Delete advanced_app.py

advanced_app.py

@@ -1,471 +0,0 @@
-# ==============================================================================
-# ADVANCED RAG WITH GPT-4o, LANGCHAIN, AND RAGAS EVALUATION - MULTI-DOCUMENT VERSION
-# ==============================================================================
-# Enhanced RAG application with quality metrics using RAGAS framework
-# Supports multiple PDF documents
-# ==============================================================================
-
-from langchain.retrievers import EnsembleRetriever
-from langchain_community.retrievers import BM25Retriever
-from langchain_community.cross_encoders import HuggingFaceCrossEncoder
-from langchain.retrievers.document_compressors import CrossEncoderReranker
-from sentence_transformers import CrossEncoder
-from langchain.retrievers import ContextualCompressionRetriever
-from langchain_community.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_openai import OpenAIEmbeddings, ChatOpenAI
-from langchain_community.vectorstores import FAISS
-from langchain.schema import Document
-from langchain.prompts import PromptTemplate
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnablePassthrough
-from datasets import Dataset
-from ragas import evaluate
-from ragas.metrics import (
-    faithfulness,
-    answer_relevancy,
-    context_precision,
-    context_recall,
-    answer_correctness,
-    answer_similarity
-)
-import gradio as gr
-import os
-import pandas as pd
-import json
-
-# ==============================================================================
-# GLOBAL VARIABLES
-# ==============================================================================
-rag_chain = None
-current_documents = []  # Changed to list for multiple documents
-openai_api_key = None
-retriever = None
-evaluation_data = []
-
-# ==============================================================================
-# HELPER FUNCTIONS
-# ==============================================================================
-
-def format_docs(docs):
-    """Format retrieved documents with source citations"""
-    out = []
-    for d in docs:
-        src = d.metadata.get("source", "unknown")
-        # Extract just the filename from the full path
-        src = os.path.basename(src)
-        page = d.metadata.get("page", d.metadata.get("page_number", "?"))
-        
-        try:
-            page_display = int(page) + 1 
-        except (ValueError, TypeError):
-            page_display = page
-            
-        out.append(f"[{src}:{page_display}] {d.page_content}")
-    return "\n\n".join(out)
-
-
-def validate_api_key(api_key):
-    """Validate that API key is provided"""
-    if not api_key or not api_key.strip():
-        return False
-    return True
-
-
-def process_documents(pdf_files, api_key):
-    """Process uploaded PDFs and create RAG chain"""
-    global rag_chain, current_documents, openai_api_key, retriever, evaluation_data
-    
-    chatbot_clear = None 
-    evaluation_data = []  # Reset evaluation data
-    
-    if not validate_api_key(api_key):
-        return "⚠️ Please provide a valid OpenAI API key.", chatbot_clear, ""
-    
-    if pdf_files is None or len(pdf_files) == 0:
-        return "⚠️ Please upload at least one PDF file.", chatbot_clear, ""
-    
-    try:
-        openai_api_key = api_key.strip()
-        os.environ["OPENAI_API_KEY"] = openai_api_key
-        
-        # Process all uploaded PDFs
-        all_docs = []
-        current_documents = []
-        total_pages = 0
-        
-        for pdf_file in pdf_files:
-            loader = PyPDFLoader(pdf_file.name)
-            docs = loader.load()
-            all_docs.extend(docs)
-            current_documents.append(os.path.basename(pdf_file.name))
-            total_pages += len(docs)
-        
-        # Split all documents
-        splitter = RecursiveCharacterTextSplitter(
-            separators=["\n\n", "\n", ". ", " ", ""],
-            chunk_size=1000,
-            chunk_overlap=100
-        )
-        chunked_docs = splitter.split_documents(all_docs)
-        
-        # Create embeddings and vector store
-        embeddings = OpenAIEmbeddings(
-            model="text-embedding-3-small",
-            openai_api_key=openai_api_key
-        )
-        
-        db = FAISS.from_documents(chunked_docs, embeddings)
-        
-        retriever_1 = db.as_retriever(search_type="similarity",search_kwargs={'k': 10})
-        
-        retriever_2 = BM25Retriever.from_documents(chunked_docs, search_kwargs={"k": 10})
-        
-        ensemble_retriever = EnsembleRetriever(retrievers=[retriever_1, retriever_2], weights=[0.7, 0.3])
-        
-        cross_encoder_model = HuggingFaceCrossEncoder(model_name="cross-encoder/ms-marco-MiniLM-L-12-v2")
-
-        reranker = CrossEncoderReranker(model=cross_encoder_model,top_n=10)
-        
-        reranking_retriever = ContextualCompressionRetriever(base_compressor=reranker,base_retriever=ensemble_retriever)
-        
-        retriever=reranking_retriever  
-              
-        # Create LLM and prompt
-        llm = ChatOpenAI(
-            model="gpt-4o-mini",
-            temperature=0.2,
-            openai_api_key=openai_api_key
-        )
-        
-        prompt_template = """You are a professional research scientist involved in document data analysis.
