diff --git a/.cursor/rules/branch-rule.mdc b/.cursor/rules/branch-rule.mdc new file mode 100644 index 000000000..93d91f14b --- /dev/null +++ b/.cursor/rules/branch-rule.mdc @@ -0,0 +1,6 @@ +--- +alwaysApply: true +--- +You always prefer to use branch development. Before writing any code - you create a feature branch to hold those changes. + +After you are done - provide instructions in a "MERGE.md" file that explains how to merge the changes back to main with both a GitHub PR route and a GitHub CLI route. \ No newline at end of file diff --git a/MERGE.md b/MERGE.md new file mode 100644 index 000000000..517368822 --- /dev/null +++ b/MERGE.md @@ -0,0 +1,187 @@ +# RAG PDF System Feature - Merge Instructions + +This document provides instructions for merging the RAG (Retrieval-Augmented Generation) PDF system from the `feature/rag-pdf-system` branch back to the `main` branch. + +## Changes Made + +### API Changes (`api/`) +- Added `PyPDF2==3.0.1` and `python-dotenv==1.0.0` dependencies to `requirements.txt` +- Enhanced `app.py` with comprehensive RAG functionality: + - **New imports**: Added aimakerspace library components and file upload handling + - **PDF Upload Endpoint** (`/api/upload-pdf-rag`): + - Accepts PDF file uploads + - Uses `aimakerspace.text_utils.PDFLoader` to extract text + - Uses `aimakerspace.text_utils.CharacterTextSplitter` to chunk text (1000 chars, 200 overlap) + - Uses `aimakerspace.vectordatabase.VectorDatabase` to create embeddings + - Returns PDF ID and processing status + - **RAG Chat Endpoint** (`/api/chat-rag`): + - Accepts user questions and PDF ID + - Uses vector similarity search to find relevant chunks + - Uses `aimakerspace.openai_utils.chatmodel.ChatOpenAI` for AI responses + - Uses `aimakerspace.openai_utils.prompts` for structured prompting + - Returns streaming responses with context-aware answers + - **PDF List Endpoint** (`/api/pdfs`): + - Lists all uploaded PDFs with metadata + - **Global storage**: In-memory storage for PDF documents and vector databases + +### Frontend Changes (`frontend/`) +- **PdfUploadRAG Component** (`frontend/src/app/components/PdfUploadRAG.tsx`): + - Drag-and-drop PDF upload interface + - File validation (PDF only, max 10MB) + - Real-time upload status and error handling + - PDF selection interface with visual feedback + - Integration with parent component for state management +- **RAGChat Component** (`frontend/src/app/components/RAGChat.tsx`): + - Chat interface specifically for PDF Q&A + - Streaming responses from RAG system + - Clear chat functionality + - Empty state when no PDF is selected +- **RAGSystem Component** (`frontend/src/app/components/RAGSystem.tsx`): + - Main orchestrator component combining upload and chat + - State management for selected PDF + - Clean separation of concerns +- **Updated Main Page** (`frontend/src/app/page.tsx`): + - Added RAG system above regular chat + - Updated description to mention PDF Q&A functionality + +### Aimakerspace Library Integration +- **Text Processing**: Uses `PDFLoader` and `CharacterTextSplitter` for document processing +- **Vector Database**: Uses `VectorDatabase` with cosine similarity for semantic search +- **Embeddings**: Uses OpenAI's text-embedding-3-small model via `EmbeddingModel` +- **Chat Model**: Uses GPT-4o-mini via `ChatOpenAI` with streaming support +- **Prompting**: Uses structured prompts with `SystemRolePrompt` and `UserRolePrompt` + +## Key Features Implemented + +### RAG System Capabilities +- ✅ **PDF Text Extraction**: Robust text extraction from PDF files +- ✅ **Intelligent Chunking**: 1000-character chunks with 200-character overlap +- ✅ **Semantic Search**: Vector similarity search using OpenAI embeddings +- ✅ **Context-Aware Responses**: AI responses based on relevant document chunks +- ✅ **Streaming Responses**: Real-time streaming of AI responses +- ✅ **Multiple PDF Support**: Upload and chat with multiple PDFs +- ✅ **Error Handling**: Comprehensive error handling throughout the system + +### User Experience +- ✅ **Drag-and-Drop Upload**: Intuitive file upload interface +- ✅ **Real-time Feedback**: Loading states and progress indicators +- ✅ **PDF Selection**: Easy switching between uploaded PDFs +- ✅ **Streaming Chat**: Real-time chat responses +- ✅ **Responsive Design**: Works on all screen sizes +- ✅ **Error Recovery**: Clear error messages and recovery options + +## Merge Instructions + +### Option 1: GitHub Pull Request (Recommended) + +1. **Push the feature branch to GitHub:** + ```bash + git push origin feature/rag-pdf-system + ``` + +2. **Create a Pull Request:** + - Go to your GitHub repository + - Click "Compare & pull request" for the `feature/rag-pdf-system` branch + - Set the base branch to `main` + - Add a descriptive title: "Add RAG system with PDF upload and intelligent document Q&A" + - Add description of the changes made + - Review the changes and create the PR + +3. **Merge the Pull Request:** + - Review any CI/CD checks + - Click "Merge pull request" + - Delete the feature branch when prompted + +4. **Update local main branch:** + ```bash + git checkout main + git pull origin main + ``` + +### Option 2: GitHub CLI + +1. **Push the feature branch:** + ```bash + git push origin feature/rag-pdf-system + ``` + +2. **Create and merge PR using GitHub CLI:** + ```bash + # Create the pull request + gh pr create --title "Add RAG system with PDF upload and intelligent document Q&A" --body "This PR adds a comprehensive RAG system that allows users to upload PDFs and chat with them using AI-powered document understanding." + + # Merge the pull request + gh pr merge --merge + + # Switch back to main and pull changes + git checkout main + git pull