winsorllc 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