From 09a66ad0ed8af9afad4714d440546c9bcb83ab5e Mon Sep 17 00:00:00 2001 From: Andrea Bravaccino Date: Sat, 28 Feb 2026 23:47:34 +0100 Subject: [PATCH 1/7] Refactor DocumentManager to integrate Orama for document storage and search - Removed DocumentIndex and related indexing logic. - Introduced OramaStore for managing document and chunk databases with persistence. - Updated DocumentManager methods to utilize OramaStore for adding, retrieving, and deleting documents. - Implemented migration from legacy JSON files to Orama. - Added new search capabilities across all documents using hybrid search (full-text + vector). - Updated server to include a new tool for cross-document search. - Adjusted types to accommodate Orama DB schema. --- README.md | 270 ++- package-lock.json | 3313 ++++++++++++++++++-------------- package.json | 4 +- src/document-manager.ts | 244 +-- src/indexing/document-index.ts | 283 --- src/orama-store.ts | 506 +++++ src/search-engine.ts | 89 - src/server.ts | 34 + src/types.ts | 22 + tsconfig.json | 4 +- 10 files changed, 2625 insertions(+), 2144 deletions(-) delete mode 100644 src/indexing/document-index.ts create mode 100644 src/orama-store.ts delete mode 100644 src/search-engine.ts diff --git a/README.md b/README.md index a98fdb2..909a0bb 100644 --- a/README.md +++ b/README.md @@ -7,43 +7,32 @@ # MCP Documentation Server -A TypeScript-based [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) server that provides local-first document management and semantic search using embeddings. The server exposes a collection of MCP tools and is optimized for performance with on-disk persistence, an in-memory index, and caching. - -## ๐Ÿš€ AI-Powered Document Intelligence - -**NEW!** Enhanced with Google Gemini AI for advanced document analysis and contextual understanding. Ask complex questions and get intelligent summaries, explanations, and insights from your documents. To get API Key go to [Google AI Studio](https://aistudio.google.com/app/apikey) - -### Key AI Features: -- **Intelligent Document Analysis**: Gemini AI understands context, relationships, and concepts -- **Natural Language Queries**: Ask a question, not just keywords -- **Smart Summarization**: Get comprehensive overviews and explanations -- **Contextual Insights**: Understand how different parts of your documents relate -- **File Mapping Cache**: Avoid re-uploading the same files to Gemini for efficiency - +A TypeScript-based [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) server that provides local-first document management and semantic search. Documents are stored in an embedded [Orama](https://orama.com/) vector database with hybrid search (full-text + vector), intelligent chunking, and local AI embeddings โ€” no external database or cloud service required. ## Core capabilities ### ๐Ÿ” Search & Intelligence -- **AI-Powered Search** ๐Ÿค–: Advanced document analysis with Gemini AI for contextual understanding and intelligent insights -- **Traditional Semantic Search**: Chunk-based search using embeddings plus in-memory keyword index -- **Context Window Retrieval**: Gather surrounding chunks for richer LLM answers +- **Hybrid Search**: Combined full-text and vector similarity powered by Orama, for both single-document and cross-document queries +- **AI-Powered Search** ๐Ÿค–: Advanced document analysis with Google Gemini AI for contextual understanding and intelligent insights (optional, requires API key) +- **Context Window Retrieval**: Fetch surrounding chunks to provide LLMs with richer context -### โšก Performance & Optimization -- **O(1) Document lookup** and keyword index through `DocumentIndex` for instant retrieval -- **LRU `EmbeddingCache`** to avoid recomputing embeddings and speed up repeated queries -- **Parallel chunking** and batch processing to accelerate ingestion of large documents -- **Streaming file reader** to process large files without high memory usage +### โšก Performance & Architecture +- **Orama Vector DB**: Embedded search engine with zero native dependencies โ€” replaces manual JSON storage and cosine similarity +- **LRU Embedding Cache**: Avoids recomputing embeddings for repeated content and queries +- **Intelligent Chunking**: Content-aware splitting (code, markdown, text) with adaptive sizing and parallel processing +- **Streaming File Reader**: Handles large files without high memory usage +- **Automatic Migration**: Legacy JSON documents are migrated to Orama on first startup โ€” no manual intervention needed ### ๐Ÿ“ File Management -- **Intelligent file handling**: copy-based storage with automatic backup preservation -- **Complete deletion**: removes both JSON files and associated original files -- **Local-only storage**: no external database required. All data resides in `~/.mcp-documentation-server/` +- **Upload processing**: Drop `.txt`, `.md`, or `.pdf` files into the uploads folder and process them with a single tool call +- **Copy-based storage**: Original files are backed up alongside the database +- **Local-only storage**: All data resides in `~/.mcp-documentation-server/` ## Quick Start ### Configure an MCP client -Example configuration for an MCP client (e.g., Claude Desktop): +Example configuration for an MCP client (e.g., Claude Desktop, VS Code): ```json { @@ -55,83 +44,93 @@ Example configuration for an MCP client (e.g., Claude Desktop): "@andrea9293/mcp-documentation-server" ], "env": { - "MCP_BASE_DIR": "/path/to/workspace", // Optional, custom data directory (default: ~/.mcp-documentation-server) - "GEMINI_API_KEY": "your-api-key-here", // Optional, enables AI-powered search - "MCP_EMBEDDING_MODEL": "Xenova/all-MiniLM-L6-v2", + "MCP_BASE_DIR": "/path/to/workspace", + "GEMINI_API_KEY": "your-api-key-here", + "MCP_EMBEDDING_MODEL": "Xenova/all-MiniLM-L6-v2" } } } } ``` +All environment variables are optional. Without `GEMINI_API_KEY`, only the local embedding-based search tools are available. + ### Basic workflow -- Add documents using the `add_document` tool or by placing `.txt`, `.md`, or `.pdf` files into the uploads folder and calling `process_uploads`. -- Search documents with `search_documents` to get ranked chunk hits. -- Use `get_context_window` to fetch neighboring chunks and provide LLMs with richer context. +1. Add documents using `add_document` or place `.txt` / `.md` / `.pdf` files in the uploads folder and call `process_uploads`. +2. Search across everything with `search_all_documents`, or within a single document with `search_documents`. +3. Use `get_context_window` to fetch neighboring chunks and give the LLM broader context. -## Exposed MCP tools +## MCP Tools -The server exposes several tools (validated with Zod schemas) for document lifecycle and search: +The server registers the following tools (all validated with Zod schemas): ### ๐Ÿ“„ Document Management -- `add_document` โ€” Add a document (title, content, metadata) -- `list_documents` โ€” List stored documents and metadata -- `get_document` โ€” Retrieve a full document by id -- `delete_document` โ€” Remove a document, its chunks, and associated original files +| Tool | Description | +| --- | --- | +| `add_document` | Add a document (title, content, optional metadata) | +| `list_documents` | List all documents with metadata and content preview | +| `get_document` | Retrieve the full content of a document by ID | +| `delete_document` | Remove a document, its chunks, database entries, and associated files | ### ๐Ÿ“ File Processing -- `process_uploads` โ€” Convert files in uploads folder into documents (chunking + embeddings + backup preservation) -- `get_uploads_path` โ€” Returns the absolute uploads folder path -- `list_uploads_files` โ€” Lists files in uploads folder +| Tool | Description | +| --- | --- | +| `process_uploads` | Process all files in the uploads folder (chunking + embeddings) | +| `get_uploads_path` | Returns the absolute path to the uploads folder | +| `list_uploads_files` | Lists files in the uploads folder with size and format info | + +### ๐Ÿ” Search +| Tool | Description | +| --- | --- | +| `search_documents` | Semantic vector search within a specific document | +| `search_all_documents` | Hybrid (full-text + vector) cross-document search | +| `get_context_window` | Returns a window of chunks around a given chunk index | +| `search_documents_with_ai` | ๐Ÿค– AI-powered search using Gemini (requires `GEMINI_API_KEY`) | + +## Configuration + +Configure via environment variables or a `.env` file in the project root: + +| Variable | Default | Description | +| --- | --- | --- | +| `MCP_BASE_DIR` | `~/.mcp-documentation-server` | Base directory for data storage | +| `MCP_EMBEDDING_MODEL` | `Xenova/all-MiniLM-L6-v2` | Embedding model name | +| `GEMINI_API_KEY` | โ€” | Google Gemini API key (enables `search_documents_with_ai`) | +| `MCP_CACHE_ENABLED` | `true` | Enable/disable LRU embedding cache | +| `MCP_PARALLEL_ENABLED` | `true` | Enable parallel chunking for large documents | +| `MCP_STREAMING_ENABLED` | `true` | Enable streaming reads for large files | +| `MCP_STREAM_CHUNK_SIZE` | `65536` | Streaming buffer size in bytes (64KB) | +| `MCP_STREAM_FILE_SIZE_LIMIT` | `10485760` | Threshold to switch to streaming (10MB) | + +### Storage layout -### ๐Ÿ” Search & Intelligence -- `search_documents_with_ai` โ€” **๐Ÿค– AI-powered search using Gemini** for advanced document analysis (requires `GEMINI_API_KEY`) -- `search_documents` โ€” Semantic search within a document (returns chunk hits and LLM hint) -- `get_context_window` โ€” Return a window of chunks around a target chunk index - -## Configuration & environment variables - -Configure behavior via environment variables. Important options: - -- `MCP_BASE_DIR` โ€” base directory for data storage (default: `~/.mcp-documentation-server`). Set this to use independent workspaces. -- `MCP_EMBEDDING_MODEL` โ€” embedding model name (default: `Xenova/all-MiniLM-L6-v2`). Changing the model requires re-adding documents. -- `GEMINI_API_KEY` โ€” **Google Gemini API key** for AI-powered search features (optional, enables `search_documents_with_ai`). -- `MCP_INDEXING_ENABLED` โ€” enable/disable the `DocumentIndex` (true/false). Default: `true`. -- `MCP_CACHE_SIZE` โ€” LRU embedding cache size (integer). Default: `1000`. -- `MCP_PARALLEL_ENABLED` โ€” enable parallel chunking (true/false). Default: `true`. -- `MCP_MAX_WORKERS` โ€” number of parallel workers for chunking/indexing. Default: `4`. -- `MCP_STREAMING_ENABLED` โ€” enable streaming reads for large files. Default: `true`. -- `MCP_STREAM_CHUNK_SIZE` โ€” streaming buffer size in bytes. Default: `65536` (64KB). -- `MCP_STREAM_FILE_SIZE_LIMIT` โ€” threshold (bytes) to switch to streaming path. Default: `10485760` (10MB). - -Example `.env` (defaults applied when variables are not set): - -```env -MCP_BASE_DIR=/path/to/workspace # Base directory for data storage (default: ~/.mcp-documentation-server) -MCP_INDEXING_ENABLED=true # Enable O(1) indexing (default: true) -GEMINI_API_KEY=your-api-key-here # Google Gemini API key (optional) -MCP_CACHE_SIZE=1000 # LRU cache size (default: 1000) -MCP_PARALLEL_ENABLED=true # Enable parallel processing (default: true) -MCP_MAX_WORKERS=4 # Parallel worker count (default: 4) -MCP_STREAMING_ENABLED=true # Enable streaming (default: true) -MCP_STREAM_CHUNK_SIZE=65536 # Stream chunk size (default: 64KB) -MCP_STREAM_FILE_SIZE_LIMIT=10485760 # Streaming threshold (default: 10MB) +``` +~/.mcp-documentation-server/ # Or custom path via MCP_BASE_DIR +โ”œโ”€โ”€ data/ +โ”‚ โ”œโ”€โ”€ orama-chunks.msp # Orama vector DB (chunks + embeddings) +โ”‚ โ”œโ”€โ”€ orama-docs.msp # Orama document DB (full content + metadata) +โ”‚ โ”œโ”€โ”€ migration-complete.flag # Written after legacy JSON migration +โ”‚ โ””โ”€โ”€ *.md # Markdown copies of documents +โ””โ”€โ”€ uploads/ # Drop .txt, .md, .pdf files here ``` -Default storage layout (data directory): +### Embedding Models -``` -~/.mcp-documentation-server/ # Or custom path via MCP_BASE_DIR -โ”œโ”€โ”€ data/ # Document JSON files -โ””โ”€โ”€ uploads/ # Drop files (.txt, .md, .pdf) to import -``` +Set via `MCP_EMBEDDING_MODEL`: -## Usage examples +| Model | Dimensions | Notes | +| --- | --- | --- | +| `Xenova/all-MiniLM-L6-v2` | 384 | Default โ€” fast, good quality | +| `Xenova/paraphrase-multilingual-mpnet-base-v2` | 768 | Recommended โ€” best quality, multilingual | + +Models are downloaded on first use (~80โ€“420 MB). The vector dimension is determined automatically from the provider. + +โš ๏ธ **Important**: Changing the embedding model requires re-adding all documents โ€” embeddings from different models are incompatible. The Orama database is recreated automatically when the dimension changes. -### Basic Document Operations +## Usage examples -Add a document via MCP tool: +### Add a document ```json { @@ -139,127 +138,91 @@ Add a document via MCP tool: "arguments": { "title": "Python Basics", "content": "Python is a high-level programming language...", - "metadata": { - "category": "programming", - "tags": ["python", "tutorial"] - } + "metadata": { "category": "programming", "tags": ["python", "tutorial"] } } } ``` -Search a document: +### Search within a document ```json { "tool": "search_documents", "arguments": { - "document_id": "doc-123", + "document_id": "abc123", "query": "variable assignment", "limit": 5 } } ``` -### ๐Ÿค– AI-Powered Search Examples - -**Advanced Analysis** (requires `GEMINI_API_KEY`): +### Cross-document search ```json { - "tool": "search_documents_with_ai", + "tool": "search_all_documents", "arguments": { - "document_id": "doc-123", - "query": "explain the main concepts and their relationships" + "query": "how to handle authentication", + "limit": 10 } } ``` -**Complex Questions**: +### Fetch context around a chunk ```json { - "tool": "search_documents_with_ai", + "tool": "get_context_window", "arguments": { - "document_id": "doc-123", - "query": "what are the key architectural patterns and how do they work together?" + "document_id": "abc123", + "chunk_index": 5, + "before": 2, + "after": 2 } } ``` -**Summarization Requests**: +### AI-powered analysis (requires `GEMINI_API_KEY`) ```json { "tool": "search_documents_with_ai", "arguments": { - "document_id": "doc-123", - "query": "summarize the core principles and provide examples" + "document_id": "abc123", + "query": "explain the main concepts and their relationships" } } ``` -### Context Enhancement - -Fetch context window: +## Architecture -```json -{ - "tool": "get_context_window", - "arguments": { - "document_id": "doc-123", - "chunk_index": 5, - "before": 2, - "after": 2 - } -} +``` +Server (FastMCP) + โ””โ”€ DocumentManager + โ”œโ”€ OramaStore โ€” Orama vector DB (chunks DB + docs DB), persistence, migration + โ”œโ”€ IntelligentChunker โ€” Content-aware splitting (code, markdown, text, PDF) + โ”œโ”€ EmbeddingProvider โ€” Local embeddings via @xenova/transformers + โ”‚ โ””โ”€ EmbeddingCache โ€” LRU in-memory cache + โ””โ”€ GeminiSearchService โ€” Optional AI search via Google Gemini ``` -### When to Use AI-Powered Search: -- **Complex Questions**: "How do these concepts relate to each other?" -- **Summarization**: "Give me an overview of the main principles" -- **Analysis**: "What are the key patterns and their trade-offs?" -- **Explanation**: "Explain this topic as if I were new to it" -- **Comparison**: "Compare these different approaches" - -### Performance Benefits: -- **Smart Caching**: File mapping prevents re-uploading the same content -- **Efficient Processing**: Only relevant sections are analyzed by Gemini -- **Contextual Results**: More accurate and comprehensive answers -- **Natural Interaction**: Ask questions in plain English - -- Embedding models are downloaded on first use; some models require several hundred MB of downloads. -- The `DocumentIndex` persists an index file and can be rebuilt if necessary. -- The `EmbeddingCache` can be warmed by calling `process_uploads`, issuing curated queries, or using a preload API when available. - -### Embedding Models - -Set via `MCP_EMBEDDING_MODEL` environment variable: - -- **`Xenova/all-MiniLM-L6-v2`** (default) - Fast, good quality (384 dimensions) -- **`Xenova/paraphrase-multilingual-mpnet-base-v2`** (recommended) - Best quality, multilingual (768 dimensions) - -The system automatically manages the correct embedding dimension for each model. Embedding providers expose their dimension via `getDimensions()`. - -โš ๏ธ **Important**: Changing models requires re-adding all documents as embeddings are incompatible. - +- **OramaStore** manages two Orama instances: one for document metadata/content and one for chunks with vector embeddings. Both are persisted to binary files on disk and restored on startup. +- **IntelligentChunker** detects content type and applies the best splitting strategy with adaptive chunk sizes. +- **EmbeddingProvider** lazily loads a Transformers.js model for local inference โ€” no API calls needed. ## Development ```bash git clone https://github.com/andrea9293/mcp-documentation-server.git -``` -```bash cd mcp-documentation-server +npm install ``` ```bash -npm run dev -``` -```bash -npm run build -``` -```bash -npm run inspect +npm run dev # FastMCP dev mode with hot reload +npm run build # TypeScript compilation +npm run inspect # FastMCP web UI for interactive tool testing +npm start # Direct tsx execution ``` ## Contributing @@ -271,14 +234,14 @@ npm run inspect ## License -MIT - see [LICENSE](LICENSE) file - +MIT โ€” see [LICENSE](LICENSE) ## Support - ๐Ÿ“– [Documentation](https://github.com/andrea9293/mcp-documentation-server) - ๐Ÿ› [Report Issues](https://github.com/andrea9293/mcp-documentation-server/issues) - ๐Ÿ’ฌ [MCP Community](https://modelcontextprotocol.io/) +- ๐Ÿค– [Google AI Studio](https://aistudio.google.com/app/apikey) โ€” get a Gemini API key --- @@ -286,5 +249,4 @@ MIT - see [LICENSE](LICENSE) file [![Star History Chart](https://api.star-history.com/svg?repos=andrea9293/mcp-documentation-server&type=Date)](https://www.star-history.com/#andrea9293/mcp-documentation-server&Date) - -**Built with [FastMCP](https://github.com/punkpeye/fastmcp) and TypeScript** ๐Ÿš€ \ No newline at end of file +**Built with [FastMCP](https://github.com/punkpeye/fastmcp), [Orama](https://orama.com/), and TypeScript** \ No newline at end of file diff --git a/package-lock.json b/package-lock.json index 5b5f70a..435c430 100644 --- a/package-lock.json +++ b/package-lock.json @@ -1,22 +1,23 @@ { "name": "@andrea9293/mcp-documentation-server", - "version": "1.11.1", + "version": "1.12.0", "lockfileVersion": 3, "requires": true, "packages": { "": { "name": "@andrea9293/mcp-documentation-server", - "version": "1.11.1", + "version": "1.12.0", "license": "MIT", "dependencies": { "@google/genai": "^1.19.0", "@modelcontextprotocol/sdk": "^1.12.3", + "@orama/orama": "^3.1.18", + "@orama/plugin-data-persistence": "^3.1.18", "@types/node": "^24.0.1", "@xenova/transformers": "^2.17.2", "dotenv": "^16.5.0", "fastmcp": "^3.24.0", "fs-extra": "^11.3.0", - 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"version": "2.7.0", - "resolved": "https://registry.npmjs.org/node-fetch/-/node-fetch-2.7.0.tgz", - "integrity": "sha512-c4FRfUm/dbcWZ7U+1Wq0AwCyFL+3nt2bEw05wfxSz+DWpWsitgmSgYmy2dQdWyKC1694ELPqMs/YzUSNozLt8A==", + "version": "3.3.2", + "resolved": "https://registry.npmjs.org/node-fetch/-/node-fetch-3.3.2.tgz", + "integrity": "sha512-dRB78srN/l6gqWulah9SrxeYnxeddIG30+GOqK/9OlLVyLg3HPnr6SqOWTWOXKRwC2eGYCkZ59NNuSgvSrpgOA==", "license": "MIT", "dependencies": { - "whatwg-url": "^5.0.0" + "data-uri-to-buffer": "^4.0.0", + "fetch-blob": "^3.1.4", + "formdata-polyfill": "^4.0.10" }, "engines": { - "node": "4.x || >=6.0.0" - }, - "peerDependencies": { - "encoding": "^0.1.0" + "node": "^12.20.0 || ^14.13.1 || >=16.0.0" }, - "peerDependenciesMeta": { - "encoding": { - "optional": true - } + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/node-fetch" } }, "node_modules/normalize-package-data": { @@ -4990,9 +5286,9 @@ } }, "node_modules/normalize-url": { - "version": "8.0.2", - "resolved": "https://registry.npmjs.org/normalize-url/-/normalize-url-8.0.2.tgz", - "integrity": "sha512-Ee/R3SyN4BuynXcnTaekmaVdbDAEiNrHqjQIA37mHU8G9pf7aaAD4ZX3XjBLo6rsdcxA/gtkcNYZLt30ACgynw==", + "version": "8.1.1", + "resolved": "https://registry.npmjs.org/normalize-url/-/normalize-url-8.1.1.tgz", + "integrity": "sha512-JYc0DPlpGWB40kH5g07gGTrYuMqV653k3uBKY6uITPWds3M0ov3GaWGp9lbE3Bzngx8+XkfzgvASb9vk9JDFXQ==", "dev": true, "license": "MIT", "engines": { @@ -5003,9 +5299,9 @@ } }, "node_modules/npm": { - "version": "10.9.2", - "resolved": "https://registry.npmjs.org/npm/-/npm-10.9.2.tgz", - "integrity": "sha512-iriPEPIkoMYUy3F6f3wwSZAU93E0Eg6cHwIR6jzzOXWSy+SD/rOODEs74cVONHKSx2obXtuUoyidVEhISrisgQ==", + "version": "10.9.4", + "resolved": "https://registry.npmjs.org/npm/-/npm-10.9.4.tgz", + "integrity": "sha512-OnUG836FwboQIbqtefDNlyR0gTHzIfwRfE3DuiNewBvnMnWEpB0VEXwBlFVgqpNzIgYo/MHh3d2Hel/pszapAA==", "bundleDependencies": [ "@isaacs/string-locale-compare", "@npmcli/arborist", @@ -5087,37 +5383,37 @@ ], "dependencies": { "@isaacs/string-locale-compare": "^1.1.0", - "@npmcli/arborist": "^8.0.0", + "@npmcli/arborist": "^8.0.1", "@npmcli/config": "^9.0.0", "@npmcli/fs": "^4.0.0", "@npmcli/map-workspaces": "^4.0.2", - "@npmcli/package-json": "^6.1.0", + "@npmcli/package-json": "^6.2.0", "@npmcli/promise-spawn": "^8.0.2", - "@npmcli/redact": "^3.0.0", - "@npmcli/run-script": "^9.0.1", - "@sigstore/tuf": "^3.0.0", - "abbrev": "^3.0.0", + "@npmcli/redact": "^3.2.2", + "@npmcli/run-script": "^9.1.0", + "@sigstore/tuf": "^3.1.1", + "abbrev": "^3.0.1", "archy": "~1.0.0", "cacache": "^19.0.1", - "chalk": "^5.3.0", - "ci-info": "^4.1.0", + "chalk": "^5.4.1", + "ci-info": "^4.2.0", "cli-columns": "^4.0.0", "fastest-levenshtein": "^1.0.16", "fs-minipass": "^3.0.3", "glob": "^10.4.5", "graceful-fs": "^4.2.11", - "hosted-git-info": "^8.0.2", + "hosted-git-info": "^8.1.0", "ini": "^5.0.0", "init-package-json": "^7.0.2", - "is-cidr": "^5.1.0", + "is-cidr": "^5.1.1", "json-parse-even-better-errors": "^4.0.0", "libnpmaccess": "^9.0.0", - "libnpmdiff": "^7.0.0", - "libnpmexec": "^9.0.0", - "libnpmfund": "^6.0.0", + "libnpmdiff": "^7.0.1", + "libnpmexec": "^9.0.1", + "libnpmfund": "^6.0.1", "libnpmhook": "^11.0.0", "libnpmorg": "^7.0.0", - "libnpmpack": "^8.0.0", + "libnpmpack": "^8.0.1", "libnpmpublish": "^10.0.1", "libnpmsearch": "^8.0.0", "libnpmteam": "^7.0.0", @@ -5127,23 +5423,23 @@ "minipass": "^7.1.1", "minipass-pipeline": "^1.2.4", "ms": "^2.1.2", - "node-gyp": "^11.0.0", - "nopt": "^8.0.0", + "node-gyp": "^11.2.0", + "nopt": "^8.1.0", "normalize-package-data": "^7.0.0", "npm-audit-report": "^6.0.0", "npm-install-checks": "^7.1.1", - "npm-package-arg": "^12.0.0", + "npm-package-arg": "^12.0.2", "npm-pick-manifest": "^10.0.0", "npm-profile": "^11.0.1", "npm-registry-fetch": "^18.0.2", "npm-user-validate": "^3.0.0", - "p-map": "^4.0.0", + "p-map": "^7.0.3", "pacote": "^19.0.1", "parse-conflict-json": "^4.0.0", "proc-log": "^5.0.0", "qrcode-terminal": "^0.12.0", - "read": "^4.0.0", - "semver": "^7.6.3", + "read": "^4.1.0", + "semver": "^7.7.2", "spdx-expression-parse": "^4.0.0", "ssri": "^12.0.0", "supports-color": "^9.4.0", @@ -5151,7 +5447,7 @@ "text-table": "~0.2.0", "tiny-relative-date": "^1.3.0", "treeverse": "^3.0.0", - "validate-npm-package-name": "^6.0.0", + "validate-npm-package-name": "^6.0.1", "which": "^5.0.0", "write-file-atomic": "^6.0.0" }, @@ -5278,7 +5574,7 @@ } }, "node_modules/npm/node_modules/@npmcli/arborist": { - "version": "8.0.0", + "version": "8.0.1", "dev": true, "inBundle": true, "license": "ISC", @@ -5358,7 +5654,7 @@ } }, "node_modules/npm/node_modules/@npmcli/git": { - "version": "6.0.1", + "version": "6.0.3", "dev": true, "inBundle": true, "license": "ISC", @@ -5368,7 +5664,6 @@ "lru-cache": "^10.0.1", "npm-pick-manifest": "^10.0.0", "proc-log": "^5.0.0", - "promise-inflight": "^1.0.1", "promise-retry": "^2.0.1", "semver": "^7.3.5", "which": "^5.0.0" @@ -5474,7 +5769,7 @@ } }, "node_modules/npm/node_modules/@npmcli/package-json": { - "version": "6.1.0", + "version": "6.2.0", "dev": true, "inBundle": true, "license": "ISC", @@ -5483,9 +5778,9 @@ "glob": "^10.2.2", "hosted-git-info": "^8.0.0", "json-parse-even-better-errors": "^4.0.0", - "normalize-package-data": "^7.0.0", "proc-log": "^5.0.0", - "semver": "^7.5.3" + "semver": "^7.5.3", + "validate-npm-package-license": "^3.0.4" }, "engines": { "node": "^18.17.0 || >=20.5.0" @@ -5504,19 +5799,19 @@ } }, "node_modules/npm/node_modules/@npmcli/query": { - "version": "4.0.0", + "version": "4.0.1", "dev": true, "inBundle": true, "license": "ISC", "dependencies": { - "postcss-selector-parser": "^6.1.2" + "postcss-selector-parser": "^7.0.0" }, "engines": { "node": "^18.17.0 || >=20.5.0" } }, "node_modules/npm/node_modules/@npmcli/redact": { - "version": "3.0.0", + "version": "3.2.2", "dev": true, "inBundle": true, "license": "ISC", @@ -5525,7 +5820,7 @@ } }, "node_modules/npm/node_modules/@npmcli/run-script": { - "version": "9.0.2", + "version": "9.1.0", "dev": true, "inBundle": true, "license": "ISC", @@ -5552,21 +5847,21 @@ } }, "node_modules/npm/node_modules/@sigstore/protobuf-specs": { - "version": "0.3.2", + "version": "0.4.3", "dev": true, "inBundle": true, "license": "Apache-2.0", "engines": { - "node": "^16.14.0 || >=18.0.0" + "node": "^18.17.0 || >=20.5.0" } }, "node_modules/npm/node_modules/@sigstore/tuf": { - "version": "3.0.0", + "version": "3.1.1", "dev": true, "inBundle": true, "license": "Apache-2.0", "dependencies": { - "@sigstore/protobuf-specs": "^0.3.2", + "@sigstore/protobuf-specs": "^0.4.1", "tuf-js": "^3.0.1" }, "engines": { @@ -5583,7 +5878,7 @@ } }, "node_modules/npm/node_modules/abbrev": { - "version": "3.0.0", + "version": "3.0.1", "dev": true, "inBundle": true, "license": "ISC", @@ -5592,30 +5887,14 @@ } }, "node_modules/npm/node_modules/agent-base": { - "version": "7.1.1", + "version": "7.1.3", "dev": true, "inBundle": true, "license": "MIT", - "dependencies": { - "debug": "^4.3.4" - }, "engines": { "node": ">= 14" } }, - "node_modules/npm/node_modules/aggregate-error": { - "version": "3.1.0", - "dev": true, - "inBundle": true, - "license": "MIT", - "dependencies": { - "clean-stack": "^2.0.0", - "indent-string": "^4.0.0" - }, - "engines": { - "node": ">=8" - } - }, "node_modules/npm/node_modules/ansi-regex": { "version": "5.0.1", "dev": true, @@ -5684,7 +5963,7 @@ } }, "node_modules/npm/node_modules/brace-expansion": { - "version": "2.0.1", + "version": "2.0.2", "dev": true, "inBundle": true, "license": "MIT", @@ -5724,19 +6003,6 @@ "node": ">=18" } }, - "node_modules/npm/node_modules/cacache/node_modules/minizlib": { - "version": "3.0.1", - "dev": true, - "inBundle": true, - "license": "MIT", - "dependencies": { - "minipass": "^7.0.4", - "rimraf": "^5.0.5" - }, - "engines": { - "node": ">= 18" - } - }, "node_modules/npm/node_modules/cacache/node_modules/mkdirp": { "version": "3.0.1", "dev": true, @@ -5752,18 +6018,6 @@ "url": "https://github.com/sponsors/isaacs" } }, - "node_modules/npm/node_modules/cacache/node_modules/p-map": { - "version": "7.0.2", - "dev": true, - "inBundle": true, - "license": "MIT", - "engines": { - "node": ">=18" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, "node_modules/npm/node_modules/cacache/node_modules/tar": { "version": "7.4.3", "dev": true, @@ -5791,7 +6045,7 @@ } }, "node_modules/npm/node_modules/chalk": { - "version": "5.3.0", + "version": "5.4.1", "dev": true, "inBundle": true, "license": "MIT", @@ -5812,7 +6066,7 @@ } }, "node_modules/npm/node_modules/ci-info": { - "version": "4.1.0", + "version": "4.2.0", "dev": true, "funding": [ { @@ -5827,24 +6081,15 @@ } }, "node_modules/npm/node_modules/cidr-regex": { - "version": "4.1.1", + "version": "4.1.3", "dev": true, "inBundle": true, "license": "BSD-2-Clause", "dependencies": { "ip-regex": "^5.0.0" - }, - "engines": { - "node": ">=14" - } - }, - "node_modules/npm/node_modules/clean-stack": { - "version": "2.2.0", - "dev": true, - "inBundle": true, - "license": "MIT", + }, "engines": { - "node": ">=6" + "node": ">=14" } }, "node_modules/npm/node_modules/cli-columns": { @@ -5935,7 +6180,7 @@ } }, "node_modules/npm/node_modules/debug": { - "version": "4.3.7", + "version": "4.4.1", "dev": true, "inBundle": true, "license": "MIT", @@ -5998,7 +6243,7 @@ "license": "MIT" }, "node_modules/npm/node_modules/exponential-backoff": { - "version": "3.1.1", + "version": "3.1.2", "dev": true, "inBundle": true, "license": "Apache-2.0" @@ -6013,12 +6258,12 @@ } }, "node_modules/npm/node_modules/foreground-child": { - "version": "3.3.0", + "version": "3.3.1", "dev": true, "inBundle": true, "license": "ISC", "dependencies": { - "cross-spawn": "^7.0.0", + "cross-spawn": "^7.0.6", "signal-exit": "^4.0.1" }, "engines": { @@ -6067,7 +6312,7 @@ "license": "ISC" }, "node_modules/npm/node_modules/hosted-git-info": { - "version": "8.0.2", + "version": "8.1.0", "dev": true, "inBundle": true, "license": "ISC", @@ -6079,7 +6324,7 @@ } }, "node_modules/npm/node_modules/http-cache-semantics": { - "version": "4.1.1", + "version": "4.2.0", "dev": true, "inBundle": true, "license": "BSD-2-Clause" @@ -6098,12 +6343,12 @@ } }, "node_modules/npm/node_modules/https-proxy-agent": { - "version": "7.0.5", + "version": "7.0.6", "dev": true, "inBundle": true, "license": "MIT", "dependencies": { - "agent-base": "^7.0.2", + "agent-base": "^7.1.2", "debug": "4" }, "engines": { @@ -6144,15 +6389,6 @@ "node": ">=0.8.19" } }, - "node_modules/npm/node_modules/indent-string": { - "version": "4.0.0", - "dev": true, - "inBundle": true, - "license": "MIT", - "engines": { - "node": ">=8" - } - }, "node_modules/npm/node_modules/ini": { "version": "5.0.0", "dev": true, @@ -6206,7 +6442,7 @@ } }, "node_modules/npm/node_modules/is-cidr": { - "version": "5.1.0", + "version": "5.1.1", "dev": true, "inBundle": true, "license": "BSD-2-Clause", @@ -6306,12 +6542,12 @@ } }, "node_modules/npm/node_modules/libnpmdiff": { - "version": "7.0.0", + "version": "7.0.1", "dev": true, "inBundle": true, "license": "ISC", "dependencies": { - "@npmcli/arborist": "^8.0.0", + "@npmcli/arborist": "^8.0.1", "@npmcli/installed-package-contents": "^3.0.0", "binary-extensions": "^2.3.0", "diff": "^5.1.0", @@ -6325,12 +6561,12 @@ } }, "node_modules/npm/node_modules/libnpmexec": { - "version": "9.0.0", + "version": "9.0.1", "dev": true, "inBundle": true, "license": "ISC", "dependencies": { - "@npmcli/arborist": "^8.0.0", + "@npmcli/arborist": "^8.0.1", "@npmcli/run-script": "^9.0.1", "ci-info": "^4.0.0", "npm-package-arg": "^12.0.0", @@ -6346,12 +6582,12 @@ } }, "node_modules/npm/node_modules/libnpmfund": { - "version": "6.0.0", + "version": "6.0.1", "dev": true, "inBundle": true, "license": "ISC", "dependencies": { - "@npmcli/arborist": "^8.0.0" + "@npmcli/arborist": "^8.0.1" }, "engines": { "node": "^18.17.0 || >=20.5.0" @@ -6384,12 +6620,12 @@ } }, "node_modules/npm/node_modules/libnpmpack": { - "version": "8.0.0", + "version": "8.0.1", "dev": true, "inBundle": true, "license": "ISC", "dependencies": { - "@npmcli/arborist": "^8.0.0", + "@npmcli/arborist": "^8.0.1", "@npmcli/run-script": "^9.0.1", "npm-package-arg": "^12.0.0", "pacote": "^19.0.0" @@ -6532,7 +6768,7 @@ } }, "node_modules/npm/node_modules/minipass-fetch": { - "version": "4.0.0", + "version": "4.0.1", "dev": true, "inBundle": true, "license": "MIT", @@ -6548,19 +6784,6 @@ "encoding": "^0.1.13" } }, - "node_modules/npm/node_modules/minipass-fetch/node_modules/minizlib": { - "version": "3.0.1", - "dev": true, - "inBundle": true, - "license": "MIT", - "dependencies": { - "minipass": "^7.0.4", - "rimraf": "^5.0.5" - }, - "engines": { - "node": ">= 18" - } - }, "node_modules/npm/node_modules/minipass-flush": { "version": "1.0.5", "dev": true, @@ -6634,28 +6857,15 @@ } }, "node_modules/npm/node_modules/minizlib": { - "version": "2.1.2", + "version": "3.0.2", "dev": true, "inBundle": true, "license": "MIT", "dependencies": { - "minipass": "^3.0.0", - "yallist": "^4.0.0" - }, - "engines": { - "node": ">= 8" - } - }, - "node_modules/npm/node_modules/minizlib/node_modules/minipass": { - "version": "3.3.6", - "dev": true, - "inBundle": true, - "license": "ISC", - "dependencies": { - "yallist": "^4.0.0" + "minipass": "^7.1.2" }, "engines": { - "node": ">=8" + "node": ">= 18" } }, "node_modules/npm/node_modules/mkdirp": { @@ -6686,20 +6896,20 @@ } }, "node_modules/npm/node_modules/node-gyp": { - "version": "11.0.0", + "version": "11.2.0", "dev": true, "inBundle": true, "license": "MIT", "dependencies": { "env-paths": "^2.2.0", "exponential-backoff": "^3.1.1", - "glob": "^10.3.10", "graceful-fs": "^4.2.6", "make-fetch-happen": "^14.0.3", "nopt": "^8.0.0", "proc-log": "^5.0.0", "semver": "^7.3.5", "tar": "^7.4.3", + "tinyglobby": "^0.2.12", "which": "^5.0.0" }, "bin": { @@ -6718,19 +6928,6 @@ "node": ">=18" } }, - "node_modules/npm/node_modules/node-gyp/node_modules/minizlib": { - "version": "3.0.1", - "dev": true, - "inBundle": true, - "license": "MIT", - "dependencies": { - "minipass": "^7.0.4", - "rimraf": "^5.0.5" - }, - "engines": { - "node": ">= 18" - } - }, "node_modules/npm/node_modules/node-gyp/node_modules/mkdirp": { "version": "3.0.1", "dev": true, @@ -6773,12 +6970,12 @@ } }, "node_modules/npm/node_modules/nopt": { - "version": "8.0.0", + "version": "8.1.0", "dev": true, "inBundle": true, "license": "ISC", "dependencies": { - "abbrev": "^2.0.0" + "abbrev": "^3.0.0" }, "bin": { "nopt": "bin/nopt.js" @@ -6787,15 +6984,6 @@ "node": "^18.17.0 || >=20.5.0" } }, - "node_modules/npm/node_modules/nopt/node_modules/abbrev": { - "version": "2.0.0", - "dev": true, - "inBundle": true, - "license": "ISC", - "engines": { - "node": "^14.17.0 || ^16.13.0 || >=18.0.0" - } - }, "node_modules/npm/node_modules/normalize-package-data": { "version": "7.0.0", "dev": true, @@ -6853,7 +7041,7 @@ } }, "node_modules/npm/node_modules/npm-package-arg": { - "version": "12.0.0", + "version": "12.0.2", "dev": true, "inBundle": true, "license": "ISC", @@ -6926,19 +7114,6 @@ "node": "^18.17.0 || >=20.5.0" } }, - "node_modules/npm/node_modules/npm-registry-fetch/node_modules/minizlib": { - "version": "3.0.1", - "dev": true, - "inBundle": true, - "license": "MIT", - "dependencies": { - "minipass": "^7.0.4", - "rimraf": "^5.0.5" - }, - "engines": { - "node": ">= 18" - } - }, "node_modules/npm/node_modules/npm-user-validate": { "version": "3.0.0", "dev": true, @@ -6949,15 +7124,12 @@ } }, "node_modules/npm/node_modules/p-map": { - "version": "4.0.0", + "version": "7.0.3", "dev": true, "inBundle": true, "license": "MIT", - "dependencies": { - "aggregate-error": "^3.0.0" - }, "engines": { - "node": ">=10" + "node": ">=18" }, "funding": { "url": "https://github.com/sponsors/sindresorhus" @@ -7040,7 +7212,7 @@ } }, "node_modules/npm/node_modules/postcss-selector-parser": { - "version": "6.1.2", + "version": "7.1.0", "dev": true, "inBundle": true, "license": "MIT", @@ -7088,12 +7260,6 @@ "url": "https://github.com/sponsors/isaacs" } }, - "node_modules/npm/node_modules/promise-inflight": { - "version": "1.0.1", - "dev": true, - "inBundle": true, - "license": "ISC" - }, "node_modules/npm/node_modules/promise-retry": { "version": "2.0.1", "dev": true, @@ -7128,7 +7294,7 @@ } }, "node_modules/npm/node_modules/read": { - "version": "4.0.0", + "version": "4.1.0", "dev": true, "inBundle": true, "license": "ISC", @@ -7170,21 +7336,6 @@ "node": ">= 4" } }, - "node_modules/npm/node_modules/rimraf": { - "version": "5.0.10", - "dev": true, - "inBundle": true, - "license": "ISC", - "dependencies": { - "glob": "^10.3.7" - }, - "bin": { - "rimraf": "dist/esm/bin.mjs" - }, - "funding": { - "url": "https://github.com/sponsors/isaacs" - } - }, "node_modules/npm/node_modules/safer-buffer": { "version": "2.1.2", "dev": true, @@ -7193,7 +7344,7 @@ "optional": true }, "node_modules/npm/node_modules/semver": { - "version": "7.6.3", + "version": "7.7.2", "dev": true, "inBundle": true, "license": "ISC", @@ -7238,29 +7389,29 @@ } }, "node_modules/npm/node_modules/sigstore": { - "version": "3.0.0", + "version": "3.1.0", "dev": true, "inBundle": true, "license": "Apache-2.0", "dependencies": { - "@sigstore/bundle": "^3.0.0", + "@sigstore/bundle": "^3.1.0", "@sigstore/core": "^2.0.0", - "@sigstore/protobuf-specs": "^0.3.2", - "@sigstore/sign": "^3.0.0", - "@sigstore/tuf": "^3.0.0", - "@sigstore/verify": "^2.0.0" + "@sigstore/protobuf-specs": "^0.4.0", + "@sigstore/sign": "^3.1.0", + "@sigstore/tuf": "^3.1.0", + "@sigstore/verify": "^2.1.0" }, "engines": { "node": "^18.17.0 || >=20.5.0" } }, "node_modules/npm/node_modules/sigstore/node_modules/@sigstore/bundle": { - "version": "3.0.0", + "version": "3.1.0", "dev": true, "inBundle": true, "license": "Apache-2.0", "dependencies": { - "@sigstore/protobuf-specs": "^0.3.2" + "@sigstore/protobuf-specs": "^0.4.0" }, "engines": { "node": "^18.17.0 || >=20.5.0" @@ -7276,15 +7427,15 @@ } }, "node_modules/npm/node_modules/sigstore/node_modules/@sigstore/sign": { - "version": "3.0.0", + "version": "3.1.0", "dev": true, "inBundle": true, "license": "Apache-2.0", "dependencies": { - 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"agent-base": "^7.1.1", + "agent-base": "^7.1.2", "debug": "^4.3.4", "socks": "^2.8.3" }, @@ -7381,7 +7532,7 @@ } }, "node_modules/npm/node_modules/spdx-license-ids": { - "version": "3.0.20", + "version": "3.0.21", "dev": true, "inBundle": true, "license": "CC0-1.0" @@ -7520,6 +7671,31 @@ "node": ">=8" } }, + "node_modules/npm/node_modules/tar/node_modules/minizlib": { + "version": "2.1.2", + "dev": true, + "inBundle": true, + "license": "MIT", + "dependencies": { + "minipass": "^3.0.0", + "yallist": "^4.0.0" + }, + "engines": { + "node": ">= 8" + } + }, + "node_modules/npm/node_modules/tar/node_modules/minizlib/node_modules/minipass": { + "version": "3.3.6", + "dev": true, + "inBundle": true, + "license": "ISC", + "dependencies": { + "yallist": "^4.0.0" + }, + "engines": { + "node": ">=8" + } + }, "node_modules/npm/node_modules/text-table": { "version": "0.2.0", "dev": true, @@ -7532,6 +7708,48 @@ "inBundle": true, "license": "MIT" }, + "node_modules/npm/node_modules/tinyglobby": { + "version": "0.2.14", + "dev": true, + "inBundle": true, + "license": "MIT", + "dependencies": { + "fdir": "^6.4.4", + "picomatch": "^4.0.2" + }, + "engines": { + "node": ">=12.0.0" + }, + "funding": { + "url": "https://github.com/sponsors/SuperchupuDev" + } + }, + "node_modules/npm/node_modules/tinyglobby/node_modules/fdir": { + "version": "6.4.6", + "dev": true, + "inBundle": true, + "license": "MIT", + "peerDependencies": { + "picomatch": "^3 || ^4" + }, + "peerDependenciesMeta": { + "picomatch": { + "optional": true + } + } + }, + "node_modules/npm/node_modules/tinyglobby/node_modules/picomatch": { + "version": "4.0.2", + "dev": true, + "inBundle": true, + "license": "MIT", + "engines": { + "node": ">=12" + }, + "funding": { + "url": "https://github.com/sponsors/jonschlinkert" + } + }, "node_modules/npm/node_modules/treeverse": { "version": "3.0.0", "dev": true, @@ -7619,7 +7837,7 @@ } }, "node_modules/npm/node_modules/validate-npm-package-name": { - 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"license": "ISC", "peerDependencies": { "zod": "^3.25 || ^4" diff --git a/package.json b/package.json index 68840da..c9d58d2 100644 --- a/package.json +++ b/package.json @@ -46,7 +46,6 @@ "@semantic-release/release-notes-generator": "^14.0.3", "@tsconfig/node22": "^22.0.1", "@types/fs-extra": "^11.0.4", - "@types/glob": "^8.1.0", "semantic-release": "^24.2.5", "shx": "^0.3.4", "tsx": "^4.20.3", @@ -55,12 +54,13 @@ "dependencies": { "@google/genai": "^1.19.0", "@modelcontextprotocol/sdk": "^1.12.3", + "@orama/orama": "^3.1.18", + "@orama/plugin-data-persistence": "^3.1.18", "@types/node": "^24.0.1", "@xenova/transformers": "^2.17.2", "dotenv": "^16.5.0", "fastmcp": "^3.24.0", "fs-extra": "^11.3.0", - "glob": "^11.0.3", "unpdf": "^1.2.1", "zod": "^3.25.64" }, diff --git a/src/document-manager.ts b/src/document-manager.ts index 410862d..ddb32c4 100644 --- a/src/document-manager.ts +++ b/src/document-manager.ts @@ -1,28 +1,26 @@ import { existsSync, mkdirSync } from "fs"; import { writeFile, readFile, copyFile, readdir, unlink } from "fs/promises"; import * as path from "path"; -import { glob } from "glob"; import { createHash } from 'crypto'; import { Document, DocumentChunk, SearchResult, EmbeddingProvider } from './types.js'; import { SimpleEmbeddingProvider } from './embedding-provider.js'; import { IntelligentChunker } from './intelligent-chunker.js'; import { extractText } from 'unpdf'; import { getDefaultDataDir } from './utils.js'; -import { DocumentIndex } from './indexing/document-index.js'; +import { OramaStore } from './orama-store.js'; import { GeminiFileMappingService } from './gemini-file-mapping-service.js'; /** - * Document manager that handles document operations with chunking, indexing, and embeddings + * Document manager that handles document operations with chunking and Orama vector DB */ export class DocumentManager { private dataDir: string; private uploadsDir: string; private embeddingProvider: EmbeddingProvider; private intelligentChunker: IntelligentChunker; - private documentIndex: DocumentIndex | null = null; - private useIndexing: boolean; - private useParallelProcessing: boolean; + private oramaStore: OramaStore; private useStreaming: boolean; + private oramaInitialized = false; constructor(embeddingProvider?: EmbeddingProvider) { // Always use default paths @@ -34,34 +32,28 @@ export class DocumentManager { this.intelligentChunker = new IntelligentChunker(this.embeddingProvider); // Feature flags with fallback - this.useIndexing = process.env.MCP_INDEXING_ENABLED !== 'false'; - this.useParallelProcessing = process.env.MCP_PARALLEL_ENABLED !== 'false'; this.useStreaming = process.env.MCP_STREAMING_ENABLED !== 'false'; + + // Create OramaStore with embedding dimensions + this.oramaStore = new OramaStore(this.embeddingProvider.getDimensions()); + console.error(`[DocumentManager] Constructed with embeddingModel=${this.embeddingProvider.getModelName()} vectorDimensions=${this.embeddingProvider.getDimensions()}`); this.ensureDataDir(); this.ensureUploadsDir(); // Initialize Gemini file mapping service GeminiFileMappingService.initialize(this.dataDir); - - // Initialize indexing with error handling - if (this.useIndexing) { - try { - this.documentIndex = new DocumentIndex(this.dataDir); - console.error('[DocumentManager] Indexing enabled'); - } catch (error) { - console.warn('[DocumentManager] Indexing disabled due to error:', error); - this.useIndexing = false; - } - } } /** - * Initialize the document index (lazy initialization) + * Ensure OramaStore is initialized (lazy init) */ - private async ensureIndexInitialized(): Promise { - if (this.documentIndex && this.useIndexing) { - await this.documentIndex.initialize(this.dataDir); + private async ensureOramaInitialized(): Promise { + if (!this.oramaInitialized) { + console.error('[DocumentManager] Initializing OramaStore...'); + await this.oramaStore.initialize(); + this.oramaInitialized = true; + console.error('[DocumentManager] OramaStore initialized'); } } @@ -90,29 +82,18 @@ export class DocumentManager { return path.resolve(this.uploadsDir); } - private getDocumentPath(id: string): string { - return path.join(this.dataDir, `${id}.json`); - } - private getDocumentMdPath(id: string): string { return path.join(this.dataDir, `${id}.md`); } async addDocument(title: string, content: string, metadata: Record = {}): Promise { - // Check for duplicate content if indexing is enabled - if (this.useIndexing && this.documentIndex) { - await this.ensureIndexInitialized(); - const duplicateId = this.documentIndex.findDuplicateContent(content); - if (duplicateId) { - console.warn(`[DocumentManager] Duplicate content detected, existing document: ${duplicateId}`); - // Optionally, you might want to return the existing document or throw an error - // For now, we'll continue with creating a new document - } - } + await this.ensureOramaInitialized(); const id = this.generateId(content); const now = new Date().toISOString(); + console.error(`[DocumentManager] Adding document id=${id} title=${title}`); + // Create chunks using intelligent chunker const chunks = await this.intelligentChunker.createChunks(id, content, { maxSize: 500, @@ -131,49 +112,22 @@ export class DocumentManager { updated_at: now, }; - const filePath = this.getDocumentPath(id); - await writeFile(filePath, JSON.stringify(document, null, 2)); + // Store in Orama + await this.oramaStore.addDocument(document); + console.error(`[DocumentManager] Stored document in Orama: ${id}`); // Create markdown file with the document content const mdFilePath = this.getDocumentMdPath(id); const mdContent = `# ${title}\n\n${content}`; await writeFile(mdFilePath, mdContent, 'utf-8'); - // Add to index if enabled - if (this.useIndexing && this.documentIndex) { - this.documentIndex.addDocument(id, filePath, content, chunks); - } - return document; } async getDocument(id: string): Promise { - if (this.useIndexing && this.documentIndex) { - await this.ensureIndexInitialized(); - - // Use index for O(1) lookup - const filePath = this.documentIndex.findDocument(id); - if (!filePath) { - return null; - } - - try { - const data = await readFile(filePath, 'utf-8'); - return JSON.parse(data); - } catch { - // File might have been deleted, remove from index - this.documentIndex.removeDocument(id); - return null; - } - } - - // Fallback to original method - try { - const data = await readFile(this.getDocumentPath(id), 'utf-8'); - return JSON.parse(data); - } catch { - return null; - } + await this.ensureOramaInitialized(); + console.error(`[DocumentManager] getDocument id=${id}`); + return this.oramaStore.getDocument(id); } async getOnlyContentDocument(id: string): Promise { @@ -182,78 +136,26 @@ export class DocumentManager { } async getAllDocuments(): Promise { - if (this.useIndexing && this.documentIndex) { - await this.ensureIndexInitialized(); - - // Use index for faster lookup - const documentIds = this.documentIndex.getAllDocumentIds(); - const documents: Document[] = []; - - for (const id of documentIds) { - const document = await this.getDocument(id); - if (document) { - documents.push(document); - } - } - - return documents; - } - - // Fallback to original method - // Use forward slashes for glob pattern to work on all platforms - const globPattern = this.dataDir.replace(/\\/g, '/') + "/*.json"; - const files = await glob(globPattern); - const documents: Document[] = []; - - for (const file of files) { - try { - const data = await readFile(file, 'utf-8'); - const document = JSON.parse(data); - if (document.id) { // Only include valid documents - documents.push(document); - } - } catch { - // Skip invalid files - } - } - - return documents; + await this.ensureOramaInitialized(); + console.error('[DocumentManager] getAllDocuments'); + return this.oramaStore.getAllDocuments(); } async searchDocuments(documentId: string, query: string, limit = 10): Promise { + await this.ensureOramaInitialized(); + console.error(`[DocumentManager] searchDocuments documentId=${documentId} query="${query}" limit=${limit}`); const queryEmbedding = await this.embeddingProvider.generateEmbedding(query); - const document = await this.getDocument(documentId); - - if (!document) { - return []; - } - - const results: SearchResult[] = document.chunks - .filter(chunk => chunk.embeddings && chunk.embeddings.length > 0) - .map(chunk => ({ - chunk, - score: this.cosineSimilarity(queryEmbedding, chunk.embeddings!) - })) - .sort((a, b) => b.score - a.score) - .slice(0, limit); - - return results; + return this.oramaStore.searchChunks(queryEmbedding, limit, documentId); } - private cosineSimilarity(a: number[], b: number[]): number { - if (a.length !