Surya

.env.example

@@ -0,0 +1,43 @@
+# === Backend Environment Variables ===
+
+# OpenAI API Key (if using OpenAI embeddings or LLM)
+OPENAI_API_KEY=your-openai-api-key
+
+# Vector database settings
+VECTOR_DB_PATH=./vector_store
+VECTOR_DB_TYPE=chromadb
+
+# PDF upload path
+PDF_UPLOAD_PATH=../data
+
+# Embedding model and provider
+EMBEDDING_MODEL=text-embedding-ada-002
+EMBEDDING_PROVIDER=openai   # Options: openai, huggingface, ollama, bedrock
+
+# LLM model and server settings
+LLM_MODEL=llama3
+LLAMA_SERVER_URL=http://localhost:11434
+LLM_TEMPERATURE=0.1
+MAX_TOKENS=1000
+
+# Chunking and retrieval
+CHUNK_SIZE=800
+CHUNK_OVERLAP=80
+RETRIEVAL_K=5
+SIMILARITY_THRESHOLD=0.7
+
+# Server settings
+HOST=0.0.0.0
+PORT=8000
+DEBUG=True
+
+# CORS allowed origins (comma separated)
+ALLOWED_ORIGINS=http://localhost:3000,http://127.0.0.1:3000
+
+# Logging
+LOG_LEVEL=INFO
+
+# === Frontend Environment Variables ===
+
+# Backend API URL (set to your backend or ngrok URL)
+NEXT_PUBLIC_BACKEND_URL=http://localhost:8000

README.md

@@ -1,329 +1,2 @@
-# RAG-based Financial Statement Q&A System Coding Challenge
-
-## Overview
-Build a full-stack application using **RAG (Retrieval Augmented Generation)** technology:
-1. **Next.js** as the frontend framework
-2. **FastAPI** as the backend API layer
-3. **PDF financial statement document** based intelligent Q&A system
-4. **Vector database** for document search and generative AI
-
-Parse and embed the provided **`FinancialStatement_2025_I_AADIpdf.pdf`** file, then build a system where users can ask questions about the financial statement and AI generates answers by retrieving relevant information.
-
----
-
-## How to Complete This Assignment
-
-### 📋 **Assignment Process**
-1. **Fork this repository** to your own GitHub account
-2. **Clone your forked repository** to your local machine
-3. **Complete the coding challenge** following the requirements below
-4. **Push your completed solution** to your forked repository
-5. **Send your repository URL via email** when completed
-
-### 🚀 **Getting Started**
-```bash
-# Fork this repository on GitHub (click "Fork" button)
-# Then clone your forked repository
-git clone https://github.com/YOUR_USERNAME/coding-test-2nd.git
-cd coding-test-2nd
-
-# Start development...
-```
-
-### ✉️ **Submission**
-When you complete the assignment:
-- Ensure your code is pushed to your forked repository
-- Test that your application runs correctly
-- **Send your GitHub repository URL via email**
-- Include any additional setup instructions if needed
-
----
-
-## Requirements
-
-### 1. **PDF Document Processing & RAG Pipeline (Required)**
-   - Parse PDF file to text and split into chunks
-   - Convert each chunk to vector embeddings and store in vector database
-   - Implement retrieval system that embeds user questions and searches for relevant document chunks
-   - Implement generation system that combines retrieved context with questions and sends to LLM
-
-### 2. **Backend API (Required)**
-   - Implement the following endpoints using **FastAPI**:
-     - `POST /api/upload`: PDF file upload and vectorization processing
-     - `GET /api/documents`: Retrieve processed document information
-     - `POST /api/chat`: Generate RAG-based answers to questions
-     - `GET /api/chunks`: Retrieve document chunks and metadata (optional)
-   - Configure CORS to allow API calls from Next.js app
-   - Integrate with vector database (e.g., Chroma, FAISS, Pinecone, etc.)
