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Merge pull request #8 from prosdevlab/feat/ollama-extraction-strategy
feat(extract): implement Ollama extraction strategy
2 parents f8fda31 + 24fc43f commit 8708809

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‎AGENTS.md‎

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@@ -26,10 +26,11 @@ The project is organized as a monorepo using pnpm workspaces:
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```
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packages/
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├── cli/ # CLI entry point and MCP server implementation
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├── core/ # Shared types, interfaces, and core logic
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├── extract/ # Document extraction logic (AI integration)
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└── vector-store/ # Vector database interaction and semantic search
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├── cli/ # CLI entry point and MCP server
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├── core/ # Shared types and interfaces
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├── extract/ # Document extraction (Gemini, Ollama)
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├── storage/ # SQLite persistence (Drizzle ORM)
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└── vector-store/ # Vector database for semantic search
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```
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## Setup Commands

‎README.md‎

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doc extract invoice.pdf
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```
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**With specific provider:**
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**With Ollama (local, privacy-first):**
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```bash
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# Ensure Ollama is running
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doc extract invoice.pdf --provider ollama
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```
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**With Gemini (cloud):**
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```bash
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export GEMINI_API_KEY=your_key_here
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doc extract invoice.pdf --provider gemini

‎package.json‎

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"@types/node": "^24.10.1",
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"@vitest/coverage-v8": "^4.0.15",
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"lint-staged": "16.2.7",
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"tsup": "^8.5.1",
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"typescript": "^5.9.3",
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"vitest": "^4.0.15"
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}

‎packages/cli/README.md‎

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# @doc-agent/cli
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Command-line interface and MCP server for document extraction.
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## Use cases
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- Extract data from documents via terminal
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- Integrate with Claude Desktop or Cursor via MCP
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- Batch process directories of documents (planned)
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## Commands
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| Command | Description |
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|---------|-------------|
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| `doc extract <file>` | Extract structured data from PDF/image |
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| `doc mcp` | Start MCP server for AI assistant integration |
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| `doc search <query>` | Search indexed documents (planned) |
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| `doc index <dir>` | Batch index directory (planned) |
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## Options
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```
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extract:
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-p, --provider <provider> AI provider: gemini, openai, ollama (default: ollama)
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-m, --model <model> Model name (default: llama3.2-vision)
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```
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## Environment variables
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| Variable | Required for |
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|----------|--------------|
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| `GEMINI_API_KEY` | `--provider gemini` |
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| `OPENAI_API_KEY` | `--provider openai` |
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## MCP tools
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When running `doc mcp`, exposes:
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- `extract_document` — Extract data from a file path
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- `search_documents` — Search indexed documents (planned)
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## Depends on
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- `@doc-agent/core` — Types
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- `@doc-agent/extract` — Extraction logic
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- `@doc-agent/vector-store` — Search (planned)
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‎packages/cli/package.json‎

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"doc-agent": "./dist/cli.js"
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},
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"scripts": {
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"build": "tsc",
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"build": "tsup",
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"dev": "tsx src/cli.ts",
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"mcp": "tsx src/mcp/server.ts"
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},
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"chalk": "^5.6.2",
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"commander": "^14.0.2",
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"ora": "^9.0.0",
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"vectordb": "^0.21.2"
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"vectordb": "^0.21.2",
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"zod": "^3.23.8"
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},
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"devDependencies": {
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"@types/node": "^24.10.1",

‎packages/cli/src/mcp/index.ts‎

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export { startMCPServer } from './server.js';
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// Server
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// SDK re-exports (for consumers who need MCP types)
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export { McpServer, StdioServerTransport } from './sdk';
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export { startMCPServer } from './server';

‎packages/cli/src/mcp/sdk.ts‎

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/**
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* MCP SDK re-exports
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* Barrel file to provide clean imports without .js extensions
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*/
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// Server
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export { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';
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export { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';

