LLMInsights.py

import os
import json
import pandas as pd
import time

import phoenix as px
from phoenix.trace.langchain import OpenInferenceTracer, LangChainInstrumentor

#from hallucinator import HallucinatonEvaluater

from langchain.embeddings import HuggingFaceEmbeddings #for using HugginFace models
from langchain.chains.question_answering import load_qa_chain
from langchain import HuggingFaceHub
from langchain.prompts import PromptTemplate

from langchain.chains import RetrievalQA
from langchain.callbacks import StdOutCallbackHandler

#from langchain.retrievers import KNNRetriever
from langchain.storage import LocalFileStore
from langchain.embeddings import CacheBackedEmbeddings
from langchain.vectorstores import FAISS


from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter


import numpy as np
import streamlit as st
import pandas as pd
# from sklearn import datasets
# from sklearn.ensemble import RandomForestClassifier

from PIL import Image


global trace_df

# Page config
st.set_page_config(page_title="RAG PoC", layout="wide")
st.sidebar.image(Image.open("./test-logo.png"), use_column_width=True)

@st.cache_resource
def tracer_config():
    #phoenix setup
    session = px.launch_app()
    # If no exporter is specified, the tracer will export to the locally running Phoenix server
    tracer = OpenInferenceTracer()
    # If no tracer is specified, a tracer is constructed for you
    LangChainInstrumentor(tracer).instrument()
    time.sleep(3)
    print(session.url)

tracer_config()




tab1, tab2, tab3 = st.tabs(["📈 **RAG**", "🗃 FactVsHallucinate", "🤖 **RAG Scoring** " ])



os.environ["HUGGINGFACEHUB_API_TOKEN"] = "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"

# embedding cache
#store = LocalFileStore("./cache/")

# define embedder
embedder  = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
#embedder=HuggingFaceHub(repo_id="sentence-transformers/all-mpnet-base-v2")
#embedder = CacheBackedEmbeddings.from_bytes_store(core_embeddings_model, store)

# define llm
llm=HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":1, "max_length":1000000})
#llm=HuggingFaceHub(repo_id="gpt2", model_kwargs={"temperature":1, "max_length":1000000})
handler =  StdOutCallbackHandler()

# set global variable
# vectorstore = None
# retriever = None


class HallucinatePromptContext:
    def __init__(self):
        self.variables_list = ["query","answer","context"]
        self.base_template = """In this task, you will be presented with a query, a reference text and an answer. The answer is
generated to the question based on the reference text. The answer may contain false information, you
must use the reference text to determine if the answer to the question contains false information,
if the answer is a hallucination of facts. Your objective is to determine whether the reference text
contains factual information and is not a hallucination. A 'hallucination' in this context refers to
an answer that is not based on the reference text or assumes information that is not available in
the reference text. Your response should be a single word: either "factual" or "hallucinated", and
it should not include any other text or characters. "hallucinated" indicates that the answer
provides factually inaccurate information to the query based on the reference text. "factual"
indicates that the answer to the question is correct relative to the reference text, and does not
contain made up information. Please read the query and reference text carefully before determining
your response.
 
    # Query: {query}
    # Reference text: {context}
    # Answer: {answer}
    Is the answer above factual or hallucinated based on the query and reference text?"""
        
        

class HallucinatonEvaluater:
    def __init__(self, item):
        self.question = item["question"]
        self.answer = item["answer"]
        #self.domain = item["domain"]
        self.context = item["context"]
        self.llm=HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":1, "max_length":1000000})

    def get_prompt_template(self):
        prompt = HallucinatePromptContext()
        template = prompt.base_template
        varialbles = prompt.variables_list
        eval_template = PromptTemplate(input_variables=varialbles, template=template)
        return eval_template

    def evaluate(self):
        prompt = self.get_prompt_template().format(query = self.question, answer = self.answer, context = self.context)
        score = self.llm(prompt)
        return score
    



@st.cache_resource
def initialize_vectorstore():

    webpage_loader = WebBaseLoader("https://www.tredence.com/case-studies/forecasting-app-installs-for-a-large-retailer-in-the-us").load()    
    webpage_chunks = _text_splitter(webpage_loader)

    global vectorstore
    global retriever

    # store embeddings in vector store
    vectorstore = FAISS.from_documents(webpage_chunks, embedder)
    print("vector store initialized with sample doc")

