AI-Powered Document Search Chat App (with PDF & OCR Support)
Budget: $15 – $25 USD
I need a secure, web-based application where users can chat with an AI assistant (ChatGPT-style interface) to ask questions and receive answers based only on the content of our internal PDF documents. These documents include both text-based and scanned files (e.g., images or scans of contracts), so OCR support is required.
Key Features:
1. End-User Chat Interface:
Clean, simple chat page.
Users ask questions (e.g., “What are the contract types above $50m?”).
The system fetches answers based only on the indexed PDFs.
The response should include references or snippets from the original documents.
2. Document Handling:
Admin uploads bulk PDF files through a secure backend.
Both text-based and scanned PDFs must be processed.
Scanned PDFs should use OCR to extract text.
Documents are automatically indexed for AI-powered search.
Files can be updated or replaced, triggering re-indexing.
3. AI & Search Integration:
Use OpenAI or any other LLM to generate answers from document embeddings.
Use vector search to find relevant chunks of content.
Must support multi-document queries.
4. Admin Dashboard:
Upload/manage documents.
View system stats (file count, indexing status, user queries).
Secure admin login (basic auth or SSO).
Technical Notes:
Open to tech stack suggestions, but prefer modern frameworks (e.g., Node.js, Python, React/Nuxt, etc.).
Backend should handle embedding generation and vector search (e.g., via PostgreSQL full-text search, Pinecone, Weaviate, or FAISS).
OCR can be done via Tesseract, Azure, Google Vision API, or other reliable libraries.
Deployment should be cloud-friendly (AWS, DigitalOcean, or similar).
System must be scalable to tens of thousands of documents.
Deliverables:
Working web application with admin and end-user views.
Document ingestion, OCR, indexing, and AI Q&A working end-to-end.
Codebase + setup instructions.
Required:
- Detailed Proposal
- Timeline of implementation
- Cost
- Deployment and post-launch support quote.
Key Features:
1. End-User Chat Interface:
Clean, simple chat page.
Users ask questions (e.g., “What are the contract types above $50m?”).
The system fetches answers based only on the indexed PDFs.
The response should include references or snippets from the original documents.
2. Document Handling:
Admin uploads bulk PDF files through a secure backend.
Both text-based and scanned PDFs must be processed.
Scanned PDFs should use OCR to extract text.
Documents are automatically indexed for AI-powered search.
Files can be updated or replaced, triggering re-indexing.
3. AI & Search Integration:
Use OpenAI or any other LLM to generate answers from document embeddings.
Use vector search to find relevant chunks of content.
Must support multi-document queries.
4. Admin Dashboard:
Upload/manage documents.
View system stats (file count, indexing status, user queries).
Secure admin login (basic auth or SSO).
Technical Notes:
Open to tech stack suggestions, but prefer modern frameworks (e.g., Node.js, Python, React/Nuxt, etc.).
Backend should handle embedding generation and vector search (e.g., via PostgreSQL full-text search, Pinecone, Weaviate, or FAISS).
OCR can be done via Tesseract, Azure, Google Vision API, or other reliable libraries.
Deployment should be cloud-friendly (AWS, DigitalOcean, or similar).
System must be scalable to tens of thousands of documents.
Deliverables:
Working web application with admin and end-user views.
Document ingestion, OCR, indexing, and AI Q&A working end-to-end.
Codebase + setup instructions.
Required:
- Detailed Proposal
- Timeline of implementation
- Cost
- Deployment and post-launch support quote.