-        Use the following context to answer the question using information provided by the documents.
-        Answer using ONLY these passages. Cite sources as [filename:page] after each claim.
-        Provide an answer in bullet points.
-        If you can't find it, say you don't know.
-
-Question:
-{question}
-
-Passages:
-{context}
-
-Answer:"""
-        
-        prompt = PromptTemplate(
-            input_variables=["context", "question"],
-            template=prompt_template,
-        )
-        
-        llm_chain = prompt | llm | StrOutputParser()
-        
-        rag_chain = (
-            {"context": reranking_retriever | format_docs, "question": RunnablePassthrough()} 
-            | llm_chain
-        )
-        
-        # Create status message with document list
-        doc_list = "\n".join([f"  • {doc}" for doc in current_documents])
-        status_msg = (
-            f"✅ Documents processed successfully!\n\n"
-            f"📄 **Documents loaded ({len(current_documents)}):**\n{doc_list}\n\n"
-            f"📊 Total pages: {total_pages}\n"
-            f"📦 Chunks created: {len(chunked_docs)}\n\n"
-            f"You can now ask questions and evaluate responses!"
-        )
-        
-        return status_msg, chatbot_clear, ""
-        
-    except Exception as e:
-        return f"❌ Error processing documents: {str(e)}", chatbot_clear, ""
-
-
-def chat_with_document(message, history):
-    """Handle chat interactions with the documents"""
-    global rag_chain, current_documents, retriever, evaluation_data
-    
-    history.append({"role": "user", "content": message})
-    
-    if rag_chain is None:
-        history.append({
-            "role": "assistant", 
-            "content": "⚠️ Please upload and process PDF documents first."
-        })
-        return history
-    
-    if not message.strip():
-        history.append({
-            "role": "assistant", 
-            "content": "⚠️ Please enter a question."
-        })
-        return history
-    
-    try:
-        # Retrieve contexts for RAGAS evaluation
-        retrieved_docs = retriever.invoke(message)
-        contexts = [doc.page_content for doc in retrieved_docs]
-        
-        # Get response from RAG chain
-        response = rag_chain.invoke(message)
-        
-        if isinstance(response, dict):
-            res_text = response.get("answer", response.get("result", str(response)))
-        else:
-            res_text = str(response)
-        
-        # Store data for RAGAS evaluation
-        evaluation_data.append({
-            "question": message,
-            "answer": res_text,
-            "contexts": contexts
-        })
-        
-        history.append({"role": "assistant", "content": res_text})
-        return history
-        
-    except Exception as e:
-        error_msg = f"❌ Error generating response: {str(e)}"
-        history.append({"role": "assistant", "content": error_msg})
-        return history
-
-
-def evaluate_rag_performance():
-    """Evaluate RAG performance using RAGAS metrics"""
-    global evaluation_data, openai_api_key
-    
-    if not evaluation_data:
-        return "⚠️ No evaluation data available. Please ask some questions first."