== b.length) return 0; - - let dotProduct = 0; - let normA = 0; - let normB = 0; - - for (let i = 0; i < a.length; i++) { - dotProduct += a[i] * b[i]; - normA += a[i] * a[i]; - normB += b[i] * b[i]; - } - - return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB)); + /** + * Search across all documents (cross-document semantic search) + */ + async searchAllDocuments(query: string, limit = 10): Promise { + await this.ensureOramaInitialized(); + console.error(`[DocumentManager] searchAllDocuments query="${query}" limit=${limit}`); + const queryEmbedding = await this.embeddingProvider.generateEmbedding(query); + return this.oramaStore.searchAllDocuments(queryEmbedding, limit, query); } private generateId(content: string): string { @@ -265,8 +167,6 @@ export class DocumentManager { /** * Extract text content from a PDF file with streaming support for large files - * @param filePath Path to the PDF file - * @returns Extracted text content */ private async extractTextFromPdf(filePath: string): Promise { try { @@ -351,8 +251,11 @@ export class DocumentManager { try { // Get all supported files from uploads directory - const pattern = this.uploadsDir.replace(/\\/g, '/') + "/*{.txt,.md,.pdf}"; - const files = await glob(pattern); + const allFiles = await readdir(this.uploadsDir); + console.error(`[DocumentManager] processUploadsFolder found ${allFiles.length} files in uploads`); + const files = allFiles + .filter(f => supportedExtensions.includes(path.extname(f).toLowerCase())) + .map(f => path.join(this.uploadsDir, f)); for (const filePath of files) { try { @@ -384,7 +287,7 @@ export class DocumentManager { // Check if document with this filename already exists and remove it const existingDoc = await this.findDocumentByTitle(title); if (existingDoc) { - await this.removeDocument(existingDoc.id); + await this.deleteDocument(existingDoc.id); } // Create new document with embeddings @@ -395,7 +298,7 @@ export class DocumentManager { processedAt: new Date().toISOString() }); - // Copy original file to data directory with same name as JSON file (keep backup in uploads) + // Copy original file to data directory with same name as document ID const documentId = document.id; const destinationFileName = `${documentId}${fileExtension}`; const destinationPath = path.join(this.dataDir, destinationFileName); @@ -424,31 +327,19 @@ export class DocumentManager { return documents.find(doc => doc.title === title) || null; } - private async removeDocument(documentId: string): Promise { - try { - const documentPath = this.getDocumentPath(documentId); - if (existsSync(documentPath)) { - await import('fs/promises').then(fs => fs.unlink(documentPath)); - } - } catch (error) { - // Ignore errors when removing non-existent files - } - } - async listUploadsFiles(): Promise<{ name: string; size: number; modified: string; supported: boolean }[]> { const supportedExtensions = ['.txt', '.md', '.pdf']; const files: { name: string; size: number; modified: string; supported: boolean }[] = []; try { - const pattern = this.uploadsDir.replace(/\\/g, '/') + "/*"; - const filePaths = await glob(pattern); + const allFiles = await readdir(this.uploadsDir); + const fsp = await import('fs/promises'); - for (const filePath of filePaths) { - const stats = await import('fs/promises').then(fs => fs.stat(filePath)); + for (const fileName of allFiles) { + const filePath = path.join(this.uploadsDir, fileName); + const stats = await fsp.stat(filePath); if (stats.isFile()) { - const fileName = path.basename(filePath); const fileExtension = path.extname(fileName).toLowerCase(); - files.push({ name: fileName, size: stats.size, @@ -465,16 +356,12 @@ export class DocumentManager { } async deleteDocument(documentId: string): Promise { + await this.ensureOramaInitialized(); + try { - const documentPath = this.getDocumentPath(documentId); - let deletedMainFile = false; - - // Delete main JSON file - if (existsSync(documentPath)) { - await unlink(documentPath); - deletedMainFile = true; - console.error(`[DocumentManager] Deleted JSON file: ${documentId}.json`); - } + console.error(`[DocumentManager] deleteDocument id=${documentId}`); + // Delete from Orama + const deleted = await this.oramaStore.deleteDocument(documentId); // Delete associated markdown file const mdPath = this.getDocumentMdPath(documentId); @@ -483,11 +370,11 @@ export class DocumentManager { console.error(`[DocumentManager] Deleted markdown file: ${documentId}.md`); } - // Delete associated original files (any extension except .json) + // Delete associated original files (any extension) try { const files = await readdir(this.dataDir); for (const file of files) { - if (file.startsWith(documentId) && !file.endsWith('.json')) { + if (file.startsWith(documentId) && !file.endsWith('.json') && !file.endsWith('.msp')) { const filePath = path.join(this.dataDir, file); await unlink(filePath); console.error(`[DocumentManager] Deleted associated file: ${file}`); @@ -497,15 +384,11 @@ export class DocumentManager { console.error(`[DocumentManager] Warning: Could not delete associated files for ${documentId}: ${fileError instanceof Error ? fileError.message : String(fileError)}`); } - // Remove from index if enabled - if (this.useIndexing && this.documentIndex) { - this.documentIndex.removeDocument(documentId); - } - // Remove Gemini file mapping if exists await GeminiFileMappingService.removeMapping(documentId); - return deletedMainFile; + console.error(`[DocumentManager] deleteDocument completed id=${documentId} deleted=${deleted}`); + return deleted; } catch (error) { throw new Error(`Failed to delete document: ${error instanceof Error ? error.message : String(error)}`); } @@ -517,16 +400,11 @@ export class DocumentManager { getStats(): any { const stats: any = { features: { - indexing: this.useIndexing, - parallelProcessing: this.useParallelProcessing, - streaming: this.useStreaming + streaming: this.useStreaming, + storage: 'orama' } }; - if (this.useIndexing && this.documentIndex) { - stats.indexing = this.documentIndex.getStats(); - } - if (this.embeddingProvider && typeof this.embeddingProvider.getCacheStats === 'function') { stats.embedding_cache = this.embeddingProvider.getCacheStats(); } diff --git a/src/indexing/document-index.ts b/src/indexing/document-index.ts deleted file mode 100644 index 736db19..0000000 --- a/src/indexing/document-index.ts +++ /dev/null @@ -1,283 +0,0 @@ -import { createHash } from 'crypto'; -import fse from 'fs-extra'; -import { promises as fsp } from 'fs'; -import * as path from 'path'; - -/** - * In-memory indexing system for O(1) document and chunk lookups - * Replaces linear searches with hash-based maps for scalability - */ -export class DocumentIndex { - private documentMap = new Map(); // id -> filePath - private chunkMap = new Map(); // chunkId -> document info - private contentHash = new Map(); // contentHash -> docId (deduplication) - private keywordIndex = new Map>(); // keyword -> docIds - private initialized = false; - private indexFilePath: string; - - constructor(dataDir: string) { - this.indexFilePath = path.join(dataDir, 'document-index.json'); - } - - /** - * Initialize the index by loading from disk or building from existing documents - */ - async initialize(dataDir: string): Promise { - if (this.initialized) return; - - try { - // Try to load existing index - await this.loadIndex(); - } catch (error) { - console.error('[DocumentIndex] Failed to load existing index, rebuilding:', error); - // Rebuild index from existing documents - await this.rebuildIndex(dataDir); - } - - this.initialized = true; - console.error(`[DocumentIndex] Initialized with ${this.documentMap.size} documents`); - } - - /** - * Add a document to the index - */ - addDocument(id: string, filePath: string, content: string, chunks?: any[]): void { - // Add to document map - this.documentMap.set(id, filePath); - - // Add content hash for deduplication - const contentHashValue = this.hashContent(content); - this.contentHash.set(contentHashValue, id); - - // Add chunks to chunk map - if (chunks) { - chunks.forEach((chunk, index) => { - this.chunkMap.set(chunk.id, { docId: id, chunkIndex: index }); - }); - } - - // Extract and index keywords - this.indexKeywords(id, content); - - // Persist index - this.saveIndex().catch(error => - console.warn('[DocumentIndex] Failed to save index:', error) - ); - } - - /** - * Remove a document from the index - */ - removeDocument(id: string): void { - const filePath = this.documentMap.get(id); - if (!filePath) return; - - // Remove from document map - this.documentMap.delete(id); - - // Remove from content hash - for (const [hash, docId] of this.contentHash.entries()) { - if (docId === id) { - this.contentHash.delete(hash); - break; - } - } - - // Remove chunks - for (const [chunkId, chunkInfo] of this.chunkMap.entries()) { - if (chunkInfo.docId === id) { - this.chunkMap.delete(chunkId); - } - } - - // Remove from keyword index - for (const [keyword, docIds] of this.keywordIndex.entries()) { - docIds.delete(id); - if (docIds.size === 0) { - this.keywordIndex.delete(keyword); - } - } - - // Persist index - this.saveIndex().catch(error => - console.warn('[DocumentIndex] Failed to save index:', error) - ); - } - - /** - * Find document file path by ID - O(1) lookup - */ - findDocument(id: string): string | undefined { - return this.documentMap.get(id); - } - - /** - * Find chunk information by chunk ID - O(1) lookup - */ - findChunk(chunkId: string): {docId: string, chunkIndex: number} | undefined { - return this.chunkMap.get(chunkId); - } - - /** - * Find duplicate content by hash - O(1) lookup - */ - findDuplicateContent(content: string): string | undefined { - const contentHashValue = this.hashContent(content); - return this.contentHash.get(contentHashValue); - } - - /** - * Search documents by keywords - much faster than full text search - */ - searchByKeywords(keywords: string[]): Set { - if (keywords.length === 0) return new Set(); - - let result = this.keywordIndex.get(keywords[0].toLowerCase()) || new Set(); - - for (let i = 1; i < keywords.length; i++) { - const keywordDocs = this.keywordIndex.get(keywords[i].toLowerCase()) || new Set(); - result = new Set([...result].filter(docId => keywordDocs.has(docId))); - } - - return result; - } - - /** - * Get all document IDs - O(1) size, O(n) iteration - */ - getAllDocumentIds(): string[] { - return Array.from(this.documentMap.keys()); - } - - /** - * Get index statistics - */ - getStats(): {documents: number, chunks: number, keywords: number} { - return { - documents: this.documentMap.size, - chunks: this.chunkMap.size, - keywords: this.keywordIndex.size - }; - } - - /** - * Hash content for deduplication - */ - private hashContent(content: string): string { - return createHash('sha256').update(content.trim()).digest('hex').substring(0, 16); - } - - /** - * Extract and index keywords from content - */ - private indexKeywords(docId: string, content: string): void { - const keywords = this.extractKeywords(content); - for (const keyword of keywords) { - const keywordLower = keyword.toLowerCase(); - if (!this.keywordIndex.has(keywordLower)) { - this.keywordIndex.set(keywordLower, new Set()); - } - this.keywordIndex.get(keywordLower)!.add(docId); - } - } - - /** - * Extract keywords from text (simple word extraction) - */ - private extractKeywords(content: string): string[] { - const words = content - .toLowerCase() - .replace(/[^\w\s]/g, ' ') - .split(/\s+/) - .filter(word => word.length > 2 && word.length < 20) - .filter(word => !this.isStopWord(word)); - - // Return unique words - return Array.from(new Set(words)); - } - - /** - * Check if word is a stop word (basic list) - */ - private isStopWord(word: string): boolean { - const stopWords = new Set([ - 'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', - 'by', 'is', 'are', 'was', 'were', 'be', 'been', 'have', 'has', 'had', 'do', 'does', - 'did', 'will', 'would', 'could', 'should', 'may', 'might', 'can', 'this', 'that', - 'these', 'those', 'from', 'up', 'down', 'out', 'off', 'over', 'under', 'again', - 'further', 'then', 'once' - ]); - return stopWords.has(word); - } - - /** - * Save index to disk - */ - private async saveIndex(): Promise { - const indexData = { - version: '1.0', - documentMap: Object.fromEntries(this.documentMap), - chunkMap: Object.fromEntries(this.chunkMap), - contentHash: Object.fromEntries(this.contentHash), - keywordIndex: Object.fromEntries( - Array.from(this.keywordIndex.entries()).map(([key, value]) => [key, Array.from(value)]) - ), - lastUpdated: new Date().toISOString() - }; - - await fse.writeJSON(this.indexFilePath, indexData, { spaces: 2 }); - } - - /** - * Load index from disk - */ - private async loadIndex(): Promise { - if (!await fse.pathExists(this.indexFilePath)) { - throw new Error('Index file does not exist'); - } - - const indexData = await fse.readJSON(this.indexFilePath); - - this.documentMap = new Map(Object.entries(indexData.documentMap || {})); - this.chunkMap = new Map(Object.entries(indexData.chunkMap || {})); - this.contentHash = new Map(Object.entries(indexData.contentHash || {})); - this.keywordIndex = new Map( - Object.entries(indexData.keywordIndex || {}).map(([key, value]) => [key, new Set(value as string[])]) - ); - } - - /** - * Rebuild index from existing documents in data directory - */ - private async rebuildIndex(dataDir: string): Promise { - console.error('[DocumentIndex] Rebuilding index from existing documents...'); - - this.documentMap.clear(); - this.chunkMap.clear(); - this.contentHash.clear(); - this.keywordIndex.clear(); - - try { - const files = await fsp.readdir(dataDir); - const documentFiles = files.filter(file => file.endsWith('.json') && file !== 'document-index.json'); - - for (const file of documentFiles) { - try { - const filePath = path.join(dataDir, file); - const document = await fse.readJSON(filePath); - - if (document.id && document.content) { - this.addDocument(document.id, filePath, document.content, document.chunks); - } - } catch (error) { - console.warn(`[DocumentIndex] Failed to index document ${file}:`, error); - } - } - - await this.saveIndex(); - console.error(`[DocumentIndex] Rebuilt index with ${this.documentMap.size} documents`); - } catch (error) { - console.error('[DocumentIndex] Failed to rebuild index:', error); - } - } -} \ No newline at end of file diff --git a/src/orama-store.ts b/src/orama-store.ts new file mode 100644 index 0000000..9ab9e87 --- /dev/null +++ b/src/orama-store.ts @@ -0,0 +1,506 @@ +import { create, insert, remove, search, save, load, count, getByID } from '@orama/orama'; +import type { AnyOrama, Results } from '@orama/orama'; +import { persistToFile, restoreFromFile } from '@orama/plugin-data-persistence/server'; +import { existsSync, mkdirSync } from 'fs'; +import { readdir, readFile } from 'fs/promises'; +import * as path from 'path'; +import { Document, DocumentChunk, SearchResult, OramaChunkDocument, OramaDocDocument } from './types.js'; +import { getDefaultDataDir } from './utils.js'; + +/** + * OramaStore: manages two Orama DB instances (chunks + documents) with persistence to disk. + */ +export class OramaStore { + private chunksDb: AnyOrama | null = null; + private docsDb: AnyOrama | null = null; + private dataDir: string; + private chunksDbPath: string; + private docsDbPath: string; + private migrationFlagPath: string; + private vectorDimensions: number; + private initialized = false; + + constructor(vectorDimensions: number) { + const baseDir = getDefaultDataDir(); + this.dataDir = path.join(baseDir, 'data'); + this.chunksDbPath = path.join(this.dataDir, 'orama-chunks.msp'); + this.docsDbPath = path.join(this.dataDir, 'orama-docs.msp'); + this.migrationFlagPath = path.join(this.dataDir, 'migration-complete.flag'); + this.vectorDimensions = vectorDimensions; + + if (!existsSync(this.dataDir)) { + mkdirSync(this.dataDir, { recursive: true }); + } + console.error(`[OramaStore] Constructed (vectorDimensions=${this.vectorDimensions}) dataDir=${this.dataDir}`); + } + + /** + * Initialize both Orama DB instances. + * Attempts to restore from disk; if not found, creates empty DBs. + * Then runs automatic migration from legacy JSON files if needed. + */ + async initialize(): Promise { + if (this.initialized) return; + + let restoredChunks = false; + let restoredDocs = false; + + // Try restoring chunks DB + if (existsSync(this.chunksDbPath)) { + try { + this.chunksDb = await restoreFromFile('binary', this.chunksDbPath, 'node'); + restoredChunks = true; + console.error('[OramaStore] Restored chunks DB from disk'); + } catch (error) { + console.error('[OramaStore] Failed to restore chunks DB, creating new:', error); + } + } + + // Try restoring docs DB + if (existsSync(this.docsDbPath)) { + try { + this.docsDb = await restoreFromFile('binary', this.docsDbPath, 'node'); + restoredDocs = true; + console.error('[OramaStore] Restored docs DB from disk'); + } catch (error) { + console.error('[OramaStore] Failed to restore docs DB, creating new:', error); + } + } + + // Create fresh DBs if restoration failed or files don't exist + if (!restoredChunks) { + this.chunksDb = this.createChunksDb(); + console.error('[OramaStore] Created new chunks DB'); + } + + if (!restoredDocs) { + this.docsDb = this.createDocsDb(); + console.error('[OramaStore] Created new docs DB'); + } + + this.initialized = true; + + console.error(`[OramaStore] Initialization finished. chunksDbPath=${this.chunksDbPath} docsDbPath=${this.docsDbPath} vectorDimensions=${this.vectorDimensions}`); + // Run migration from legacy JSON files if not already done + if (!restoredChunks && !restoredDocs && !existsSync(this.migrationFlagPath)) { + await this.migrateFromJson(); + } + } + + /** + * Create a new chunks Orama DB with the vector schema + */ + private createChunksDb(): AnyOrama { + return create({ + schema: { + document_id: 'string', + document_title: 'string', + chunk_index: 'number', + content: 'string', + embedding: `vector[${this.vectorDimensions}]` as `vector[${number}]`, + start_position: 'number', + end_position: 'number', + metadata: 'string', + }, + }); + } + + /** + * Create a new documents Orama DB + */ + private createDocsDb(): AnyOrama { + return create({ + schema: { + title: 'string', + content: 'string', + created_at: 'string', + updated_at: 'string', + metadata: 'string', + }, + }); + } + + /** + * Persist both DBs to disk + */ + private async persistToDisk(): Promise { + try { + if (this.chunksDb) { + await persistToFile(this.chunksDb, 'binary', this.chunksDbPath, 'node'); + } + if (this.docsDb) { + await persistToFile(this.docsDb, 'binary', this.docsDbPath, 'node'); + } + console.error('[OramaStore] Persisted DBs to disk'); + } catch (error) { + console.error('[OramaStore] Failed to persist DBs to disk:', error); + } + } + + /** + * Migrate legacy JSON documents into Orama. + * Scans the data directory for *.json files and imports them. + */ + private async migrateFromJson(): Promise { + try { + const files = await readdir(this.dataDir); + const jsonFiles = files.filter(f => f.endsWith('.json') && f !