rag-doc-analyzer/README.md

A RAG (Retrieval-Augmented Generation) library for document analysis and processing

# RAG Document Analyzer

A powerful TypeScript library for document analysis and processing using Retrieval-Augmented Generation (RAG) techniques. This library provides tools for document loading, text extraction, embedding generation, and semantic search.

## Features

- 📄 Document loading and processing (PDF support included)
- 🔍 Semantic search capabilities
- 🤖 Integration with various LLM providers (OpenAI, Ollama, Gemini)
- 🧠 In-memory vector storage
- 🚀 Built with TypeScript for type safety

## Installation

```bash
npm install rag-doc-analyzer
# or
yarn add rag-doc-analyzer
```

## Prerequisites

- Node.js >= 16.0.0
- API keys for your chosen LLM provider (OpenAI, Ollama, or Gemini)

## Quick Start

```typescript
import { DocAnalyzer } from 'rag-doc-analyzer';
import { readFileSync } from 'fs';
import { join } from 'path';

async function main() {
  try {
    // Initialize the analyzer
    const analyzer = await DocAnalyzer.init({
      pdfs: [readFileSync(join(process.cwd(), 'path-to-your-document.pdf'))],
      llm: {
        provider: 'openai', // or 'ollama' or 'gemini'
        apiKey: 'your-api-key-here',
        model: 'gpt-3.5-turbo' // or your preferred model
      },
      embedder: 'openai' // or 'ollama'
    });

    // Ask a question about the document
    const answer = await analyzer.ask('What is the main topic of this document?');
    console.log('Answer:', answer);

    // Or have a conversation
    const messages = [
      { role: 'user' as const, content: 'What are the key points?' },
      // The response will be added to the messages array
    ];
    
    const response = await analyzer.chat(messages);
    console.log('Chat response:', response);
  } catch (error) {
    console.error('Error:', error);
  }
}

main();
```

## Configuration Options

```typescript
interface RagOptions {
  // Array of PDF documents (as Buffer, File, or file path string)
  pdfs: (Buffer | File | string)[];
  
  // LLM configuration
  llm: {
    // The LLM provider to use
    provider: 'openai' | 'ollama' | 'gemini';
    
    // API key for the provider (optional for some providers like Ollama)
    apiKey?: string;
    
    // Model to use (e.g., 'gpt-3.5-turbo', 'llama2', 'gemini-pro')
    model: string;
  };
  
  // Embedding model to use (optional, defaults to 'openai')
  embedder?: 'openai' | 'ollama';
  
  // Vector store to use (currently only 'memory' is supported)
  vectorStore?: 'memory';
}
```

## API Reference

### `DocAnalyzer.init(options: RagOptions): Promise<DocAnalyzer>`

Initialize a new DocAnalyzer instance with the provided options.

### `analyzer.ask(question: string): Promise<string>`

Ask a question about the loaded documents.

### `analyzer.chat(messages: Message[]): Promise<string>`

Have a conversation about the loaded documents.

## License

MIT

For detailed documentation and API reference, please visit our [documentation website](https://github.com/yourusername/rag-doc-analyzer#readme).

## Contributing

Contributions are welcome! Please read our [contributing guidelines](CONTRIBUTING.md) to get started.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.