    # instantiate a retriever
    retriever = vectorstore.as_retriever()
    st.session_state['vectorstore'] = vectorstore
    st.session_state['docadd'] = 0

    return retriever


def _text_splitter(doc):
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=600,
        chunk_overlap=50,
        length_function=len,
    )
    return text_splitter.transform_documents(doc)

def _load_docs(path: str):
    load_doc = WebBaseLoader(path).load()
    doc = _text_splitter(load_doc)
    return doc






def rag_response(response):
    #st.markdown("""<hr style="height:10px;border:none;color:#333;background-color:#333;" /> """, unsafe_allow_html=True)

    #st.markdown(".stTextInput > label {font-size:105%; font-weight:bold; color:blue;} ",unsafe_allow_html=True) #for all text-input label sections
    
    question_title = '<h1 style="color:#33ff33;font-size:24px;">Question</h1>'
    


    st.markdown('<h1 style="color:#100170;font-size:48px;text-align:center;">RAG Response</h1>', unsafe_allow_html=True)
    st.markdown('<h1 style="color:#100170;font-size:24px;">Question</h1>', unsafe_allow_html=True)
    st.text_area(label="", value=response["query"], height=30)
    st.markdown('<h1 style="color:#100170;font-size:24px;">RAG Output</h1>', unsafe_allow_html=True)
    st.text_area(label="", value=response["result"])
    # st.markdown('<h1 style="color:#100170;font-size:24px;">Augmented knowledge</h1>', unsafe_allow_html=True)
    # st.text_area(label="", value=response["source_documents"])
    
    #st.button("Check Hallucination")





# Create extractor instance
def _create_hallucination_scenario(item):
    score = HallucinatonEvaluater(item).evaluate()
    return score

def hallu_eval(question: str, answer: str, context: str):
    print("in hallu eval")
    hallucination_score = _create_hallucination_scenario({
            "question": question,
            "answer": answer,
            "context": context            
        }        
    )
    print("got hallu score")
    st.markdown('<h1 style="color:#100170;font-size:24px;">Hallucinated?</h1>', unsafe_allow_html=True)
    st.text_area(label=" ", value=hallucination_score, height=30)
    #return {"hallucination_score": hallucination_score}    
    #time.sleep(10)


def scoring_eval(question: str, answer: str, context: str):
    print("in scoring eval")
    score = _create_evaluation_scenario({
            "question": question,
            "answer": answer,
            "context": context            
        }        
    )
    print("got score")
    st.markdown('<h1 style="color:#100170;font-size:24px;">Score</h1>', unsafe_allow_html=True)
    st.text_area(label=" ", value=score, height=30)
    #return {"hallucination_score": hallucination_score}    
    #time.sleep(10)



# if 'clicked' not in st.session_state:
#     print("set state to False")
#     st.session_state.clicked = False


def click_button(response):
    # print("set state to True")
    # st.session_state.clicked = True

    hallu_eval(response["query"], response["result"], "blah blah")


class BasePromptContext:
    def __init__(self):
        self.variables_list = ["question","answer","context"]
        self.base_template = """Please act as an impartial judge and evaluate the quality of the provided answer which attempts to answer the provided question based on a provided context.
And you'll need to submit your grading for the correctness, comprehensiveness and readability of the answer, using JSON format with the 2 items in parenthesis:
("score": [your score number for the correctness of the answer], "reasoning": [your one line step by step reasoning about the correctness of the answer])
  Below is your grading rubric:
- Correctness: If the answer correctly answer the question, below are the details for different scores:
  - Score 0: the answer is completely incorrect, doesn’t mention anything about the question or is completely contrary to the correct answer.
      - For example, when asked “How to terminate a databricks cluster”, the answer is empty string, or content that’s completely irrelevant, or sorry I don’t know the answer.
  - Score 4: the answer provides some relevance to the question and answer one aspect of the question correctly.
      - Example:
          - Question: How to terminate a databricks cluster
          - Answer: Databricks cluster is a cloud-based computing environment that allows users to process big data and run distributed data processing tasks efficiently.
          - Or answer:  In the Databricks workspace, navigate to the "Clusters" tab. And then this is a hard question that I need to think more about it
  - Score 7: the answer mostly answer the question but is missing or hallucinating on one critical aspect.
      - Example:
          - Question: How to terminate a databricks cluster”
          - Answer: “In the Databricks workspace, navigate to the "Clusters" tab.
          Find the cluster you want to terminate from the list of active clusters.
          And then you’ll find a button to terminate all clusters at once”
  - Score 10: the answer correctly answer the question and not missing any major aspect
      - Example:
          - Question: How to terminate a databricks cluster
          - Answer: In the Databricks workspace, navigate to the "Clusters" tab.
          Find the cluster you want to terminate from the list of active clusters.
          Click on the down-arrow next to the cluster name to open the cluster details.
          Click on the "Terminate" button. A confirmation dialog will appear. Click "Terminate" again to confirm the action.”
Provided question:
{question}
Provided answer:
{answer}
Provided context:
{context}
Please provide your grading for the correctness and explain you gave the particular grading"""

