Building an AI Document Analysis Platform with RAG and Multiple LLMs

The DocumentAI Analysis Platform project aims to assist users in quickly analyzing documents by uploading files and posing natural language questions. Supported formats include PDF, Word, Excel, and scanned documents. The platform employs Retrieval Augmented Generation (RAG) to extract relevant information and generate responses using AI models such as OpenAI, Claude, and Gemini.

Key engineering challenges in this project involve processing large documents efficiently, improving answer relevance, handling AI model failures, and building a maintainable full stack application. To address these challenges, the following solutions were implemented:

1. Document processing pipeline: The system processes large documents efficiently by extracting content from various formats, dividing documents into smaller chunks, and storing vector embeddings for rapid retrieval. The target is to process 100-page PDFs in under 30 seconds.

2. Enhancing answer relevance: RAG with Pinecone vector search is utilized to retrieve pertinent document content prior to supplying the context to the AI model. This approach ensures that answers are grounded in the source material rather than solely relying on the model's general knowledge.

3. Mitigating AI model failures: The application incorporates multiple AI providers along with an intelligent fallback mechanism. This allows the system to switch models when necessary, improving resilience and reducing dependence on a single provider.

4. Creating a maintainable full stack application: The project is organized into distinct modules for document processing, AI workflows, database access, vector search, API routes, and UI components. Additionally, unit, integration, and end-to-end testing support is provided to ensure the application remains maintainable and reliable.

Through this project, the author discovered that developing a valuable AI product encompasses more than just linking an LLM to a user interface. Document processing, retrieval quality, error handling, security, and system architecture are equally vital. The author finds satisfaction in overcoming these engineering challenges and building AI-powered products that are not only effective but also dependable and maintainable.

For potential collaborations involving AI applications, RAG systems, LLM integrations, backend development, and full stack engineering, contact can be made via GitHub: https://github.com/goal31121.