Supported File Types

• Documents: PDF, EPUB, TXT, TEXT
• Markdown: MD, MDX, Markdown, MDown, MKD, MKDN
• Web Content: HTM, HTML, XHTML
• Images (with OCR): BMP, JPEG, JPG, PNG
• Archives: RAR (planned - currently not implemented)

Installation

• Navigate to the plugin directory:

cd big-rag-plugin

• Install dependencies:

npm install

• Build the plugin:

npm run build

• Run in development mode:

npm run dev

Configuration

The plugin provides the following configuration options in LM Studio:

Required Settings

• Documents Directory: Root directory containing your documents (read access required)
• Vector Store Directory: Where the vector database will be stored (read/write access required)

Embedding model

• Embedding Model (plugin setting): String passed to LM Studio’s embedding load API. Both common forms can work for the same weights—for example mixedbread-ai/mxbai-embed-large-v1 (Hub / lms get) and text-embedding-mxbai-embed-large-v1 (as shown in lms ls). Use one spelling consistently for indexing and retrieval so it matches .big-rag-embedding.json; switching spelling without reindexing can trigger a mismatch warning. Default: nomic-ai/nomic-embed-text-v1.5-GGUF.
• After changing the embedding model, run a full reindex (toggle Manual Reindex Trigger with Skip Previously Indexed Files off, or clear the vector store and let first-run indexing rebuild). Vectors from different models are not comparable in the same index.
• .big-rag-embedding.json: Written under the vector store directory when the index has at least one chunk; records the model id and vector length used to build the index. If the configured model no longer matches this file, retrieval is blocked until you reindex or revert the setting. If the index has zero chunks, this file is removed so metadata cannot drift (including after manual shard deletion).
• Indexes built with older plugin versions may have chunks but no manifest; retrieval still works, and a full reindex will create the manifest.

Retrieval Settings

• Retrieval Limit (1-20, default: 5): Maximum number of chunks to return
• Retrieval Affinity Threshold (0.0-1.0, default: 0.5): Minimum similarity score for relevance
• Chunk Size (128-2048 tokens, default: 512): Size of text chunks for embedding
• Chunk Overlap (0-512 tokens, default: 100): Overlap between consecutive chunks

Performance Settings

• Max Concurrent Files (1-10, default: 1): Number of files to process simultaneously
• Enable OCR (default: true): Enable OCR for image files and image-based PDFs using LM Studio's built-in document parser

Reindexing Controls

• Manual Reindex Trigger (toggle): Turn this ON and submit any chat message to force indexing to run on every chat session where the plugin is enabled. Flip it OFF once you’re done to stop the automatic reindex loop.
• Skip Previously Indexed Files (default: true): If enabled while "Manual Reindex Trigger" is enabled, each manual run touches just the documents that are new or have changed since the last index; if disabled, every chat rebuilds the entire index from scratch. Combine "Skip Previously Indexed Files" and "Manual Reindex Trigger" to choose between incremental updates or repeated full refreshes.
• Automatic First-Run: If the vector store is empty, the plugin automatically indexes the configured documents the first time any chat message is processed—no manual input is required.

Usage

• Configure the Plugin:

  • Open LM Studio settings
  • Navigate to the Big RAG plugin configuration
  • Set your documents directory (e.g., /Users/user/Documents/MyLibrary)
  • Set your vector store directory (e.g., /Users/user/.lmstudio/big-rag-db)

• Initial Indexing:

  • The first time you send a message, the plugin will automatically scan and index your documents
  • This process may take a while depending on the size of your document collection
  • Progress will be shown in the LM Studio interface

• Query Your Documents:

  • Simply chat with your LM Studio model as usual
  • The plugin will automatically search your indexed documents for relevant content
  • Retrieved passages will be injected into the context for the model to use

Architecture

Components

• File Scanner (src/ingestion/fileScanner.ts):

  • Recursively scans directories
  • Filters for supported file types
  • Collects file metadata

• Document Parsers (src/parsers/):

  • htmlParser.ts: Extracts text from HTML/HTM files
  • pdfParser.ts: Extracts text from PDF files
  • epubParser.ts: Extracts text from EPUB files
  • textParser.ts: Reads plain text & Markdown files with optional Markdown stripping
  • imageParser.ts: OCR for image files
  • documentParser.ts: Routes to appropriate parser

• Vector Store (src/vectorstore/vectorStore.ts):

  • Uses Vectra with sharded indexes (one shard in memory at a time; avoids V8 string size limits)
  • Supports incremental updates
  • Efficient similarity search

• Index Manager (src/ingestion/indexManager.ts):