class Evaluater:
    def __init__(self, item):
        self.question = item["question"]
        self.answer = item["answer"]
        #self.domain = item["domain"]
        self.context = item["context"]
        self.llm=HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":1, "max_length":1000000})

    def get_prompt_template(self):
        prompt = BasePromptContext()
        template = prompt.base_template
        varialbles = prompt.variables_list
        eval_template = PromptTemplate(input_variables=varialbles, template=template)
        return eval_template

    def evaluate(self):
        prompt = self.get_prompt_template().format(question = self.question, answer = self.answer, context = self.context)
        score = self.llm(prompt)
        return score
        
# Create extractor instance
def _create_evaluation_scenario(item):
    score = Evaluater(item).evaluate()
    return score


# Create extractor instance
def _create_hallucination_scenario(item):
    score = HallucinatonEvaluater(item).evaluate()
    return score

#st.write(''' # RAG App''')

with tab1:

  with st.form(" RAG with evaluation - scoring & hallucination "):
    #tab1.subheader(''' # RAG App''')
    initialize_vectorstore()
    if st.session_state['docadd'] == 1:
        retriever = st.session_state['retriever']
    else:
        retriever = initialize_vectorstore()
    
    #print("lenght in tab1,  ", len(vectorstore.serialize_to_bytes()))
    options = ["true", "false"]
    
    st.markdown('<h1 style="color:#100170;font-size:24px;">User Query</h1>', unsafe_allow_html=True)

    question = st.text_input(label="", value="", placeholder="Type in question",label_visibility="visible", disabled=False)
    #st.markdown('<h2 style="color:#3a0aa6;font-size:24px;">Evaluation</h2>', unsafe_allow_html=True)
    evaluate = st.selectbox(label="***Perform  Evaluation?***",options=options, index=1,  placeholder="Choose an option", disabled=False, label_visibility="visible")

    m = st.markdown("""
                    <style>
                    div.stButton > button:first-child {
                        background-color: #100170;
                        color:#ffffff;
                    }
                    div.stButton > button:hover {
                        background-color: #00ff00;
                        color:#ff0000;
                        }
                    </style>""", unsafe_allow_html=True)

    #st.markdown("----", unsafe_allow_html=True)
    columns = st.columns([2,1,2])
    
    if columns[1].form_submit_button(" Start RAG "):
  
        st.markdown("""<hr style="height:10px;border:none;color:#333;background-color: #100170;" /> """, unsafe_allow_html=True)

        print("retrie  ,", retriever)
        chain = RetrievalQA.from_chain_type(
        llm=llm,
        retriever=retriever,
        callbacks=[handler],
        return_source_documents=True
        )

        #response = chain("how tredence brought good insight?")
        response = chain(question)
        print(response["result"])

        
        rag_response(response)
        #click_button(response)


        time.sleep(4)
        
        df = px.active_session().get_spans_dataframe()
        #print(px.active_session())
        #print(px.active_session().get_spans_dataframe())
        print(df.count())        
        df_sorted = df.sort_values(by='end_time',ascending=False) 

        model_input = json.loads(df_sorted[df_sorted["name"] == "LLMChain"]["attributes.input.value"][0])
        context = model_input["context"]

        print(context)

