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import os
import requests
import streamlit as st
import pickle
from langchain.chains import 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
VECTOR_DB_PATH = "/tmp/chroma_db"
CHUNKS_FILE = "/tmp/chunks.pkl"
# Session State Initialization
if "vector_store" not in st.session_state:
st.session_state.vector_store = None
if "documents" not in st.session_state:
st.session_state.documents = None
if "pdf_path" not in st.session_state:
st.session_state.pdf_path = None
if "pdf_loaded" not in st.session_state:
st.session_state.pdf_loaded = False
if "chunked" not in st.session_state:
st.session_state.chunked = False
if "vector_created" not in st.session_state:
st.session_state.vector_created = False
st.title("Blah-2")
# Step 1: Choose PDF Source
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:
st.session_state.pdf_path = "temp.pdf"
with open(st.session_state.pdf_path, "wb") as f:
f.write(uploaded_file.getbuffer())
st.session_state.pdf_loaded = False
st.session_state.chunked = False
st.session_state.vector_created = False
elif pdf_source == "Enter a PDF URL":
pdf_url = st.text_input("Enter PDF URL:")
if pdf_url and not st.session_state.pdf_path:
with st.spinner("Downloading PDF..."):
try:
response = requests.get(pdf_url)
if response.status_code == 200:
st.session_state.pdf_path = "temp.pdf"
with open(st.session_state.pdf_path, "wb") as f:
f.write(response.content)
st.session_state.pdf_loaded = False
st.session_state.chunked = False
st.session_state.vector_created = False
st.success("✅ PDF Downloaded Successfully!")
else:
st.error("❌ Failed to download PDF. Check the URL.")
except Exception as e:
st.error(f"❌ Error downloading PDF: {e}")
# Step 2: Load & Process PDF (Only Once)
if st.session_state.pdf_path and not st.session_state.pdf_loaded:
with st.spinner("Loading PDF..."):
try:
loader = PDFPlumberLoader(st.session_state.pdf_path)
docs = loader.load()
st.session_state.documents = docs
st.session_state.pdf_loaded = True
st.success(f"✅ **PDF Loaded!** Total Pages: {len(docs)}")
except Exception as e:
st.error(f"❌ Error processing PDF: {e}")
# Load Cached Chunks if Available
def load_chunks():
if os.path.exists(CHUNKS_FILE):
with open(CHUNKS_FILE, "rb") as f:
return pickle.load(f)
return None
if not st.session_state.chunked: # Ensure chunking only happens once
cached_chunks = load_chunks()
if cached_chunks:
st.session_state.documents = cached_chunks
st.session_state.chunked = True
# Step 3: Chunking (Only Happens Once)
if st.session_state.pdf_loaded and not st.session_state.chunked:
with st.spinner("Chunking the document..."):
try:
model_name = "nomic-ai/modernbert-embed-base"
embedding_model = HuggingFaceEmbeddings(model_name=model_name, model_kwargs={'device': 'cpu'})
text_splitter = SemanticChunker(embedding_model)
if st.session_state.documents:
documents = text_splitter.split_documents(st.session_state.documents)
st.session_state.documents = documents
st.session_state.chunked = True
# Save chunks for persistence
with open(CHUNKS_FILE, "wb") as f:
pickle.dump(documents, f)
st.success(f"✅ **Document Chunked!** Total Chunks: {len(documents)}")
except Exception as e:
st.error(f"❌ Error chunking document: {e}")
# Step 4: Setup Vectorstore
def load_vector_store():
return Chroma(persist_directory=VECTOR_DB_PATH, collection_name="deepseek_collection", embedding_function=HuggingFaceEmbeddings(model_name="nomic-ai/modernbert-embed-base"))
if st.session_state.chunked and not st.session_state.vector_created:
with st.spinner("Creating vector store..."):
try:
if st.session_state.vector_store is None: # Prevent unnecessary reloading
st.session_state.vector_store = load_vector_store()
if len(st.session_state.vector_store.get()["documents"]) == 0: # Prevent duplicate insertions
st.session_state.vector_store.add_documents(st.session_state.documents)
num_documents = len(st.session_state.vector_store.get()["documents"])
st.session_state.vector_created = True
st.success(f"✅ **Vector Store Created!** Total documents stored: {num_documents}")
except Exception as e:
st.error(f"❌ Error creating vector store: {e}")
# Debugging Logs
st.write("📄 **PDF Loaded:**", st.session_state.pdf_loaded)
