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import streamlit as st
from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate
import os
import nltk
nltk.download("punkt")
st.title(':blue[Langchain:] A Rag System on “Leave No Context Behind” Paper')
st.header("AI Chatbot :robot_face:")
os.environ["GOOGLE_API_KEY"] = os.getenv("k4")
# Creating a template
chat_template = ChatPromptTemplate.from_messages([
# System Message establishes bot's role and general behavior guidelines
SystemMessage(content="""You are a Helpful AI Bot.
You take the context and question from user. Your answer should be based on the specific context."""),
# Human Message Prompt Template
HumanMessagePromptTemplate.from_template("""Answer the question based on the given context.
Context:
{context}
Question:
{question}
Answer: """)
])
#user's question.
#how many results we want to print.
from langchain_google_genai import ChatGoogleGenerativeAI
chat_model = ChatGoogleGenerativeAI(model="gemini-1.5-pro-latest")
from langchain_core.output_parsers import StrOutputParser
output_parser = StrOutputParser()
chain = chat_template | chat_model | output_parser
from langchain_community.document_loaders import PDFMinerLoader
from langchain_text_splitters import NLTKTextSplitter
uploaded_file = st.file_uploader("Choose a pdf file",type = "pdf")
pdf_loader = PDFMinerLoader(uploaded_file)
dat_nik = pdf_loader.load()
text_splitter = NLTKTextSplitter(chunk_size = 500,chunk_overlap = 100)
chunks = test_splitter.split_documents(dat_nik)
# dat = PDFMinerLoader("2404.07143.pdf")
# dat_nik =dat.load()
# # Split the document into chunks
# text_splitter = NLTKTextSplitter(chunk_size=500, chunk_overlap=100)
# chunks = text_splitter.split_documents(dat_nik)
# Creating Chunks Embedding
# We are just loading OpenAIEmbeddings
from langchain_google_genai import GoogleGenerativeAIEmbeddings
embedding_model = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
# vectors = embeddings.embed_documents(chunks)
# Store the chunks in vector store
from langchain_community.vectorstores import Chroma
# Creating a New Chroma Database
db = Chroma.from_documents(chunks, embedding_model, persist_directory="./chroma_db_1")
# saving the database on drive
db.persist()
# Setting a Connection with the ChromaDB
db_connection = Chroma(persist_directory="./chroma_db_", embedding_function=embedding_model)
# Converting CHROMA db_connection to Retriever Object, which retrieves top 5 results
retriever = db_connection.as_retriever(search_kwargs={"k": 5})
from langchain_core.runnables import RunnablePassthrough #takes user's question.
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
# format chunks: takes the 5 results, combines all the chunks and displays one output.
rag_chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| chat_template
| chat_model
| output_parser
)
user_input = st.text_area("Ask Questions to AI")
if st.button("Submit"):
st.subheader(":green[Query:]")
st.subheader(user_input)
response = rag_chain.invoke(user_input)
st.subheader(":green[Response:-]")
st.write(response) |