-skills-hub

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.