st.write("🔹 **Chunked:**", st.session_state.chunked)
st.write("📂 **Vector Store Created:**", st.session_state.vector_created)
# ----------------- Query Input -----------------
query = st.text_input("🔍 Ask a question about the document:")
if query:
with st.spinner("🔄 Retrieving relevant context..."):
retriever = st.session_state.vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
contexts = retriever.invoke(query)
# Debugging: Check what was retrieved
st.write("Retrieved Contexts:", contexts)
st.write("Number of Contexts:", len(contexts))
context = [d.page_content for d in contexts]
# Debugging: Check extracted context
st.write("Extracted Context (page_content):", context)
st.write("Number of Extracted Contexts:", len(context))
# ----------------- Run Individual Chains Explicitly -----------------
context_relevancy_checker_prompt = PromptTemplate(input_variables=["retriever_query","context"],template=relevancy_prompt)
if "context_relevancy_evaluation_chain" in st.session_state:
del st.session_state["context_relevancy_evaluation_chain"]
context_relevancy_evaluation_chain = LLMChain( llm=llm_judge, prompt=PromptTemplate(input_variables=["retriever_query","context"],template=relevancy_prompt), output_key="relevancy_response" )
st.write("🚀 Debugging Expected Keys for `context_relevancy_evaluation_chain`:")
st.write(context_relevancy_evaluation_chain.input_keys)
response_crisis = context_relevancy_evaluation_chain.invoke({"context":context,"retriever_query":query})
relevant_prompt = 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":response_crisis['relevancy_response']})
context_prompt = PromptTemplate(input_variables=["context_number"],template=response_synth)
relevant_contexts_chain = LLMChain(llm=llm_judge, prompt=context_prompt, output_key="relevant_contexts")
contexts = relevant_contexts_chain.invoke({"context_number":relevant_response['context_number'],"context":context})
#temp
st.subheader("Relevant Contexts")
st.json(contexts['relevant_contexts'])
response_chain = LLMChain(llm=rag_llm,prompt=final_prompt,output_key="final_response")
#temp
st.subheader("Response Chain")
st.json(response_chain)
#response = chain.invoke({"query":query,"context":contexts['relevant_contexts']})
#temp
#st.subheader("blah response")
#st.json(response.content)
# Orchestrate using SequentialChain
context_management_chain = SequentialChain(
chains=[context_relevancy_evaluation_chain ,pick_relevant_context_chain, relevant_contexts_chain,response_chain],
input_variables=["context","retriever_query","query"],
output_variables=["relevancy_response", "context_number","relevant_contexts","final_response"]
)
final_output = context_management_chain({"context":context,"retriever_query":query,"query":query})
st.subheader("Final Output from Context Management chain")
st.json(final_output)
st.subheader("Context of Final Output from Context Management chain")
st.json(final_output['context'])
st.header("Relevancy Response")
st.json(final_output['relevancy_response'])
st.subheader("Relevant Context")
st.json(final_output['relevant_contexts'])
response = chain.invoke({"query":query,"context":final_output['relevant_contexts']})
st.subheader("Final Response")
st.json(response.content)
# ----------------- Display All Outputs -----------------
#st.subheader("response_crisis")
#st.json((response_crisis))
#st.subheader("response_crisis['relevancy_response']")
#st.json((response_crisis['relevancy_response']))
#st.markdown("### Context Relevancy Evaluation")
#st.json(response_crisis["relevancy_response"])
#st.markdown("### Picked Relevant Contexts")
#st.json(relevant_response["context_number"])
#st.markdown("### Extracted Relevant Contexts")
#st.json(contexts["relevant_contexts"])
#st.subheader("context_relevancy_evaluation_chain Statement")
#st.json(final_response["relevancy_response"])
#st.subheader("pick_relevant_context_chain Statement")
#st.json(final_response["context_number"])
#st.subheader("relevant_contexts_chain Statement")
#st.json(final_response["relevant_contexts"])
#st.subheader("RAG Response Statement")
#st.json(final_response["final_response"])
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