-
-### 3. **Frontend UI/UX (Required)**
-   - Implement user-friendly chat interface using **Next.js**
-   - Real-time Q&A functionality (chat format)
-   - Document upload status display and processing progress
-   - Display referenced document chunk sources with answers
-   - Loading states and error handling
-
-### 4. **Recommended Tech Stack**
-   - **Document Processing**: PyPDF2, pdfplumber, or langchain Document Loaders
-   - **Embedding Models**: OpenAI embeddings, Sentence Transformers, or HuggingFace embeddings
-   - **Vector Database**: ChromaDB (local), FAISS, or Pinecone
-   - **LLM**: OpenAI GPT, Google Gemini, Anthropic Claude, or open-source models
-   - **Frameworks**: LangChain or LlamaIndex (for RAG pipeline construction)
-
-### 5. **Bonus Features (Optional)**
-   - Multi-PDF file support
-   - Conversation history maintenance and context continuity
-   - Answer quality evaluation and feedback system
-   - Visual highlighting of document chunks
-   - Financial metrics calculator integration
-   - Chart and graph generation functionality
-
----
-
-## Free LLM APIs and Embedding Services
-
-### LLM Services
-- **OpenAI API**: GPT-3.5/4 (free credits provided)
-- **Google Gemini API**: Free tier available
-- **Anthropic Claude**: Free credits provided
-- **Cohere**: Free API available
-- **Hugging Face**: Free open-source models
-
-### Embedding Services
-- **OpenAI Embeddings**: text-embedding-ada-002
-- **Cohere Embeddings**: Free tier available
-- **Sentence Transformers**: Open-source models for local execution
-- **Hugging Face Embeddings**: Various free models available
-
-### Vector Databases
-- **ChromaDB**: Free local and cloud usage
-- **FAISS**: Free open-source by Meta
-- **Weaviate**: Free cloud tier available
-- **Pinecone**: Free starter plan available
-
----
-
-## Project Structure
-
-```
-coding-test-2nd/
-├── backend/
-│   ├── main.py              # FastAPI application
-│   ├── models/              # Data models
-│   │   └── schemas.py       # Pydantic schemas
-│   ├── services/            # RAG service logic
-│   │   ├── pdf_processor.py # PDF processing and chunking
-│   │   ├── vector_store.py  # Vector database integration
-│   │   └── rag_pipeline.py  # RAG pipeline
-│   ├── requirements.txt     # Python dependencies
-│   └── config.py           # Configuration file
-├── frontend/
-│   ├── pages/              # Next.js pages
-│   │   ├── index.tsx       # Main page
-│   │   └── _app.tsx        # App component
-│   ├── components/         # React components
-│   │   ├── ChatInterface.tsx
-│   │   └── FileUpload.tsx
-│   ├── styles/             # CSS files
-│   │   └── globals.css     # Global styles
-│   ├── package.json        # Node.js dependencies
-│   ├── tsconfig.json       # TypeScript configuration
-│   ├── next.config.js      # Next.js configuration
-│   ├── next-env.d.ts       # Next.js type definitions
-│   └── .eslintrc.json      # ESLint configuration
-├── data/
-│   └── FinancialStatement_2025_I_AADIpdf.pdf
-└── README.md
-```
-
----
-
-## Getting Started
-
-### 1. **Environment Setup**
-```bash
-# Clone repository
-git clone <your-repository-url>
-cd coding-test-2nd
-
-# Set up Python virtual environment (recommended)
-python -m venv venv
-source venv/bin/activate  # Windows: venv\Scripts\activate
-```
-
-### 2. **Backend Setup**
-```bash
-cd backend
-
-# Install dependencies
-pip install -r requirements.txt
-
-# Set up environment variables (create .env file)
-OPENAI_API_KEY=your_openai_api_key
-VECTOR_DB_PATH=./vector_store
-PDF_UPLOAD_PATH=../data
-
-# Run server
-uvicorn main:app --host 0.0.0.0 --port 8000 --reload
-```
-
-### 3. **Frontend Setup**
-```bash
-cd frontend
-
-# Install dependencies
-npm install
-
-# Run development server
-npm run dev
-```
-
-**Note**: If you encounter TypeScript/linting errors:
-- Make sure `npm install` completed successfully