== 'document-index.json' && f !== 'gemini_file_mappings.json'); + + if (jsonFiles.length === 0) { + console.error('[OramaStore] No legacy JSON files found for migration'); + return; + } + + console.error(`[OramaStore] Migrating ${jsonFiles.length} legacy JSON documents...`); + let migrated = 0; + + for (const file of jsonFiles) { + try { + const filePath = path.join(this.dataDir, file); + const data = await readFile(filePath, 'utf-8'); + const doc: Document = JSON.parse(data); + + if (!doc.id || !doc.title || !doc.content) { + console.error(`[OramaStore] Skipping invalid JSON file: ${file}`); + continue; + } + + await this.addDocumentInternal(doc); + migrated++; + } catch (error) { + console.error(`[OramaStore] Failed to migrate ${file}:`, error); + } + } + + // Persist after migration + await this.persistToDisk(); + + // Write migration flag + const { writeFile } = await import('fs/promises'); + await writeFile(this.migrationFlagPath, `Migration completed at ${new Date().toISOString()}\nMigrated ${migrated} documents\n`); + + console.error(`[OramaStore] Migrated ${migrated} documents from JSON to Orama`); + } catch (error) { + console.error('[OramaStore] Migration failed:', error); + } + } + + /** + * Internal method to add a full Document (with chunks) to both DBs. + * Used by migration and by addDocument. + */ + private async addDocumentInternal(doc: Document): Promise { + if (!this.docsDb || !this.chunksDb) throw new Error('OramaStore not initialized'); + + console.error(`[OramaStore] Inserting document ${doc.id} (${doc.title}) with ${doc.chunks?.length || 0} chunks`); + // Insert into docs DB + await insert(this.docsDb, { + id: doc.id, + title: doc.title, + content: doc.content, + created_at: doc.created_at, + updated_at: doc.updated_at, + metadata: JSON.stringify(doc.metadata || {}), + }); + + // Insert chunks into chunks DB + for (const chunk of doc.chunks) { + const embedding = chunk.embeddings || []; + // Skip chunks with missing or wrong-dimension embeddings + if (embedding.length !== this.vectorDimensions) { + console.error(`[OramaStore] Skipping chunk ${chunk.id} (embedding dim ${embedding.length} != ${this.vectorDimensions})`); + continue; + } + + await insert(this.chunksDb, { + id: chunk.id, + document_id: doc.id, + document_title: doc.title, + chunk_index: chunk.chunk_index, + content: chunk.content, + embedding, + start_position: chunk.start_position, + end_position: chunk.end_position, + metadata: JSON.stringify(chunk.metadata || {}), + }); + } + console.error(`[OramaStore] Inserted document ${doc.id} into Orama (chunks inserted)`); + } + + // โ”€โ”€โ”€ Public API โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ + + /** + * Add a document and its chunks to the store + */ + async addDocument(doc: Document): Promise { + if (!this.initialized) await this.initialize(); + await this.addDocumentInternal(doc); + await this.persistToDisk(); + } + + /** + * Delete a document and all associated chunks + */ + async deleteDocument(documentId: string): Promise { + if (!this.initialized) await this.initialize(); + if (!this.docsDb || !this.chunksDb) return false; + + let deleted = false; + + // Remove from docs DB + try { + const docExists = getByID(this.docsDb, documentId); + if (docExists) { + await remove(this.docsDb, documentId); + deleted = true; + } + } catch (error) { + console.error(`[OramaStore] Error removing document ${documentId}:`, error); + } + + // Remove associated chunks + try { + // Find all chunks for this document + const chunkResults = await search(this.chunksDb, { + term: documentId, + properties: ['document_id'], + limit: 10000, + threshold: 0, + }) as Results; + + for (const hit of chunkResults.hits) { + if (hit.document.document_id === documentId) { + try { + await remove(this.chunksDb, hit.id); + } catch { + // chunk may already be removed + } + } + } + } catch (error) { + console.error(`[OramaStore] Error removing chunks for ${documentId}:`, error); + } + + if (deleted) { + await this.persistToDisk(); + } + return deleted; + } + + /** + * Get a document by ID (full Document with chunks reconstructed) + */ + async getDocument(documentId: string): Promise { + if (!this.initialized) await this.initialize(); + if (!this.docsDb || !this.chunksDb) return null; + + try { + const doc = getByID(this.docsDb, documentId) as OramaDocDocument | undefined; + if (!doc) return null; + + // Get chunks for this document + const chunks = await this.getChunksByDocumentId(documentId); + + return { + id: doc.id, + title: doc.title, + content: doc.content, + metadata: this.safeJsonParse(doc.metadata), + chunks, + created_at: doc.created_at, + updated_at: doc.updated_at, + }; + } catch { + return null; + } + } + + /** + * Get all documents (metadata only, no embeddings) + */ + async getAllDocuments(): Promise { + if (!this.initialized) await this.initialize(); + if (!this.docsDb || !this.chunksDb) return []; + + try { + // Use full-text search with empty term to get all docs + const totalDocs = count(this.docsDb); + if (totalDocs === 0) return []; + + const result = await search(this.docsDb, { + term: '', + limit: totalDocs, + threshold: 0, + }) as Results; + + const documents: Document[] = []; + for (const hit of result.hits) { + const d = hit.document; + // Get chunk count for this document + const chunks = await this.getChunksByDocumentId(d.id); + documents.push({ + id: d.id, + title: d.title, + content: d.content, + metadata: this.safeJsonParse(d.metadata), + chunks, + created_at: d.created_at, + updated_at: d.updated_at, + }); + } + + return documents; + } catch (error) { + console.error('[OramaStore] Error getting all documents:', error); + return []; + } + } + + /** + * Search chunks using vector similarity, optionally filtered by document_id + */ + async searchChunks(embedding: number[], limit: number = 10, documentId?: string): Promise { + if (!this.initialized) await this.initialize(); + if (!this.chunksDb) return []; + + try { + const searchParams: any = { + mode: 'vector', + vector: { + value: embedding, + property: 'embedding', + }, + similarity: 0.5, + limit, + includeVectors: false, + }; + + // Add document_id filter if specified + if (documentId) { + searchParams.where = { + document_id: documentId, + }; + } + + const results = await search(this.chunksDb, searchParams) as Results; + + return results.hits.map(hit => ({ + chunk: { + id: hit.document.id, + document_id: hit.document.document_id, + chunk_index: hit.document.chunk_index, + content: hit.document.content, + start_position: hit.document.start_position, + end_position: hit.document.end_position, + metadata: this.safeJsonParse(hit.document.metadata), + } as DocumentChunk, + score: hit.score, + })); + } catch (error) { + console.error('[OramaStore] Error searching chunks:', error); + return []; + } + } + + /** + * Search across all documents using hybrid search (full-text + vector) + */ + async searchAllDocuments(embedding: number[], limit: number = 10, term?: string): Promise { + if (!this.initialized) await this.initialize(); + if (!this.chunksDb) return []; + + try { + let results: Results; + + if (term) { + // Hybrid search: full-text + vector + results = await search(this.chunksDb, { + term, + mode: 'hybrid', + vector: { + value: embedding, + property: 'embedding', + }, + properties: ['content', 'document_title'], + similarity: 0.5, + limit, + includeVectors: false, + } as any) as Results; + } else { + // Pure vector search + results = await search(this.chunksDb, { + mode: 'vector', + vector: { + value: embedding, + property: 'embedding', + }, + similarity: 0.5, + limit, + includeVectors: false, + } as any) as Results; + } + + return results.hits.map(hit => ({ + chunk: { + id: hit.document.id, + document_id: hit.document.document_id, + chunk_index: hit.document.chunk_index, + content: hit.document.content, + start_position: hit.document.start_position, + end_position: hit.document.end_position, + metadata: this.safeJsonParse(hit.document.metadata), + } as DocumentChunk, + score: hit.score, + })); + } catch (error) { + console.error('[OramaStore] Error in searchAllDocuments:', error); + return []; + } + } + + /** + * Get all chunks for a specific document, sorted by chunk_index + */ + async getChunksByDocumentId(documentId: string): Promise { + if (!this.initialized) await this.initialize(); + if (!this.chunksDb) return []; + + try { + const results = await search(this.chunksDb, { + term: documentId, + properties: ['document_id'], + limit: 10000, + threshold: 0, + }) as Results; + + const chunks: DocumentChunk[] = results.hits + .filter(hit => hit.document.document_id === documentId) + .map(hit => ({ + id: hit.document.id, + document_id: hit.document.document_id, + chunk_index: hit.document.chunk_index, + content: hit.document.content, + start_position: hit.document.start_position, + end_position: hit.document.end_position, + metadata: this.safeJsonParse(hit.document.metadata), + })) + .sort((a, b) => a.chunk_index - b.chunk_index); + + return chunks; + } catch (error) { + console.error(`[OramaStore] Error getting chunks for ${documentId}:`, error); + return []; + } + } + + /** + * Safely parse JSON string, returning empty object on failure + */ + private safeJsonParse(str: string): Record { + try { + return JSON.parse(str); + } catch { + return {}; + } + } +} diff --git a/src/search-engine.ts b/src/search-engine.ts deleted file mode 100644 index 3a14cab..0000000 --- a/src/search-engine.ts +++ /dev/null @@ -1,89 +0,0 @@ -import { DocumentManager } from './document-manager.js'; -import { EmbeddingProvider, SearchResult } from './types.js'; - -/** - * Search engine that provides semantic search capabilities across all documents - */ -export class SearchEngine { - private documentManager: DocumentManager; - private embeddingProvider: EmbeddingProvider; - - constructor(documentManager: DocumentManager, embeddingProvider: EmbeddingProvider) { - this.documentManager = documentManager; - this.embeddingProvider = embeddingProvider; - } - - /** - * Perform semantic search across all documents - */ - // async searchAllDocuments(query: string, limit = 10): Promise { - // try { - // const allDocuments = await this.documentManager.getAllDocuments(); - // const allResults: SearchResult[] = []; - - // for (const document of allDocuments) { - // const results = await this.documentManager.searchDocuments(document.id, query, limit); - // allResults.push(...results); - // } - - // // Sort all results by score and limit - // allResults.sort((a, b) => b.score - a.score); - // return allResults.slice(0, limit); - // } catch (error) { - // console.error('Search failed:', error); - // throw new Error(`Search failed: ${error}`); - // } - // } - - /** - * Search within a specific document - */ - async searchDocument(documentId: string, query: string, limit = 10): Promise { - return this.documentManager.searchDocuments(documentId, query, limit); - } - - /** - * Add a document with automatic embedding generation - */ - async addDocument( - title: string, - content: string, - metadata: Record = {} - ) { - try { - // Use the DocumentManager's addDocument method which handles chunking and embeddings - return await this.documentManager.addDocument(title, content, metadata); - } catch (error) { - console.error('Failed to add document:', error); - throw new Error(`Failed to add document: ${error}`); - } - } - - /** - * Get document by ID - */ - async getDocument(id: string) { - return this.documentManager.getDocument(id); - } - - /** - * List all documents - */ - async listDocuments() { - return this.documentManager.getAllDocuments(); - } - - /** - * Delete a document - */ - async deleteDocument(id: string) { - return this.documentManager.deleteDocument(id); - } - - /** - * Check if the search engine is ready - */ - isReady(): boolean { - return this.embeddingProvider.isAvailable(); - } -} diff --git a/src/server.ts b/src/server.ts index 4b5a7f6..5c72c40 100644 --- a/src/server.ts +++ b/src/server.ts @@ -243,6 +243,40 @@ server.addTool({ }); +// Search across all documents tool +server.addTool({ + name: "search_all_documents", + description: "Search for relevant chunks across ALL documents in the knowledge base using semantic similarity (hybrid: full-text + vector). Useful for cross-document search when you don't know which document contains the answer.", + parameters: z.object({ + query: z.string().describe("The search query"), + limit: z.number().optional().default(10).describe("Maximum number of chunk results to return"), + }), execute: async (args) => { + try { + const manager = await initializeDocumentManager(); + const results = await manager.searchAllDocuments(args.query, args.limit); + + if (results.length === 0) { + return "No chunks found matching your query across all documents."; + } + + const searchResults = results.map(result => ({ + document_id: result.chunk.document_id, + chunk_index: result.chunk.chunk_index, + score: result.score, + content: result.chunk.content, + })); + + const res = { + hint_for_llm: "After identifying the relevant chunks, use the get_context_window tool to retrieve additional context around each chunk of interest. You can call get_context_window multiple times until you have gathered enough context to answer the question.", + results: searchResults, + }; + return JSON.stringify(res, null, 2); + } catch (error) { + throw new Error(`Cross-document search failed: ${error instanceof Error ? error.message : String(error)}`); + } + }, +}); + // MCP tool: get_context_window server.addTool({ name: "get_context_window", diff --git a/src/types.ts b/src/types.ts index 4be033d..74ea3b1 100644 --- a/src/types.ts +++ b/src/types.ts @@ -93,6 +93,28 @@ export interface DocumentStorage { search(embedding: number[], options?: SearchOptions): Promise; } +// Orama DB schema types +export interface OramaChunkDocument { + id: string; + document_id: string; + document_title: string; + chunk_index: number; + content: string; + embedding: number[]; + start_position: number; + end_position: number; + metadata: string; // stringified JSON +} + +export interface OramaDocDocument { + id: string; + title: string; + content: string; + created_at: string; + updated_at: string; + metadata: string; // stringified JSON +} + export interface ServerConfig { dataDir?: string; embeddingProvider?: EmbeddingProvider; diff --git a/tsconfig.json b/tsconfig.json index 71dc84a..1914aae 100644 --- a/tsconfig.json +++ b/tsconfig.json @@ -1,8 +1,8 @@ { "compilerOptions": { "target": "es2022", - "module": "esnext", - "moduleResolution": "node", + "module": "Node16", + "moduleResolution": "Node16", "allowSyntheticDefaultImports": true, "esModuleInterop": true, "allowJs": true, From 34aee75a71b4a1332bc15d93ed308502fba4c515 Mon Sep 17 00:00:00 2001 From: Andrea Bravaccino Date: Sun, 1 Mar 2026 00:18:16 +0100 Subject: [PATCH 2/7] feat: add web server for document management and search API - Introduced a new web server module (`web-server.ts`) to handle REST API routes for document management, including CRUD operations and search functionalities. - Integrated the web server startup in the main server file (`server.ts`), allowing it to run alongside the main application. - Implemented