app.py

import streamlit as st  
from streamlit_chat import message
import os
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.llms import Ollama
from pinecone import Pinecone
from langchain_community.embeddings import OllamaEmbeddings
from langchain.chains import ConversationChain
from langchain.chains.conversation.memory import ConversationBufferWindowMemory
from langchain.prompts import (
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
    ChatPromptTemplate,
    MessagesPlaceholder
)
# from langchain_huggingface.llms import HuggingFacePipeline
from langchain_google_genai import GoogleGenerativeAI
from langchain_groq import ChatGroq
from dotenv import load_dotenv
load_dotenv()

os.environ["PINECONE_API_KEY"] = os.environ['PINECONE_API_KEY']
os.environ["GROQ_API_KEY"] = os.environ['GROQ_API_KEY']
# os.environ["GOOGLE_API_KEY"] = os.environ['GOOGLE_AI_API_KEY']

embeddings = OllamaEmbeddings(model="all-minilm:latest")
pc = Pinecone()
index_name = "rag-pdf"
index = pc.Index(index_name)

def retriever(input):
    input_em = embeddings.embed_query(input)
    result = index.query(vector=input_em, top_k=4, includeMetadata=True)
    texts = []
    for match in result["matches"]:
        if "metadata" in match and "text" in match["metadata"]:
            texts.append(match["metadata"]["text"])
    return " ".join(texts)

# st.title("RAG-PDF Chatbot")
st.markdown("<h1 style='text-align: center;'>RAG-PDF Chatbot</h1><br>", unsafe_allow_html=True)

if 'responses' not in st.session_state:
    st.session_state['responses'] = ["How can I assist you?"]

if 'requests' not in st.session_state:
    st.session_state['requests'] = []

# llm = HuggingFacePipeline.from_model_id(
#     model_id="gpt2",
#     task="text-generation",
#     pipeline_kwargs={"max_new_tokens": 10},
# )

llm = ChatGroq(temperature=0, model_name="mixtral-8x7b-32768")
# llm = GoogleGenerativeAI(temperature=0.1, model="models/text-bison-001")

if 'buffer_memory' not in st.session_state:
    st.session_state.buffer_memory = ConversationBufferWindowMemory(k=3, return_messages=True)

system_msg_template = SystemMessagePromptTemplate.from_template(template="""Answer the question as truthfully as possible using the provided context, 
and if the answer is not contained within the text below, say 'I don't know'""")

human_msg_template = HumanMessagePromptTemplate.from_template(template="{input}")

prompt_template = ChatPromptTemplate.from_messages([system_msg_template, MessagesPlaceholder(variable_name="history"), human_msg_template])

conversation = ConversationChain(memory=st.session_state.buffer_memory, prompt=prompt_template, llm=llm, verbose=True)

response_container = st.container()
textcontainer = st.container()

with textcontainer:
    query = st.text_input("Query: ", key="input")
    if query:
        with st.spinner("typing..."):
            context = retriever(query)
            if len(st.session_state.requests) == 0:
                response = conversation.predict(input=f"Context:\n{context}\n\nQuery:\n{query}")
            else:
                response = conversation.predict(input=f"Context:\n{context}\n\nQuery:\n{query}")
        st.session_state.requests.append(query)
        st.session_state.responses.append(response)

with response_container:
    if st.session_state['responses']:
        for i in range(len(st.session_state['responses'])):
            message(st.session_state['responses'][i], key=str(i))
            if i < len(st.session_state['requests']):
                message(st.session_state["requests"][i], is_user=True, key=str(i) + '_user')