-- The project includes all necessary configuration files (`tsconfig.json`, `.eslintrc.json`, `next-env.d.ts`)
-- Check that all dependencies are properly installed in `node_modules`
-
-### 4. **Initial Data Processing**
-```bash
-# Process and vectorize PDF file via API
-curl -X POST "http://localhost:8000/api/upload" \
-     -F "file=@../data/FinancialStatement_2025_I_AADIpdf.pdf"
-```
-
----
-
-## API Endpoints
-
-### **POST /api/upload**
-Upload PDF file and store in vector database
-```json
-{
-  "file": "multipart/form-data"
-}
-```
-
-### **POST /api/chat**
-Generate RAG-based answer to question
-```json
-{
-  "question": "What is the total revenue for 2025?",
-  "chat_history": [] // optional
-}
-```
-
-Response:
-```json
-{
-  "answer": "The total revenue for 2025 is 123.4 billion won...",
-  "sources": [
-    {
-      "content": "Related document chunk content",
-      "page": 1,
-      "score": 0.85
-    }
-  ],
-  "processing_time": 2.3
-}
-```
-
-### **GET /api/documents**
-Retrieve processed document information
-```json
-{
-  "documents": [
-    {
-      "filename": "FinancialStatement_2025_I_AADIpdf.pdf",
-      "upload_date": "2024-01-15T10:30:00Z",
-      "chunks_count": 125,
-      "status": "processed"
-    }
-  ]
-}
-```
-
----
-
-## Evaluation Criteria
-
-### 1. **RAG System Implementation (30%)**
-   - PDF processing and chunking quality
-   - Embedding and vector search accuracy
-   - LLM integration and answer quality
-
-### 2. **Code Quality & Structure (30%)**
-   - Code readability and maintainability
-   - Modularization and separation of concerns
-   - Error handling and logging
-
-### 3. **User Experience (20%)**
-   - Intuitive chat interface
-   - Real-time feedback and loading states
-   - Answer source display and reliability
-
-### 4. **Technical Implementation (20%)**
-   - API design and documentation
-   - Performance optimization
-   - Scalable architecture
-
----
-
-## Submission
-
-### 📦 **What to Submit**
-1. **Your forked GitHub repository** with complete implementation
-2. **All source code** (frontend, backend, configurations)
-3. **Updated documentation** with any additional setup instructions
-4. **Runnable demo** that works locally
-
-### 📧 **How to Submit**
-1. **Complete your implementation** in your forked repository
-2. **Test thoroughly** to ensure everything works
-3. **Push all changes** to your GitHub repository
-4. **Send an email** with your repository URL to the designated contact
-
-### 📝 **Repository Should Include**
-- Complete frontend and backend implementation
-- All necessary configuration files
-- Clear installation and execution instructions
-- Any additional documentation or notes
-
-### 🎥 **Optional Extras**
-- **Demo video** showing your system in action
-- **Performance analysis** or optimization notes
-- **Future improvement suggestions**
-
----
-
-## Sample Questions
-
-Your system should be able to handle questions like these about the financial statement PDF:
-
-- "What is the total revenue for 2025?"
-- "What is the year-over-year operating profit growth rate?"
-- "What are the main cost items?"
-- "How is the cash flow situation?"
-- "What is the debt ratio?"
-
----
-
-## Troubleshooting
-
-### Common Issues
-
-**Frontend TypeScript Errors**:
-- Ensure `npm install` was completed successfully
-- Check that `node_modules` directory exists and is populated
-- Verify all configuration files are present
-
-**Backend Import Errors**:
-- Activate Python virtual environment
-- Install all requirements: `pip install -r requirements.txt`
-- Check Python path and module imports
-
-**CORS Issues**:
-- Ensure backend CORS settings allow frontend origin
-- Check that API endpoints are accessible from frontend
-
----
-
-**Build a smarter document Q&A system with RAG technology!** 
+# Handoko
+#Ini Surya yang mengubah readme di branch sendiri