diff --git a/.env.example b/.env.example new file mode 100644 index 00000000..cae486db --- /dev/null +++ b/.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 diff --git a/README.md b/README.md index 8f4f427e..83f0c9f8 100644 --- a/README.md +++ b/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 - -### πŸ“‹ **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 - -### πŸš€ **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... -``` - -### βœ‰οΈ **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/ -β”œβ”€β”€ backend/ -β”‚ β”œβ”€β”€ main.py # FastAPI application -β”‚ β”œβ”€β”€ models/ # Data models -β”‚ β”‚ └── schemas.py # Pydantic schemas -β”‚ β”œβ”€β”€ services/ # RAG service logic -β”‚ β”‚ β”œβ”€β”€ pdf_processor.py # PDF processing and chunking -β”‚ β”‚ β”œβ”€β”€ vector_store.py # Vector database integration -β”‚ β”‚ └── rag_pipeline.py # RAG pipeline -β”‚ β”œβ”€β”€ requirements.txt # Python dependencies -β”‚ └── config.py # Configuration file -β”œβ”€β”€ frontend/ -β”‚ β”œβ”€β”€ pages/ # Next.js pages -β”‚ β”‚ β”œβ”€β”€ index.tsx # Main page -β”‚ β”‚ └── _app.tsx # App component -β”‚ β”œβ”€β”€ components/ # React components -β”‚ β”‚ β”œβ”€β”€ ChatInterface.tsx -β”‚ β”‚ └── FileUpload.tsx -β”‚ β”œβ”€β”€ styles/ # CSS files -β”‚ β”‚ └── globals.css # Global styles -β”‚ β”œβ”€β”€ package.json # Node.js dependencies -β”‚ β”œβ”€β”€ tsconfig.json # TypeScript configuration -β”‚ β”œβ”€β”€ next.config.js # Next.js configuration -β”‚ β”œβ”€β”€ next-env.d.ts # Next.js type definitions -β”‚ └── .eslintrc.json # ESLint configuration -β”œβ”€β”€ data/ -β”‚ └── FinancialStatement_2025_I_AADIpdf.pdf -└── README.md -``` - ---- - -## Getting Started - -### 1. **Environment Setup** -```bash -# Clone repository -git clone -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 - -### πŸ“¦ **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 - -### πŸ“§ **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 - -### πŸ“ **Repository Should Include** -- Complete frontend and backend implementation -- All necessary configuration files -- Clear installation and execution instructions -- Any additional documentation or notes - -### πŸŽ₯ **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!** \ No newline at end of file +# Handoko +#Ini Surya yang mengubah readme di branch sendiri diff --git a/Video_2025_06_13-3.webm b/Video_2025_06_13-3.webm new file mode 100644 index 00000000..01991516 Binary files /dev/null and b/Video_2025_06_13-3.webm differ diff --git a/backend/config.py b/backend/config.py index ad3beb7b..3975f90b 100644 --- a/backend/config.py +++ b/backend/config.py @@ -2,7 +2,6 @@ from typing import List from pydantic_settings import BaseSettings - class Settings(BaseSettings): # OpenAI API configuration openai_api_key: str = os.getenv("OPENAI_API_KEY", "") @@ -16,15 +15,20 @@ class Settings(BaseSettings): # Embedding model configuration embedding_model: str = os.getenv("EMBEDDING_MODEL", "text-embedding-ada-002") + embedding_provider: str = os.getenv("EMBEDDING_PROVIDER", "openai") # NEW: provider field # LLM configuration - llm_model: str = os.getenv("LLM_MODEL", "gpt-3.5-turbo") + llm_provider: str = os.getenv("LLM_PROVIDER", "ollama") + google_api_key: str = os.getenv("GOOGLE_API_KEY", "") + + llm_model: str = os.getenv("LLM_MODEL", "llama3") + ollama_base_url: str = os.getenv("LLAMA_SERVER_URL", "http://localhost:11434") llm_temperature: float = float(os.getenv("LLM_TEMPERATURE", "0.1")) max_tokens: int = int(os.getenv("MAX_TOKENS", "1000")) # Chunking configuration - chunk_size: int = int(os.getenv("CHUNK_SIZE", "1000")) - chunk_overlap: int = int(os.getenv("CHUNK_OVERLAP", "200")) + chunk_size: int = int(os.getenv("CHUNK_SIZE", "800")) + chunk_overlap: int = int(os.getenv("CHUNK_OVERLAP", "80")) # Retrieval configuration retrieval_k: int = int(os.getenv("RETRIEVAL_K", "5")) @@ -47,5 +51,4 @@ class Settings(BaseSettings): class Config: env_file = ".env" - -settings = Settings() \ No newline at end of file +settings = Settings() diff --git a/backend/data/Sample Coding Test.pdf b/backend/data/Sample Coding Test.pdf new file mode 100644 index 00000000..c1b463ba Binary files /dev/null and b/backend/data/Sample Coding Test.pdf differ diff --git a/backend/data/sample.pdf b/backend/data/sample.pdf new file mode 100644 index 00000000..c1b463ba Binary files /dev/null and b/backend/data/sample.pdf differ diff --git a/backend/main.py b/backend/main.py index 669d9e77..c176ecf3 100644 --- a/backend/main.py +++ b/backend/main.py @@ -1,16 +1,20 @@ +from dotenv import load_dotenv +load_dotenv() +from typing import List from fastapi import FastAPI, UploadFile, File, HTTPException from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import JSONResponse -from models.schemas import ChatRequest, ChatResponse, DocumentsResponse, UploadResponse +from models.schemas import ChatRequest, ChatResponse, DocumentsResponse, UploadResponse, DocumentInfo, DocumentSource, ChunkInfo, ChunksResponse, MultiUploadResponse from services.pdf_processor import PDFProcessor from services.vector_store import VectorStoreService from services.rag_pipeline import RAGPipeline +from utils.get_embedding_function import get_embedding_function from config import settings import logging import time import os +from datetime import datetime -# Configure logging logging.basicConfig(level=settings.log_level) logger = logging.getLogger(__name__) @@ -20,7 +24,7 @@ version="1.0.0" ) -# Configure CORS +# CORS configuration app.add_middleware( CORSMiddleware, allow_origins=settings.allowed_origins, @@ -29,64 +33,115 @@ allow_headers=["*"], ) -# Initialize services -# TODO: Initialize your services here -pdf_processor = None -vector_store = None -rag_pipeline = None +pdf_processor = PDFProcessor() +vector_store = VectorStoreService( + persist_directory=settings.vector_db_path, + embedding_function=get_embedding_function() +) +rag_pipeline = RAGPipeline() +UPLOAD_DIR = getattr(settings, "upload_dir", "./data") +os.makedirs(UPLOAD_DIR, exist_ok=True) @app.on_event("startup") async def startup_event(): - """Initialize services on startup""" - # TODO: Initialize your services logger.info("Starting RAG Q&A System...") - @app.get("/") async def root(): - """Health check endpoint""" return {"message": "RAG-based Financial Statement Q&A System is running"} - -@app.post("/api/upload") -async def upload_pdf(file: UploadFile = File(...)): - """Upload and process PDF file""" - # TODO: Implement PDF upload and processing - # 1. Validate file type (PDF) - # 2. Save uploaded file - # 3. Process PDF and extract text - # 4. Store documents in vector database - # 5. Return processing results - pass - - -@app.post("/api/chat") +@app.post("/api/upload", response_model=MultiUploadResponse) +async def upload_pdfs(files: List[UploadFile] = File(...)): + responses = [] + for file in files: + if not file.filename.lower().endswith(".pdf"): + responses.append(UploadResponse( + message="Only PDF files are supported.", + filename=file.filename, + chunks_count=0, + processing_time=0 + )) + continue + save_path = os.path.join(UPLOAD_DIR, file.filename) + with open(save_path, "wb") as f: + content = await file.read() + f.write(content) + start_time = time.time() + try: + chunks = pdf_processor.process_pdf(save_path) + vector_store.add_documents(chunks) + processing_time = round(time.time() - start_time, 2) + responses.append(UploadResponse( + message="File processed successfully", + filename=file.filename, + chunks_count=len(chunks), + processing_time=processing_time + )) + except Exception as e: + logger.error(f"PDF processing or vector store add failed: {e}") + responses.append(UploadResponse( + message="Failed to process PDF.", + filename=file.filename, + chunks_count=0, + processing_time=0 + )) + return MultiUploadResponse(results=responses) + +@app.post("/api/chat", response_model=ChatResponse) async def chat(request: ChatRequest): - """Process chat request and return AI response""" - # TODO: Implement chat functionality - # 1. Validate request - # 2. Use RAG pipeline to generate answer - # 3. Return response with sources - pass - - -@app.get("/api/documents") + if not request.question or not request.question.strip(): + raise HTTPException(status_code=400, detail="Question is required.") + start_time = time.time() + try: + result = rag_pipeline.generate_answer(request.question, chat_history=request.chat_history) + except Exception as e: + logger.error(f"RAG pipeline failed: {e}") + raise HTTPException(status_code=500, detail="Failed to generate answer.") + processing_time = round(time.time() - start_time, 2) + sources = [ + DocumentSource( + content=src["content"], + page=src.get("page", 0), + score=src.get("score", 0.0), + metadata=src.get("metadata", {}) + ) + for src in result["sources"] + ] + return ChatResponse( + answer=result["answer"], + sources=sources, + processing_time=processing_time + ) + +@app.get("/api/documents", response_model=DocumentsResponse) async def get_documents(): - """Get list of processed documents""" - # TODO: Implement document listing - # - Return list of uploaded and processed documents - pass - - -@app.get("/api/chunks") + docs = [] + for fname in os.listdir(UPLOAD_DIR): + if fname.lower().endswith(".pdf"): + items = vector_store.db.get(include=[]) + chunk_count = sum(1 for id in items["ids"] if fname in id) + docs.append(DocumentInfo( + filename=fname, + upload_date=datetime.utcfromtimestamp(os.path.getmtime(os.path.join(UPLOAD_DIR, fname))), + chunks_count=chunk_count, + status="processed" + )) + return DocumentsResponse(documents=docs) + +@app.get("/api/chunks", response_model=ChunksResponse) async def get_chunks(): - """Get document chunks (optional endpoint)""" - # TODO: Implement chunk listing - # - Return document chunks with metadata - pass - + items = vector_store.db.get(include=["metadatas", "documents"]) + chunks = [] + for doc, meta in zip(items["documents"], items["metadatas"]): + chunks.append(ChunkInfo( + id=meta.get("id", ""), + content=doc, + page=meta.get("page", 0), + metadata=meta + )) + return ChunksResponse(chunks=chunks, total_count=len(chunks)) if __name__ == "__main__": import uvicorn - uvicorn.run(app, host=settings.host, port=settings.port, reload=settings.debug) \ No newline at end of file + uvicorn.run(app, host=settings.host, port=settings.port, reload=settings.debug) diff --git a/backend/models/schemas.py b/backend/models/schemas.py index ce088b5a..9f58169d 100644 --- a/backend/models/schemas.py +++ b/backend/models/schemas.py @@ -38,6 +38,8 @@ class UploadResponse(BaseModel): chunks_count: int processing_time: float +class MultiUploadResponse(BaseModel): + results: List[UploadResponse] class ChunkInfo(BaseModel): id: str @@ -48,4 +50,4 @@ class ChunkInfo(BaseModel): class ChunksResponse(BaseModel): chunks: List[ChunkInfo] - total_count: int \ No newline at end of file + total_count: int diff --git a/backend/services/pdf_processor.py b/backend/services/pdf_processor.py index 761eb86e..45276014 100644 --- a/backend/services/pdf_processor.py +++ b/backend/services/pdf_processor.py @@ -1,39 +1,54 @@ import os from typing import List, Dict, Any -import PyPDF2 -import pdfplumber -from langchain.text_splitter import RecursiveCharacterTextSplitter -from langchain.schema import Document +from langchain.document_loaders.pdf import PyPDFDirectoryLoader +from langchain_text_splitters import RecursiveCharacterTextSplitter +from langchain.schema.document import Document +from langchain_community.document_loaders import PyPDFLoader + from config import settings import logging logger = logging.getLogger(__name__) - class PDFProcessor: def __init__(self): - # TODO: Initialize text splitter with chunk size and overlap settings - pass - - def extract_text_from_pdf(self, file_path: str) -> List[Dict[str, Any]]: - """Extract text from PDF and return page-wise content""" - # TODO: Implement