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3d57eb3 29b9253 3d57eb3 7278323 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | import os
import streamlit as st
from dotenv import load_dotenv
# LangChain + Hugging Face + Groq
from langchain_chroma import Chroma
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
from langchain_groq import ChatGroq
from langchain_huggingface import HuggingFaceEmbeddings
# Load environment variables
load_dotenv()
# Hugging Face token
#HUGGINGFACEHUB_API_TOKEN = st.secrets["HF_TOKEN"]
#st.secrets["HF_TOKEN"]
# Streamlit UI
st.title("Conversational RAG with PDF Uploads")
st.write("Upload PDFs and chat with their content using Hugging Face embeddings + Groq LLM")
# Groq API key input
api_key = st.text_input("Enter your Groq API key:", type="password")
if api_key:
# Initialize Groq LLM (use supported model)
llm = ChatGroq(groq_api_key=api_key, model_name="llama-3.3-70b-versatile")
# Session management
session_id = st.text_input("Session ID", value="default_session")
if "store" not in st.session_state:
st.session_state.store = {}
# File uploader
uploaded_files = st.file_uploader("Choose PDF files", type="pdf", accept_multiple_files=True)
if uploaded_files:
documents = []
for uploaded_file in uploaded_files:
temppdf = "./temp.pdf"
with open(temppdf, "wb") as file:
file.write(uploaded_file.getvalue())
loader = PyPDFLoader(temppdf)
docs = loader.load()
documents.extend(docs)
# Split and embed
text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=200)
splits = text_splitter.split_documents(documents)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L12-v2")
vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)
retriever = vectorstore.as_retriever()
# Prompt template
qa_prompt = ChatPromptTemplate.from_messages([
("system", "Use retrieved context to answer concisely:\n\n{context}"),
MessagesPlaceholder("chat_history"),
("human", "{input}"),
])
def rag_chain(question, chat_history):
retrieved_docs = retriever.invoke(question)
context = "\n".join([doc.page_content for doc in retrieved_docs])
prompt = qa_prompt.format(chat_history=chat_history, input=question, context=context)
return llm.invoke(prompt)
def get_session_history(session: str) -> BaseChatMessageHistory:
if session not in st.session_state.store:
st.session_state.store[session] = ChatMessageHistory()
return st.session_state.store[session]
# User input
user_input = st.text_input("Your question:")
if user_input:
session_history = get_session_history(session_id)
response = rag_chain(user_input, session_history.messages)
session_history.add_user_message(user_input)
session_history.add_ai_message(response.content)
st.write("Assistant:", response.content)
st.write("Chat History:", session_history.messages)
else:
st.warning("Please enter your Groq API Key")
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