Query your local document knowledge base using semantic search and get AI-powered answers.
Query your local document knowledge base using semantic search to get AI-powered answers with citations. Use this when you need to search through indexed documents to answer questions about your codebase or documentation.
/plugin marketplace add ItMeDiaTech/rag-cli/plugin install rag-cli@rag-cliThis skill inherits all available tools. When active, it can use any tool Claude has access to.
__init__.pyretrieve.pyQuery your local document knowledge base using semantic search and get AI-powered answers.
This skill enables RAG (Retrieval-Augmented Generation) queries against your locally indexed documents. It uses semantic search to find relevant documents and generates answers using Claude Haiku.
/skill rag-retrieval "How to configure the API?"
query (required): Your question or search query--top-k (optional): Number of documents to retrieve (default: 5)--threshold (optional): Minimum similarity score (default: 0.7)--mode (optional): Search mode - "hybrid", "vector", or "keyword" (default: "hybrid")/skill rag-retrieval "What is the authentication process?"
/skill rag-retrieval "How to handle errors?" --top-k 10
/skill rag-retrieval "API rate limits" --mode vector
The skill uses the following configuration from config/default.yaml:
retrieval.top_k: Default number of documents to retrieveretrieval.hybrid_ratio: Balance between vector and keyword search (0.7 = 70% vector)claude.model: LLM model for response generationclaude.max_tokens: Maximum response lengthTypical latencies:
data/vectors/python scripts/index.py --input data/documents--threshold 0.5python -m src.monitoring.tcp_serverlogs/rag_cli.log