first commit
Browse files- ragchatbot.py +84 -0
- ragfunctions.py +130 -0
- requirements.txt +3 -0
ragchatbot.py
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import streamlit as st
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import os
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import ragfunctions
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st.title("RAG Chatbot")
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# Setting the LLM
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with st.expander("Setting the LLM"):
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st.markdown("This page is used to have a chat with the uploaded documents")
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with st.form("setting"):
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row_1 = st.columns(3)
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with row_1[0]:
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token = st.text_input("Hugging Face Token", type="password")
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with row_1[1]:
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llm_model = st.text_input("LLM model", value="tiiuae/falcon-7b-instruct")
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with row_1[2]:
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instruct_embeddings = st.text_input("Instruct Embeddings", value="hkunlp/instructor-xl")
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row_2 = st.columns(3)
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with row_2[0]:
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vector_store_list = os.listdir("vector store/")
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default_choice = (
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vector_store_list.index('naruto_snake')
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if 'naruto_snake' in vector_store_list
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else 0
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)
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existing_vector_store = st.selectbox("Vector Store", vector_store_list, default_choice)
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with row_2[1]:
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temperature = st.number_input("Temperature", value=1.0, step=0.1)
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with row_2[2]:
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max_length = st.number_input("Maximum character length", value=300, step=1)
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create_chatbot = st.form_submit_button("Create chatbot")
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# Prepare the LLM model
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if "conversation" not in st.session_state:
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st.session_state.conversation = None
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if token:
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st.session_state.conversation = ragfunctions.prepare_rag_llm(
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token, llm_model, instruct_embeddings, existing_vector_store, temperature, max_length
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)
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# Chat history
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if "history" not in st.session_state:
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st.session_state.history = []
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# Source documents
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if "source" not in st.session_state:
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st.session_state.source = []
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# Display chats
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for message in st.session_state.history:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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# Ask a question
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if question := st.chat_input("Ask a question"):
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# Append user question to history
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st.session_state.history.append({"role": "user", "content": question})
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# Add user question
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with st.chat_message("user"):
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st.markdown(question)
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# Answer the question
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answer, doc_source = ragfunctions.generate_answer(question, token)
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with st.chat_message("assistant"):
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st.write(answer)
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# Append assistant answer to history
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st.session_state.history.append({"role": "assistant", "content": answer})
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# Append the document sources
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st.session_state.source.append({"question": question, "answer": answer, "document": doc_source})
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# Source documents
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with st.expander("Source documents"):
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st.write(st.session_state.source)
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ragfunctions.py
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@@ -0,0 +1,130 @@
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import streamlit as st
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from langchain.document_loaders import TextLoader
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from pypdf import PdfReader
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from langchain import HuggingFaceHub
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.embeddings import HuggingFaceInstructEmbeddings
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from langchain.vectorstores import FAISS
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from langchain.chains import ConversationalRetrievalChain
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from langchain.memory import ConversationBufferWindowMemory
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def read_pdf(file):
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document = ""
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reader = PdfReader(file)
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for page in reader.pages:
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document += page.extract_text()
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return document
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class llmClass():
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def __init__(self, token, llm_model, instruct_embeddings, existing_vector_store, temperature, max_length):
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print('Created')
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def generate_answer(self, question, token):
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return 'LLm not connected'
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def read_txt(file):
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document = str(file.getvalue())
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document = document.replace("\\n", " \\n ").replace("\\r", " \\r ")
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return document
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def split_doc(document, chunk_size, chunk_overlap):
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splitter = RecursiveCharacterTextSplitter(
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap
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)
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split = splitter.split_text(document)
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split = splitter.create_documents(split)
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return split
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def embedding_storing(model_name, split, create_new_vs, existing_vector_store, new_vs_name):
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if create_new_vs is not None:
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# Load embeddings instructor
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instructor_embeddings = HuggingFaceInstructEmbeddings(
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model_name=model_name, model_kwargs={"device":"cuda"}
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)
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# Implement embeddings
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db = FAISS.from_documents(split, instructor_embeddings)
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if create_new_vs == True:
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# Save db
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db.save_local("vector store/" + new_vs_name)
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else:
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# Load existing db
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load_db = FAISS.load_local(
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"vector store/" + existing_vector_store,
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instructor_embeddings,
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allow_dangerous_deserialization=True
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)
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# Merge two DBs and save
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load_db.merge_from(db)
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load_db.save_local("vector store/" + new_vs_name)
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st.success("The document has been saved.")
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def prepare_rag_llm(
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token, llm_model, instruct_embeddings, vector_store_list, temperature, max_length
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):
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# # Load embeddings instructor
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# instructor_embeddings = HuggingFaceInstructEmbeddings(
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# model_name=instruct_embeddings, model_kwargs={"device":"cuda"}
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# )
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# # Load db
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# loaded_db = FAISS.load_local(
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# f"vector store/{vector_store_list}", instructor_embeddings, allow_dangerous_deserialization=True
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# )
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# # Load LLM
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# llm = HuggingFaceHub(
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# repo_id=llm_model,
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# model_kwargs={"temperature": temperature, "max_length": max_length},
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# huggingfacehub_api_token=token
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# )
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# memory = ConversationBufferWindowMemory(
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# k=2,
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# memory_key="chat_history",
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# output_key="answer",
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# return_messages=True,
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# )
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# # Create the chatbot
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# qa_conversation = ConversationalRetrievalChain.from_llm(
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# llm=llm,
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# chain_type="stuff",
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# retriever=loaded_db.as_retriever(),
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# return_source_documents=True,
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# memory=memory,
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# )
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llm_object = llmClass(token, llm_model, instruct_embeddings, vector_store_list, temperature, max_length)
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return llm_object
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def generate_answer(question, token):
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return ["Not connected to An LLm", "No documentation available"]
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answer = "An error has occured"
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if token == "":
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answer = "Insert the Hugging Face token"
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doc_source = ["no source"]
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else:
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response = st.session_state.conversation({"question": question})
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answer = response.get("answer").split("Helpful Answer:")[-1].strip()
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explanation = response.get("source_documents", [])
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doc_source = [d.page_content for d in explanation]
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return answer, doc_source
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requirements.txt
ADDED
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streamlit
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pypdf
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langchain
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