PDF text extraction - # - Use pdfplumber or PyPDF2 to extract text from each page - # - Return list of dictionaries with page content and metadata - pass - - def split_into_chunks(self, pages_content: List[Dict[str, Any]]) -> List[Document]: - """Split page content into chunks""" - # TODO: Implement text chunking - # - Split each page content into smaller chunks - # - Create Document objects with proper metadata - # - Return list of Document objects - pass - + self.text_splitter = RecursiveCharacterTextSplitter( + chunk_size=getattr(settings, "chunk_size", 800), + chunk_overlap=getattr(settings, "chunk_overlap", 80), + length_function=len, + is_separator_regex=False, + ) + + def extract_text_from_pdf(self, file_path: str) -> List[Document]: + """Extract text from PDF and return as list of Document objects with metadata.""" + loader = PyPDFLoader(file_path) + docs = loader.load() + logger.info(f"Extracted {len(docs)} pages from {file_path}") + return docs + + def split_into_chunks(self, documents: List[Document]) -> List[Document]: + """Split page content into chunks.""" + chunks = self.text_splitter.split_documents(documents) + logger.info(f"Split into {len(chunks)} chunks") + return self._add_chunk_ids(chunks) + + def _add_chunk_ids(self, chunks: List[Document]) -> List[Document]: + last_page_id = None + current_chunk_index = 0 + for chunk in chunks: + source = chunk.metadata.get("source") + page = chunk.metadata.get("page") + current_page_id = f"{source}:{page}" + if current_page_id == last_page_id: + current_chunk_index += 1 + else: + current_chunk_index = 0 + chunk_id = f"{current_page_id}:{current_chunk_index}" + last_page_id = current_page_id + chunk.metadata["id"] = chunk_id + return chunks + def process_pdf(self, file_path: str) -> List[Document]: - """Process PDF file and return list of Document objects""" - # TODO: Implement complete PDF processing pipeline - # 1. Extract text from PDF - # 2. Split text into chunks - # 3. Return processed documents - pass \ No newline at end of file + docs = self.extract_text_from_pdf(file_path) + chunks = self.split_into_chunks(docs) + return chunks diff --git a/backend/services/rag_pipeline.py b/backend/services/rag_pipeline.py index d6a56cf0..c194c722 100644 --- a/backend/services/rag_pipeline.py +++ b/backend/services/rag_pipeline.py @@ -4,43 +4,77 @@ from config import settings import logging +from langchain.prompts import ChatPromptTemplate +from langchain_community.llms.ollama import Ollama +from utils.get_embedding_function import get_embedding_function +from utils.llm_provider import get_llm +from langchain_google_genai import ChatGoogleGenerativeAI + + logger = logging.getLogger(__name__) +PROMPT_TEMPLATE = """ +You are a financial reporting expert assistant. Use the following context as the primary source of information to answer the question. If necessary, you may carefully apply standard financial reasoning to interpret incomplete data, but always clearly indicate when you are inferring or estimating. + +Context: +{context} + +--- + +Instructions: +- Base your answer primarily on the provided context. +- If you make any assumptions or interpretations, clearly state them as such. +- Provide numeric answers with appropriate units where possible (e.g. USD, %, etc). +- If calculations are involved, show simple calculation steps if helpful. +- If the context lacks key data, acknowledge the limitation and suggest what additional data would be helpful. +- Keep your tone professional, concise, and helpful. + +Question: {question} +Answer: +""" class RAGPipeline: def __init__(self): - # TODO: Initialize RAG pipeline components - # - Vector store service - # - LLM client - # - Prompt templates - pass - + self.vector_store = VectorStoreService( + persist_directory=settings.vector_db_path, + embedding_function=get_embedding_function() + ) + self.llm = get_llm() + self.prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE) + self.k = getattr(settings, "RETRIEVAL_TOP_K", 5) + def generate_answer(self, question: str, chat_history: List[Dict[str, str]] = None) -> Dict[str, Any]: - """Generate answer using RAG pipeline""" - # TODO: Implement RAG pipeline - # 1. Retrieve relevant documents - # 2. Generate context from retrieved documents - # 3. Generate answer using LLM - # 4. Return answer with sources - pass - - def _retrieve_documents(self, query: str) -> List[Document]: - """Retrieve relevant documents for the query""" - # TODO: Implement document retrieval - # - Search vector store for similar documents - # - Filter by similarity threshold - # - Return top-k documents - pass - - def _generate_context(self, documents: List[Document]) -> str: - """Generate context from retrieved documents""" - # TODO: Generate context string from documents - pass - + docs_with_scores = self._retrieve_documents(question) + context = self._generate_context(docs_with_scores) + answer = self._generate_llm_response(question, context, chat_history) + sources = [] + for doc, score in docs_with_scores: + sources.append({ + "content": doc.page_content, + "page": doc.metadata.get("page", 0), + "score": float(score), + "metadata": doc.metadata + }) + return { + "answer": answer, + "sources": sources + } + + def _retrieve_documents(self, query: str) -> List[tuple]: + results = self.vector_store.similarity_search_with_score(query, k=self.k) + logger.info(f"Retrieved {len(results)} documents for query: {query}") + return results + + def _generate_context(self, docs_with_scores: List[tuple]) -> str: + return "\n\n---\n\n".join([doc.page_content for doc, _score in docs_with_scores]) + def _generate_llm_response(self, question: str, context: str, chat_history: List[Dict[str, str]] = None) -> str: - """Generate response using LLM""" - # TODO: Implement LLM response generation - # - Create prompt with question and context - # - Call LLM API - # - Return generated response - pass \ No newline at end of file + history_text = "" + if chat_history: + for turn in chat_history: + role = turn.get("role", "user").capitalize() + content = turn.get("content", "") + history_text += f"{role}: {content}\n" + prompt = f"{history_text}\nContext:\n{context}\n\nQuestion: {question}\nAnswer:" + response = self.llm.invoke(prompt) + return getattr(response, "content", response) diff --git a/backend/services/vector_store.py b/backend/services/vector_store.py index b2c9abf5..523160c2 100644 --- a/backend/services/vector_store.py +++ b/backend/services/vector_store.py @@ -1,38 +1,47 @@ from typing import List, Tuple from langchain.schema import Document -from langchain.vectorstores import VectorStore +from langchain.vectorstores.chroma import Chroma from config import settings import logging logger = logging.getLogger(__name__) - class VectorStoreService: - def __init__(self): - # TODO: Initialize vector store (ChromaDB, FAISS, etc.) - pass - - def add_documents(self, documents: List[Document]) -> None: - """Add documents to the vector store""" - # TODO: Implement document addition to vector store - # - Generate embeddings for documents - # - Store documents with embeddings in vector database - pass - - def similarity_search(self, query: str, k: int = None) -> List[Tuple[Document, float]]: - """Search for similar documents""" - # TODO: Implement similarity search - # - Generate embedding for query - # - Search for similar documents in vector store - # - Return documents with similarity scores - pass - + def __init__(self, persist_directory: str = None, embedding_function=None): + self.persist_directory = persist_directory or settings.vector_db_path + self.embedding_function = embedding_function or settings.get_embedding_function() + self.db = Chroma( + persist_directory=self.persist_directory, + embedding_function=self.embedding_function + ) + + def add_documents(self, documents: List[Document], batch_size: int = 100) -> None: + """Add documents to the vector store in batches to avoid exceeding max batch size.""" + ids = [doc.metadata.get("id") for doc in documents] + for i in range(0, len(documents), batch_size): + batch_docs = documents[i:i+batch_size] + batch_ids = ids[i:i+batch_size] + self.db.add_documents(batch_docs, ids=batch_ids) + self.db.persist() + logger.info(f"Added {len(documents)} documents to vector store.") + + def similarity_search_with_score(self, query: str, k: int = 5) -> List[Tuple[Document, float]]: + results = self.db.similarity_search_with_score(query, k=k) + logger.info(f"Similarity search returned {len(results)} results for query: {query}") + return results + + def similarity_search(self, query: str, k: int = 5) -> List[Document]: + docs = self.db.similarity_search(query, k=k) + logger.info(f"Similarity search returned {len(docs)} documents for query: {query}") + return docs + def delete_documents(self, document_ids: List[str]) -> None: - """Delete documents from vector store""" - # TODO: Implement document deletion - pass - + self.db.delete(ids=document_ids) + self.db.persist() + logger.info(f"Deleted {len(document_ids)} documents from vector store.") + def get_document_count(self) -> int: - """Get total number of documents in vector store""" - # TODO: Return document count - pass \ No newline at end of file + items = self.db.get(include=[]) + count = len(items["ids"]) + logger.info(f"Vector store contains {count} documents.") + return count diff --git a/backend/services/__init__.py b/backend/utils/__init__.py similarity index 100% rename from backend/services/__init__.py rename to backend/utils/__init__.py diff --git a/backend/utils/get_embedding_function.py b/backend/utils/get_embedding_function.py new file mode 100644 index 00000000..ad4ff0e4 --- /dev/null +++ b/backend/utils/get_embedding_function.py @@ -0,0 +1,18 @@ +from langchain_community.embeddings.ollama import OllamaEmbeddings +from langchain_community.embeddings.bedrock import BedrockEmbeddings +from langchain_openai.embeddings import OpenAIEmbeddings +from langchain_community.embeddings import HuggingFaceEmbeddings +from config import settings + +def get_embedding_function(): + provider = getattr(settings, "embedding_provider", "huggingface").lower() + model_name = getattr(settings, "embedding_model", "all-MiniLM-L6-v2") + + if provider == "openai": + return OpenAIEmbeddings(model=model_name, openai_api_key=settings.openai_api_key) + elif provider == "ollama": + return OllamaEmbeddings(model=model_name) + elif provider == "bedrock": + return BedrockEmbeddings(model_id=model_name) + else: + return HuggingFaceEmbeddings(model_name=model_name) diff --git a/backend/utils/llm_provider.py b/backend/utils/llm_provider.py new file mode 100644 index 00000000..5f804e19 --- /dev/null +++ b/backend/utils/llm_provider.py @@ -0,0 +1,23 @@ +from langchain_community.llms.ollama import Ollama +from langchain_google_genai import ChatGoogleGenerativeAI +from config import settings + + +def get_llm(): + provider = getattr(settings, "llm_provider", "ollama").lower() + + if provider == "ollama": + return Ollama( + model=settings.llm_model, + base_url=settings.ollama_base_url, + temperature=settings.llm_temperature + ) + + elif provider == "google": + return ChatGoogleGenerativeAI( + model=settings.llm_model, + google_api_key=settings.google_api_key + ) + + else: + raise ValueError(f"Unsupported LLM provider: {provider}") diff --git a/backend/utils/text_splitter.py b/backend/utils/text_splitter.py new file mode 100644 index 00000000..0ba3560c --- /dev/null +++ b/backend/utils/text_splitter.py @@ -0,0 +1,30 @@ +import spacy +from spacy_chunks import SpacyChunker + +class SemanticChunker: + def __init__(self, chunk_size=800, overlap=80): + self.chunk_size = chunk_size + self.overlap = overlap + + # Load spacy model and spacy-chunks + self.nlp = spacy.load("en_core_web_sm") + self.chunker = SpacyChunker(self.nlp) + + def chunk(self, text: str): + doc = self.nlp(text) + semantic_chunks = self.chunker.chunk_text(doc) + + # fallback if semantic chunks are too large β†’ split again by char + final_chunks = [] + for chunk in semantic_chunks: + if len(chunk) > self.chunk_size: + final_chunks.extend(self._split_by_char(chunk)) + else: + final_chunks.append(chunk) + return final_chunks + + def _split_by_char(self, text): + chunks = [] + for i in range(0, len(text), self.chunk_size - self.overlap): + chunks.append(text[i:i+self.chunk_size]) + return chunks diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 00000000..e19f86d0 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,14 @@ +version: "3.9" + +services: + ollama: + image: ollama/ollama + container_name: ollama + restart: always + ports: + - "11434:11434" + volumes: + - ollama_data:/root/.ollama + +volumes: + ollama_data: diff --git a/frontend/package-lock.json b/frontend/package-lock.json index b1485dcf..61107086 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -8,6 +8,7 @@ "name": "rag-frontend", "version": "0.1.0", "dependencies": { + "@tailwindcss/forms": "^0.5.10", "@types/node": "^20", "@types/react": "^18", "@types/react-dom": "^18", @@ -15,21 +16,21 @@ "next": "14.0.0", "react": "^18", "react-dom": "^18", + "react-markdown": "^10.1.0", "typescript": "5.3.3" }, "devDependencies": { - "autoprefixer": "^10.0.1", + "autoprefixer": "^10.4.21", "eslint": "^8", "eslint-config-next": "14.0.0", - "postcss": "^8.4.31", - "tailwindcss": "^3.3.0" + "postcss": "^8.5.5", + "tailwindcss": "^3.4.17" } }, "node_modules/@alloc/quick-lru": { "version": "5.2.0", "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz", "integrity": "sha512-UrcABB+4bUrFABwbluTIBErXwvbsU/V7TZWfmbgJfbkwiBuziS9gxdODUyuiecfdGQ85jglMW6juS3+z5TsKLw==", - "dev": true, "license": "MIT", "engines": { "node": ">=10" @@ -177,7 +178,6 @@ "version": "8.0.2", "resolved": "https://registry.npmjs.org/@isaacs/cliui/-/cliui-8.0.2.tgz", "integrity": "sha512-O8jcjabXaleOG9DQ0+ARXWZBTfnP4WNAqzuiJK7ll44AmxGKv/J2M4TPjxjY3znBCfvBXFzucm1twdyFybFqEA==", - "dev": true, "license": "ISC", "dependencies": { "string-width": "^5.1.2", @@ -195,7 +195,6 @@ "version": "6.1.0", "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-6.1.0.tgz", "integrity": "sha512-7HSX4QQb4CspciLpVFwyRe79O3xsIZDDLER21kERQ71oaPodF8jL725AgJMFAYbooIqolJoRLuM81SpeUkpkvA==", - "dev": true, "license": "MIT", "engines": { "node": ">=12" @@ -208,7 +207,6 @@ "version": "7.1.0", "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-7.1.0.tgz", "integrity": "sha512-iq6eVVI64nQQTRYq2KtEg2d2uU7LElhTJwsH4YzIHZshxlgZms/wIc4VoDQTlG/IvVIrBKG06CrZnp0qv7hkcQ==", - "dev": true, "license": "MIT", "dependencies": { "ansi-regex": "^6.0.1" @@ -224,7 +222,6 @@ "version": "0.3.8", "resolved": "https://registry.npmjs.org/@jridgewell/gen-mapping/-/gen-mapping-0.3.8.tgz", "integrity": "sha512-imAbBGkb+ebQyxKgzv5Hu2nmROxoDOXHh80evxdoXNOrvAnVx7zimzc1Oo5h9RlfV4vPXaE2iM5pOFbvOCClWA==", - "dev": true, "license": "MIT", "dependencies": { "@jridgewell/set-array": "^1.2.1", @@ -239,7 +236,6 @@ "version": "3.1.2", "resolved": "https://registry.npmjs.org/@jridgewell/resolve-uri/-/resolve-uri-3.1.2.tgz", "integrity": 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"node_modules/@tybys/wasm-util": { "version": "0.9.0", "resolved": "https://registry.npmjs.org/@tybys/wasm-util/-/wasm-util-0.9.0.tgz", @@ -539,6 +540,39 @@ "tslib": "^2.4.0" } }, + "node_modules/@types/debug": { + "version": "4.1.12", + "resolved": "https://registry.npmjs.org/@types/debug/-/debug-4.1.12.tgz", + "integrity": "sha512-vIChWdVG3LG1SMxEvI/AK+FWJthlrqlTu7fbrlywTkkaONwk/UAGaULXRlf8vkzFBLVm0zkMdCquhL5aOjhXPQ==", + "license": "MIT", + "dependencies": { + "@types/ms": "*" + } + }, + "node_modules/@types/estree": { + "version": "1.0.8", + "resolved": "https://registry.npmjs.org/@types/estree/-/estree-1.0.8.tgz", + "integrity": "sha512-dWHzHa2WqEXI/O1E9OjrocMTKJl2mSrEolh1Iomrv6U+JuNwaHXsXx9bLu5gG7BUWFIN0skIQJQ/L1rIex4X6w==", + "license": "MIT" + }, + "node_modules/@types/estree-jsx": { + "version": "1.0.5", + "resolved": "https://registry.npmjs.org/@types/estree-jsx/-/estree-jsx-1.0.5.tgz", + "integrity": 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"node_modules/@types/ms": { + "version": "2.1.0", + "resolved": "https://registry.npmjs.org/@types/ms/-/ms-2.1.0.tgz", + "integrity": "sha512-GsCCIZDE/p3i96vtEqx+7dBUGXrc7zeSK3wwPHIaRThS+9OhWIXRqzs4d6k1SVU8g91DrNRWxWUGhp5KXQb2VA==", + "license": "MIT" + }, "node_modules/@types/node": { "version": "20.19.0", "resolved": "https://registry.npmjs.org/@types/node/-/node-20.19.0.tgz", @@ -580,6 +629,12 @@ "@types/react": "^18.0.0" } }, + "node_modules/@types/unist": { + "version": "3.0.3", + "resolved": "https://registry.npmjs.org/@types/unist/-/unist-3.0.3.tgz", + "integrity": "sha512-ko/gIFJRv177XgZsZcBwnqJN5x/Gien8qNOn0D5bQU/zAzVf9Zt3BlcUiLqhV9y4ARk0GbT3tnUiPNgnTXzc/Q==", + "license": "MIT" + }, "node_modules/@typescript-eslint/parser": { "version": "6.21.0", "resolved": "https://registry.npmjs.org/@typescript-eslint/parser/-/parser-6.21.0.tgz", @@ -718,7 +773,6 @@ "version": "1.3.0", "resolved": "https://registry.npmjs.org/@ungap/structured-clone/-/structured-clone-1.3.0.tgz", 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"sha512-kRj8B+YHZCc9kQYdWfJB2/oUl9rA99qbowYYBtr4ui4mZyAQ2JpvVBd/6U2YloATfqBhBTSMhTpgBHtU0Mf3Rg==", + "license": "MIT", + "funding": { + "type": "github", + "url": "https://github.com/sponsors/wooorm" + } + }, + "node_modules/trough": { + "version": "2.2.0", + "resolved": "https://registry.npmjs.org/trough/-/trough-2.2.0.tgz", + "integrity": "sha512-tmMpK00BjZiUyVyvrBK7knerNgmgvcV/KLVyuma/SC+TQN167GrMRciANTz09+k3zW8L8t60jWO1GpfkZdjTaw==", + "license": "MIT", + "funding": { + "type": "github", + "url": "https://github.com/sponsors/wooorm" + } + }, "node_modules/ts-api-utils": { "version": "1.4.3", "resolved": "https://registry.npmjs.org/ts-api-utils/-/ts-api-utils-1.4.3.tgz", @@ -5583,7 +6554,6 @@ "version": "0.1.13", "resolved": "https://registry.npmjs.org/ts-interface-checker/-/ts-interface-checker-0.1.13.tgz", "integrity": "sha512-Y/arvbn+rrz3JCKl9C4kVNfTfSm2/mEp5FSz5EsZSANGPSlQrpRI5M4PKF+mJnE52jOO90PnPSc3Ur3bTQw0gA==", - "dev": true, "license": "Apache-2.0" }, "node_modules/tsconfig-paths": { @@ -5747,6 +6717,93 @@ "integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==", "license": "MIT" }, + "node_modules/unified": { + "version": "11.0.5", + "resolved": "https://registry.npmjs.org/unified/-/unified-11.0.5.tgz", + "integrity": "sha512-xKvGhPWw3k84Qjh8bI3ZeJjqnyadK+GEFtazSfZv/rKeTkTjOJho6mFqh2SM96iIcZokxiOpg78GazTSg8+KHA==", + "license": "MIT", + "dependencies": { + "@types/unist": "^3.0.0", + "bail": "^2.0.0", + "devlop": "^1.0.0", + "extend": "^3.0.0", + "is-plain-obj": "^4.0.0", + "trough": "^2.0.0", + "vfile": "^6.