RAG Search
Semantic search using embeddings and vector storage. Search documents semantically using similarity matching.
Setup
No additional setup required. Uses in-memory vector storage with optional embedding providers.
Usage
Index Documents
{baseDir}/rag-search.js --index --path ./docs --chunk-size 500
Search Documents
{baseDir}/rag-search.js --search "how to configure authentication"
Query with Filters
{baseDir}/rag-search.js --search "deployment steps" --limit 5
Options
Option Description Required
--index
Index documents No
--path
Path to documents For index
--chunk-size
Chunk size for splitting No
--search
Search query For search
--limit
Max results to return No
--list
List indexed documents No
--clear
Clear index No
Supported Formats
-
Plain text (.txt)
-
Markdown (.md)
-
JSON (.json)
-
JavaScript/TypeScript (.js, .ts)
-
Python (.py)
-
HTML (.html)
-
YAML (.yaml, .yml)
Embedding Providers
-
OpenAI (default, requires API key)
-
Cohere (requires API key)
-
Local (TF-IDF based, no API key needed)
Output Format
{ "results": [ { "file": "docs/config.md", "chunk": "To configure authentication...", "score": 0.92, "line": 15 } ] }
When to Use
-
Semantic search across codebase
-
Finding relevant documentation
-
Knowledge base queries
-
RAG applications