app.py

import os
import streamlit as st
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain.schema import Document
from langchain_openai import ChatOpenAI
from langchain_community.tools.tavily_search import TavilySearchResults
from langgraph.graph import StateGraph, END
from graphviz import Digraph  # For workflow visualization
from typing_extensions import TypedDict
from typing import List
from utils.build_rag import RAG


# Fetch API Keys
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")

# Check for Missing API Keys
if not OPENAI_API_KEY or not TAVILY_API_KEY:
    st.error("❌ API keys missing! Please set `OPENAI_API_KEY` and `TAVILY_API_KEY` in your `.env` file.")
    st.stop()  # Stop the app execution

# Set up LLM and Tools
llm = ChatOpenAI(model="gpt-4-1106-preview", openai_api_key=OPENAI_API_KEY)
web_search_tool = TavilySearchResults(api_key=TAVILY_API_KEY, k=2)

# Prompt templates
def get_prompt():
    template = """Answer the question based only on the following context:
    {context}

    Question: {question}
    """
    return ChatPromptTemplate.from_template(template)

# Define Graph State
class GraphState(TypedDict):
    question: str
    generation: str
    web_search: str
    documents: List[Document]

# RAG Setup
rag = RAG()
retriever = rag.get_retriever()
prompt = get_prompt()
output_parser = StrOutputParser()

# Nodes
def retrieve(state):
    question = state["question"]
    documents = retriever.get_relevant_documents(question)
    st.sidebar.write(f"Retrieved Documents: {len(documents)}")
    return {"documents": documents, "question": question}

def grade_documents(state):
    question = state["question"]
    documents = state["documents"]
    filtered_docs = []
    web_search = "No"

    for doc in documents:
        score = {"binary_score": "yes"}  # Dummy grader; integrate as needed
        if score["binary_score"] == "yes":
            filtered_docs.append(doc)
        else:
            web_search = "Yes"
    st.sidebar.write(f"Document Grading Results: {len(filtered_docs)} relevant")
    return {"documents": filtered_docs, "web_search": web_search, "question": question}

def generate(state):
    context = "\n".join([doc.page_content for doc in state["documents"]])
    response = output_parser.parse(llm.invoke({"context": context, "question": state["question"]}).content)
    return {"generation": response}

def transform_query(state):
    question = state["question"]
    new_question = llm.invoke(f"Rewrite: {question}").content
    st.sidebar.write(f"Rewritten Question: {new_question}")
    return {"question": new_question}

def web_search(state):
    question = state["question"]
    results = web_search_tool.invoke({"query": question})
    docs = "\n".join([result["content"] for result in results])
    state["documents"].append(Document(page_content=docs))
    st.sidebar.write("Web Search Completed")
    return {"documents": state["documents"], "question": question}

def decide_to_generate(state):
    return "generate" if state["web_search"] == "No" else "transform_query"

# Build Graph
workflow = StateGraph(GraphState)
workflow.add_node("retrieve", retrieve)
workflow.add_node("grade_documents", grade_documents)
workflow.add_node("generate", generate)
workflow.add_node("transform_query", transform_query)
workflow.add_node("web_search_node", web_search)

workflow.set_entry_point("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", "web_search_node")
workflow.add_edge("web_search_node", "generate")
workflow.add_edge("generate", END)

app = workflow.compile()

# Visualize Workflows
def plot_workflow():
    graph = Digraph()
    graph.attr(size='6,6')

    # Add nodes
    graph.node("retrieve", "Retrieve Documents")
    graph.node("grade_documents", "Grade Documents")
    graph.node("generate", "Generate Answer")
    graph.node("transform_query", "Transform Query")
    graph.node("web_search_node", "Web Search")
    graph.node("END", "End")

    # Add edges
    graph.edge("retrieve", "grade_documents")
    graph.edge("grade_documents", "generate", label="Relevant Docs")
    graph.edge("grade_documents", "transform_query", label="No Relevant Docs")
    graph.edge("transform_query", "web_search_node")
    graph.edge("web_search_node", "generate")
    graph.edge("generate", "END")

    return graph

# Streamlit App
st.title("Self-Corrective RAG")
st.write("### Compare RAG Pipeline Outputs (With and Without Self-Correction)")

# Plot Workflow
st.subheader("Workflow Visualization")
st.graphviz_chart(plot_workflow().source)

# User Input
question = st.text_input("Enter your question:", "What is Llama2?")

if st.button("Run Comparison"):
    # Run Basic RAG
    st.subheader("Without Self-Correction:")
    docs = retriever.invoke(question)
    basic_context = "\n".join([doc.page_content for doc in docs])
    basic_response = output_parser.parse(llm.invoke({"context": basic_context, "question": question}).content)
    st.write(basic_response)

    # Run Self-Corrective RAG
    st.subheader("With Self-Correction:")
    inputs = {"question": question}
    final_generation = ""
    for output in app.stream(inputs):
        for key, value in output.items():
            if key == "generation":
                final_generation = value
    st.write(final_generation)