origin main + ``` + +## Testing the Feature + +After merging, you can test the RAG system: + +1. **Start the API server:** + ```bash + cd api + pip install -r requirements.txt + python app.py + ``` + +2. **Start the frontend:** + ```bash + cd frontend + npm install + npm run dev + ``` + +3. **Test the RAG system:** + - Upload a PDF file using the drag-and-drop interface + - Wait for indexing to complete + - Select the uploaded PDF + - Ask questions about the PDF content + - Verify that responses are contextually relevant + - Test with multiple PDFs + +## Environment Variables Required + +Ensure these environment variables are set: +- `OPENAI_API_KEY`: Your OpenAI API key for embeddings and chat +- `NEXT_PUBLIC_OPENAI_API_KEY`: Frontend access to OpenAI API key + +## Deployment Notes + +- The API endpoints will be automatically deployed to Vercel when merged to main +- The frontend changes will also be deployed automatically +- Ensure the `PyPDF2` and `python-dotenv` dependencies are properly installed +- The aimakerspace library is included in the repository and will be deployed + +## Performance Considerations + +- **Memory Usage**: PDF documents and vector databases are stored in memory +- **File Size Limits**: 10MB maximum PDF size +- **Chunking Strategy**: 1000-character chunks with 200-character overlap +- **Search Results**: Top 3 most relevant chunks used for context +- **Streaming**: Real-time response streaming for better UX + +## Cleanup + +After successful merge: +```bash +# Delete the local feature branch +git branch -d feature/rag-pdf-system + +# Delete the remote feature branch (if using GitHub PR method) +git push origin --delete feature/rag-pdf-system +``` + +## Future Enhancements + +Potential improvements for future iterations: +- Persistent storage for PDF documents and vector databases +- Support for more document types (DOCX, TXT, etc.) +- Advanced chunking strategies (semantic chunking) +- User authentication and document ownership +- Conversation history persistence +- Advanced search filters and metadata \ No newline at end of file diff --git a/aimakerspace/__init__.py b/aimakerspace/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/aimakerspace/openai_utils/__init__.py b/aimakerspace/openai_utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/aimakerspace/openai_utils/chatmodel.py b/aimakerspace/openai_utils/chatmodel.py new file mode 100644 index 000000000..5754be9f7 --- /dev/null +++ b/aimakerspace/openai_utils/chatmodel.py @@ -0,0 +1,45 @@ +from openai import OpenAI, AsyncOpenAI +from dotenv import load_dotenv +import os + +load_dotenv() + + +class ChatOpenAI: + def __init__(self, model_name: str = "gpt-4o-mini"): + self.model_name = model_name + self.openai_api_key = os.getenv("OPENAI_API_KEY") + if self.openai_api_key is None: + raise ValueError("OPENAI_API_KEY is not set") + + def run(self, messages, text_only: bool = True, **kwargs): + if not isinstance(messages, list): + raise ValueError("messages must be a list") + + client = OpenAI() + response = client.chat.completions.create( + model=self.model_name, messages=messages, **kwargs + ) + + if text_only: + return response.choices[0].message.content + + return response + + async def astream(self, messages, **kwargs): + if not isinstance(messages, list): + raise ValueError("messages must be a list") + + client = AsyncOpenAI() + + stream = await client.chat.completions.create( + model=self.model_name, + messages=messages, + stream=True, + **kwargs + ) + + async for chunk in stream: + content = chunk.choices[0].delta.content + if content is not None: + yield content diff --git a/aimakerspace/openai_utils/embedding.py b/aimakerspace/openai_utils/embedding.py new file mode 100644 index 000000000..77d890bff --- /dev/null +++ b/aimakerspace/openai_utils/embedding.py @@ -0,0 +1,59 @@ +from dotenv import load_dotenv +from openai import AsyncOpenAI, OpenAI +import openai +from typing import List +import os +import asyncio + + +class EmbeddingModel: + def __init__(self, embeddings_model_name: str = "text-embedding-3-small"): + load_dotenv() + self.openai_api_key = os.getenv("OPENAI_API_KEY") + self.async_client = AsyncOpenAI() + self.client = OpenAI() + + if self.openai_api_key is None: + raise ValueError( + "OPENAI_API_KEY environment variable is not set. Please set it to your OpenAI API key." + ) + openai.api_key = self.openai_api_key + self.embeddings_model_name = embeddings_model_name + + async def async_get_embeddings(self, list_of_text: List[str]) -> List[List[float]]: + embedding_response = await self.async_client.embeddings.create( + input=list_of_text, model=self.embeddings_model_name + ) + + return [embeddings.embedding for embeddings in embedding_response.data] + + async def async_get_embedding(self, text: str) -> List[float]: + embedding = await self.async_client.embeddings.create( + input=text, model=self.embeddings_model_name + ) + + return embedding.data[0].embedding + + def get_embeddings(self, list_of_text: List[str]) -> List[List[float]]: + embedding_response = self.client.embeddings.create( + input=list_of_text, model=self.embeddings_model_name + ) + + return [embeddings.embedding for embeddings in embedding_response.data] + + def get_embedding(self, text: str) -> List[float]: + embedding = self.client.embeddings.create( + input=text, model=self.embeddings_model_name + ) + + return embedding.data[0].embedding + + +if __name__ == "__main__": + embedding_model = EmbeddingModel() + print(asyncio.run(embedding_model.async_get_embedding("Hello, world!"))) + print( + asyncio.run( + embedding_model.async_get_embeddings(["Hello, world!", "Goodbye, world!"]) + ) + ) diff --git a/aimakerspace/openai_utils/prompts.py