app.py

import os
import dotenv
import logging
import gradio as gr
import glob
import concurrent.futures
from typing import List, Any
from tqdm import tqdm

# LangChain imports
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.output_parsers import StrOutputParser
from langchain import hub
from langgraph.graph import END, StateGraph, START
from typing_extensions import TypedDict
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.callbacks import get_openai_callback

# Load environment variables
dotenv.load_dotenv()

# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Check if OpenAI API key is set
if os.getenv("OPENAI_API_KEY") is None:
    raise ValueError("OPENAI_API_KEY is not set in .env file")

# Initialize Retriever for all Markdown files in /MarkdownOutput
def initialize_retriever():
    from langchain_community.document_loaders import UnstructuredMarkdownLoader
    from langchain_text_splitters import RecursiveCharacterTextSplitter

    # Find all markdown files in /MarkdownOutput
    markdown_files = glob.glob("./MarkdownOutput/**/*.md", recursive=True)
    logger.info(f"Found {len(markdown_files)} markdown files in ./MarkdownOutput.")

    # Load and split all markdown documents
    all_doc_splits = []
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    for idx, md_path in enumerate(markdown_files, 1):
        logger.info(f"Loading and splitting file {idx}/{len(markdown_files)}: {md_path}")
        loader = UnstructuredMarkdownLoader(md_path)
        docs = loader.load()
        splits = text_splitter.split_documents(docs)
        all_doc_splits.extend(splits)
        logger.info(f"File {md_path} loaded and split into {len(splits)} chunks.")

    logger.info(f"Total document splits: {len(all_doc_splits)}. Creating vector store...")

    # Create vector store
    vectorstore = Chroma.from_documents(
        documents=all_doc_splits,
        collection_name="rag-chroma",
        embedding=OpenAIEmbeddings(
            model="text-embedding-3-large",
            dimensions=3072,
            timeout=120,
        ),
        persist_directory="./chroma_rag_cache"
    )

    logger.info("Vector store created and persisted to ./chroma_rag_cache.")

    # Configure retriever
    retriever = vectorstore.as_retriever(
        search_type="mmr",
        search_kwargs={
            "k": 40,
            "fetch_k": 200,
            "lambda_mult": 0.2,
            "filter": None,
            "score_threshold": 0.7,
        }
    )

    logger.info("Retriever configured and ready to use.")

    return retriever

# Define graders and components
def setup_components(retriever, model_choice):
    # Data models for grading
    class GradeDocuments(BaseModel):
        """Binary score for relevance check on retrieved documents."""
        binary_score: str = Field(
            description="Documents are relevant to the question, 'yes' or 'no'"
        )
    
    class GradeHallucinations(BaseModel):
        """Binary score for hallucination present in generation answer."""
        binary_score: str = Field(
            description="Answer is grounded in the facts, 'yes' or 'no'"
        )
    
    class GradeAnswer(BaseModel):
        """Binary score to assess answer addresses question."""
        binary_score: str = Field(
            description="Answer addresses the question, 'yes' or 'no'"
        )
    
    # LLM models
    llm = ChatOpenAI(model=model_choice, temperature=0)
    doc_grader = llm.with_structured_output(GradeDocuments)
    hallucination_grader_llm = llm.with_structured_output(GradeHallucinations)
    answer_grader_llm = llm.with_structured_output(GradeAnswer)
    
    # Prompts
    # Document grading prompt
    system_doc = """You are a grader assessing relevance of a retrieved document to a user question. \n 
        It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \n
        If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \n
        Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question."""
    grade_prompt = ChatPromptTemplate.from_messages(
        [
            ("system", system_doc),
            ("human", "Retrieved document: \n\n {document} \n\n User question: {question}"),
        ]
    )
    retrieval_grader = grade_prompt | doc_grader
    
    # Hallucination grading prompt
    system_hallucination = """You are a grader assessing whether an LLM generation is grounded in / supported by a set of retrieved facts. \n 
         Give a binary score 'yes' or 'no'. 'Yes' means that the answer is grounded in / supported by the set of facts."""
    hallucination_prompt = ChatPromptTemplate.from_messages(
        [
            ("system", system_hallucination),
            ("human", "Set of facts: \n\n {documents} \n\n LLM generation: {generation}"),
        ]
    )
    hallucination_grader = hallucination_prompt | hallucination_grader_llm
    
