Chroma
技能 已验证 活跃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.
To serve as a powerful, self-hosted, and open-source embedding database for AI applications, enabling efficient semantic search and retrieval.
功能
- Store embeddings and metadata
- Perform vector and full-text search
- Filter search results by metadata
- Support for multiple embedding functions (OpenAI, HuggingFace, custom)
- Integration with LangChain and LlamaIndex
使用场景
- Building RAG (retrieval-augmented generation) applications
- Implementing semantic search over documents
- Prototyping AI applications locally
- Storing and querying vector embeddings with associated metadata
非目标
- Acting as a managed cloud vector database (alternatives like Pinecone are mentioned)
- Performing similarity search without metadata filtering (like FAISS)
- Replacing production ML-native databases (like Weaviate or Qdrant, though Chroma scales to production)
安装
请先添加 Marketplace
/plugin marketplace add Orchestra-Research/AI-Research-SKILLs/plugin install AI-Research-SKILLs@ai-research-skills质量评分
已验证类似扩展
Embedding Strategies
100Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
Rag Implementation
98Build 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.
Vector Embed
96Generate embeddings via npx ruvector@0.2.25 embed text (ONNX all-MiniLM-L6-v2, 384-dim), normalize, and store in HNSW index
AgentDB Vector Search
99Implement 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.
Sentence Transformers
98Framework 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.
Hybrid Search Implementation
98Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.