R&D RAG AI Agent by consuming vary documents. This serve as a personal experiment with AI
A Retrieval-Augmented Generation (RAG) application that enables users to chat with their PDF documents using Google's Gemini AI model.
This is for my personal experiment use only & educational purposes.
graph TB
subgraph Frontend["Frontend (Next.js)"]
UI[User Interface]
Upload[PDF Upload Component]
Chat[Chat Component]
Theme[Theme Provider]
end
subgraph Backend["Backend (FastAPI)"]
API[FastAPI Endpoints]
RAG[RAG Service]
PDF[PDF Processor]
LLM[Gemini LLM]
end
subgraph Database["Vector Database (Supabase)"]
PG[PostgreSQL]
Vector[pgvector]
Embed[Embeddings Storage]
end
%% User Flow
User((User)) -->|1. Uploads PDF| Upload
Upload -->|2. Send PDF| API
API -->|3. Process| PDF
PDF -->|4. Generate Embeddings| RAG
RAG -->|5. Store Vectors| Vector
%% Chat Flow
User -->|6. Ask Question| Chat
Chat -->|7. Query| API
API -->|8. Get Context| RAG
RAG -->|9. Search Similar| Vector
Vector -->|10. Return Context| RAG
RAG -->|11. Generate Answer| LLM
LLM -->|12. Response| API
API -->|13. Display Answer| Chat
%% Styling
classDef frontend fill:#47B4B6,stroke:#333,stroke-width:2px;
classDef backend fill:#FF9776,stroke:#333,stroke-width:2px;
classDef database fill:#7C3AED,stroke:#333,stroke-width:2px;
classDef user fill:#4CAF50,stroke:#333,stroke-width:2px;
class UI,Uplo
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