    # Answer grading prompt
    system_answer = """You are a grader assessing whether an answer addresses / resolves a question \n 
         Give a binary score 'yes' or 'no'. Yes' means that the answer resolves the question."""
    answer_prompt = ChatPromptTemplate.from_messages(
        [
            ("system", system_answer),
            ("human", "User question: \n\n {question} \n\n LLM generation: {generation}"),
        ]
    )
    answer_grader = answer_prompt | answer_grader_llm
    
    # Question rewriter prompt
    system_rewrite = """You a question re-writer that converts an input question to a better version that is optimized \n 
         for vectorstore retrieval. Look at the input and try to reason about the underlying semantic intent / meaning."""
    re_write_prompt = ChatPromptTemplate.from_messages(
        [
            ("system", system_rewrite),
            (
                "human",
                "Here is the initial question: \n\n {question} \n Formulate an improved question.",
            ),
        ]
    )
    question_rewriter = re_write_prompt | llm | StrOutputParser()
    
    # RAG generation prompt and chain
    prompt = hub.pull("rlm/rag-prompt")
    rag_chain = prompt | llm | StrOutputParser()
    
    return {
        "retriever": retriever,
        "retrieval_grader": retrieval_grader,
        "hallucination_grader": hallucination_grader,
        "answer_grader": answer_grader,
        "question_rewriter": question_rewriter,
        "rag_chain": rag_chain
    }

# Build the RAG graph
def build_rag_graph(components):
    # Define graph state
    class GraphState(TypedDict):
        """Represents the state of our graph."""
        question: str
        generation: str
        documents: List[str]
    
    # Node functions
    def retrieve(state):
        """Retrieve documents"""
        question = state["question"]
        documents = components["retriever"].get_relevant_documents(question)
        return {"documents": documents, "question": question}
    
    def generate(state):
        """Generate answer"""
        question = state["question"]
        documents = state["documents"]
        generation = components["rag_chain"].invoke({"context": documents, "question": question})
        return {"documents": documents, "question": question, "generation": generation}
    
    def grade_documents(state):
        """Determines whether the retrieved documents are relevant to the question."""
        question = state["question"]
        documents = state["documents"]
        
        # Score each doc
        filtered_docs = []
        for d in documents:
            score = components["retrieval_grader"].invoke(
                {"question": question, "document": d.page_content}
            )
            grade = score.binary_score
            if grade == "yes":
                filtered_docs.append(d)
        
        return {"documents": filtered_docs, "question": question}
    
    def transform_query(state):
        """Transform the query to produce a better question."""
        question = state["question"]
        documents = state["documents"]
        better_question = components["question_rewriter"].invoke({"question": question})
        return {"documents": documents, "question": better_question}
    
    # Edge functions
    def decide_to_generate(state):
        """Determines whether to generate an answer, or re-generate a question."""
        filtered_documents = state["documents"]
        
        if not filtered_documents:
            # All documents have been filtered out
            return "transform_query"
        else:
            # We have relevant documents, so generate answer
            return "generate"
    
    def grade_generation_v_documents_and_question(state):
        """Determines whether the generation is grounded in the document and answers question."""
        question = state["question"]
        documents = state["documents"]
        generation = state["generation"]
        
        score = components["hallucination_grader"].invoke(
            {"documents": documents, "generation": generation}
        )
        grade = score.binary_score
        
        # Check hallucination
        if grade == "yes":
            # Check question-answering
            score = components["answer_grader"].invoke({"question": question, "generation": generation})
            grade = score.binary_score
            if grade == "yes":
                return "useful"
            else:
                return "not useful"
        else:
            return "not supported"
    
    # Build the graph
    workflow = StateGraph(GraphState)
    
    # Add nodes
    workflow.add_node("retrieve", retrieve)
    workflow.add_node("grade_documents", grade_documents)
    workflow.add_node("generate", generate)
    workflow.add_node("transform_query", transform_query)
    
    # Add edges
    workflow.add_edge(START, "retrieve")
    workflow.add_edge("retrieve", "grade_documents")
    workflow.add_conditional_edges(
        "grade_documents",
        decide_to_generate,
        {
            "transform_query": "transform_query",
            "generate": "generate",
        },
    )
    workflow.add_edge("transform_query", "retrieve")
    workflow.add_conditional_edges(
        "generate",
        grade_generation_v_documents_and_question,
        {
            "not supported": "generate",
            "useful": END,
            "not useful": "transform_query",
        },
    )
    
    # Compile the graph
    return workflow.compile()

# Initialize global variables
retriever = None
rag_app = None
components = None
current_model_choice = "gpt-4.1"  # Default

# Run PDF processing and RAG setup ONCE at startup, with default model
retriever = initialize_retriever()
if retriever is not None:
    components = setup_components(retriever, current_model_choice)
    rag_app = build_rag_graph(components)
else:
    logger.error("No retriever could be initialized. Please add PDF files to the Data directory.")