file upload handling and processing capabilities. - Added endpoints for listing documents, searching within documents, and managing uploads. --- package-lock.json | 220 ++++++++ package.json | 8 +- src/public/index.html | 1213 +++++++++++++++++++++++++++++++++++++++++ src/server.ts | 10 + src/web-server.ts | 323 +++++++++++ 5 files changed, 1773 insertions(+), 1 deletion(-) create mode 100644 src/public/index.html create mode 100644 src/web-server.ts diff --git a/package-lock.json b/package-lock.json index 435c430..3651aae 100644 --- a/package-lock.json +++ b/package-lock.json @@ -13,11 +13,15 @@ "@modelcontextprotocol/sdk": "^1.12.3", "@orama/orama": "^3.1.18", "@orama/plugin-data-persistence": "^3.1.18", + "@types/express": "^5.0.6", + "@types/multer": "^2.0.0", "@types/node": "^24.0.1", "@xenova/transformers": "^2.17.2", "dotenv": "^16.5.0", + "express": "^5.2.1", "fastmcp": "^3.24.0", "fs-extra": "^11.3.0", + "multer": "^2.1.0", "unpdf": "^1.2.1", "zod": "^3.25.64" }, @@ -1434,6 +1438,48 @@ "dev": true, "license": "MIT" }, + "node_modules/@types/body-parser": { + "version": "1.19.6", + "resolved": "https://registry.npmjs.org/@types/body-parser/-/body-parser-1.19.6.tgz", + "integrity": "sha512-HLFeCYgz89uk22N5Qg3dvGvsv46B8GLvKKo1zKG4NybA8U2DiEO3w9lqGg29t/tfLRJpJ6iQxnVw4OnB7MoM9g==", + "license": "MIT", + "dependencies": { + "@types/connect": "*", + "@types/node": "*" + } + }, + "node_modules/@types/connect": { + "version": "3.4.38", + "resolved": "https://registry.npmjs.org/@types/connect/-/connect-3.4.38.tgz", + "integrity": "sha512-K6uROf1LD88uDQqJCktA4yzL1YYAK6NgfsI0v/mTgyPKWsX1CnJ0XPSDhViejru1GcRkLWb8RlzFYJRqGUbaug==", + "license": "MIT", + "dependencies": { + "@types/node": "*" + } + }, + "node_modules/@types/express": { + "version": "5.0.6", + "resolved": "https://registry.npmjs.org/@types/express/-/express-5.0.6.tgz", + "integrity": "sha512-sKYVuV7Sv9fbPIt/442koC7+IIwK5olP1KWeD88e/idgoJqDm3JV/YUiPwkoKK92ylff2MGxSz1CSjsXelx0YA==", + "license": "MIT", + "dependencies": { + "@types/body-parser": "*", + "@types/express-serve-static-core": "^5.0.0", + "@types/serve-static": "^2" + } + }, + "node_modules/@types/express-serve-static-core": { + "version": "5.1.1", + "resolved": "https://registry.npmjs.org/@types/express-serve-static-core/-/express-serve-static-core-5.1.1.tgz", + "integrity": "sha512-v4zIMr/cX7/d2BpAEX3KNKL/JrT1s43s96lLvvdTmza1oEvDudCqK9aF/djc/SWgy8Yh0h30TZx5VpzqFCxk5A==", + "license": "MIT", + "dependencies": { + "@types/node": "*", + "@types/qs": "*", + "@types/range-parser": "*", + "@types/send": "*" + } + }, "node_modules/@types/fs-extra": { "version": "11.0.4", "resolved": "https://registry.npmjs.org/@types/fs-extra/-/fs-extra-11.0.4.tgz", @@ -1445,6 +1491,12 @@ "@types/node": "*" } }, + "node_modules/@types/http-errors": { + "version": "2.0.5", + "resolved": "https://registry.npmjs.org/@types/http-errors/-/http-errors-2.0.5.tgz", + "integrity": "sha512-r8Tayk8HJnX0FztbZN7oVqGccWgw98T/0neJphO91KkmOzug1KkofZURD4UaD5uH8AqcFLfdPErnBod0u71/qg==", + "license": "MIT" + }, "node_modules/@types/jsonfile": { "version": "6.1.4", "resolved": "https://registry.npmjs.org/@types/jsonfile/-/jsonfile-6.1.4.tgz", @@ -1461,6 +1513,15 @@ "integrity": "sha512-MqTGEo5bj5t157U6fA/BiDynNkn0YknVdh48CMPkTSpFTVmvao5UQmm7uEF6xBEo7qIMAlY/JSleYaE6VOdpaA==", "license": "MIT" }, + "node_modules/@types/multer": { + "version": "2.0.0", + "resolved": "https://registry.npmjs.org/@types/multer/-/multer-2.0.0.tgz", + "integrity": "sha512-C3Z9v9Evij2yST3RSBktxP9STm6OdMc5uR1xF1SGr98uv8dUlAL2hqwrZ3GVB3uyMyiegnscEK6PGtYvNrjTjw==", + "license": "MIT", + "dependencies": { + "@types/express": "*" + } + }, "node_modules/@types/node": { "version": "24.11.0", "resolved": "https://registry.npmjs.org/@types/node/-/node-24.11.0.tgz", @@ -1477,12 +1538,43 @@ "dev": true, "license": "MIT" }, + "node_modules/@types/qs": { + "version": "6.14.0", + "resolved": "https://registry.npmjs.org/@types/qs/-/qs-6.14.0.tgz", + "integrity": "sha512-eOunJqu0K1923aExK6y8p6fsihYEn/BYuQ4g0CxAAgFc4b/ZLN4CrsRZ55srTdqoiLzU2B2evC+apEIxprEzkQ==", + "license": "MIT" + }, + "node_modules/@types/range-parser": { + "version": "1.2.7", + "resolved": "https://registry.npmjs.org/@types/range-parser/-/range-parser-1.2.7.tgz", + "integrity": "sha512-hKormJbkJqzQGhziax5PItDUTMAM9uE2XXQmM37dyd4hVM+5aVl7oVxMVUiVQn2oCQFN/LKCZdvSM0pFRqbSmQ==", + "license": "MIT" + }, "node_modules/@types/retry": { "version": "0.12.0", "resolved": "https://registry.npmjs.org/@types/retry/-/retry-0.12.0.tgz", "integrity": "sha512-wWKOClTTiizcZhXnPY4wikVAwmdYHp8q6DmC+EJUzAMsycb7HB32Kh9RN4+0gExjmPmZSAQjgURXIGATPegAvA==", "license": "MIT" }, + "node_modules/@types/send": { + "version": "1.2.1", + "resolved": "https://registry.npmjs.org/@types/send/-/send-1.2.1.tgz", + "integrity": "sha512-arsCikDvlU99zl1g69TcAB3mzZPpxgw0UQnaHeC1Nwb015xp8bknZv5rIfri9xTOcMuaVgvabfIRA7PSZVuZIQ==", + "license": "MIT", + "dependencies": { + "@types/node": "*" + } + }, + "node_modules/@types/serve-static": { + "version": "2.2.0", + "resolved": "https://registry.npmjs.org/@types/serve-static/-/serve-static-2.2.0.tgz", + "integrity": "sha512-8mam4H1NHLtu7nmtalF7eyBH14QyOASmcxHhSfEoRyr0nP/YdoesEtU+uSRvMe96TW/HPTtkoKqQLl53N7UXMQ==", + "license": "MIT", + "dependencies": { + "@types/http-errors": "*", + "@types/node": "*" + } + }, "node_modules/@xenova/transformers": { "version": "2.17.2", "resolved": "https://registry.npmjs.org/@xenova/transformers/-/transformers-2.17.2.tgz", @@ -1613,6 +1705,12 @@ "dev": true, "license": "MIT" }, + "node_modules/append-field": { + "version": "1.0.0", + "resolved": "https://registry.npmjs.org/append-field/-/append-field-1.0.0.tgz", + "integrity": "sha512-klpgFSWLW1ZEs8svjfb7g4qWY0YS5imI82dTg+QahUvJ8YqAY0P10Uk8tTyh9ZGuYEZEMaeJYCF5BFuX552hsw==", + "license": "MIT" + }, "node_modules/argparse": { "version": "2.0.1", "resolved": "https://registry.npmjs.org/argparse/-/argparse-2.0.1.tgz", @@ -1902,6 +2000,23 @@ "integrity": "sha512-zRpUiDwd/xk6ADqPMATG8vc9VPrkck7T07OIx0gnjmJAnHnTVXNQG3vfvWNuiZIkwu9KrKdA1iJKfsfTVxE6NA==", "license": "BSD-3-Clause" }, + "node_modules/buffer-from": { + "version": "1.1.2", + "resolved": "https://registry.npmjs.org/buffer-from/-/buffer-from-1.1.2.tgz", + "integrity": "sha512-E+XQCRwSbaaiChtv6k6Dwgc+bx+Bs6vuKJHHl5kox/BaKbhiXzqQOwK4cO22yElGp2OCmjwVhT3HmxgyPGnJfQ==", + "license": "MIT" + }, + "node_modules/busboy": { + "version": "1.6.0", + "resolved": "https://registry.npmjs.org/busboy/-/busboy-1.6.0.tgz", + "integrity": "sha512-8SFQbg/0hQ9xy3UNTB0YEnsNBbWfhf7RtnzpL7TkBiTBRfrQ9Fxcnz7VJsleJpyp6rVLvXiuORqjlHi5q+PYuA==", + "dependencies": { + "streamsearch": "^1.1.0" + }, + "engines": { + "node": ">=10.16.0" + } + }, "node_modules/bytes": { "version": "3.1.2", "resolved": "https://registry.npmjs.org/bytes/-/bytes-3.1.2.tgz", @@ -2334,6 +2449,35 @@ "dev": true, "license": "MIT" }, + "node_modules/concat-stream": { + "version": "2.0.0", + "resolved": "https://registry.npmjs.org/concat-stream/-/concat-stream-2.0.0.tgz", + "integrity": "sha512-MWufYdFw53ccGjCA+Ol7XJYpAlW6/prSMzuPOTRnJGcGzuhLn4Scrz7qf6o8bROZ514ltazcIFJZevcfbo0x7A==", + "engines": [ + "node >= 6.0" + ], + "license": "MIT", + "dependencies": { + "buffer-from": "^1.0.0", + "inherits": "^2.0.3", + "readable-stream": "^3.0.2", + "typedarray": "^0.0.6" + } + }, + "node_modules/concat-stream/node_modules/readable-stream": { + "version": "3.6.2", + "resolved": "https://registry.npmjs.org/readable-stream/-/readable-stream-3.6.2.tgz", + "integrity": "sha512-9u/sniCrY3D5WdsERHzHE4G2YCXqoG5FTHUiCC4SIbr6XcLZBY05ya9EKjYek9O5xOAwjGq+1JdGBAS7Q9ScoA==", + "license": "MIT", + "dependencies": { + "inherits": "^2.0.3", + "string_decoder": "^1.1.1", + "util-deprecate": "^1.0.1" + }, + "engines": { + "node": ">= 6" + } + }, "node_modules/config-chain": { "version": "1.1.13", "resolved": "https://registry.npmjs.org/config-chain/-/config-chain-1.1.13.tgz", @@ -5157,6 +5301,68 @@ "integrity": "sha512-6FlzubTLZG3J2a/NVCAleEhjzq5oxgHyaCU9yYXvcLsvoVaHJq/s5xXI6/XXP6tz7R9xAOtHnSO/tXtF3WRTlA==", "license": "MIT" }, + "node_modules/multer": { + "version": "2.1.0", + "resolved": "https://registry.npmjs.org/multer/-/multer-2.1.0.tgz", + "integrity": "sha512-TBm6j41rxNohqawsxlsWsNNh/VdV4QFXcBvRcPhXaA05EZ79z0qJ2bQFpync6JBoHTeNY5Q1JpG7AlTjdlfAEA==", + "license": "MIT", + "dependencies": { + "append-field": "^1.0.0", + "busboy": "^1.6.0", + "concat-stream": "^2.0.0", + "type-is": "^1.6.18" + }, + "engines": { + "node": ">= 10.16.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/express" + } + }, + "node_modules/multer/node_modules/media-typer": { + "version": "0.3.0", + "resolved": "https://registry.npmjs.org/media-typer/-/media-typer-0.3.0.tgz", + "integrity": "sha512-dq+qelQ9akHpcOl/gUVRTxVIOkAJ1wR3QAvb4RsVjS8oVoFjDGTc679wJYmUmknUF5HwMLOgb5O+a3KxfWapPQ==", + "license": "MIT", + "engines": { + "node": ">= 0.6" + } + }, + "node_modules/multer/node_modules/mime-db": { + "version": "1.52.0", + "resolved": "https://registry.npmjs.org/mime-db/-/mime-db-1.52.0.tgz", + "integrity": "sha512-sPU4uV7dYlvtWJxwwxHD0PuihVNiE7TyAbQ5SWxDCB9mUYvOgroQOwYQQOKPJ8CIbE+1ETVlOoK1UC2nU3gYvg==", + "license": "MIT", + "engines": { + "node": ">= 0.6" + } + }, + "node_modules/multer/node_modules/mime-types": { + "version": "2.1.35", + "resolved": "https://registry.npmjs.org/mime-types/-/mime-types-2.1.35.tgz", + "integrity": "sha512-ZDY+bPm5zTTF+YpCrAU9nK0UgICYPT0QtT1NZWFv4s++TNkcgVaT0g6+4R2uI4MjQjzysHB1zxuWL50hzaeXiw==", + "license": "MIT", + "dependencies": { + "mime-db": "1.52.0" + }, + "engines": { + "node": ">= 0.6" + } + }, + "node_modules/multer/node_modules/type-is": { + "version": "1.6.18", + "resolved": "https://registry.npmjs.org/type-is/-/type-is-1.6.18.tgz", + "integrity": "sha512-TkRKr9sUTxEH8MdfuCSP7VizJyzRNMjj2J2do2Jr3Kym598JVdEksuzPQCnlFPW4ky9Q+iA+ma9BGm06XQBy8g==", + "license": "MIT", + "dependencies": { + "media-typer": "0.3.0", + "mime-types": "~2.1.24" + }, + "engines": { + "node": ">= 0.6" + } + }, "node_modules/mz": { "version": "2.7.0", "resolved": "https://registry.npmjs.org/mz/-/mz-2.7.0.tgz", @@ -9888,6 +10094,14 @@ "readable-stream": "^2.0.2" } }, + "node_modules/streamsearch": { + "version": "1.1.0", + "resolved": "https://registry.npmjs.org/streamsearch/-/streamsearch-1.1.0.tgz", + "integrity": "sha512-Mcc5wHehp9aXz1ax6bZUyY5afg9u2rv5cqQI3mRrYkGC8rW2hM02jWuwjtL++LS5qinSyhj2QfLyNsuc+VsExg==", + "engines": { + "node": ">=10.0.0" + } + }, "node_modules/streamx": { "version": "2.23.0", "resolved": "https://registry.npmjs.org/streamx/-/streamx-2.23.0.tgz", @@ -10453,6 +10667,12 @@ "node": ">= 0.6" } }, + "node_modules/typedarray": { + "version": "0.0.6", + "resolved": "https://registry.npmjs.org/typedarray/-/typedarray-0.0.6.tgz", + "integrity": "sha512-/aCDEGatGvZ2BIk+HmLf4ifCJFwvKFNb9/JeZPMulfgFracn9QFcAf5GO8B/mweUjSoblS5In0cWhqpfs/5PQA==", + "license": "MIT" + }, "node_modules/typescript": { "version": "5.9.3", "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.9.3.tgz", diff --git a/package.json b/package.json index c9d58d2..4825c32 100644 --- a/package.json +++ b/package.json @@ -16,8 +16,10 @@ "url": "https://github.com/andrea9293/mcp-documentation-server/issues" }, "scripts": { - "build": "tsc && shx chmod +x dist/*.js", + "build": "tsc && shx chmod +x dist/*.js && shx cp -r src/public dist/public", "start": "tsx src/server.ts", + "web": "tsx src/web-server.ts", + "web:build": "npm run build && node dist/web-server.js", "dev": "fastmcp dev src/server.ts", "dev:direct": "npx tsx src/server.ts", "dev:build": "npm run build && node dist/server.js", @@ -56,11 +58,15 @@ "@modelcontextprotocol/sdk": "^1.12.3", "@orama/orama": "^3.1.18", "@orama/plugin-data-persistence": "^3.1.18", + "@types/express": "^5.0.6", + "@types/multer": "^2.0.0", "@types/node": "^24.0.1", "@xenova/transformers": "^2.17.2", "dotenv": "^16.5.0", + "express": "^5.2.1", "fastmcp": "^3.24.0", "fs-extra": "^11.3.0", + "multer": "^2.1.0", "unpdf": "^1.2.1", "zod": "^3.25.64" }, diff --git a/src/public/index.html b/src/public/index.html new file mode 100644 index 0000000..9bc32a7 --- /dev/null +++ b/src/public/index.html @@ -0,0 +1,1213 @@ + + + + + + MCP Documentation Server + + + + + +
+
+

๐Ÿ“š MCP Docs Server

+

Document Management & Search

+
+
+
Overview
+
+ ๐Ÿ“Š + Dashboard +
+ +
Documents
+
+ ๐Ÿ“„ + All Documents + 0 +
+
+ โž• + Add Document +
+ +
Search
+
+ ๐Ÿ” + Search All +
+
+ ๐ŸŽฏ + Search in Doc +
+ + +
Uploads
+
+ ๐Ÿ“ + Upload Files +
+ +
Tools
+
+ ๐ŸชŸ + Context Window +
+
+
+ MCP Documentation Server v2.0 +
+
+ + +
+
+

Dashboard

+
+
+
+ + +
+
+
+
๐Ÿ“Š Recent Documents
+
+ + + +
TitleIDChunksCreated
+
+ +
+
+ + +
+
+
+
๐Ÿ“„ All Documents
+ +
+
+ + + +
TitleIDChunksCreatedActions
+
+ +
+
+ + +
+
+
โž• Add New Document
+
+
+ + +
+
+ + +
+
+ + +
+
+ + +
+
+
+
+ + +
+
+
๐Ÿ” Search Across All Documents
+
+
+
+ + +
+
+ + +
+
+ +
+
+
+
+ + +
+
+
๐ŸŽฏ Search Within Document
+
+
+ + +
+
+
+ + +
+
+ + +
+
+ +
+
+
+
+ + +
+
+
๐Ÿค– AI-Powered Search (Gemini)
+
+
+ + +
+
+ + +
+ +
+
+
+
+ + +
+
+
๐Ÿ“‚ Upload Files
+
+
๐Ÿ“Ž
+
Drop files here or click to browse
+
Supports .txt, .md, .pdf
+ +
+
+
+
+
๐Ÿ“ Files in Uploads Folder
+
+ + +
+
+
+
+ + + +
FilenameSizeModifiedStatus
+
+ +
+
+ + +
+
+
๐ŸชŸ Context Window Explorer
+
+
+ + +
+
+
+ + +
+
+ + +
+
+ + +
+
+ +
+
+
+
+ +
+
+ + + + + + + + +
+ + + + diff --git a/src/server.ts b/src/server.ts index 5c72c40..6c14846 100644 --- a/src/server.ts +++ b/src/server.ts @@ -6,6 +6,7 @@ import { z } from "zod"; import { createLazyEmbeddingProvider } from './embedding-provider.js'; import { DocumentManager } from './document-manager.js'; import { GeminiSearchService } from './gemini-search-service.js'; +import { startWebServer } from './web-server.js'; // Initialize server const server = new FastMCP({ @@ -424,6 +425,15 @@ if (process.env.GEMINI_API_KEY) { // }); // Start the server +// Optionally start web UI alongside MCP server +if (process.env.START_WEB_UI !== 'false') { + startWebServer().then(() => { + console.error('[Server] Web UI started (port=' + (process.env.WEB_PORT || '3080') + ')'); + }).catch(err => { + console.error('[Server] Failed to start Web UI:', err instanceof Error ? err.message : String(err)); + }); +} + server.start({ transportType: "stdio", }); diff --git a/src/web-server.ts b/src/web-server.ts new file mode 100644 index 0000000..a3d5ec4 --- /dev/null +++ b/src/web-server.ts @@ -0,0 +1,323 @@ +#!