Create app.py

app.py

@@ -0,0 +1,183 @@
+"""
+Question Answering with Retrieval QA and LangChain Language Models featuring FAISS vector stores.
+This script uses the LangChain Language Model API to answer questions using Retrieval QA 
+and FAISS vector stores. It also uses the Mistral huggingface inference endpoint to 
+generate responses.
+"""
+
+import os
+import streamlit as st
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from htmlTemplates import css, bot_template, user_template
+from langchain.llms import HuggingFaceHub
+
+
+def get_pdf_text(pdf_docs):
+    """
+    Extract text from a list of PDF documents.
+    Parameters
+    ----------
+    pdf_docs : list
+        List of PDF documents to extract text from.
+    Returns
+    -------
+    str
+        Extracted text from all the PDF documents.
+    """
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+
+def get_text_chunks(text):
+    """
+    Split the input text into chunks.
+    Parameters
+    ----------
+    text : str
+        The input text to be split.
+    Returns
+    -------
+    list
+        List of text chunks.
+    """
+    text_splitter = CharacterTextSplitter(
+        separator="\n", chunk_size=1500, chunk_overlap=300, length_function=len
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+
+def get_vectorstore(text_chunks):
+    """
+    Generate a vector store from a list of text chunks using HuggingFace BgeEmbeddings.
+    Parameters
+    ----------
+    text_chunks : list
+        List of text chunks to be embedded.
+    Returns
+    -------
+    FAISS
+        A FAISS vector store containing the embeddings of the text chunks.
+    """
+    model = "BAAI/bge-base-en-v1.5"
+    encode_kwargs = {
+        "normalize_embeddings": True
+    }  # set True to compute cosine similarity
+    embeddings = HuggingFaceBgeEmbeddings(
+        model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"}
+    )
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+
+def get_conversation_chain(vectorstore):
+    """
+    Create a conversational retrieval chain using a vector store and a language model.
+    Parameters
+    ----------
+    vectorstore : FAISS
+        A FAISS vector store containing the embeddings of the text chunks.
+    Returns
+    -------
+    ConversationalRetrievalChain
+        A conversational retrieval chain for generating responses.
+    """
+    llm = HuggingFaceHub(
+        repo_id="mistralai/Mistral-7B-Instruct-v0.1",
+        model_kwargs={"temperature": 0.005, "max_length": 512},
+    )
+    # llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
+
+    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+    conversation_chain = ConversationalRetrievalChain.from_llm(
+        llm=llm, retriever=vectorstore.as_retriever(), memory=memory
+    )
+    return conversation_chain
+
+
+def handle_userinput(user_question):
+    """
+    Handle user input and generate a response using the conversational retrieval chain.
+    Parameters
+    ----------
+    user_question : str
+        The user's question.
+    """
+    response = st.session_state.conversation({"question": user_question})
+    st.session_state.chat_history = response["chat_history"]
+
+    for i, message in enumerate(st.session_state.chat_history):
+        if i % 2 == 0:
+            st.write(
+                user_template.replace("{{MSG}}", message.content),
+                unsafe_allow_html=True,
+            )
+        else:
+            st.write(
+                bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True
+            )
+
+
+def main():
+    """
+    Putting it all together.
+    """
+    st.set_page_config(
+        page_title="Chat with a Bot that tries to answer questions about multiple PDFs",
+        page_icon=":books:",
+    )
+
+    st.markdown("# Chat with Contracts Bot")
+    st.markdown("This bot tries to answer questions about multiple PDFs using Open Source Mistral 7B")
+
+    st.write(css, unsafe_allow_html=True)
+
+    # set huggingface hub token in st.text_input widget
+    # then hide the input
+    huggingface_token = os.environ["HUGGINGFACEHUB_API_TOKEN"]
+    #openai_api_key = st.text_input("Enter your OpenAI API key", type="password")
+
+
+    if "conversation" not in st.session_state:
+        st.session_state.conversation = None
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = None
+
+    st.header("Chat with a Bot 🤖🦾 that tries to answer questions about multiple PDFs :books:")
+    user_question = st.text_input("Ask a question about your contracts:")
+    if user_question:
+        handle_userinput(user_question)
+
+    with st.sidebar:
+        st.subheader("Your Contracts")
+        pdf_docs = st.file_uploader(
+            "Upload your PDFs here and click on 'Index'", accept_multiple_files=True
+        )
+        if st.button("Index"):
+            with st.spinner("Processing"):
+                # get pdf text
+                raw_text = get_pdf_text(pdf_docs)
+
+                # get the text chunks
+                text_chunks = get_text_chunks(raw_text)
+
+                # create vector store
+                vectorstore = get_vectorstore(text_chunks)
+
+                # create conversation chain
+                st.session_state.conversation = get_conversation_chain(vectorstore)
+
+
+if __name__ == "__main__":
+    main()