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app.py

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+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
+
+# set this key as an environment variable
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets['huggingface_token']
+
+def get_pdf_text(pdf_docs : list) -> str:
+    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:str) ->list:
+    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 : list) -> FAISS:
+    model = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
+    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:FAISS) -> ConversationalRetrievalChain:
+    # llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
+    llm = HuggingFaceHub(
+        repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
+        #repo_id="TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF"
+        model_kwargs={"temperature": 0.5, "max_length": 1048},
+    )
+
+    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:str):
+    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("   Usuario: " + message.content)
+        else:
+            st.write("🤖 ChatBot: " + message.content)
+
+
+def main():
+    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)
+
+    
+    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()

htmlTemplates.py

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+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.txt

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+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