/usr/bin/env node + +import 'dotenv/config'; +import express from 'express'; +import multer from 'multer'; +import path from 'path'; +import { fileURLToPath } from 'url'; +import { existsSync } from 'fs'; +import { createLazyEmbeddingProvider } from './embedding-provider.js'; +import { DocumentManager } from './document-manager.js'; +import { GeminiSearchService } from './gemini-search-service.js'; + +const __filename = fileURLToPath(import.meta.url); +const __dirname = path.dirname(__filename); + +const app = express(); + +/** + * Start the web UI server. Returns the http.Server instance. + * Call with `START_WEB_UI=false` to disable automatic start. + */ +export async function startWebServer(portArg?: number) { + const PORT = parseInt(String(portArg || process.env.WEB_PORT || '3080')); + +// Middleware +app.use(express.json({ limit: '50mb' })); +app.use(express.urlencoded({ extended: true, limit: '50mb' })); + +// Serve static files from public directory +const publicDir = path.join(__dirname, '..', 'src', 'public'); +const distPublicDir = path.join(__dirname, 'public'); +// Try dist/public first (production), fall back to src/public (dev) + +const staticDir = existsSync(distPublicDir) ? distPublicDir : publicDir; +app.use(express.static(staticDir)); + +// Lazy document manager initialization +let documentManager: DocumentManager; + +async function getManager(): Promise { + if (!documentManager) { + const embeddingModel = process.env.MCP_EMBEDDING_MODEL || 'Xenova/all-MiniLM-L6-v2'; + const embeddingProvider = createLazyEmbeddingProvider(embeddingModel); + documentManager = new DocumentManager(embeddingProvider); + console.error(`[WebServer] Document manager initialized with: ${embeddingProvider.getModelName()}`); + } + return documentManager; +} + +// Configure multer for file uploads +const storage = multer.diskStorage({ + destination: async (_req, _file, cb) => { + const manager = await getManager(); + cb(null, manager.getUploadsDir()); + }, + filename: (_req, file, cb) => { + cb(null, file.originalname); + } +}); +const upload = multer({ storage }); + +// ============ REST API ROUTES ============ + +// GET /api/documents โ€” list all documents +app.get('/api/documents', async (_req, res) => { + try { + const manager = await getManager(); + const documents = await manager.getAllDocuments(); + const documentList = documents.map(doc => ({ + id: doc.id, + title: doc.title, + created_at: doc.created_at, + updated_at: doc.updated_at, + metadata: doc.metadata, + content_preview: doc.content.substring(0, 700), + chunks_count: doc.chunks.length, + })); + res.json(documentList); + } catch (error) { + res.status(500).json({ error: error instanceof Error ? error.message : String(error) }); + } +}); + +// GET /api/documents/:id โ€” get a single document +app.get('/api/documents/:id', async (req, res): Promise => { + try { + const manager = await getManager(); + const document = await manager.getDocument(req.params.id); + if (!document) { + res.status(404).json({ error: `Document not found: ${req.params.id}` }); + return; + } + res.json(document); + } catch (error) { + res.status(500).json({ error: error instanceof Error ? error.message : String(error) }); + } +}); + +// POST /api/documents โ€” add a new document +app.post('/api/documents', async (req, res): Promise => { + try { + const { title, content, metadata } = req.body; + if (!title || !content) { + res.status(400).json({ error: 'Title and content are required' }); + return; + } + const manager = await getManager(); + const document = await manager.addDocument(title, content, metadata || {}); + res.json({ id: document.id, title: document.title, message: 'Document added successfully' }); + } catch (error) { + res.status(500).json({ error: error instanceof Error ? error.message : String(error) }); + } +}); + +// DELETE /api/documents/:id โ€” delete a document +app.delete('/api/documents/:id', async (req, res): Promise => { + try { + const manager = await getManager(); + const document = await manager.getDocument(req.params.id); + if (!document) { + res.status(404).json({ error: `Document not found: ${req.params.id}` }); + return; + } + const success = await manager.deleteDocument(req.params.id); + res.json({ success, message: success ? `Document "${document.title}" deleted` : 'Delete failed' }); + } catch (error) { + res.status(500).json({ error: error instanceof Error ? error.message : String(error) }); + } +}); + +// POST /api/search โ€” search within a specific document +app.post('/api/search', async (req, res): Promise => { + try { + const { document_id, query, limit } = req.body; + if (!document_id || !query) { + res.status(400).json({ error: 'document_id and query are required' }); + return; + } + const manager = await getManager(); + const document = await manager.getDocument(document_id); + if (!document) { + res.status(404).json({ error: `Document not found: ${document_id}` }); + return; + } + const results = await manager.searchDocuments(document_id, query, limit || 10); + res.json(results.map(r => ({ + document_id: r.chunk.document_id, + chunk_index: r.chunk.chunk_index, + score: r.score, + content: r.chunk.content, + }))); + } catch (error) { + res.status(500).json({ error: error instanceof Error ? error.message : String(error) }); + } +}); + +// POST /api/search-all โ€” search across all documents +app.post('/api/search-all', async (req, res): Promise => { + try { + const { query, limit } = req.body; + if (!query) { + res.status(400).json({ error: 'query is required' }); + return; + } + const manager = await getManager(); + const results = await manager.searchAllDocuments(query, limit || 10); + res.json(results.map(r => ({ + document_id: r.chunk.document_id, + chunk_index: r.chunk.chunk_index, + score: r.score, + content: r.chunk.content, + }))); + } catch (error) { + res.status(500).json({ error: error instanceof Error ? error.message : String(error) }); + } +}); + +// POST /api/context-window โ€” get context window around a chunk +app.post('/api/context-window', async (req, res): Promise => { + try { + const { document_id, chunk_index, before, after } = req.body; + if (!document_id || chunk_index === undefined) { + res.status(400).json({ error: 'document_id and chunk_index are required' }); + return; + } + const manager = await getManager(); + const document = await manager.getDocument(document_id); + if (!document || !document.chunks) { + res.status(404).json({ error: 'Document or chunks not found' }); + return; + } + const total = document.chunks.length; + const start = Math.max(0, chunk_index - (before || 1)); + const end = Math.min(total, chunk_index + (after || 1) + 1); + const windowChunks = document.chunks.slice(start, end).map(chunk => ({ + chunk_index: chunk.chunk_index, + content: chunk.content, + })); + res.json({ window: windowChunks, center: chunk_index, total_chunks: total }); + } catch (error) { + res.status(500).json({ error: error instanceof Error ? error.message : String(error) }); + } +}); + +// GET /api/uploads โ€” list files in uploads folder +app.get('/api/uploads', async (_req, res) => { + try { + const manager = await getManager(); + const files = await manager.listUploadsFiles(); + res.json(files); + } catch (error) { + res.status(500).json({ error: error instanceof Error ? error.message : String(error) }); + } +}); + +// GET /api/uploads/path โ€” get uploads path +app.get('/api/uploads/path', async (_req, res) => { + try { + const manager = await getManager(); + res.json({ path: manager.getUploadsPath() }); + } catch (error) { + res.status(500).json({ error: error instanceof Error ? error.message : String(error) }); + } +}); + +// POST /api/uploads/process โ€” process uploads folder +app.post('/api/uploads/process', async (_req, res) => { + try { + const manager = await getManager(); + const result = await manager.processUploadsFolder(); + res.json(result); + } catch (error) { + res.status(500).json({ error: error instanceof Error ? error.message : String(error) }); + } +}); + +// POST /api/uploads/upload โ€” upload files to the uploads folder +app.post('/api/uploads/upload', upload.array('files', 20), async (req, res): Promise => { + try { + const files = req.files as Express.Multer.File[]; + if (!files || files.length === 0) { + res.status(400).json({ error: 'No files uploaded' }); + return; + } + res.json({ + uploaded: files.length, + files: files.map(f => ({ name: f.originalname, size: f.size })), + }); + } catch (error) { + res.status(500).json({ error: error instanceof Error ? error.message : String(error) }); + } +}); + +// POST /api/search-ai โ€” AI-powered search (Gemini) +app.post('/api/search-ai', async (req, res): Promise => { + try { + if (!process.env.GEMINI_API_KEY) { + res.status(400).json({ error: 'GEMINI_API_KEY is not set. AI search is not available.' }); + return; + } + const { document_id, query } = req.body; + if (!document_id || !query) { + res.status(400).json({ error: 'document_id and query are required' }); + return; + } + const manager = await getManager(); + const document = await manager.getDocument(document_id); + if (!document) { + res.status(404).json({ error: `Document not found: ${document_id}` }); + return; + } + const dataDir = manager.getDataDir(); + const fs = await import('fs/promises'); + const files = await fs.readdir(dataDir); + const originalFile = files.find(file => + file.startsWith(document_id) && !file.endsWith('.json') && !file.endsWith('.msp') + ); + if (!originalFile) { + res.status(404).json({ error: 'Original file for this document not found.' }); + return; + } + const result = await GeminiSearchService.searchDocumentWithGemini( + document_id, query, dataDir, process.env.GEMINI_API_KEY + ); + res.json({ + document_id, + document_title: document.title, + search_query: query, + ai_analysis: JSON.parse(result), + }); + } catch (error) { + res.status(500).json({ error: error instanceof Error ? error.message : String(error) }); + } +}); + +// GET /api/config โ€” server config info +app.get('/api/config', (_req, res) => { + res.json({ + gemini_available: !!process.env.GEMINI_API_KEY, + embedding_model: process.env.MCP_EMBEDDING_MODEL || 'Xenova/all-MiniLM-L6-v2', + }); +}); + +// Catch-all: serve index.html for SPA routing +app.get('/{*splat}', (_req, res) => { + res.sendFile(path.join(staticDir, 'index.html')); +}); + + // Start server + const server = app.listen(PORT, () => { + console.log(`\n ๐ŸŒ MCP Documentation Server - Web UI`); + console.log(` โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€`); + console.log(` Local: http://localhost:${PORT}`); + console.log(` Network: http://0.0.0.0:${PORT}\n`); + }); + + return server; +} + +// If run directly, start the web server +if (process.argv[1] === __filename) { + startWebServer().catch(err => console.error('[WebServer] Failed to start', err)); +} From 9e3a2c860274e3f19cc91a2aad7451edc39cc41a Mon Sep 17 00:00:00 2001 From: Andrea Bravaccino Date: Sun, 1 Mar 2026 00:19:04 +0100 Subject: [PATCH 3/7] feat: enhance README with detailed web UI features and usage instructions --- README.md | 120 +++++++++++++++++++++--------------------------------- 1 file changed, 46 insertions(+), 74 deletions(-) diff --git a/README.md b/README.md index 909a0bb..e37c712 100644 --- a/README.md +++ b/README.md @@ -11,6 +11,12 @@ A TypeScript-based [Model Context Protocol (MCP)](https://modelcontextprotocol.i ## Core capabilities +### ๐ŸŒ Web UI +- **Built-in Web Interface**: A full-featured web dashboard starts automatically alongside the MCP server โ€” no additional setup required +- **Complete Tool Coverage**: Every MCP tool is accessible from the browser: add/view/delete documents, semantic search, AI search, file uploads, and context window exploration +- **Drag & Drop Uploads**: Upload `.txt`, `.md`, and `.pdf` files directly from the browser +- **Configurable**: Disable with `START_WEB_UI=false` or change the port with `WEB_PORT` + ### ๐Ÿ” Search & Intelligence - **Hybrid Search**: Combined full-text and vector similarity powered by Orama, for both single-document and cross-document queries - **AI-Powered Search** ๐Ÿค–: Advanced document analysis with Google Gemini AI for contextual understanding and intelligent insights (optional, requires API key) @@ -55,6 +61,31 @@ Example configuration for an MCP client (e.g., Claude Desktop, VS Code): All environment variables are optional. Without `GEMINI_API_KEY`, only the local embedding-based search tools are available. +### Web UI + +The web interface starts automatically on port **3080** when the MCP server launches. Open your browser at: + +``` +http://localhost:3080 +``` + +From the web UI you can: +- ๐Ÿ“Š **Dashboard** โ€” overview of all documents and stats +- ๐Ÿ“„ **Documents** โ€” browse, view, and delete documents +- โž• **Add Document** โ€” create documents with title, content, and metadata +- ๐Ÿ” **Search All** โ€” semantic search across all documents +- ๐ŸŽฏ **Search in Doc** โ€” search within a specific document +- ๐Ÿค– **AI Search** โ€” Gemini-powered analysis (if `GEMINI_API_KEY` is set) +- ๐Ÿ“ **Upload Files** โ€” drag & drop files and process them into the knowledge base +- ๐ŸชŸ **Context Window** โ€” explore chunks around a specific index + +To run the web UI standalone (without the MCP server): + +```bash +npm run web # Development (tsx) +npm run web:build # Production (compiled) +``` + ### Basic workflow 1. Add documents using `add_document` or place `.txt` / `.md` / `.pdf` files in the uploads folder and call `process_uploads`. @@ -98,6 +129,8 @@ Configure via environment variables or a `.env` file in the project root: | `MCP_EMBEDDING_MODEL` | `Xenova/all-MiniLM-L6-v2` | Embedding model name | | `GEMINI_API_KEY` | โ€” | Google Gemini API key (enables `search_documents_with_ai`) | | `MCP_CACHE_ENABLED` | `true` | Enable/disable LRU embedding cache | +| `START_WEB_UI` | `true` | Set to `false` to disable the built-in web interface | +| `WEB_PORT` | `3080` | Port for the web UI | | `MCP_PARALLEL_ENABLED` | `true` | Enable parallel chunking for large documents | | `MCP_STREAMING_ENABLED` | `true` | Enable streaming reads for large files | | `MCP_STREAM_CHUNK_SIZE` | `65536` | Streaming buffer size in bytes (64KB) | @@ -128,82 +161,19 @@ Models are downloaded on first use (~80โ€“420 MB). The vector dimension is deter โš ๏ธ **Important**: Changing the embedding model requires re-adding all documents โ€” embeddings from different models are incompatible. The Orama database is recreated automatically when the dimension changes. -## Usage examples - -### Add a document - -```json -{ - "tool": "add_document", - "arguments": { - "title": "Python Basics", - "content": "Python is a high-level programming language...", - "metadata": { "category": "programming", "tags": ["python", "tutorial"] } - } -} -``` - -### Search within a document - -```json -{ - "tool": "search_documents", - "arguments": { - "document_id": "abc123", - "query": "variable assignment", - "limit": 5 - } -} -``` - -### Cross-document search - -```json -{ - "tool": "search_all_documents", - "arguments": { - "query": "how to handle authentication", - "limit": 10 - } -} -``` - -### Fetch context around a chunk - -```json -{ - "tool": "get_context_window", - "arguments": { - "document_id": "abc123", - "chunk_index": 5, - "before": 2, - "after": 2 - } -} -``` - -### AI-powered analysis (requires `GEMINI_API_KEY`) - -```json -{ - "tool": "search_documents_with_ai", - "arguments": { - "document_id": "abc123", - "query": "explain the main concepts and their relationships" - } -} -``` - ## Architecture ``` -Server (FastMCP) - โ””โ”€ DocumentManager - โ”œโ”€ OramaStore โ€” Orama vector DB (chunks DB + docs DB), persistence, migration - โ”œโ”€ IntelligentChunker โ€” Content-aware splitting (code, markdown, text, PDF) - โ”œโ”€ EmbeddingProvider โ€” Local embeddings via @xenova/transformers - โ”‚ โ””โ”€ EmbeddingCache โ€” LRU in-memory cache - โ””โ”€ GeminiSearchService โ€” Optional AI search via Google Gemini +Server (FastMCP, stdio) + โ”œโ”€ Web UI (Express, port 3080) + โ”‚ โ””โ”€ REST API โ†’ DocumentManager + โ””โ”€ MCP Tools + โ””โ”€ DocumentManager + โ”œโ”€ OramaStore โ€” Orama vector DB (chunks DB + docs DB), persistence, migration + โ”œโ”€ IntelligentChunker โ€” Content-aware splitting (code, markdown, text, PDF) + โ”œโ”€ EmbeddingProvider โ€” Local embeddings via @xenova/transformers + โ”‚ โ””โ”€ EmbeddingCache โ€” LRU in-memory cache + โ””โ”€ GeminiSearchService โ€” Optional AI search via Google Gemini ``` - **OramaStore** manages two Orama instances: one for document metadata/content and one for chunks with vector embeddings. Both are persisted to binary files on disk and restored on startup. @@ -222,7 +192,9 @@ npm install npm run dev # FastMCP dev mode with hot reload npm run build # TypeScript compilation npm run inspect # FastMCP web UI for interactive tool testing -npm start # Direct tsx execution +npm start # Direct tsx execution (MCP server + web UI) +npm run web # Run only the web UI (development) +npm run web:build # Run only the web UI (compiled) ``` ## Contributing From 6a6b7ca43ba2a71a7f0f1165cf887ee33f97e0a2 Mon Sep 17 00:00:00 2001 From: Andrea Bravaccino Date: Sun, 1 Mar 2026 00:44:22 +0100 Subject: [PATCH 4/7] feat: implement parent-child chunking pattern with separate parents DB - Added OramaParentDocument and ParentChunkData interfaces to define parent chunk structure. - Introduced parentsDb in OramaStore to manage parent chunks separately. - Implemented methods to add and retrieve parent chunks in OramaStore. - Updated deleteDocument to remove associated parent chunks. - Modified search results handling in server to support parent-child deduplication and provide full context. --- README.md | 14 +- src/document-manager.ts | 30 +- src/intelligent-chunker.ts | 1122 +++++++++++------------------------- src/orama-store.ts | 114 +++- src/server.ts | 94 ++- src/types.ts | 27 + 6 files changed, 586 insertions(+), 815 deletions(-) diff --git a/README.md b/README.md index e37c712..87d4a04 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,7 @@ A TypeScript-based [Model Context Protocol (MCP)](https://modelcontextprotocol.i ### โšก Performance & Architecture - **Orama Vector DB**: Embedded search engine with zero native dependencies โ€” replaces manual JSON storage and cosine similarity - **LRU Embedding Cache**: Avoids recomputing embeddings for repeated content and queries -- **Intelligent Chunking**: Content-aware