app.py

import os
import streamlit as st
import asyncio
from typing_extensions import TypedDict, List
from IPython.display import Image, display
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain.schema import Document
from langgraph.graph import START, END, StateGraph
from langchain.prompts import PromptTemplate
import uuid
from langchain_groq import ChatGroq
from langchain_community.utilities import GoogleSerperAPIWrapper
from langchain_chroma import Chroma
from langchain_community.document_loaders import NewsURLLoader
from langchain_community.retrievers.wikipedia import WikipediaRetriever
from sentence_transformers import SentenceTransformer
from langchain.vectorstores import Chroma
from langchain_community.document_loaders import UnstructuredURLLoader, NewsURLLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import WebBaseLoader
from langchain_core.output_parsers import StrOutputParser
from langchain_core.output_parsers import JsonOutputParser
from langchain_community.vectorstores.utils import filter_complex_metadata
from langchain.schema import Document
from langgraph.graph import START, END, StateGraph
from langchain_community.document_loaders.directory import DirectoryLoader
from langchain.document_loaders import TextLoader
from functions import *


lang_api_key = os.getenv("lang_api_key")
SERPER_API_KEY = os.getenv("SERPER_API_KEY")
groq_api_key = os.getenv("groq_api_key")




os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_ENDPOINT"] = "https://api.langchain.plus"
os.environ["LANGCHAIN_API_KEY"] = lang_api_key
os.environ["LANGCHAIN_PROJECT"] = "Lithuanian_Law_RAG_QA"
os.environ["GROQ_API_KEY"] = groq_api_key
os.environ["SERPER_API_KEY"] = SERPER_API_KEY





def main():


    

    st.set_page_config(page_title="Info Assistant: ",
                       page_icon=":books:")
    

    st.header("Info Assistant :" ":books:")
    
    st.markdown("""
        ###### Get support of **"Info Assistant"**, who has in memory a lot of Data Science related articles. 
        If it can't answer based on its knowledge base, information will be found on the internet :books:
    """)


    if "messages" not in st.session_state:
        st.session_state["messages"] = [
        {"role": "assistant", "content": "Hi, I'm a chatbot who is  based on respublic of Lithuania law documents. How can I help you?"}
    ]


    class GraphState(TypedDict):
        """
        Represents the state of our graph.

        Attributes:
            question: question
            generation: LLM generation
            search: whether to add search
            documents: list of documents
            generations_count : generations count
        """

        question: str
        generation: str
        search: str
        documents: List[str]
        steps: List[str]
        generation_count: int

    
    search_type = st.selectbox(
        "Choose search type. Options are [Max marginal relevance search (similarity) , Similarity search (similarity). Default value (similarity)]", 
        options=["mmr", "similarity"], 
        index=1  
    )

    k = st.select_slider(
        "Select amount of documents to be retrieved. Default value (5): ", 
        options=list(range(2, 16)), 
        value=4  
    )

    llm = ChatGroq(
    model="gemma2-9b-it",  # Specify the Gemma2 9B model
    temperature=0.0,
    max_tokens=400,
    max_retries=3
    )
    
    retriever = create_retriever_from_chroma(vectorstore_path="docs/chroma/", search_type=search_type, k=k, chunk_size=550, chunk_overlap=40)
    


    # Graph
    workflow = StateGraph(GraphState)

    # Define the nodes
    workflow.add_node("ask_question", lambda state: ask_question(state, retriever))
    workflow.add_node("retrieve", lambda state: retrieve(state, retriever))  
    workflow.add_node("grade_documents", lambda state: grade_documents(state, retrieval_grader_grader(llm) ))  # grade documents
    workflow.add_node("generate", lambda state: generate(state,QA_chain(llm) ))  # generatae
    workflow.add_node("web_search", web_search)  # web search
    workflow.add_node("transform_query", lambda state: transform_query(state,create_question_rewriter(llm) ))


    # Build graph
    workflow.set_entry_point("ask_question")
    workflow.add_conditional_edges(
        "ask_question",
        lambda state: grade_question_toxicity(state, create_toxicity_checker(llm)),
    
        {
        "good": "retrieve",
        'bad': END,
        
        },
    )

    workflow.add_edge("retrieve", "grade_documents")
    workflow.add_conditional_edges(
        "grade_documents",
        decide_to_generate,
        {
            "search": "web_search",
            "generate": "generate",
        
        },
    )
    workflow.add_edge("web_search", "generate")
    workflow.add_conditional_edges(
        "generate",
        lambda state: grade_generation_v_documents_and_question(state, create_hallucination_checker(llm), create_helpfulness_checker(llm)),
        {
            "not supported": "generate",
            "useful": END,
            "not useful": "transform_query",
        },
    )

    workflow.add_edge("transform_query", "retrieve")

    custom_graph = workflow.compile()
    
    

    
    if user_question := st.text_input("Ask a question about your documents:"):
        asyncio.run(handle_userinput(user_question, custom_graph))            



if __name__ == "__main__":
    main()