b/aimakerspace/openai_utils/prompts.py new file mode 100644 index 000000000..05f80df5a --- /dev/null +++ b/aimakerspace/openai_utils/prompts.py @@ -0,0 +1,78 @@ +import re + + +class BasePrompt: + def __init__(self, prompt): + """ + Initializes the BasePrompt object with a prompt template. + + :param prompt: A string that can contain placeholders within curly braces + """ + self.prompt = prompt + self._pattern = re.compile(r"\{([^}]+)\}") + + def format_prompt(self, **kwargs): + """ + Formats the prompt string using the keyword arguments provided. + + :param kwargs: The values to substitute into the prompt string + :return: The formatted prompt string + """ + matches = self._pattern.findall(self.prompt) + return self.prompt.format(**{match: kwargs.get(match, "") for match in matches}) + + def get_input_variables(self): + """ + Gets the list of input variable names from the prompt string. + + :return: List of input variable names + """ + return self._pattern.findall(self.prompt) + + +class RolePrompt(BasePrompt): + def __init__(self, prompt, role: str): + """ + Initializes the RolePrompt object with a prompt template and a role. + + :param prompt: A string that can contain placeholders within curly braces + :param role: The role for the message ('system', 'user', or 'assistant') + """ + super().__init__(prompt) + self.role = role + + def create_message(self, format=True, **kwargs): + """ + Creates a message dictionary with a role and a formatted message. + + :param kwargs: The values to substitute into the prompt string + :return: Dictionary containing the role and the formatted message + """ + if format: + return {"role": self.role, "content": self.format_prompt(**kwargs)} + + return {"role": self.role, "content": self.prompt} + + +class SystemRolePrompt(RolePrompt): + def __init__(self, prompt: str): + super().__init__(prompt, "system") + + +class UserRolePrompt(RolePrompt): + def __init__(self, prompt: str): + super().__init__(prompt, "user") + + +class AssistantRolePrompt(RolePrompt): + def __init__(self, prompt: str): + super().__init__(prompt, "assistant") + + +if __name__ == "__main__": + prompt = BasePrompt("Hello {name}, you are {age} years old") + print(prompt.format_prompt(name="John", age=30)) + + prompt = SystemRolePrompt("Hello {name}, you are {age} years old") + print(prompt.create_message(name="John", age=30)) + print(prompt.get_input_variables()) diff --git a/aimakerspace/text_utils.py b/aimakerspace/text_utils.py new file mode 100644 index 000000000..d648d7136 --- /dev/null +++ b/aimakerspace/text_utils.py @@ -0,0 +1,136 @@ +import os +from typing import List +import PyPDF2 + + +class TextFileLoader: + def __init__(self, path: str, encoding: str = "utf-8"): + self.documents = [] + self.path = path + self.encoding = encoding + + def load(self): + if os.path.isdir(self.path): + self.load_directory() + elif os.path.isfile(self.path) and self.path.endswith(".txt"): + self.load_file() + else: + raise ValueError( + "Provided path is neither a valid directory nor a .txt file." + ) + + def load_file(self): + with open(self.path, "r", encoding=self.encoding) as f: + self.documents.append(f.read()) + + def load_directory(self): + for root, _, files in os.walk(self.path): + for file in files: + if file.endswith(".txt"): + with open( + os.path.join(root, file), "r", encoding=self.encoding + ) as f: + self.documents.append(f.read()) + + def load_documents(self): + self.load() + return self.documents + + +class CharacterTextSplitter: + def __init__( + self, + chunk_size: int = 1000, + chunk_overlap: int = 200, + ): + assert ( + chunk_size > chunk_overlap + ), "Chunk size must be greater than chunk overlap" + + self.chunk_size = chunk_size + self.chunk_overlap = chunk_overlap + + def split(self, text: str) -> List[str]: + chunks = [] + for i in range(0, len(text), self.chunk_size - self.chunk_overlap): + chunks.append(text[i : i + self.chunk_size]) + return chunks + + def split_texts(self, texts: List[str]) -> List[str]: + chunks = [] + for text in texts: + chunks.extend(self.split(text)) + return chunks + + +class PDFLoader: + def __init__(self, path: str): + self.documents = [] + self.path = path + print(f"PDFLoader initialized with path: {self.path}") + + def load(self): + print(f"Loading PDF from path: {self.path}") + print(f"Path exists: {os.path.exists(self.path)}") + print(f"Is file: {os.path.isfile(self.path)}") + print(f"Is directory: {os.path.isdir(self.path)}") + print(f"File permissions: {oct(os.stat(self.path).st_mode)[-3:]}") + + try: + # Try to open the file first to verify access + with open(self.path, 'rb') as test_file: + pass + + # If we can open it, proceed with loading + self.load_file() + + except IOError as e: + raise ValueError(f"Cannot access file at '{self.path}': {str(e)}") + except Exception as e: + raise ValueError(f"Error processing file at '{self.path}': {str(e)}") + + def load_file(self): + with open(self.path, 'rb') as file: + # Create PDF reader object + pdf_reader = PyPDF2.PdfReader(file) + + # Extract text from each page + text = "" + for page in pdf_reader.pages: + text += page.extract_text() + "\n" + + self.documents.append(text) + + def load_directory(self): + for root, _, files in os.walk(self.path): + for file in files: + if file.lower().endswith('.pdf'): + file_path = os.path.join(root, file) + with open(file_path, 'rb') as f: + pdf_reader = PyPDF2.PdfReader(f) + + # Extract text from each page + text = "" + for page in pdf_reader.pages: + text += page.extract_text() + "\n" + + self.documents.append(text) + + def load_documents(self): + self.load() + return self.documents + + +if __name__ == "__main__": + loader = TextFileLoader("data/KingLear.txt") + loader.load() + splitter = CharacterTextSplitter() + chunks = splitter.split_texts(loader.documents) + print(len(chunks)) + print(chunks[0]) + print("--------") + print(chunks[1]) + print("--------") + print(chunks[-2]) + print("--------") + print(chunks[-1]) diff --git a/aimakerspace/vectordatabase.py b/aimakerspace/vectordatabase.py new file mode 100644 index 000000000..5f4060263 --- /dev/null +++ b/aimakerspace/vectordatabase.py @@ -0,0 +1,81 @@ +import numpy as np +from collections import defaultdict +from typing import List, Tuple, Callable +from aimakerspace.openai_utils.embedding import EmbeddingModel +import asyncio + + +def cosine_similarity(vector_a: np.array, vector_b: np.array) -> float: + """Computes the cosine similarity between two vectors.""" + dot_product = np.dot(vector_a, vector_b) + norm_a = np.linalg.norm(vector_a) + norm_b = np.linalg.norm(vector_b) + return dot_product / (norm_a * norm_b) + + +class VectorDatabase: + def __init__(self, embedding_model: EmbeddingModel = None): + self.vectors = defaultdict(np.array) + self.embedding_model = embedding_model or EmbeddingModel() + + def insert(self, key: str, vector: np.array) -> None: + self.vectors[key] = vector + + def search( + self, + query_vector: np.array, + k: int, + distance_measure: Callable = cosine_similarity, + ) -> List[Tuple[str, float]]: + scores = [ + (key, distance_measure(query_vector, vector)) + for key, vector in self.vectors.items() + ] + return sorted(scores, key=lambda x: x[1], reverse=True)[:k] + + def search_by_text( + self, + query_text: str, + k: int, + distance_measure: Callable = cosine_similarity, + return_as_text: bool = False, + ) -> List[Tuple[str, float]]: + query_vector = self.embedding_model.get_embedding(query_text) + results = self.search(query_vector, k, distance_measure) + return [result[0] for result in results] if return_as_text else results + + def retrieve_from_key(self, key: str) -> np.array: + return self.vectors.get(key, None) + + async def abuild_from_list(self, list_of_text: List[str]) -> "VectorDatabase": + embeddings = await self.embedding_model.async_get_embeddings(list_of_text) + for text, embedding in zip(list_of_text, embeddings): + self.insert(text, np.array(embedding)) + return self + + +if __name__ == "__main__": + list_of_text = [ + "I like to eat broccoli and bananas.", + "I ate a banana and spinach smoothie for breakfast.", + "Chinchillas and kittens are cute.", + "My sister adopted a kitten yesterday.", + "Look at this cute hamster munching on a piece of broccoli.", + ] + + vector_db = VectorDatabase() + vector_db = asyncio.run(vector_db.abuild_from_list(list_of_text)) + k = 2 + + searched_vector = vector_db.search_by_text("I think fruit is awesome!", k=k) + print(f"Closest {k} vector(s):", searched_vector) + + retrieved_vector = vector_db.retrieve_from_key( + "I like to eat broccoli and bananas." + ) + print("Retrieved vector:", retrieved_vector) + + relevant_texts = vector_db.search_by_text( + "I think fruit is awesome!", k=k, return_as_text=True + ) + print(f"Closest {k} text(s):", relevant_texts) diff --git a/api/app.py b/api/app.py index 4fe8d0ba8..a83b0baf9 100644 --- a/api/app.py +++ b/api/app.py @@ -1,13 +1,25 @@ # Import required FastAPI components for building the API -from fastapi import FastAPI, HTTPException -from fastapi.responses import StreamingResponse +from fastapi import FastAPI, HTTPException, UploadFile, File +from fastapi.responses import StreamingResponse, JSONResponse from fastapi.middleware.cors import CORSMiddleware # Import Pydantic for data validation and settings management from pydantic import BaseModel # Import OpenAI client for interacting with OpenAI's API from openai import OpenAI import os -from typing import Optional +import io +import tempfile +import uuid +from typing import Optional, Dict, List +import asyncio + +# Import aimakerspace components +import sys +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from aimakerspace.text_utils import PDFLoader, CharacterTextSplitter +from aimakerspace.vectordatabase import VectorDatabase +from aimakerspace.openai_utils.chatmodel import ChatOpenAI +from aimakerspace.openai_utils.prompts import SystemRolePrompt, UserRolePrompt # Initialize FastAPI application with a title app = FastAPI(title="OpenAI Chat API") @@ -30,13 +42,23 @@ class ChatRequest(BaseModel): model: Optional[str] = "gpt-4.1-mini" # Optional model selection with default api_key: str # OpenAI API key for authentication +# Define the data model for RAG chat requests +class RAGChatRequest(BaseModel): + user_message: str # Message from the user + pdf_id: str # ID of the uploaded PDF to chat with + api_key: str # OpenAI API key for authentication + model: Optional[str] = "gpt-4o-mini" # Optional model selection with default + +# Global storage for PDF documents and their vector databases +pdf_documents: Dict[str, Dict] = {} + # Define the main chat endpoint that handles POST requests @app.post("/api/chat") async def chat(request: ChatRequest): try: # Initialize OpenAI client with the provided API key client = OpenAI(api_key=request.api_key) - + print('GOT API REQUEST') # Create an async generator function for streaming responses async def generate(): # Create a streaming chat completion request @@ -66,6 +88,138 @@ async def generate(): async def health_check(): return {"status": "ok"} +# Define the PDF upload endpoint for RAG system +@app.post("/api/upload-pdf-rag") +async def upload_pdf_rag(file: UploadFile = File(...)): + """ + Upload and process a PDF file for RAG system. + Returns the PDF ID and processing status. + """ + try: + # Validate file type + if not file.filename.lower().endswith('.pdf'): + raise