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import os
import requests
import streamlit as st
from langchain.chains import SequentialChain, LLMChain
from langchain.prompts import PromptTemplate
from langchain_groq import ChatGroq
from langchain.document_loaders import PDFPlumberLoader
from langchain_experimental.text_splitter import SemanticChunker
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_chroma import Chroma
from prompts import rag_prompt, relevancy_prompt, relevant_context_picker_prompt, response_synth
# Set API Keys
os.environ["GROQ_API_KEY"] = st.secrets.get("GROQ_API_KEY", "")
# Load LLM models
llm_judge = ChatGroq(model="deepseek-r1-distill-llama-70b")
rag_llm = ChatGroq(model="mixtral-8x7b-32768")
llm_judge.verbose = True
rag_llm.verbose = True
st.title("❓")
# Step 1: Choose PDF Source
#### Initialize pdf_path
pdf_path = None
pdf_source = st.radio("Upload or provide a link to a PDF:", ["Upload a PDF file", "Enter a PDF URL"], index=0, horizontal=True)
if pdf_source == "Upload a PDF file":
uploaded_file = st.file_uploader("Upload your PDF file", type="pdf")
if uploaded_file:
with open("temp.pdf", "wb") as f:
f.write(uploaded_file.getbuffer())
pdf_path = "temp.pdf"
elif pdf_source == "Enter a PDF URL":
pdf_url = st.text_input("Enter PDF URL:")
if pdf_url:
with st.spinner("Downloading PDF..."):
try:
response = requests.get(pdf_url)
if response.status_code == 200:
with open("temp.pdf", "wb") as f:
f.write(response.content)
pdf_path = "temp.pdf"
st.success("✅ PDF Downloaded Successfully!")
else:
st.error("❌ Failed to download PDF. Check the URL.")
pdf_path = None
except Exception as e:
st.error(f"Error downloading PDF: {e}")
pdf_path = None
else:
pdf_path = None
# Step 2: Process PDF
if pdf_path:
with st.spinner("Loading PDF..."):
loader = PDFPlumberLoader(pdf_path)
docs = loader.load()
st.success(f"✅ **PDF Loaded!** Total Pages: {len(docs)}")
# Step 3: Chunking
with st.spinner("Chunking the document..."):
model_name = "nomic-ai/modernbert-embed-base"
embedding_model = HuggingFaceEmbeddings(model_name=model_name, model_kwargs={'device': 'cpu'})
text_splitter = SemanticChunker(embedding_model)
documents = text_splitter.split_documents(docs)
st.success(f"✅ **Document Chunked!** Total Chunks: {len(documents)}")
# Step 4: Setup Vectorstore
with st.spinner("Creating vector store..."):
vector_store = Chroma(
collection_name="deepseek_collection",
collection_metadata={"hnsw:space": "cosine"},
embedding_function=embedding_model
)
vector_store.add_documents(documents)
num_documents = len(vector_store.get()["documents"])
st.success(f"✅ **Vector Store Created!** Total documents stored: {num_documents}")
# Step 5: Query Input
query = st.text_input("🔍 Enter a Query:")
if query:
with st.spinner("Retrieving relevant contexts..."):
retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
contexts = retriever.invoke(query)
context_texts = [doc.page_content for doc in contexts]
st.success(f"✅ **Retrieved {len(context_texts)} Contexts!**")
for i, text in enumerate(context_texts, 1):
st.write(f"**Context {i}:** {text[:500]}...")
# Step 6: Context Relevancy Checker
with st.spinner("Evaluating context relevancy..."):
context_relevancy_checker_prompt = PromptTemplate(
input_variables=["retriever_query", "context"], template=relevancy_prompt
)
context_relevancy_chain = LLMChain(llm=llm_judge, prompt=context_relevancy_checker_prompt, output_key="relevancy_response")
relevancy_response = context_relevancy_chain.invoke({"context": context_texts, "retriever_query": query})
st.subheader("🟥 Context Relevancy Evaluation")
st.json(relevancy_response['relevancy_response'])
# Step 7: Selecting Relevant Contexts
with st.spinner("Selecting the most relevant contexts..."):
relevant_prompt = PromptTemplate(
input_variables=["relevancy_response"], template=relevant_context_picker_prompt
)
pick_relevant_context_chain = LLMChain(llm=llm_judge, prompt=relevant_prompt, output_key="context_number")
relevant_response = pick_relevant_context_chain.invoke({"relevancy_response": relevancy_response['relevancy_response']})
st.subheader("🟦 Pick Relevant Context Chain")
st.json(relevant_response['context_number'])
# Step 8: Retrieving Context for Response Generation
with st.spinner("Retrieving final context..."):
context_prompt = PromptTemplate(
input_variables=["context_number", "context"], template=response_synth
)
relevant_contexts_chain = LLMChain(llm=llm_judge, prompt=context_prompt, output_key="relevant_contexts")
final_contexts = relevant_contexts_chain.invoke({"context_number": relevant_response['context_number'], "context": context_texts})
st.subheader("🟥 Relevant Contexts Extracted")
st.json(final_contexts['relevant_contexts'])
# Step 9: Generate Final Response
with st.spinner("Generating the final answer..."):
final_prompt = PromptTemplate(
input_variables=["query", "context"], template=rag_prompt
)
response_chain = LLMChain(llm=rag_llm, prompt=final_prompt, output_key="final_response")
final_response = response_chain.invoke({"query": query, "context": final_contexts['relevant_contexts']})
st.subheader("🟥 RAG Final Response")
st.success(final_response['final_response'])
# Step 10: Display Workflow Breakdown
st.subheader("🔍 **Workflow Breakdown:**")
st.json({
"Context Relevancy Evaluation": relevancy_response["relevancy_response"],
"Relevant Contexts": relevant_response["context_number"],
"Extracted Contexts": final_contexts["relevant_contexts"],
"Final Answer": final_response["final_response"]
}) |