-    
-    try:
-        # Prepare dataset for RAGAS
-        dataset_dict = {
-            "question": [item["question"] for item in evaluation_data],
-            "answer": [item["answer"] for item in evaluation_data],
-            "contexts": [item["contexts"] for item in evaluation_data],
-        }
-        
-        dataset = Dataset.from_dict(dataset_dict)
-        
-        # Run RAGAS evaluation
-        # Using only metrics that don't require ground truth (reference answers)
-        result = evaluate(
-            dataset,
-            metrics=[
-                faithfulness,
-                answer_relevancy,
-            ],
-            llm=ChatOpenAI(model="gpt-4o", openai_api_key=openai_api_key),
-            embeddings=OpenAIEmbeddings(openai_api_key=openai_api_key),
-        )
-        
-        # Convert to DataFrame for better display
-        df = result.to_pandas()
-        
-        # Calculate average scores from the result directly
-        metrics_summary = "## 📊 RAGAS Evaluation Results\n\n"
-        metrics_summary += "### Average Scores:\n"
-        
-        # Get metric scores safely
-        metric_cols = ['faithfulness', 'answer_relevancy']
-        metric_scores = {}
-        
-        for col in metric_cols:
-            if col in df.columns:
-                # Convert to numeric, handling any non-numeric values
-                numeric_values = pd.to_numeric(df[col], errors='coerce')
-                avg_score = numeric_values.mean()
-                if not pd.isna(avg_score):
-                    metric_scores[col] = avg_score
-                    metrics_summary += f"- **{col.replace('_', ' ').title()}**: {avg_score:.4f}\n"
-        
-        metrics_summary += "\n### Metric Explanations:\n"
-        metrics_summary += "- **Faithfulness** (0-1): Measures if the answer is factually consistent with the retrieved context. Higher scores mean the answer doesn't hallucinate or contradict the source.\n"
-        metrics_summary += "- **Answer Relevancy** (0-1): Measures how relevant the answer is to the question asked. Higher scores mean better alignment with the user's query.\n"
-        
-        
-        metrics_summary += "\n### Interpretation Guide:\n"
-        metrics_summary += "- **0.9 - 1.0**: Excellent performance\n"
-        metrics_summary += "- **0.7 - 0.9**: Good performance\n"
-        metrics_summary += "- **0.5 - 0.7**: Moderate performance (needs improvement)\n"
-        metrics_summary += "- **< 0.5**: Poor performance (requires significant optimization)\n"
-        
-        metrics_summary += f"\n### Total Questions Evaluated: {len(evaluation_data)}\n"
-        
-        # Add document info
-        if current_documents:
-            metrics_summary += f"\n### Documents in Index: {len(current_documents)}\n"
-        
-        return metrics_summary
-        
-    except Exception as e:
-        return f"❌ Error during evaluation: {str(e)}"
-
-
-def export_evaluation_data():
-    """Export evaluation data as JSON"""
-    global evaluation_data, current_documents
-    
-    if not evaluation_data:
-        return None
-    
-    try:
-        # Create a temporary file with metadata
-        output_data = {
-            "documents": current_documents,
-            "evaluation_data": evaluation_data,
-            "total_questions": len(evaluation_data)
-        }
-        
-        output_path = "ragas_evaluation_data.json"
-        with open(output_path, 'w') as f:
-            json.dump(output_data, f, indent=2)
-        return output_path
-    except Exception as e:
-        print(f"Error exporting data: {str(e)}")
-        return None
-
-
-def clear_chat():
-    """Clear the chat history and evaluation data"""
-    global evaluation_data
-    evaluation_data = []  # Reset evaluation data when clearing chat
-    return [], ""  # Return empty chatbot and empty eval_summary
-
-
-
-# ==============================================================================
-# GRADIO INTERFACE
-# ==============================================================================
-
-with gr.Blocks(title="RAG with RAGAS Evaluation", theme=gr.themes.Soft()) as demo:
-    
-    gr.Markdown(
-        """
-        # 📚 Multi-Document Q&A Analysis 
-        ### Advanced RAG System Powered by OpenAI GPT models, LangChain & RAGAS
-        
-        Upload multiple PDFs, ask questions across all documents, and evaluate your RAG system's performance with industry-standard metrics.