‎packages/cli/src/mcp/server.ts‎

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import type { Config } from '@doc-agent/core';
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import { extractDocument } from '@doc-agent/extract';
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import { Server } from '@modelcontextprotocol/sdk/server';
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import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
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import {
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type CallToolRequest,
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CallToolRequestSchema,
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ListToolsRequestSchema,
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} from '@modelcontextprotocol/sdk/types.js';
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import { z } from 'zod';
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import { McpServer, StdioServerTransport } from './sdk';
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const server = new Server(
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{
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name: 'doc-agent',
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version: '0.1.0',
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},
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{
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capabilities: {
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tools: {},
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},
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}
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);
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// List available tools
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server.setRequestHandler(ListToolsRequestSchema, async () => {
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return {
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tools: [
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{
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name: 'extract_document',
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description: 'Extract structured data from invoice, receipt, or bank statement',
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inputSchema: {
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type: 'object',
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properties: {
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filepath: {
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type: 'string',
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description: 'Path to the document file',
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},
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provider: {
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type: 'string',
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enum: ['gemini', 'openai', 'ollama'],
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description: 'AI provider to use',
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default: 'gemini',
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},
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},
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required: ['filepath'],
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},
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},
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{
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name: 'search_documents',
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description: 'Search indexed documents using natural language',
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inputSchema: {
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type: 'object',
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properties: {
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query: {
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type: 'string',
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description: 'Search query in natural language',
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},
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limit: {
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type: 'number',
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description: 'Maximum number of results',
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default: 10,
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},
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},
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required: ['query'],
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},
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},
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],
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};
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const server = new McpServer({
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name: 'doc-agent',
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version: '0.1.0',
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});
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// Handle tool calls
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server.setRequestHandler(CallToolRequestSchema, async (request: CallToolRequest) => {
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if (request.params.name === 'extract_document') {
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const { filepath, provider = 'gemini' } = request.params.arguments as {
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filepath: string;
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provider?: string;
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};
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// Register extract_document tool
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server.registerTool(
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'extract_document',
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{
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description: 'Extract structured data from invoice, receipt, or bank statement',
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inputSchema: {
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filepath: z.string().describe('Path to the document file'),
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provider: z
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.enum(['gemini', 'openai', 'ollama'])
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.default('gemini')
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.describe('AI provider to use'),
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},
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},
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async ({ filepath, provider }) => {
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const config: Config = {
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aiProvider: provider as 'gemini' | 'openai' | 'ollama',
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aiProvider: provider,
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geminiApiKey: process.env.GEMINI_API_KEY,
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openaiApiKey: process.env.OPENAI_API_KEY,
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};
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try {
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const result = await extractDocument(filepath, config);
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return {
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content: [
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{
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type: 'text',
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text: JSON.stringify(result, null, 2),
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},
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],
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content: [{ type: 'text', text: JSON.stringify(result, null, 2) }],
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};
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} catch (error) {
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return {
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content: [
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{
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type: 'text',
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text: `Error: ${(error as Error).message}`,
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},
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],
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content: [{ type: 'text', text: `Error: ${(error as Error).message}` }],
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isError: true,
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};
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}
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}
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);
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if (request.params.name === 'search_documents') {
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// Register search_documents tool
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server.registerTool(
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'search_documents',
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{
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description: 'Search indexed documents using natural language',
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inputSchema: {
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query: z.string().describe('Search query in natural language'),
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limit: z.number().default(10).describe('Maximum number of results'),
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},
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},
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async () => {
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return {
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content: [
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{
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type: 'text',
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text: 'Search functionality not yet implemented',
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},
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],
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content: [{ type: 'text', text: 'Search functionality not yet implemented' }],
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};
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}
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throw new Error(`Unknown tool: ${request.params.name}`);
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});
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);
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export async function startMCPServer() {
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const transport = new StdioServerTransport();

‎packages/cli/tsconfig.json‎

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"rootDir": "./src"
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},
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"include": ["src/**/*"],
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"exclude": ["node_modules", "dist"],
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"references": [{ "path": "../core" }, { "path": "../extract" }, { "path": "../vector-store" }]
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"exclude": ["node_modules", "dist", "**/*.test.ts"],
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"references": [{ "path": "../core" }, { "path": "../extract" }, { "path": "../vector-store" }],
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"ts-node": {
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"esm": true
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}
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}

‎packages/cli/tsup.config.ts‎

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import { defineConfig } from 'tsup';
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export default defineConfig({
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entry: {
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cli: 'src/cli.ts',
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'mcp/server': 'src/mcp/server.ts',
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'mcp/index': 'src/mcp/index.ts',
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},
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format: ['esm'],
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dts: false,
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sourcemap: true,
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clean: true,
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splitting: false,
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treeshake: true,
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external: [
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'@doc-agent/core',
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'@doc-agent/extract',
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'@doc-agent/vector-store',
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'@google/generative-ai',
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'@modelcontextprotocol/sdk',
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'chalk',
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'commander',
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'ora',
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'vectordb',
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],
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tsconfig: './tsconfig.json',
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});

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