app.py

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain.retrievers.document_compressors import DocumentCompressorPipeline
from langchain_community.document_transformers import EmbeddingsRedundantFilter
from langchain.retrievers.document_compressors import EmbeddingsFilter
#from langchain_text_splitters import CharacterTextSplitter
from langchain.retrievers import ContextualCompressionRetriever
from langchain_groq import ChatGroq
#from langchain.document_loaders import HuggingFaceDatasetLoader
# from langchain_community.document_loaders import UnstructuredExcelLoader
# from langchain.document_loaders import CSVLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
# from transformers import AutoTokenizer, AutoModelForQuestionAnswering
# from transformers import AutoTokenizer, pipeline
# from langchain import HuggingFacePipeline
import re
import os
import streamlit as st
import requests


# Define the path to the pre-trained model you want to use
modelPath = "sentence-transformers/all-MiniLM-l6-v2"

# Create a dictionary with model configuration options, specifying to use the CPU for computations
model_kwargs = {'device': 'cpu'}

# Create a dictionary with encoding options, specifically setting 'normalize_embeddings' to False
encode_kwargs = {'normalize_embeddings': False}

# Initialize an instance of HuggingFaceEmbeddings with the specified parameters
embeddings = HuggingFaceEmbeddings(
    model_name=modelPath,  # Provide the pre-trained model's path
    model_kwargs=model_kwargs,  # Pass the model configuration options
    encode_kwargs=encode_kwargs  # Pass the encoding options
)


# Initialize the HuggingFaceEmbeddings
model_path = "sentence-transformers/all-MiniLM-l6-v2"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
embeddings = HuggingFaceEmbeddings(
    model_name=model_path,
    model_kwargs=model_kwargs,
    encode_kwargs=encode_kwargs
)

# Load the FAISS index
db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)

retriever = db.as_retriever(search_kwargs={"k": 2})
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)
relevant_filter = EmbeddingsFilter(embeddings=embeddings)
pipeline_compressor = DocumentCompressorPipeline(transformers=[text_splitter, redundant_filter, relevant_filter])
compression_retriever = ContextualCompressionRetriever(base_compressor=pipeline_compressor, base_retriever=retriever)

chat = ChatGroq(temperature=0, groq_api_key="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",
                model_name="llama3-70b-8192")

rag_template_str = ("""
Answer the following query based on the context given.
Stylization:
1)Don't say "According to context provided" or "Here is the answer to the query"
2)Include the source URLs 
3)Include the Category it belongs to
Formatting:
1)Start answer with "ANSR:"
2)Use bullet points
Restriction:
1)Only use context to answer the question 
2)If you don't know the answer,reply with "No answer found, you can contact us on https://www.i2econsulting.com/contact-us/" 
context: {context}
query:{query}
""")

rag_prompt = ChatPromptTemplate.from_template(rag_template_str)
rag_chain = rag_prompt | chat | StrOutputParser()

llm = ChatGroq(groq_api_key="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",
            model_name="mixtral-8x7b-32768")

# prompt = ChatPromptTemplate.from_template(
#     """
#     Answer the questions based on the provided context only.
#     Please provide the most accurate response based on the question
#     <context>
#     {context}
#     <context>
#     Questions:{input}
#     """
# )
rag_prompt = ChatPromptTemplate.from_template(rag_template_str)
rag_chain = rag_prompt | chat | StrOutputParser()

col1, col2 = st.columns([1, 7])

# Display the robot image
with col1:
    st.image(image="image.png", width=80)

# Display the title
with col2:
    st.title("ANSR Chatbot")

#st.title("I2E Enterprise Chatbot")
# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []
    intro = {"role": "assistant",
             "content": """Hello there! Welcome to i2e, your intelligent guide to unlocking productivity and efficiency! Whether you're seeking quick answers, expert assistance, or simply exploring our services, I'm here to assist you every step of the way. Let's dive in and discover how i2e can empower you to achieve your goals effortlessly. How can I assist you today?"""}
    st.session_state.messages.append(intro)


# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])


# st.title("i2e Enterprise Chatbot")

# prompt = st.text_input("Ask Question")

def api_py_function(query):
    context = compression_retriever.get_relevant_documents(query)
    #print(context)
    l = []
    for documents in context[:5]:
        if documents.state['query_similarity_score'] > 0.1:
            content = documents.page_content + str(documents.metadata)
            l.append(content)
    final_context = ''.join(l)
    if l != []:
        response = rag_chain.invoke({"query": query, "context": final_context})
    else:
        response = "No answer found, Please rephrase your question or you can contact us on https://www.i2econsulting.com/contact-us/"
    for word in response.split():
        yield word + " "
        # time.sleep(0.05)
    # return yield word + " "

# def response_generator():
    
#     response=response.replace("\\n\\n","  \\n")
#     for word in response.split():
#         yield word + " "
#         time.sleep(0.05)
#     print(response)
# if prompt:
#     print("processing request")
#     full_response=api_py_function(prompt)
#     # full_response = response.text.replace(u"\u2000", "")
#     # full_response=response.text.replace("\\n\\n","  \\n")
#     # full_response = full_response.replace("\\n", "  \\n")
#     st.write(full_response)
# Accept user input
if prompt := st.chat_input("ANSR is ready to answer your question"):
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})
    # Display user message in chat message container
    with st.chat_message("user"):
        st.markdown(prompt)
    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        response = st.write_stream(api_py_function(prompt))
    # Add assistant response to chat history
    print(st.session_state.messages)
    st.session_state.messages.append({"role": "assistant", "content": response})