Our Solution

Advanced PDF Analysis and Conversational Agent

Executive Summary

Vision PDF is an AI-driven document processing platform that allows users to upload complex PDF files and extract all content from them, including images, text, and tables. The system converts this multi-modal content into vector embeddings and exposes it through a conversational chat interface. Vision PDF provides intelligent, context-aware answers to document-related queries in real-time using Vision AI and LLMs.

Challenges

Process mixed content(text,images,tables) only for complex PDFs.

By preserving the context the visual elements have been extracted accurately.

Managing API costs of multiple AI services (LLMs, embeddings, Unstructured API).

Responses to be delivered Real-time streaming conversional

Maintaining separate stores for per-service data isolation.

Solution Overview

To handle all these challenges, GenAI Protos has developed a full-stack application. The system processes each uploaded PDF in parallel. The Unstructured API is used for high-resolution text and table extraction. Image analysis models capture visual content. All the extracted data is stored in a FAISS vector store (created per user) via embeddings. Queries are intelligently routed between OpenAI and Google Gemini LLMs, with built-in token usage and cost tracking. Reranking and contextual compression using a streaming chat interface are employed to ensure maximum relevance of responses.

How it Works

User Setup: The user enters API keys and selects the LLM provider (OpenAI/Gemini).
Upload: The PDF is uploaded and saved to the user's specific directory.
Parallel Extraction: Text/tables are extracted using an unstructured API; images are processed by CV models.
Indexing: All the extracted content is embedded, and a FAISS vector store is built.
Question Suggestion: Sample questions are auto-generated for user exploration.
Conversational Query: The user asks document-related questions in natural language.
Streaming Response: Answers are streamed in real-time, along with token usage and cost metadata.

Key Benefits

Document analysis has been significantly accelerated.

Drastically reduces the time of manual PDF review.

Possible natural language interaction with complex documents.

Complete cost transparency of AI operations.

Scalable, per-user document workflows

Key Outcomes with Advanced PDF and Conversational Agent

Multi-modal PDF processing that handles text, images, and tables seamlessly.

Per-user FAISS vector stores for strict data isolation and faster retrieval.

Multiple LLM support (OpenAI, Gemini) with automatic cost calculation.

Streaming, context-aware chat responses that pull relevant content from the document.

Automatic question generation to prompt user engagement with the document.

Full token and cost tracking for all operations, ensuring cost transparency.

Technical Foundation

Backend

FastAPI with Python

Frontend

React (built with Vite)

AI/ML

LangChain orchestration with OpenAI and Google Gemini models, plus Cohere for reranking

Vector Store

FAISS for indexing embeddings

Document Processing

Unstructured API for high-res text/table extraction

Embeddings

OpenAI/Gemini text embeddings generation

Deployment

Uvicorn server with CORS enabled for API endpoints

Conclusion

This Vision PDF demonstrates how document consumption and analysis can be modernized using multi-modal AI and conversational interfaces. The system provides an accurate and explainable understanding of complex PDFs by unifying text, visuals, and semantic retrieval. Its modular design, user-level isolation, and cost-aware execution create a strong foundation for enterprise document intelligence platforms – moving beyond static search to true conversational understanding.