HTTPException(status_code=400, detail="Only PDF files are allowed") + + # Generate unique ID for the PDF + pdf_id = str(uuid.uuid4()) + + # Read the uploaded file + content = await file.read() + + # Save to temporary file for processing + with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as temp_file: + temp_file.write(content) + temp_file_path = temp_file.name + + try: + # Load and process the PDF + pdf_loader = PDFLoader(temp_file_path) + documents = pdf_loader.load_documents() + + if not documents: + raise HTTPException(status_code=400, detail="No text content found in PDF") + + # Split text into chunks + text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200) + chunks = text_splitter.split_texts(documents) + + # Create vector database + vector_db = VectorDatabase() + await vector_db.abuild_from_list(chunks) + + # Store the processed data + pdf_documents[pdf_id] = { + "filename": file.filename, + "chunks": chunks, + "vector_db": vector_db, + "document_count": len(chunks) + } + + return JSONResponse(content={ + "pdf_id": pdf_id, + "filename": file.filename, + "chunks": len(chunks), + "message": "PDF processed and indexed successfully" + }) + + finally: + # Clean up temporary file + os.unlink(temp_file_path) + + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error processing PDF: {str(e)}") + +# Define the RAG chat endpoint +@app.post("/api/chat-rag") +async def chat_rag(request: RAGChatRequest): + """ + Chat with a PDF using RAG system. + """ + try: + # Check if PDF exists + if request.pdf_id not in pdf_documents: + raise HTTPException(status_code=404, detail="PDF not found") + + pdf_data = pdf_documents[request.pdf_id] + vector_db = pdf_data["vector_db"] + + # Search for relevant chunks + relevant_chunks = vector_db.search_by_text( + request.user_message, + k=3, + return_as_text=True + ) + + # Create context from relevant chunks + context = "\n\n".join(relevant_chunks) + + # Create system prompt for RAG + system_prompt = SystemRolePrompt( + "You are a helpful job interview AI assistant that helps people best prepare for their interviews. " + "Use only the information from the context to answer questions. " + "If the answer cannot be found in the context, say so. " + "Always cite specific parts of the document when possible.\n\n" + "Document Context:\n{context}" + ) + + # Create user prompt + user_prompt = UserRolePrompt("Question: {question}") + + # Prepare messages for chat + messages = [ + system_prompt.create_message(context=context), + user_prompt.create_message(question=request.user_message) + ] + + # Initialize chat model + chat_model = ChatOpenAI(model_name=request.model or "gpt-4o-mini") + + # Create async generator for streaming response + async def generate(): + async for chunk in chat_model.astream(messages): + yield chunk + + return StreamingResponse(generate(), media_type="text/plain") + + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error in RAG chat: {str(e)}") + +# Define endpoint to list uploaded PDFs +@app.get("/api/pdfs") +async def list_pdfs(): + """ + List all uploaded PDFs with their metadata. + """ + pdf_list = [] + for pdf_id, data in pdf_documents.items(): + pdf_list.append({ + "pdf_id": pdf_id, + "filename": data["filename"], + "chunks": data["document_count"] + }) + + return JSONResponse(content={"pdfs": pdf_list}) + # Entry point for running the application directly if __name__ == "__main__": import uvicorn diff --git a/api/requirements.txt b/api/requirements.txt index f2d9a1cbc..963c08d3d 100644 --- a/api/requirements.txt +++ b/api/requirements.txt @@ -2,4 +2,6 @@ fastapi==0.115.12 uvicorn==0.34.2 openai==1.77.0 pydantic==2.11.4 -python-multipart==0.0.18 \ No newline at end of file +python-multipart==0.0.18 +PyPDF2==3.0.1 +python-dotenv==1.0.0 \ No newline at end of file diff --git a/frontend/next-env.d.ts b/frontend/next-env.d.ts new file mode 100644 index 000000000..4f11a03dc --- /dev/null +++ b/frontend/next-env.d.ts @@ -0,0 +1,5 @@ +/// +/// + +// NOTE: This file should not be edited +// see https://nextjs.org/docs/basic-features/typescript for more information. diff --git a/frontend/next.config.js b/frontend/next.config.js new file mode 100644 index 000000000..8d4c37caf --- /dev/null +++ b/frontend/next.config.js @@ -0,0 +1,7 @@ +/** @type {import('next').NextConfig} */ +const nextConfig = { + reactStrictMode: true, + swcMinify: true, +} + +module.exports = nextConfig \ No newline at end of file diff --git a/frontend/package-lock.json b/frontend/package-lock.json new file mode 100644 index 000000000..021110f41 --- /dev/null +++ b/frontend/package-lock.json @@ -0,0 +1,5473 @@ +{ + "name": "ai-engineer-challenge-frontend", + "version": "0.1.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "ai-engineer-challenge-frontend", + "version": "0.1.0", + "dependencies": { + "@heroicons/react": "^2.1.1", + "autoprefixer": "^10.4.17", + "next": "14.1.0", + "postcss": "^8.4.35", + "react": "^18.2.0", + "react-dom": "^18.2.0", + "tailwindcss": "^3.4.1" + }, + "devDependencies": { + "@types/node": "^20.11.19", + "@types/react": "^18.2.57", + "@types/react-dom": "^18.2.19", + "eslint": "^8.56.0", + 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"14.1.0" + } +} \ No newline at end of file diff --git a/frontend/postcss.config.js b/frontend/postcss.config.js new file mode 100644 index 000000000..0cc9a9ded --- /dev/null +++ b/frontend/postcss.config.js @@ -0,0 +1,6 @@ +module.exports = { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} \ No newline at end of file diff --git a/frontend/src/app/components/Chat.tsx b/frontend/src/app/components/Chat.tsx new file mode 100644 index 000000000..e26339532 --- /dev/null +++ b/frontend/src/app/components/Chat.tsx @@ -0,0 +1,133 @@ +'use client'; + +import { useState, useRef, useEffect } from 'react'; + +interface Message { + role: 'user' | 'assistant'; + content: string; +} + +export default function Chat() { + const [messages, setMessages] = useState([]); + const [input, setInput] = useState(''); + const [isLoading, setIsLoading] = useState(false); + const messagesEndRef = useRef(null); + + const scrollToBottom = () => { + messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' }); + }; + + useEffect(() => { + scrollToBottom(); + }, [messages]); + + const handleSubmit = async (e: React.FormEvent) => { + e.preventDefault(); + if (!input.trim() || isLoading) return; + + const userMessage = input.trim(); + setInput(''); + setMessages(prev => [...prev, { role: 'user', content: userMessage }]); + setIsLoading(true); + + try { + const response = await fetch('https://the-ai-engineer-challenge-six.vercel.app/api/chat', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ + developer_message: "You are a helpful AI assistant.", + user_message: userMessage, + api_key: process.env.NEXT_PUBLIC_OPENAI_API_KEY, + }), + }); + + if (!response.ok) throw new Error('Failed to get response'); + + const reader = response.body?.getReader(); + if (!reader) throw new Error('No reader available'); + + let assistantMessage = ''; + + while (true) { + const { done, value } = await reader.read(); + if (done) break; + + const text = new TextDecoder().decode(value); + assistantMessage += text; + setMessages(prev => { + const newMessages = [...prev]; + const lastMessage = newMessages[newMessages.length - 1]; + if (lastMessage.role === 'assistant') { + lastMessage.content = assistantMessage; + return newMessages; + } else { + return [...newMessages, { role: 'assistant', content: assistantMessage }]; + } + }); + } + } catch (error) { + console.error('Error:', error); + setMessages(prev => [...prev, { role: 'assistant', content: 'Sorry, there was an error processing your request.' }]); + } finally { + setIsLoading(false); + } + }; + + return ( +
{/*className="flex flex-col h-[600px] w-full max-w-2xl mx-auto bg-white rounded-lg shadow-lg"> +
+ {messages.map((message, index) => ( +
+
+ {message.content} +
+
+ ))} + {isLoading && ( +
+
+
+
+
+
+
+
+
+ )} +
+
+
+
+ setInput(e.target.value)} + placeholder="Type your message..." + className="flex-1 p-2 border rounded-lg focus:outline-none focus:ring-2 focus:ring-blue-500" + disabled={isLoading} + /> + +
+
*/} +
+ ); +} \ No newline at end of file diff --git a/frontend/src/app/components/PdfUploadRAG.tsx b/frontend/src/app/components/PdfUploadRAG.tsx new file mode 100644 index 000000000..1f73fd0d8 --- /dev/null +++ b/frontend/src/app/components/PdfUploadRAG.tsx @@ -0,0 +1,270 @@ +'use client'; + +import { useState, useRef, useEffect } from 'react'; + +interface PdfUploadResponse { + pdf_id: string; + filename: string; + chunks: number; + message: string; +} + +interface PdfInfo { + pdf_id: string; + filename: string; + chunks: number; +} + +interface PdfUploadRAGProps { + onPdfSelect: (pdfId: string) => void; + selectedPdfId: string | null; +} + +export default function PdfUploadRAG({ onPdfSelect, selectedPdfId }: PdfUploadRAGProps) { + const [isUploading, setIsUploading] = useState(false); + const [uploadResult, setUploadResult] = useState(null); + const [error, setError] = useState(null); + const [uploadedPdfs, setUploadedPdfs] = useState([]); + const fileInputRef = useRef(null); + + // Load uploaded PDFs on component mount + useEffect(() => { + loadUploadedPdfs(); + }, []); + + const loadUploadedPdfs = async () => { + try { + const response = await fetch('https://the-ai-engineer-challenge-six.vercel.app/api/pdfs'); + if (response.ok) { + const data = await response.json(); + setUploadedPdfs(data.pdfs); + if (data.pdfs.length > 0 && !selectedPdfId) { + onPdfSelect(data.pdfs[0].pdf_id); + } + } + } catch (err) { + console.error('Error loading PDFs:', err); + } + }; + + const handleFileUpload = async (event: React.ChangeEvent) => { + const file = event.target.files?.[0]; + if (!file) return; + + // Validate file type + if (!file.name.toLowerCase().endsWith('.pdf')) { + setError('Please select a PDF file'); + return; + } + + // Validate file size (max 10MB) + if (file.size > 10 * 1024 * 1024) { + setError('File size must be less than 10MB'); + return; + } + + setIsUploading(true); + setError(null); + setUploadResult(null); + + try { + const formData = new FormData(); + formData.append('file', file); + + const response = await fetch('https://the-ai-engineer-challenge-six.vercel.app/api/upload-pdf-rag', { + method: 'POST', + body: formData, + }); + + if (!response.ok) { + const errorData = await response.json(); + throw new Error(errorData.detail || 'Failed to upload PDF'); + } + + const result: PdfUploadResponse = await response.json(); + setUploadResult(result); + onPdfSelect(result.pdf_id); + + // Reload the list of uploaded PDFs + await loadUploadedPdfs(); + } catch (err) { + setError(err instanceof Error ? err.message : 'An error occurred while uploading the PDF'); + } finally { + setIsUploading(false); + } + }; + + const handleDrop = async (event: React.DragEvent) => { + event.preventDefault(); + const files = event.dataTransfer.files; + if (files.length > 0) { + const file = files[0]; + if (fileInputRef.current) { + fileInputRef.current.files = files; + await handleFileUpload({ target: { files } } as React.ChangeEvent); + } + } + }; + + const handleDragOver = (event: React.DragEvent) => { + event.preventDefault(); + }; + + const clearResult = () => { + setUploadResult(null); + setError(null); + if (fileInputRef.current) { + fileInputRef.current.value = ''; + } + }; + + return ( +
+