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, + "node_modules/unist-util-is": { + "version": "6.0.0", + "resolved": "https://registry.npmjs.org/unist-util-is/-/unist-util-is-6.0.0.tgz", + "integrity": "sha512-2qCTHimwdxLfz+YzdGfkqNlH0tLi9xjTnHddPmJwtIG9MGsdbutfTc4P+haPD7l7Cjxf/WZj+we5qfVPvvxfYw==", + "license": "MIT", + "dependencies": { + "@types/unist": "^3.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, + "node_modules/unist-util-position": { + "version": "5.0.0", + "resolved": "https://registry.npmjs.org/unist-util-position/-/unist-util-position-5.0.0.tgz", + "integrity": "sha512-fucsC7HjXvkB5R3kTCO7kUjRdrS0BJt3M/FPxmHMBOm8JQi2BsHAHFsy27E0EolP8rp0NzXsJ+jNPyDWvOJZPA==", + "license": "MIT", + "dependencies": { + "@types/unist": "^3.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, + "node_modules/unist-util-stringify-position": { + "version": "4.0.0", + "resolved": "https://registry.npmjs.org/unist-util-stringify-position/-/unist-util-stringify-position-4.0.0.tgz", + "integrity": "sha512-0ASV06AAoKCDkS2+xw5RXJywruurpbC4JZSm7nr7MOt1ojAzvyyaO+UxZf18j8FCF6kmzCZKcAgN/yu2gm2XgQ==", + "license": "MIT", + "dependencies": { + "@types/unist": "^3.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, + "node_modules/unist-util-visit": { + "version": "5.0.0", + "resolved": "https://registry.npmjs.org/unist-util-visit/-/unist-util-visit-5.0.0.tgz", + "integrity": "sha512-MR04uvD+07cwl/yhVuVWAtw+3GOR/knlL55Nd/wAdblk27GCVt3lqpTivy/tkJcZoNPzTwS1Y+KMojlLDhoTzg==", + "license": "MIT", + "dependencies": { + "@types/unist": "^3.0.0", + "unist-util-is": "^6.0.0", + "unist-util-visit-parents": "^6.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, + "node_modules/unist-util-visit-parents": { + "version": "6.0.1", + "resolved": "https://registry.npmjs.org/unist-util-visit-parents/-/unist-util-visit-parents-6.0.1.tgz", + "integrity": "sha512-L/PqWzfTP9lzzEa6CKs0k2nARxTdZduw3zyh8d2NVBnsyvHjSX4TWse388YrrQKbvI8w20fGjGlhgT96WwKykw==", + "license": "MIT", + "dependencies": { + "@types/unist": "^3.0.0", + "unist-util-is": "^6.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, "node_modules/unrs-resolver": { "version": "1.7.11", "resolved": "https://registry.npmjs.org/unrs-resolver/-/unrs-resolver-1.7.11.tgz", @@ -5825,9 +6882,36 @@ "version": "1.0.2", "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz", "integrity": "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==", - "dev": true, "license": "MIT" }, + "node_modules/vfile": { + "version": "6.0.3", + "resolved": "https://registry.npmjs.org/vfile/-/vfile-6.0.3.tgz", + "integrity": "sha512-KzIbH/9tXat2u30jf+smMwFCsno4wHVdNmzFyL+T/L3UGqqk6JKfVqOFOZEpZSHADH1k40ab6NUIXZq422ov3Q==", + "license": "MIT", + "dependencies": { + "@types/unist": "^3.0.0", + "vfile-message": "^4.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, + "node_modules/vfile-message": { + "version": "4.0.2", + "resolved": "https://registry.npmjs.org/vfile-message/-/vfile-message-4.0.2.tgz", + "integrity": "sha512-jRDZ1IMLttGj41KcZvlrYAaI3CfqpLpfpf+Mfig13viT6NKvRzWZ+lXz0Y5D60w6uJIBAOGq9mSHf0gktF0duw==", + "license": "MIT", + "dependencies": { + "@types/unist": "^3.0.0", + "unist-util-stringify-position": "^4.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, "node_modules/watchpack": { "version": "2.4.0", "resolved": "https://registry.npmjs.org/watchpack/-/watchpack-2.4.0.tgz", @@ -5845,7 +6929,6 @@ "version": "2.0.2", "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz", "integrity": "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==", - "dev": true, "license": "ISC", "dependencies": { "isexe": "^2.0.0" @@ -5960,7 +7043,6 @@ "version": "8.1.0", "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-8.1.0.tgz", "integrity": "sha512-si7QWI6zUMq56bESFvagtmzMdGOtoxfR+Sez11Mobfc7tm+VkUckk9bW2UeffTGVUbOksxmSw0AA2gs8g71NCQ==", - "dev": true, "license": "MIT", "dependencies": { "ansi-styles": "^6.1.0", @@ -5979,7 +7061,6 @@ "version": "7.0.0", "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-7.0.0.tgz", "integrity": "sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==", - "dev": true, "license": "MIT", "dependencies": { "ansi-styles": "^4.0.0", @@ -5997,14 +7078,12 @@ "version": "8.0.0", "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz", "integrity": "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==", - "dev": true, "license": "MIT" }, "node_modules/wrap-ansi-cjs/node_modules/string-width": { "version": "4.2.3", "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz", "integrity": "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==", - "dev": true, "license": "MIT", "dependencies": { "emoji-regex": "^8.0.0", @@ -6019,7 +7098,6 @@ "version": "6.1.0", "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-6.1.0.tgz", "integrity": "sha512-7HSX4QQb4CspciLpVFwyRe79O3xsIZDDLER21kERQ71oaPodF8jL725AgJMFAYbooIqolJoRLuM81SpeUkpkvA==", - "dev": true, "license": "MIT", "engines": { "node": ">=12" @@ -6032,7 +7110,6 @@ "version": "6.2.1", "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-6.2.1.tgz", "integrity": "sha512-bN798gFfQX+viw3R7yrGWRqnrN2oRkEkUjjl4JNn4E8GxxbjtG3FbrEIIY3l8/hrwUwIeCZvi4QuOTP4MErVug==", - "dev": true, "license": "MIT", "engines": { "node": ">=12" @@ -6045,7 +7122,6 @@ "version": "7.1.0", "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-7.1.0.tgz", "integrity": "sha512-iq6eVVI64nQQTRYq2KtEg2d2uU7LElhTJwsH4YzIHZshxlgZms/wIc4VoDQTlG/IvVIrBKG06CrZnp0qv7hkcQ==", - "dev": true, "license": "MIT", "dependencies": { "ansi-regex": "^6.0.1" @@ -6068,7 +7144,6 @@ "version": "2.8.0", "resolved": "https://registry.npmjs.org/yaml/-/yaml-2.8.0.tgz", "integrity": "sha512-4lLa/EcQCB0cJkyts+FpIRx5G/llPxfP6VQU5KByHEhLxY3IJCH0f0Hy1MHI8sClTvsIb8qwRJ6R/ZdlDJ/leQ==", - "dev": true, "license": "ISC", "bin": { "yaml": "bin.mjs" @@ -6089,6 +7164,16 @@ "funding": { "url": "https://github.com/sponsors/sindresorhus" } + }, + "node_modules/zwitch": { + "version": "2.0.4", + "resolved": "https://registry.npmjs.org/zwitch/-/zwitch-2.0.4.tgz", + "integrity": "sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A==", + "license": "MIT", + "funding": { + "type": "github", + "url": "https://github.com/sponsors/wooorm" + } } } } diff --git a/frontend/package.json b/frontend/package.json index f830ac82..bd151c3a 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -9,20 +9,22 @@ "lint": "next lint" }, "dependencies": { - "next": "14.0.0", - "react": "^18", - "react-dom": "^18", - "axios": "^1.6.0", + "@tailwindcss/forms": "^0.5.10", "@types/node": "^20", "@types/react": "^18", "@types/react-dom": "^18", + "axios": "^1.6.0", + "next": "14.0.0", + "react": "^18", + "react-dom": "^18", + "react-markdown": "^10.1.0", "typescript": "5.3.3" }, "devDependencies": { + "autoprefixer": "^10.4.21", "eslint": "^8", "eslint-config-next": "14.0.0", - "tailwindcss": "^3.3.0", - "autoprefixer": "^10.0.1", - "postcss": "^8.4.31" + "postcss": "^8.5.5", + "tailwindcss": "^3.4.17" } -} \ No newline at end of file +} diff --git a/frontend/pages/_app.tsx b/frontend/pages/_app.tsx index 224b352d..c055f25c 100644 --- a/frontend/pages/_app.tsx +++ b/frontend/pages/_app.tsx @@ -3,4 +3,4 @@ import type { AppProps } from 'next/app' export default function App({ Component, pageProps }: AppProps) { return -} \ No newline at end of file +} diff --git a/frontend/pages/index.tsx b/frontend/pages/index.tsx index 5d3e7ad1..ce2cf19f 100644 --- a/frontend/pages/index.tsx +++ b/frontend/pages/index.tsx @@ -1,31 +1,519 @@ -import React from 'react'; -import Head from 'next/head'; +import React, { useRef, useState } from "react"; +import Head from "next/head"; +import ReactMarkdown from "react-markdown"; + +const BACKEND_URL = process.env.NEXT_PUBLIC_BACKEND_URL || "http://localhost:8000"; export default function Home() { + const [pdfFiles, setPdfFiles] = useState([]); + const [uploadProgress, setUploadProgress] = useState(0); + const [uploading, setUploading] = useState(false); + const [uploadStatus, setUploadStatus] = useState(null); + + const [chatInput, setChatInput] = useState(""); + // Initialize chat with welcome message + const [chatHistory, setChatHistory] = useState<{ role: string; content: string }[]>([ + { + role: "assistant", + content: + "Hello! I'm ready to answer your questions about the uploaded financial statement. What would you like to know?", + }, + ]); + const [aiSources, setAiSources] = useState([]); + const [loadingAnswer, setLoadingAnswer] = useState(false); + + const fileInputRef = useRef(null); + + // Handle PDF file selection + const handleFileChange = (e: React.ChangeEvent) => { + if (e.target.files) { + // Append new files to the existing list, avoiding duplicates by name + const newFiles = Array.from(e.target.files); + setPdfFiles((prev) => { + const existingNames = new Set(prev.map((f) => f.name)); + const filteredNew = newFiles.filter((f) => !existingNames.has(f.name)); + return [...prev, ...filteredNew]; + }); + setUploadStatus(null); + setUploadProgress(0); + } + }; + + // Handle PDF upload + const handleUpload = async () => { + if (!pdfFiles.length) return; + setUploading(true); + setUploadStatus(null); + setUploadProgress(0); + + const formData = new FormData(); + pdfFiles.forEach((file) => { + formData.append("files", file); + }); + + try { + const res = await fetch(`${BACKEND_URL}/api/upload`, { + method: "POST", + body: formData, + }); + if (!res.ok) { + setUploadStatus("Upload failed"); + setUploading(false); + return; + } + const data = await res.json(); + setUploadStatus(data); + setUploadProgress(100); + setPdfFiles([]); // Clear the list after upload finishes + } catch (err) { + setUploadStatus("Upload failed"); + } + setUploading(false); + }; + + // Handle chat send + const handleSend = async () => { + if (!chatInput.trim()) return; + setLoadingAnswer(true); + setAiSources([]); + // Add user message to chat history + setChatHistory((prev) => [...prev, { role: "user", content: chatInput }]); + const currentHistory = [...chatHistory, { role: "user", content: chatInput }]; + + try { + const res = await fetch(`${BACKEND_URL}/api/chat`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ + question: chatInput, + chat_history: currentHistory, + }), + }); + if (!res.ok) { + // Add error as AI message + setChatHistory((prev) => [ + ...prev, + { role: "assistant", content: "Sorry, something went wrong." }, + ]); + setLoadingAnswer(false); + setChatInput(""); + return; + } + const data = await res.json(); + // Add AI answer to chat history + setChatHistory((prev) => [ + ...prev, + { role: "assistant", content: data.answer }, + ]); + setAiSources(data.sources || []); + } catch (err) { + setChatHistory((prev) => [ + ...prev, + { role: "assistant", content: "Sorry, something went wrong." }, + ]); + } + setLoadingAnswer(false); + setChatInput(""); + }; + return ( -
+ <> - RAG-based Financial Q&A System + Financial Insights AI + + { + (e.currentTarget as HTMLLinkElement).rel = "stylesheet"; + }} + href="https://fonts.googleapis.com/css2?display=swap&family=Inter:wght@400;500;700;900&family=Noto+Sans:wght@400;500;700;900" + /> - -
-

