Upload 4 files

app (1).py

@@ -0,0 +1,194 @@
+"""
+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/Mixtral-8x7B-Instruct-v0.1",
+        model_kwargs={"temperature": 0.5, "max_length": 1048},
+    )
+    # 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: " + message.content)
+        else:
+            st.write("🤖 ChatBot: " + message.content)
+
+
+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 a Bot")
+    st.markdown("This bot tries to answer questions about multiple PDFs. Let the processing of the PDF finish before adding your question. 🙏🏾")
+
+    st.write(css, unsafe_allow_html=True)
+
+    # set huggingface hub token in st.text_input widget
+    # then hide the input
+    huggingface_token = st.text_input("Enter your HuggingFace Hub token", type="password")
+    #openai_api_key = st.text_input("Enter your OpenAI API key", type="password")
+
+    # set this key as an environment variable
+    os.environ["HUGGINGFACEHUB_API_TOKEN"] = huggingface_token
+    #os.environ["OPENAI_API_KEY"] = openai_api_key
+
+
+    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 documents:")
+    if user_question:
+        handle_userinput(user_question)
+
+    with st.sidebar:
+        st.subheader("Your documents")
+        pdf_docs = st.file_uploader(
+            "Upload your PDFs here and click on 'Process'", accept_multiple_files=True
+        )
+        if st.button("Process"):
+            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()

gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

htmlTemplates (1).py

@@ -0,0 +1,44 @@
+css = """
+<style>
+.chat-message {
+    padding: 1.5rem; border-radius: 0.5rem; margin-bottom: 1rem; display: flex
+}
+.chat-message.user {
+    background-color: #2b313e
+}
+.chat-message.bot {
+    background-color: #475063
+}
+.chat-message .avatar {
+  width: 20%;
+}
+.chat-message .avatar img {
+  max-width: 78px;
+  max-height: 78px;
+  border-radius: 50%;
+  object-fit: cover;
+}
+.chat-message .message {
+  width: 80%;
+  padding: 0 1.5rem;
+  color: #fff;
+}
+"""
+
+bot_template = """
+<div class="chat-message bot">
+    <div class="avatar">
+        <img src="https://i.ibb.co/cN0nmSj/Screenshot-2023-05-28-at-02-37-21.png" style="max-height: 78px; max-width: 78px; border-radius: 50%; object-fit: cover;">
+    </div>
+    <div class="message">{{MSG}}</div>
+</div>
+"""
+
+user_template = """
+<div class="chat-message user">
+    <div class="avatar">
+        <img src="https://i.ibb.co/rdZC7LZ/Photo-logo-1.png">
+    </div>    
+    <div class="message">{{MSG}}</div>
+</div>
+"""

requirements (1).txt

@@ -0,0 +1,16 @@
+langchain==0.0.335
+PyPDF2==3.0.1
+python-dotenv==1.0.0
+streamlit==1.28.2
+openai==1.2.4
+faiss-cpu==1.7.4
+altair==5.1.2
+tiktoken==0.5.1
+black==23.11.0
+# uncomment to use huggingface llms
+huggingface-hub==0.17.3
+
+# uncomment to use instructor embeddings
+InstructorEmbedding==1.0.1
+sentence-transformers==2.2.2
+transformers