Job Description Evaluation

+ {/* Upload/Select Job Description */} +
+

Upload or Select Job Description PDF

+ {/* Reuse upload area, but for job description */} +
+
+
+ + + +
+ +
+ + +
+ +

+ PDF files only, max 10MB. Files will be indexed for AI chat. +

+
+
+ + {/* Loading State */} + {isUploading && ( +
+
+
+
+
+
+

Processing and indexing your PDF...

+
+ )} + + {/* Error Display */} + {error && ( +
+
+
+ + + +
+
+

{error}

+
+
+
+ )} + + {/* Success Result */} + {uploadResult && ( +
+
+
+ + + +

+ PDF Indexed Successfully +

+
+ +
+ +
+

Filename: {uploadResult.filename}

+

Chunks Created: {uploadResult.chunks}

+

PDF ID: {uploadResult.pdf_id}

+
+
+ )} + + {/* Uploaded PDFs List */} + {uploadedPdfs.length > 0 && ( +
+

Uploaded PDFs

+
+ {uploadedPdfs.map((pdf) => ( +
onPdfSelect(pdf.pdf_id)} + > +
+
+

{pdf.filename}

+

{pdf.chunks} chunks indexed

+
+ {selectedPdfId === pdf.pdf_id && ( + + + + )} +
+
+ ))} +
+
+ )} + + {/* Selected PDF Info */} + {selectedPdfId && ( +
+

+ Selected PDF: {uploadedPdfs.find(p => p.pdf_id === selectedPdfId)?.filename} +

+

+ You can now chat with this PDF using the chat interface below. +

+
+ )} +
+
+ ); +} \ No newline at end of file diff --git a/frontend/src/app/components/RAGChat.tsx b/frontend/src/app/components/RAGChat.tsx new file mode 100644 index 000000000..862520b3e --- /dev/null +++ b/frontend/src/app/components/RAGChat.tsx @@ -0,0 +1,183 @@ +'use client'; + +import { useState, useRef, useEffect } from 'react'; + +interface Message { + role: 'user' | 'assistant'; + content: string; +} + +interface RAGChatProps { + selectedPdfId: string | null; +} + +export default function RAGChat({ selectedPdfId }: RAGChatProps) { + const [messages, setMessages] = useState([]); + const [input, setInput] = useState(''); + const [isLoading, setIsLoading] = useState(false); + const messagesEndRef = useRef(null); + + const scrollToBottom = () => { + messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' }); + }; + + useEffect(() => { + scrollToBottom(); + }, [messages]); + + const handleSubmit = async (e: React.FormEvent) => { + e.preventDefault(); + if (!input.trim() || isLoading || !selectedPdfId) return; + + const userMessage = input.trim(); + setInput(''); + setMessages(prev => [...prev, { role: 'user', content: userMessage }]); + setIsLoading(true); + + try { + const response = await fetch('https://the-ai-engineer-challenge-six.vercel.app/api/chat-rag', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ + user_message: userMessage, + pdf_id: selectedPdfId, + api_key: process.env.NEXT_PUBLIC_OPENAI_API_KEY, + }), + }); + + if (!response.ok) throw new Error('Failed to get response'); + + const reader = response.body?.getReader(); + if (!reader) throw new Error('No reader available'); + + let assistantMessage = ''; + + while (true) { + const { done, value } = await reader.read(); + if (done) break; + + const text = new TextDecoder().decode(value); + assistantMessage += text; + setMessages(prev => { + const newMessages = [...prev]; + const lastMessage = newMessages[newMessages.length - 1]; + if (lastMessage.role === 'assistant') { + lastMessage.content = assistantMessage; + return newMessages; + } else { + return [...newMessages, { role: 'assistant', content: assistantMessage }]; + } + }); + } + } catch (error) { + console.error('Error:', error); + setMessages(prev => [...prev, { role: 'assistant', content: 'Sorry, there was an error processing your request.' }]); + } finally { + setIsLoading(false); + } + }; + + const clearChat = () => { + setMessages([]); + }; + + if (!selectedPdfId) { + return ( +
+
+
+ + + +

No PDF Selected

+

Please upload and select a PDF to start chatting with it.