  • Orchestrates the indexing pipeline
  • Manages concurrent processing
  • Handles progress reporting

• Prompt Preprocessor (src/promptPreprocessor.ts):

  • Intercepts user queries
  • Performs vector search
  • Injects relevant context

Performance Considerations

Large Datasets

• Disk Space: The vector store requires additional disk space (typically 10-20% of original document size)
• Initial Indexing: Can take several hours for TB-scale collections
• Memory Usage: Scales with concurrent processing (reduce maxConcurrentFiles if needed)

Optimization Tips

• Start Small: Test with a subset of documents first
• Disable OCR: Unless you have many image-based documents, keep OCR disabled
• Adjust Concurrency: Lower maxConcurrentFiles on systems with limited resources
• Chunk Size: Larger chunks (1024-2048) work better for technical documents
• Threshold Tuning: Adjust retrievalAffinityThreshold based on result quality

Troubleshooting

No Results Found

• Check that documents directory is correctly configured
• Verify that indexing completed successfully
• Try lowering the retrieval affinity threshold
• Check LM Studio logs for errors

Embedding model mismatch

• If you see a message that the index was built with a different embedding model than the one in settings, either change Embedding Model back to the value recorded in .big-rag-embedding.json or run a full reindex after changing the model.
• Dimension mismatch means the model’s output size changed; reindex after switching models or quantizations.

Slow Indexing

• Reduce maxConcurrentFiles
• Disable OCR if not needed
• Ensure vector store directory is on a fast drive (SSD recommended)

Out of Memory

• Reduce maxConcurrentFiles to 1 or 2
• Process documents in batches by organizing them into subdirectories
• Increase system swap space

OCR Not Working

• Tesseract.js downloads language data on first use
• Ensure internet connectivity during first OCR operation
• Check that image files are valid and readable

Failure Reason Reporting

• The CLI logs cumulative success / failed counts after each processed document.
• Set BIG_RAG_FAILURE_REPORT_PATH=/absolute/path/report.json when running npm run index (or via LM Studio env settings) to emit a JSON report containing all failure reasons and counts after indexing completes. This is useful when triaging stubborn PDFs such as blueprints or large scanned books.
• BIG_RAG_EMBEDDING_MODEL: Optional. When set for headless indexing (npm run index:cli / dist/cliIndex.js), overrides the default embedding model id (same default as the plugin’s Embedding Model setting). Empty/unset uses the built-in default from config.ts.

Limitations

• RAR Archives: Not yet implemented (files are skipped)
• Password-Protected Files: Not supported
• Very Large Files: Individual files >100MB may cause memory issues
• Non-English OCR: Currently only English OCR is configured

Development

Project Structure

big-rag-plugin/
├── src/
│   ├── config.ts              # Plugin configuration schema
│   ├── index.ts               # Main entry point
│   ├── promptPreprocessor.ts  # RAG integration
│   ├── ingestion/
│   │   ├── fileScanner.ts     # Directory scanning
│   │   └── indexManager.ts    # Indexing orchestration
│   ├── parsers/
│   │   ├── documentParser.ts  # Parser router
│   │   ├── htmlParser.ts      # HTML parsing
│   │   ├── pdfParser.ts       # PDF parsing
│   │   ├── epubParser.ts      # EPUB parsing
│   │   ├── textParser.ts      # Text parsing
│   │   └── imageParser.ts     # OCR parsing
│   ├── vectorstore/
│   │   └── vectorStore.ts     # Vectra sharded index integration
│   └── utils/
│       ├── coerceEmbedding.ts       # Normalize embedding API vectors
│       ├── embeddingIndexManifest.ts # Index embedding metadata on disk
│       ├── fileHash.ts              # File hashing
│       └── textChunker.ts           # Text chunking
├── manifest.json              # Plugin manifest
├── package.json               # Dependencies
├── tsconfig.json              # TypeScript config
└── README.md                  # This file

Testing

Automated parser smoke tests cover HTML, Markdown, and plain text ingestion:

npm run test

For end-to-end validation:

• Create a test directory with sample documents
• Configure the plugin to use this directory
• Send a test query to verify retrieval works
• Check LM Studio logs for any errors

Contributing

This plugin is based on the LM Studio plugin SDK. For more information:

• lmstudio-js GitHub
• Documentation
• Discord

License

ISC

Acknowledgments

• Built using the LM Studio SDK
• Uses Vectra for vector storage (sharded indexes)
• OCR powered by Tesseract.js
• PDF parsing via pdf-parse
• EPUB parsing via epub2
• HTML parsing via cheerio