splitting (code, markdown, text) with adaptive sizing and parallel processing +- **Parent-Child Chunking**: Documents are split into large context-preserving parent chunks and small precise child chunks for vector search โ€” search results include both the matched fragment and the full surrounding context - **Streaming File Reader**: Handles large files without high memory usage - **Automatic Migration**: Legacy JSON documents are migrated to Orama on first startup โ€” no manual intervention needed @@ -131,7 +131,6 @@ Configure via environment variables or a `.env` file in the project root: | `MCP_CACHE_ENABLED` | `true` | Enable/disable LRU embedding cache | | `START_WEB_UI` | `true` | Set to `false` to disable the built-in web interface | | `WEB_PORT` | `3080` | Port for the web UI | -| `MCP_PARALLEL_ENABLED` | `true` | Enable parallel chunking for large documents | | `MCP_STREAMING_ENABLED` | `true` | Enable streaming reads for large files | | `MCP_STREAM_CHUNK_SIZE` | `65536` | Streaming buffer size in bytes (64KB) | | `MCP_STREAM_FILE_SIZE_LIMIT` | `10485760` | Threshold to switch to streaming (10MB) | @@ -141,8 +140,9 @@ Configure via environment variables or a `.env` file in the project root: ``` ~/.mcp-documentation-server/ # Or custom path via MCP_BASE_DIR โ”œโ”€โ”€ data/ -โ”‚ โ”œโ”€โ”€ orama-chunks.msp # Orama vector DB (chunks + embeddings) +โ”‚ โ”œโ”€โ”€ orama-chunks.msp # Orama vector DB (child chunks + embeddings) โ”‚ โ”œโ”€โ”€ orama-docs.msp # Orama document DB (full content + metadata) +โ”‚ โ”œโ”€โ”€ orama-parents.msp # Orama parent chunks DB (context sections) โ”‚ โ”œโ”€โ”€ migration-complete.flag # Written after legacy JSON migration โ”‚ โ””โ”€โ”€ *.md # Markdown copies of documents โ””โ”€โ”€ uploads/ # Drop .txt, .md, .pdf files here @@ -169,15 +169,15 @@ Server (FastMCP, stdio) โ”‚ โ””โ”€ REST API โ†’ DocumentManager โ””โ”€ MCP Tools โ””โ”€ DocumentManager - โ”œโ”€ OramaStore โ€” Orama vector DB (chunks DB + docs DB), persistence, migration - โ”œโ”€ IntelligentChunker โ€” Content-aware splitting (code, markdown, text, PDF) + โ”œโ”€ OramaStore โ€” Orama vector DB (chunks DB + docs DB + parents DB), persistence, migration + โ”œโ”€ IntelligentChunker โ€” Parent-child chunking (code, markdown, text, PDF) โ”œโ”€ EmbeddingProvider โ€” Local embeddings via @xenova/transformers โ”‚ โ””โ”€ EmbeddingCache โ€” LRU in-memory cache โ””โ”€ GeminiSearchService โ€” Optional AI search via Google Gemini ``` -- **OramaStore** manages two Orama instances: one for document metadata/content and one for chunks with vector embeddings. Both are persisted to binary files on disk and restored on startup. -- **IntelligentChunker** detects content type and applies the best splitting strategy with adaptive chunk sizes. +- **OramaStore** manages three Orama instances: one for document metadata/content, one for child chunks with vector embeddings, and one for parent chunks (context sections). All are persisted to binary files on disk and restored on startup. +- **IntelligentChunker** implements the Parent-Child Chunking pattern: documents are first split into large parent chunks that preserve full context (sections, paragraphs), then each parent is further split into small child chunks for precise vector search. At query time, results are deduplicated by parent so that the LLM receives both the matched fragment and the broader context. - **EmbeddingProvider** lazily loads a Transformers.js model for local inference โ€” no API calls needed. ## Development diff --git a/src/document-manager.ts b/src/document-manager.ts index ddb32c4..22d894f 100644 --- a/src/document-manager.ts +++ b/src/document-manager.ts @@ -94,28 +94,30 @@ export class DocumentManager { console.error(`[DocumentManager] Adding document id=${id} title=${title}`); - // Create chunks using intelligent chunker - const chunks = await this.intelligentChunker.createChunks(id, content, { - maxSize: 500, - overlap: 75, - adaptiveSize: true, - addContext: true - }); + // Create chunks using parent-child chunking pattern + // Returns children (small, embedded) and parents (large, context-preserving) + const { children, parents } = await this.intelligentChunker.createChunks(id, content); const document: Document = { id, title, content, metadata, - chunks, + chunks: children, created_at: now, updated_at: now, }; - // Store in Orama + // Store document + children in Orama await this.oramaStore.addDocument(document); console.error(`[DocumentManager] Stored document in Orama: ${id}`); + // Store parent chunks in separate parents DB + if (parents.length > 0) { + await this.oramaStore.addParents(id, parents); + console.error(`[DocumentManager] Stored ${parents.length} parent chunks for: ${id}`); + } + // Create markdown file with the document content const mdFilePath = this.getDocumentMdPath(id); const mdContent = `# ${title}\n\n${content}`; @@ -148,6 +150,16 @@ export class DocumentManager { return this.oramaStore.searchChunks(queryEmbedding, limit, documentId); } + /** + * Get parent chunk content by document_id and parent_index. + * Returns null if the parent is not found (legacy documents pre-parent-child chunking). + */ + async getParentContent(documentId: string, parentIndex: number): Promise { + await this.ensureOramaInitialized(); + const parent = await this.oramaStore.getParentChunk(documentId, parentIndex); + return parent ? parent.content : null; + } + /** * Search across all documents (cross-document semantic search) */ diff --git a/src/intelligent-chunker.ts b/src/intelligent-chunker.ts index 6b39da3..eb8d79b 100644 --- a/src/intelligent-chunker.ts +++ b/src/intelligent-chunker.ts @@ -1,25 +1,19 @@ -import { EmbeddingProvider, DocumentChunk } from './types.js'; - -export interface ChunkOptions { - maxSize?: number; - overlap?: number; - minSize?: number; - preserveCodeBlocks?: boolean; - preserveMarkdown?: boolean; - adaptiveSize?: boolean; - addContext?: boolean; -} - -export interface ChunkMetadata { - type: 'code' | 'text' | 'markdown' | 'structured' | 'mixed'; - language?: string; - section?: string; - heading?: string; - complexity_score?: number; - semantic_topic?: string; - surrounding_context?: string; - chunk_size: number; - original_document_type?: string; +import { EmbeddingProvider, DocumentChunk, ParentChunkData, ChunkingResult } from './types.js'; + +/** + * Options for parent-child chunking. + * Parent chunks are large context-preserving segments (not embedded directly). + * Child chunks are small precise segments used for vector search (embedded). + */ +export interface ParentChildChunkOptions { + /** Max size of parent chunks in characters */ + parentMaxSize?: number; + /** Max size of child chunks in characters */ + childMaxSize?: number; + /** Overlap between consecutive child chunks within the same parent */ + childOverlap?: number; + /** Overlap between consecutive parent chunks */ + parentOverlap?: number; } export enum ContentType { @@ -31,6 +25,34 @@ export enum ContentType { MIXED = 'mixed' } +interface ParentChunk { + index: number; + content: string; + startPosition: number; + endPosition: number; + heading?: string; + contentType: ContentType; +} + +type ResolvedOptions = Required; + +/** + * IntelligentChunker implements the Parent-Child Chunking pattern for RAG. + * + * Pattern (from "Parent-Child Chunking in LangChain for Advanced RAG"): + * 1. The document is split into large "parent" chunks that preserve full context + * (entire sections, multiple paragraphs, or conceptually complete units). + * 2. Each parent is further split into small "child" chunks for precise matching. + * 3. Only child chunks are embedded and indexed for vector search. + * 4. Each child stores its parent's content in metadata. + * 5. At search time, child chunks enable precise query matching, but the + * corresponding parent chunk is returned for richer context. + * + * Benefits: + * - Better context preservation (parent chunks maintain the broader narrative) + * - Higher search precision (child chunks match specific queries accurately) + * - Adaptive granularity (complex queries get full context, simple ones get focused answers) + */ export class IntelligentChunker { private embeddingProvider: EmbeddingProvider; @@ -39,843 +61,387 @@ export class IntelligentChunker { } /** - * Main chunking method that automatically detects content type and applies best strategy + * Main entry point: creates parent-child chunks from document content. + * Returns a ChunkingResult with: + * - children: small chunks with embeddings (indexed for vector search) + * - parents: large context chunks (stored separately, referenced by index) */ async createChunks( - documentId: string, - content: string, - options: ChunkOptions = {} - ): Promise { + documentId: string, + content: string, + options: ParentChildChunkOptions = {} + ): Promise { const contentType = this.detectContentType(content); - - // Set default options based on content type - const mergedOptions = this.getOptimalOptions(contentType, options); - - console.error(`[IntelligentChunker] Processing ${contentType} content with ${mergedOptions.maxSize} max size`); - - // Check if parallel processing should be used - const useParallel = process.env.MCP_PARALLEL_ENABLED !== 'false' && - content.length > 10000; // Use parallel for large documents - - if (useParallel) { - try { - console.error(`[IntelligentChunker] Using parallel processing for large document (${content.length} chars)`); - return await this.createChunksParallel(documentId, content, contentType, mergedOptions); - } catch (error) { - console.warn('[IntelligentChunker] Parallel processing failed, falling back to sequential:', error); - // Fall through to sequential processing - } - } - - return await this.createChunksSequential(documentId, content, contentType, mergedOptions); - } + const opts = this.resolveOptions(contentType, options); - /** - * Sequential chunking (original implementation) - */ - private async createChunksSequential( - documentId: string, - content: string, - contentType: ContentType, - options: ChunkOptions - ): Promise { - let chunks: DocumentChunk[]; - - switch (contentType) { - case ContentType.CODE: - chunks = await this.chunkCode(documentId, content, options); - break; - case ContentType.MARKDOWN: - chunks = await this.chunkMarkdown(documentId, content, options); - break; - case ContentType.HTML: - chunks = await this.chunkHtml(documentId, content, options); - break; - case ContentType.MIXED: - chunks = await this.chunkMixed(documentId, content, options); - break; - default: - chunks = await this.chunkText(documentId, content, options); - } - - // Apply semantic chunking if enabled - if (options.adaptiveSize) { - chunks = await this.applySemanticRefinement(chunks, options); - } - - // Add contextual information if enabled - if (options.addContext) { - chunks = await this.enrichWithContext(chunks, content); - } - - console.error(`[IntelligentChunker] Created ${chunks.length} chunks (sequential)`); - return chunks; - } - - /** - * Parallel chunking for improved performance on large documents - */ - private async createChunksParallel( - documentId: string, - content: string, - contentType: ContentType, - options: ChunkOptions - ): Promise { - const maxWorkers = parseInt(process.env.MCP_MAX_WORKERS || '4'); - - // For parallel processing, we first create basic chunks quickly - let initialChunks: DocumentChunk[]; - - // Create initial chunks using appropriate strategy - switch (contentType) { - case ContentType.CODE: - initialChunks = await this.chunkCode(documentId, content, options); - break; - case ContentType.MARKDOWN: - initialChunks = await this.chunkMarkdown(documentId, content, options); - break; - case ContentType.HTML: - initialChunks = await this.chunkHtml(documentId, content, options); - break; - case ContentType.MIXED: - initialChunks = await this.chunkMixed(documentId, content, options); - break; - default: - initialChunks = await this.chunkText(documentId, content, options); - } + console.error( + `[IntelligentChunker] Parent-child chunking: type=${contentType}, ` + + `len=${content.length}, parentMax=${opts.parentMaxSize}, childMax=${opts.childMaxSize}` + ); - // Now process chunks in parallel batches - const batchSize = Math.max(1, Math.ceil(initialChunks.length / maxWorkers)); - const batches: DocumentChunk[][] = []; - - for (let i = 0; i < initialChunks.length; i += batchSize) { - batches.push(initialChunks.slice(i, i + batchSize)); + // Step 1: Split document into large parent chunks + const internalParents = this.createParentChunks(content, contentType, opts); + + // Step 2: Build ParentChunkData array for separate storage + const parentData: ParentChunkData[] = internalParents.map(p => ({ + index: p.index, + content: p.content.trim(), + startPosition: p.startPosition, + endPosition: p.endPosition, + heading: p.heading, + contentType: p.contentType, + })); + + // Step 3: For each parent, split into small children and generate embeddings + // Children only store a lightweight reference (parent_index) to their parent. + const allChildren: DocumentChunk[] = []; + let globalChildIndex = 0; + + for (const parent of internalParents) { + const childTexts = this.createChildTexts(parent.content, contentType, opts); + + let searchFrom = 0; + for (const childText of childTexts) { + const trimmed = childText.trim(); + if (!trimmed) continue; + + // Track child position within parent content + const posInParent = parent.content.indexOf(trimmed, searchFrom); + const globalStart = parent.startPosition + Math.max(0, posInParent); + const globalEnd = globalStart + trimmed.length; + if (posInParent >= 0) searchFrom = posInParent + 1; + + const embeddings = await this.embeddingProvider.generateEmbedding(trimmed); + + allChildren.push({ + id: `${documentId}_chunk_${globalChildIndex}`, + document_id: documentId, + chunk_index: globalChildIndex, + content: trimmed, + embeddings, + start_position: globalStart, + end_position: globalEnd, + metadata: { + type: parent.contentType, + parent_index: parent.index, + heading: parent.heading, + } + }); + globalChildIndex++; + } } - console.error(`[IntelligentChunker] Processing ${initialChunks.length} chunks in ${batches.length} parallel batches`); - - // Process batches in parallel - const processedBatches = await Promise.all( - batches.map(async (batch, batchIndex) => { - try { - return await this.processChunkBatch(batch, options, content); - } catch (error) { - console.warn(`[IntelligentChunker] Batch ${batchIndex} failed, using original chunks:`, error); - return batch; // Return original chunks if processing fails - } - }) + console.error( + `[IntelligentChunker] Created ${parentData.length} parents โ†’ ${allChildren.length} children` ); - - // Flatten results - const chunks = processedBatches.flat(); - - console.error(`[IntelligentChunker] Created ${chunks.length} chunks (parallel)`); - return chunks; + return { children: allChildren, parents: parentData }; } - /** - * Process a batch of chunks (can be run in parallel) - */ - private async processChunkBatch( - chunks: DocumentChunk[], - options: ChunkOptions, - originalContent: string - ): Promise { - let processedChunks = chunks; - - // Apply semantic refinement to this batch - if (options.adaptiveSize) { - processedChunks = await this.applySemanticRefinement(processedChunks, options); - } - - // Add contextual information if enabled - if (options.addContext) { - processedChunks = await this.enrichWithContext(processedChunks, originalContent); - } - - return processedChunks; - } + // โ”€โ”€โ”€ Parent Chunk Creation โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ /** - * Detect the type of content to determine chunking strategy + * Split the full document into large parent chunks that preserve context. + * Uses high-level structural separators (headings, sections, function/class defs). */ - private detectContentType(content: string): ContentType { - const codePatterns = [ - /^import\s+/m, - /^from\s+\w+\s+import/m, - /^\s*def\s+\w+/m, - /^\s*function\s+\w+/m, - /^\s*class\s+\w+/m, - /^\s*public\s+class/m, - /^\s*interface\s+\w+/m, - /^\s*export\s+(class|function|interface)/m, - /\{[\s\S]*\}/, - /^\s*if\s*\(/m, - /^\s*for\s*\(/m, - /^\s*while\s*\(/m - ]; - - const markdownPatterns = [ - /^#{1,6}\s+/m, - /^\*\s+/m, - /^\d+\.\s+/m, - /\[.*\]\(.*\)/, - /```[\s\S]*?```/, - /^\|.*\|.*\|/m, - /^>\s+/m - ]; - - const htmlPatterns = [ - //i, - //i, - /