# Processing function for Gradio
def process_query(question, display_logs=False, model_choice="gpt-4.1"):
    logs = []
    answer = ""
    token_usage = {}
    try:
        global retriever, rag_app, components, current_model_choice
        if retriever is None:
            logs.append("Error: No PDF files found. Please add PDF files to the Data directory and restart the app.")
            return "Error: No PDF files found. Please add PDF files to the Data directory.", "\n".join(logs), token_usage
        
        # If model_choice changed, re-initialize components and rag_app
        if model_choice != current_model_choice:
            logs.append(f"Switching model to {model_choice} ...")
            components = setup_components(retriever, model_choice)
            rag_app = build_rag_graph(components)
            current_model_choice = model_choice

        logs.append("Processing query: " + question)
        logs.append(f"Using model: {model_choice}")
        logs.append("Starting RAG pipeline...")
        final_output = None
        
        with get_openai_callback() as cb:
            for i, output in enumerate(rag_app.stream({"question": question})):
                step_info = f"Step {i+1}: "
                if 'retrieve' in output:
                    step_info += f"Retrieved {len(output['retrieve']['documents'])} documents"
                elif 'grade_documents' in output:
                    step_info += f"Graded documents, {len(output['grade_documents']['documents'])} deemed relevant"
                elif 'transform_query' in output:
                    step_info += f"Transformed query to: {output['transform_query']['question']}"
                elif 'generate' in output:
                    step_info += "Generated answer"
                    final_output = output
                logs.append(step_info)
            
            # Store token usage information
            token_usage = {
                "total_tokens": cb.total_tokens,
                "prompt_tokens": cb.prompt_tokens,
                "completion_tokens": cb.completion_tokens,

                "total_cost": cb.total_cost
            }
            logs.append(f"Token usage: {token_usage}")
            
        if final_output and 'generate' in final_output:
            answer = final_output['generate']['generation']
            logs.append("Final answer generated successfully")
        else:
            answer = "No answer could be generated. Please try rephrasing your question."
            logs.append("Failed to generate answer")
    except Exception as e:
        logs.append(f"Error: {str(e)}")
        answer = f"An error occurred: {str(e)}"
    return answer, "\n".join(logs) if display_logs else "", token_usage

# Create Gradio interface
with gr.Blocks(title="Self-RAG Document Assistant", theme=gr.themes.Base()) as demo:
    with gr.Row():
        gr.Markdown("# Self-RAG Document Assistant")
    
    with gr.Row():
        gr.Markdown("""This application uses a Self-RAG (Retrieval Augmented Generation) system to
        provide accurate answers by:
        1. Retrieving relevant documents from your PDF database
        2. Grading document relevance to your question
        3. Generating answers grounded in these documents
        4. Self-checking for hallucinations and question addressing""")
    
    with gr.Row():
        with gr.Column(scale=3):
            query_input = gr.Textbox(
                label="Your Question",
                placeholder="Ask a question about your documents...",
                lines=4
            )
        
        with gr.Column(scale=1):
            model_choice_input = gr.Dropdown(
                label="Model",
                choices=["gpt-4.1", "gpt-4.1-mini", "gpt-4.1-nano"],
                value="gpt-4.1"
            )
            show_logs = gr.Checkbox(label="Show Debugging Logs", value=False)
            submit_btn = gr.Button("Submit", variant="primary")
    
    with gr.Row():
        with gr.Column():
            answer_output = gr.Textbox(
                label="Answer",
                lines=10,
                placeholder="Your answer will appear here...",
            )
    
    with gr.Row():
        logs_output = gr.Textbox(
            label="Process Logs",
            lines=15,
            visible=False
        )
        
    with gr.Row():
        token_usage_output = gr.JSON(
            label="Token Usage Statistics",
            visible=True
        )
    
    # Event handlers
    submit_btn.click(
        fn=process_query,
        inputs=[query_input, show_logs, model_choice_input],
        outputs=[answer_output, logs_output, token_usage_output]
    )
    
    show_logs.change(
        fn=lambda x: gr.update(visible=x),
        inputs=[show_logs],
        outputs=[logs_output]
    )

# Launch the app
if __name__ == "__main__":
    demo.launch(share=False)