+
+
+
+ ); + } + + return ( +
+ {/* Header */} +
+
+ + + +

Chat with PDF

+
+ +
+ + {/* Messages */} +
+ {messages.length === 0 && ( +
+

Start asking questions about your PDF!

+
+ )} + + {messages.map((message, index) => ( +
+
+ {message.content} +
+
+ ))} + + {isLoading && ( +
+
+
+
+
+
+
+
+
+ )} +
+
+ + {/* Input Form */} +
+
+ setInput(e.target.value)} + placeholder="Ask a question about your PDF..." + className="flex-1 p-2 border rounded-lg focus:outline-none focus:ring-2 focus:ring-blue-500" + disabled={isLoading} + /> + +
+
+
+ ); +} \ No newline at end of file diff --git a/frontend/src/app/components/RAGSystem.tsx b/frontend/src/app/components/RAGSystem.tsx new file mode 100644 index 000000000..818573efc --- /dev/null +++ b/frontend/src/app/components/RAGSystem.tsx @@ -0,0 +1,25 @@ +'use client'; + +import { useState } from 'react'; +import PdfUploadRAG from './PdfUploadRAG'; +import RAGChat from './RAGChat'; + +export default function RAGSystem() { + const [selectedPdfId, setSelectedPdfId] = useState(null); + + const handlePdfSelect = (pdfId: string) => { + setSelectedPdfId(pdfId); + }; + + return ( +
+ {/* PDF Upload Section */} + + + {/* RAG Chat Section */} +
+ +
+
+ ); +} \ No newline at end of file diff --git a/frontend/src/app/globals.css b/frontend/src/app/globals.css new file mode 100644 index 000000000..5cda3ccbd --- /dev/null +++ b/frontend/src/app/globals.css @@ -0,0 +1,19 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +:root { + --foreground-rgb: 0, 0, 0; + --background-start-rgb: 214, 219, 220; + --background-end-rgb: 255, 255, 255; +} + +body { + color: rgb(var(--foreground-rgb)); + background: linear-gradient( + to bottom, + transparent, + rgb(var(--background-end-rgb)) + ) + rgb(var(--background-start-rgb)); +} \ No newline at end of file diff --git a/frontend/src/app/layout.tsx b/frontend/src/app/layout.tsx new file mode 100644 index 000000000..13e8f396a --- /dev/null +++ b/frontend/src/app/layout.tsx @@ -0,0 +1,26 @@ +import './globals.css' +import type { Metadata } from 'next' +import { Inter } from 'next/font/google' + +const inter = Inter({ subsets: ['latin'] }) + +export const metadata: Metadata = { + title: 'AI Engineer Challenge', + description: 'Frontend for the AI Engineer Challenge application', +} + +export default function RootLayout({ + children, +}: { + children: React.ReactNode +}) { + return ( + + +
+ {children} +
+ + + ) +} \ No newline at end of file diff --git a/frontend/src/app/page.tsx b/frontend/src/app/page.tsx new file mode 100644 index 000000000..e6a1396d5 --- /dev/null +++ b/frontend/src/app/page.tsx @@ -0,0 +1,29 @@ +import Chat from './components/Chat'; +import RAGSystem from './components/RAGSystem'; + +export default function Home() { + return ( +
+
+
+

+ Job Prep AI +

+

+ Chat with our AI assistant and upload job descriptions for intelligent analysis and your preparation +

+
+ + {/* RAG System Section */} +
+ +
+ + {/* Regular Chat Section */} +
+ +
+
+
+ ) +} \ No newline at end of file diff --git a/frontend/tailwind.config.js b/frontend/tailwind.config.js new file mode 100644 index 000000000..4b163df0b --- /dev/null +++ b/frontend/tailwind.config.js @@ -0,0 +1,16 @@ +/** @type {import('tailwindcss').Config} */ +module.exports = { + content: [ + './src/pages/**/*.{js,ts,jsx,tsx,mdx}', + './src/components/**/*.{js,ts,jsx,tsx,mdx}', + './src/app/**/*.{js,ts,jsx,tsx,mdx}', + ], + theme: { + extend: { + colors: { + primary: '#0070f3', + }, + }, + }, + plugins: [], +} \ No newline at end of file diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json new file mode 100644 index 000000000..a0dcb396c --- /dev/null +++ b/frontend/tsconfig.json @@ -0,0 +1,28 @@ +{ + "compilerOptions": { + "target": "es5", + "lib": ["dom", "dom.iterable", "esnext"], + "allowJs": true, + "skipLibCheck": true, + "strict": true, + "forceConsistentCasingInFileNames": true, + "noEmit": true, + "esModuleInterop": true, + "module": "esnext", + "moduleResolution": "node", + "resolveJsonModule": true, + "isolatedModules": true, + "jsx": "preserve", + "incremental": true, + "plugins": [ + { + "name": "next" + } + ], + "paths": { + "@/*": ["./src/*"] + } + }, + "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"], + "exclude": ["node_modules"] +} \ No newline at end of file