-        """
-    )
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown(
-                """
-                ### 📋 How to Use
-                1. Enter your OpenAI API key
-                2. Upload one or more PDF documents
-                3. Process the documents
-                4. Ask questions in the chat
-                5. Click "Evaluate" to see performance metrics
-                
-                ---
-                
-                💡 **RAGAS Metrics**:
-                - Faithfulness: Factual accuracy
-                - Answer Relevancy: Question alignment
-                
-                📁 **Multi-Document Support**:
-                - Upload multiple PDFs at once
-                - Search across all documents
-                - Get citations with document names
-                """
-            )
-            
-            gr.Markdown("### 🔑 API Configuration")
-            api_key_input = gr.Textbox(
-                label="OpenAI API Key",
-                type="password",
-                placeholder="sk-...",
-                info="Required for GPT models and RAGAS evaluation"
-            )
-            
-            gr.Markdown("### 📤 Upload Documents")
-            pdf_input = gr.File(
-                label="Upload PDF Documents",
-                file_types=[".pdf"],
-                type="filepath",
-                file_count="multiple"  # Enable multiple file upload
-            )
-            process_btn = gr.Button("📄 Process Documents", variant="primary", size="lg")
-            
-            status_output = gr.Textbox(
-                label="Status",
-                lines=8,  # Increased to show multiple documents
-                interactive=False,
-                placeholder="Enter API key, upload PDFs, and click 'Process Documents'..."
-            )
-            
-            gr.Markdown("### 📈 Evaluation")
-            evaluate_btn = gr.Button("🔍 Evaluate RAG Performance", variant="secondary", size="lg")
-            export_btn = gr.Button("💾 Export Evaluation Data", size="sm")
-            export_file = gr.File(label="Download Evaluation Data", visible=True)
-        
-        with gr.Column(scale=2):
-            gr.Markdown("### 💬 Chat with Your Documents")
-            chatbot = gr.Chatbot(
-                height=400,
-                placeholder="Upload and process documents to start...",
-                show_label=False,
-                type="messages" 
-            )
-            
-            msg = gr.Textbox(
-                label="Enter your question",
-                placeholder="Type your question here (searches across all uploaded documents)...",
-                lines=2
-            )
-            
-            with gr.Row():
-                submit_btn = gr.Button("📤 Send", variant="primary", scale=4)
-                clear_btn = gr.Button("🗑️ Clear Chat", scale=1)
-            
-            gr.Markdown("### 📊 Evaluation Results")
-            eval_summary = gr.Markdown(value="")
-    
-    # Event handlers
-    process_btn.click(
-        fn=process_documents,  # Changed function name
-        inputs=[pdf_input, api_key_input],
-        outputs=[status_output, chatbot, eval_summary] 
-    )
-    
-    submit_btn.click(
-        fn=chat_with_document,
-        inputs=[msg, chatbot], 
-        outputs=[chatbot] 
-    ).then(
-        lambda: "", 
-        outputs=[msg] 
-    )
-    
-    msg.submit(
-        fn=chat_with_document,
-        inputs=[msg, chatbot], 
-        outputs=[chatbot] 
-    ).then(
-        lambda: "", 
-        outputs=[msg] 
-    )
-    
-    clear_btn.click(
-        fn=clear_chat,
-        outputs=[chatbot, eval_summary]
-    )
-    
-    evaluate_btn.click(
-        fn=evaluate_rag_performance,
-        outputs=[eval_summary]
-    )
-    
-    export_btn.click(
-        fn=export_evaluation_data,
-        outputs=[export_file]
-    )
-
-# ==============================================================================
-# LAUNCH APPLICATION
-# ==============================================================================
-
-if __name__ == "__main__":
-    demo.launch(share=False, debug=True)