        if evaluate:
            score = _create_evaluation_scenario({
                "question": question,
                "answer": response['result'],
                "context": context            
            })
            hallucination_score = _create_hallucination_scenario({
                "question": question,
                "answer": response['result'],
                "context": context            
                }        
            )
        else:
            score = "Evaluation is Turned OFF"
        st.markdown('<h1 style="color:#100170;font-size:24px;">Completeness Score</h1>', unsafe_allow_html=True)
        st.text_area(label=" ", value=score, height=30)            
        st.markdown('<h1 style="color:#100170;font-size:24px;">Hallucinated?</h1>', unsafe_allow_html=True)
        st.text_area(label=" ", value=hallucination_score, height=30)
        st.markdown('<h1 style="color:#100170;font-size:24px;">context</h1>', unsafe_allow_html=True)
        st.text_area(label="", value=context)
        st.markdown('<h1 style="color:#100170;font-size:24px;">Augmented knowledge</h1>', unsafe_allow_html=True)
        st.text_area(label="", value=response["source_documents"])



# if st.session_state.clicked:
  
#   # The message and nested widget will remain on the page
#   hallu_eval(response["query"], response["result"], "blah blah")
  

#   print("in if for hallu")
      


with tab2:
  
  
  
  with st.form(" LLM-aasisted evaluation of Hallucination"):
      
    
    #print("lenght in tab2,  ", len(vectorstore.serialize_to_bytes()))    
    question = st.text_input(label="**Question**", value="", label_visibility="visible", disabled=False)
    answer = st.text_input(label="**answer**", value="", label_visibility="visible", disabled=False)
    context = st.text_input(label="**context**", value="", label_visibility="visible", disabled=False)


    if st.form_submit_button("Evaluate"):
        hallu_eval(question, answer, context)
      

with tab3: 
  
  
  with st.form("RAG scoring"):
      
    
    #print("lenght in tab2,  ", len(vectorstore.serialize_to_bytes()))    
    question = st.text_input(label="**Question**", value="", label_visibility="visible", disabled=False)
    answer = st.text_input(label="**answer**", value="", label_visibility="visible", disabled=False)
    context = st.text_input(label="**context**", value="", label_visibility="visible", disabled=False)


    if st.form_submit_button("Evaluate"):
        scoring_eval(question, answer, context)
      


print("activ session:  ", px.active_session().get_spans_dataframe())  
trace_df = px.active_session().get_spans_dataframe()

st.session_state['trace_df'] = trace_df

# with tab3:
  
  
  
#   with st.form(" trace"):

#     if px.active_session():  
#         df0 = px.active_session().get_spans_dataframe()
#         if not df0.empty:
#             df= df0.fillna('')
#             st.dataframe(df)

    



def rag():       
  print("in rag")
  options = ["true", "false"]
  question = st.text_input(label="user question", value="", label_visibility="visible", disabled=False)
  evaluate = st.selectbox(label="select evaluation",options=options, index=0,  placeholder="Choose an option", disabled=False, label_visibility="visible")



  if st.button("do RAG"):
    chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever,
    callbacks=[handler],
    return_source_documents=True
    )

    #response = chain("how tredence brought good insight?")
    response = chain(question)
    print(response["result"])

    # time.sleep(4)
    
    # df = px.active_session().get_spans_dataframe()
    # print(px.active_session())
    # print(px.active_session().get_spans_dataframe())
    # print(df.count())        
    # df_sorted = df.sort_values(by='end_time',ascending=False) 

    # model_input = json.loads(df_sorted[df_sorted["name"] == "LLMChain"]["attributes.input.value"][0])
    # context = model_input["context"]

    # print(context)

    # if evaluate:
    #     score = _create_evaluation_scenario({
    #         "question": question,
    #         "answer": response['result'],
    #         "context": context            
    #     })
    # else:
    #     score = "Evaluation is Turned OFF"

    # return {"question": question, "answer": response['result'], "context": context, "score": score}
    rag_response(response)

    # if st.button("click me"):
    #   click_button(response)

    click = st.button("Do you want to see more?")
    if click:
        st.session_state.more_stuff = True

    if st.session_state.more_stuff:
        click_button(response)
        #st.write("Doing more optional stuff")


    return(response) 
  

a = st.markdown("""
            <style>
            div.stTextArea > textarea {
                background-color: #0099ff;     
                height: 1400px;
                width: 800px;
            }               
            </style>""", unsafe_allow_html=True)