pinecone
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
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.
sentence-transformers
Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.
google-gemini-file-search
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google-gemini-embeddings
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chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
AgentDB Vector Search
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
AILANG Inbox
Cross-agent communication system with semantic search and GitHub sync. Check messages, find similar content, deduplicate, and sync with GitHub Issues for AI workflows across sessions.
semantic-intelligence
Use Julie's semantic search capabilities for conceptual code understanding. Activates when searching for concepts, cross-language patterns, business logic, or exploring unfamiliar code. Combines text and semantic search for optimal results.
smart-search
Intelligently choose between semantic and text search based on query intent. Automatically selects the best search mode (semantic for concepts, text for exact terms, symbols for definitions) and provides relevant results. Use when user wants to find code.
explore-codebase
Autonomously explore unfamiliar codebases using Julie's code intelligence. Use semantic search, symbol navigation, and call path tracing to understand architecture without reading entire files. Activates when user asks to understand, explore, or learn about a codebase.
helix-memory
Long-term memory system for Claude Code using HelixDB graph-vector database. Store and retrieve facts, preferences, context, and relationships across sessions using semantic search, reasoning chains, and time-window filtering.
vector-databases
Vector database selection, indexing strategies, and semantic search optimization.
octocode
AI-powered code indexer with semantic search, knowledge graphs (GraphRAG), and persistent memory system. Use when you need to (1) perform semantic code searches across large codebases, (2) analyze file relationships and dependencies through GraphRAG, (3) store and retrieve code insights with memory system. This skill uses Bash to call octocode CLI directly with automatic index management.
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.
Knowledge Graph Builder
Design and build knowledge graphs. Use when modeling complex relationships, building semantic search, or creating knowledge bases. Covers schema design, entity relationships, and graph database selection.
Convex Agents RAG
Implements Retrieval-Augmented Generation (RAG) patterns to enhance agents with custom knowledge bases. Use this when agents need to search through documents, retrieve context from a knowledge base, or ground responses in specific data.
sear
Semantic search and RAG for documents. Use when user needs to index PDF/DOCX/text files, perform semantic search, extract relevant content from document corpuses, or build RAG applications. Supports multi-corpus search, GPU acceleration, line-level citations, and document conversion with OCR.
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