RAG-based Financial Statement Q&A System

- - {/* TODO: Implement your components here */} - {/* - Suggested components to implement: - - FileUpload component for PDF upload - - ChatInterface component for Q&A - - DocumentViewer component for document display - */} - -
-

Welcome to the RAG-based Q&A System!

-

Upload a financial statement PDF and start asking questions.

+
+
+
+
+
+ + + +
+

+ Financial Insights AI +

+
+ +
+
+
+
+

+ Financial Statement Q&A +

+
+ {/* File Upload */} +
+ + {pdfFiles.length > 0 && ( +
    + {pdfFiles.map((file, idx) => ( +
  • +
    + {/* File icon */} + + + + + + {file.name} + +
    + +
    • + ))} +
+ )} + +
+ {/* Upload Progress */} + {uploading && ( +
+
+

+ Uploading... +

+
+ {/* Spinner widget */} +
+ + + + +
+
+ )} + {uploadStatus && ( +
+

+ Upload Results +

+ + + + + + + + + + + {uploadStatus.results && + uploadStatus.results.map((r: any, idx: number) => ( + + + + + + + ))} + +
+ File + + Status + + Chunks + + Time (s) +
{r.filename} + {r.message.includes("success") ? ( + + + + + {r.message} + + ) : ( + + + + + {r.message} + + )} + {r.chunks_count}{r.processing_time}
+
+ )} + {/* Chat Section */} +

+ Chat +

+ {/* Chat Bubbles */} +
+ {chatHistory.map((msg, idx) => ( +
+ {msg.role === "assistant" && ( +
+ )} +
+

+ {msg.role === "user" ? "You" : "AI Assistant"} +

+
+ {msg.content} +
+
+ {msg.role === "user" && ( +
+ )} +
+ ))} + {/* Loading spinner for AI answer */} + {loadingAnswer && ( +
+
+
+

+ AI Assistant +

+
+ + + + + + Generating answer... + +
+
+
+ )} +
+ {/* Chat Input */} +
+ +
+
+
-
-
+ + ); -} \ No newline at end of file +} diff --git a/frontend/postcss.config.js b/frontend/postcss.config.js new file mode 100644 index 00000000..33ad091d --- /dev/null +++ b/frontend/postcss.config.js @@ -0,0 +1,6 @@ +module.exports = { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/frontend/styles/globals.css b/frontend/styles/globals.css index 34afd612..b5c61c95 100644 --- a/frontend/styles/globals.css +++ b/frontend/styles/globals.css @@ -1,18 +1,3 @@ -html, -body { - padding: 0; - margin: 0; - font-family: -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Oxygen, - Ubuntu, Cantarell, Fira Sans, Droid Sans, Helvetica Neue, sans-serif; -} - -a { - color: inherit; - text-decoration: none; -} - -* { - box-sizing: border-box; -} - -/* TODO: Add your custom styles here */ \ No newline at end of file +@tailwind base; +@tailwind components; +@tailwind utilities; diff --git a/frontend/tailwind.config.js b/frontend/tailwind.config.js new file mode 100644 index 00000000..d26096ca --- /dev/null +++ b/frontend/tailwind.config.js @@ -0,0 +1,11 @@ +/** @type {import('tailwindcss').Config} */ +module.exports = { + content: [ + "./pages/**/*.{js,ts,jsx,tsx}", + "./components/**/*.{js,ts,jsx,tsx}", + ], + theme: { + extend: {}, + }, + plugins: [require('@tailwindcss/forms')], +} diff --git a/requirements.txt b/requirements.txt index 6102af3c..96ad9242 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,48 +1,38 @@ -# FastAPI 및 μ›Ή ν”„λ ˆμž„μ›Œν¬ -fastapi==0.104.1 -uvicorn[standard]==0.24.0 -python-multipart==0.0.6 -python-jose[cryptography]==3.3.0 +# FastAPI stack +fastapi==0.111.0 +uvicorn[standard]==0.29.0 +python-multipart==0.0.9 +python-dotenv==1.0.1 -# PDF 처리 +# PDF processing PyPDF2==3.0.1 -pdfplumber==0.10.0 -pymupdf==1.23.26 # fitz - -# RAG 및 AI κ΄€λ ¨ -langchain==0.1.0 -langchain-openai==0.0.2 -langchain-community==0.0.10 -openai==1.6.1 -tiktoken==0.5.2 - -# 벑터 λ°μ΄ν„°λ² μ΄μŠ€ -chromadb==0.4.18 -faiss-cpu==1.7.4 - -# μž„λ² λ”© 및 ν…μŠ€νŠΈ 처리 -sentence-transformers==2.2.2 -transformers==4.36.2 -torch==2.1.2 - -# 데이터 처리 -numpy==1.24.4 -pandas==2.1.4 - -# ν™˜κ²½ λ³€μˆ˜ 및 μ„€μ • -python-dotenv==1.0.0 -pydantic==2.5.2 -pydantic-settings==2.1.0 - -# μœ ν‹Έλ¦¬ν‹° -requests==2.31.0 -aiofiles==23.2.0 -tqdm==4.66.1 - -# λ‘œκΉ… 및 λͺ¨λ‹ˆν„°λ§ +pdfplumber==0.11.0 +pymupdf==1.23.26 + +# Embedding + LLM +openai==1.30.5 +sentence-transformers==2.7.0 +transformers==4.41.2 +torch==2.3.0 +tiktoken==0.7.0 + +# Vectorstore +chromadb==0.4.24 + +# LangChain full stack +langchain==0.1.17 +langchain-community==0.0.36 +langchain-openai==0.1.5 +langchain-google-genai==1.0.3 + +# Utilities +numpy==1.26.4 +pandas==2.2.2 +pydantic==2.7.1 +pydantic-settings==2.2.1 +requests==2.32.3 +tqdm==4.66.4 loguru==0.7.2 -# ν…ŒμŠ€νŠΈ (선택사항) -pytest==7.4.3 -pytest-asyncio==0.21.1 -httpx==0.25.2 \ No newline at end of file +ollama==0.1.8 +pypdf