Intelligent AI/ML Document Analysis & QA Platform

# Build an AI-Powered Document Analysis & Q&A Platform (Python, LLM, RAG)

**Project Type:** Fixed Price
**Budget:** $1,500 USD
**Timeline:** 3 Weeks
**Experience Level:** Expert

### Project Description

We are looking for an experienced AI/ML developer to build a web-based intelligent document analysis platform using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG).

The application should allow users to upload PDF documents, ask questions in natural language, and receive accurate, context-aware answers based on the uploaded content.

We want a functional MVP with clean, maintainable code and a user-friendly interface.

### Core Requirements

**1. Document Processing**
- Upload and process text-based PDF documents.
- Support up to 30 documents per workspace, with a maximum of 10 MB per document.
- Extract, clean, and split document text into meaningful chunks.
- Generate and store embeddings for semantic retrieval.

**2. AI/ML Integration**
- Integrate an LLM API such as OpenAI.
- Implement a RAG pipeline using LangChain or LlamaIndex.
- Use a vector database such as ChromaDB or FAISS.
- Support semantic search and context-based question answering.
- Display source references with document names and page numbers.
- Clearly indicate when the documents do not contain enough information to answer a question.

**3. Backend Development**
- Python with FastAPI.
- REST APIs for document uploading, management, and querying.
- Basic error handling, logging, and secure API key management.

**4. Frontend Development**
- React.js with a clean, responsive UI.
- Document upload and management interface.
- Chat interface for asking questions.
- Answer display with source citations.

### Expected Deliverables

1. Fully functional web application with frontend and backend.
2. Working document processing and RAG pipeline.
3. Source code delivered through a Git repository.
4. Docker-based local setup instructions.
5. README with installation and configuration instructions.
6. Demonstration using sample documents and at least 10 agreed test questions.
7. Bug fixes for issues identified during final acceptance testing.

### Acceptance Criteria

- Users can upload, view, and delete supported PDF documents.
- Uploaded documents can be searched through natural-language questions.
- Responses include verifiable references to relevant documents and pages.
- The system handles unsupported files and API errors gracefully.
- The full application can be started following the supplied setup documentation.
- All agreed demonstration test cases are completed successfully.

### Preferred Technical Skills

Python, FastAPI, React.js, LLMs, OpenAI API, LangChain, LlamaIndex, RAG, Embeddings, Vector Databases, Docker, and REST APIs.

### Payment Milestones

- **Milestone 1 — $300:** Architecture, environment setup, PDF extraction, and chunking.
- **Milestone 2 — $700:** Working embeddings, vector retrieval, RAG pipeline, and backend APIs.
- **Milestone 3 — $500:** Completed frontend, testing, documentation, and final delivery.

### Additional Information

The fixed budget covers development and delivery of the defined MVP. Third-party API usage and production hosting costs are excluded.

Only text-based PDFs are required. OCR, authentication, payment integration, custom model training, and production-scale infrastructure are outside the initial scope.

Please include relevant AI/ML project examples, your proposed technical approach, and confirmation that you can deliver within the timeline.

We are interested in working with developers who write clean code, communicate clearly, and have practical experience building AI-powered applications.