app.py

import gradio as gr
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
from groq import Groq
from langchain_groq import ChatGroq
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import JsonOutputParser, StrOutputParser
import os
from langchain_community.tools.tavily_search import TavilySearchResults
from typing_extensions import TypedDict
from typing import List
from langchain.schema import Document
from langgraph.graph import END, StateGraph

# Environment setup
os.environ['TAVILY_API_KEY'] = "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"

# Model and embedding setup
embed_model = FastEmbedEmbeddings(model_name="BAAI/bge-base-en-v1.5")
llm = ChatGroq(temperature=0, model_name="Llama3-8b-8192", api_key="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")

# Load documents from URLs
urls = ["https://lilianweng.github.io/posts/2023-06-23-agent/",
        "https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/",
        "https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/"]

docs = [WebBaseLoader(url).load() for url in urls]
docs_list = [item for sublist in docs for item in sublist]

# Document splitting
text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(chunk_size=512, chunk_overlap=0)
doc_splits = text_splitter.split_documents(docs_list)

# Vectorstore setup
vectorstore = Chroma.from_documents(documents=doc_splits, embedding=embed_model, collection_name="local-rag")
retriever = vectorstore.as_retriever(search_kwargs={"k": 2})

# Prompt templates
question_router_prompt = PromptTemplate(
    template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are an expert at routing a 
    user question to a vectorstore or web search. Use the vectorstore for questions on LLM agents, 
    prompt engineering, and adversarial attacks. Otherwise, use web-search. Give a binary choice 'web_search' 
    or 'vectorstore' based on the question. Return a JSON with a single key 'datasource' and no preamble. 
    Question to route: {question} <|eot_id|><|start_header_id|>assistant<|end_header_id|>""",
    input_variables=["question"],
)

question_router = question_router_prompt | llm | JsonOutputParser()

rag_chain_prompt = PromptTemplate(
    template="""<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are an assistant for question-answering tasks. 
    Use the following pieces of retrieved context to answer the question concisely. <|eot_id|><|start_header_id|>user<|end_header_id|>
    Question: {question} 
    Context: {context} 
    Answer: <|eot_id|><|start_header_id|>assistant<|end_header_id|>""",
    input_variables=["question", "document"],
)

# Chain
rag_chain = rag_chain_prompt | llm | StrOutputParser()

# Web search tool
web_search_tool = TavilySearchResults(k=3)

# Workflow functions
def retrieve(state):
    question = state["question"]
    documents = retriever.invoke(question)
    return {"documents": documents, "question": question}

def generate(state):
    question = state["question"]
    documents = state["documents"]
    generation = rag_chain.invoke({"context": documents, "question": question})
    return {"documents": documents, "question": question, "generation": generation}

def route_question(state):
    question = state["question"]
    source = question_router.invoke({"question": question})
    return "websearch" if source['datasource'] == 'web_search' else "vectorstore"

def web_search(state):
    question = state["question"]
    docs = web_search_tool.invoke({"query": question})
    web_results = Document(page_content="\n".join([d["content"] for d in docs]))
    documents = state.get("documents", [])
    documents.append(web_results)
    return {"documents": documents, "question": question}

workflow = StateGraph(TypedDict("GraphState", {"question": str, "generation": str, "documents": List[Document]}))

# Define the nodes
workflow.add_node("websearch", web_search)
workflow.add_node("retrieve", retrieve)
workflow.add_node("generate", generate)

workflow.set_conditional_entry_point(
    route_question,
    {
        "websearch": "websearch",
        "vectorstore": "retrieve",
    },
)

workflow.add_edge("retrieve", "generate")
workflow.add_edge("websearch", "generate")

# Compile the app
app = workflow.compile()

# Gradio integration with Chatbot

# Updated ask_question_conversation function
def ask_question_conversation(history, question):
    inputs = {"question": question}
    generation_result = None
    
    # Run the workflow and get the generation result
    for output in app.stream(inputs):
        for key, value in output.items():
            generation_result = value.get("generation", "No generation found")
    
    # Append the new question and response to the history
    history.append((question, generation_result))
    
    # Return the updated history to chatbot and clear the question textbox
    return history, ""

# Gradio conversation UI
'''
with gr.Blocks() as demo:
    gr.Markdown("🤖 Multi-Agent Knowledge Assistant: Powered by RAG for Smart Answers!")
    
    chatbot = gr.Chatbot(label="Chat with AI Assistant")
    question = gr.Textbox(label="Your Question", placeholder="Ask your question here...")
    clear = gr.Button("Clear Conversation")
    
    # Submit action for the question textbox
    question.submit(ask_question_conversation, [chatbot, question], [chatbot, question])
    clear.click(lambda: [], None, chatbot)  # Clear conversation history

demo.launch()
'''

with gr.Blocks(css="""
    #title { 
        font-size: 26px; 
        font-weight: bold; 
        text-align: center; 
        color: #4A90E2;
    }
    #subtitle {
        font-size: 18px; 
        text-align: center; 
        margin-top: -15px; 
        color: #7D7D7D;
    }
    .gr-chatbot, .gr-textbox, .gr-button {
        max-width: 600px;
        margin: 0 auto;
    }
    .gr-chatbot {
        height: 400px;
    }
    .gr-button {
        display: block;
        width: 100px;
        margin: 20px auto;
        background-color: #4A90E2;
        color: white;
    }
""") as demo:
    gr.Markdown("<div id='title'>🤖 Multi-Agent Knowledge Assistant: Powered by RAG for Smart Answers!</div>")
    
    chatbot = gr.Chatbot(label="Chat with AI Assistant")
    question = gr.Textbox(label="Ask a Question", placeholder="Type your question here...", lines=1)
    clear = gr.Button("Clear Chat")
    
    # Submit action for the question textbox
    question.submit(ask_question_conversation, [chatbot, question], [chatbot, question])
    clear.click(lambda: [], None, chatbot)  # Clear conversation history

demo.launch()