Create app.py

app.py

@@ -0,0 +1,165 @@
+import os
+import gradio as gr
+from langchain.chains import RetrievalQA
+from langchain_community.document_loaders import TextLoader
+from langchain.document_loaders import PyPDFLoader
+from langchain.document_loaders import PyMuPDFLoader
+
+import chromadb #==0.4.24
+from langchain.memory import ConversationBufferMemory
+
+
+from langchain.indexes import VectorstoreIndexCreator
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain import HuggingFaceHub
+from dotenv import find_dotenv, load_dotenv 
+
+from langchain.chains import create_retrieval_chain, RetrievalQA
+from langchain.chains import ConversationalRetrievalChain
+
+from langchain_community.vectorstores import FAISS
+
+from langchain_community.vectorstores import LanceDB
+import lancedb #==0.6.5
+
+from langchain_community.vectorstores import Chroma
+
+_=load_dotenv(find_dotenv())
+hf_api = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+llms = ["Google/flan-t5-xxl", "Mistralai/Mistral-7B-Instruct-v0.2", "Mistralai/Mistral-7B-Instruct-v0.1", \
+    "Google/gemma-7b-it","Google/gemma-2b-it", "HuggingFaceH4/zephyr-7b-beta",  \
+    "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "Mosaicml/mpt-7b-instruct", "Tiiuae/falcon-7b-instruct", \
+]
+
+def indexdocs (docs,chunk_size,chunk_overlap,vector_store, progress=gr.Progress()):
+    
+    progress(0.1,desc="Loading documents...")
+    
+    loaders = [PyPDFLoader(x) for x in docs]
+    pages = []
+    for loader in loaders:
+        pages.extend(loader.load())
+        
+    progress(0.2,desc="Splitting documents...")
+    
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size = chunk_size, 
+        chunk_overlap = chunk_overlap)
+    doc_splits = text_splitter.split_documents(pages)
+    
+    progress(0.3,desc="Generating embeddings...")
+
+    embedding = HuggingFaceEmbeddings()
+
+    progress(0.5,desc="Generating vectorstore...")
+   
+    if vector_store== 0:
+        new_client = chromadb.EphemeralClient()# "Chroma"
+        vector_store_db = Chroma.from_documents(
+        documents=doc_splits,
+        embedding=embedding,
+        client=new_client #,
+        )
+    elif vector_store==1: #"FAISS"
+        vector_store_db = FAISS.from_documents(
+            documents=doc_splits,
+            embedding=embedding
+            )
+    else: #Lance
+        vector_store_db = LanceDB.from_documents(
+                documents=doc_splits,
+                embedding=embedding
+                )
+    
+    progress(0.9,desc="Vector store generated from the documents.")
+    
+    return vector_store_db, gr.Column(visible=True), "Vector store generated from the documents"
+
+def setup_llm(vector_store,llm_model,temp,max_tokens):
+    retriever=vector_store.as_retriever()
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        output_key='answer',
+        return_messages=True
+    )
+    llm = HuggingFaceEndpoint(
+            repo_id=llms[llm_model], 
+            temperature = temp, 
+            max_new_tokens = max_tokens, 
+            top_k = 3 #top_k,
+        )
+    qa_chain = ConversationalRetrievalChain.from_llm(
+        llm,
+        retriever=retriever,
+        chain_type="stuff", 
+        memory=memory,
+        return_source_documents=True,
+        verbose=False,
+    )
+    return qa_chain,gr.Column(visible=True)
+
+def format_chat_history(chat_history):
+    formatted_chat_history = []
+    for user_message, bot_message in chat_history:
+        formatted_chat_history.append(f"User: {user_message}")
+        formatted_chat_history.append(f"Assistant: {bot_message}")
+    return formatted_chat_history
+
+def chat(qa_chain,msg,history):
+    formatted_chat_history = format_chat_history(history)
+    response = qa_chain.invoke({"question": msg, "chat_history": formatted_chat_history})
+    response_answer = response["answer"]
+    response_sources=response["source_documents"]
+    response_source1=response_sources[0].page_content.strip()
+    response_source_page=response_sources[0].metadata["page"]+1
+    new_history = history + [(msg, response_answer)]
+    return qa_chain, gr.update(value=""), new_history, response_source1, response_source_page
+
+with gr.Blocks() as demo:
+    vector_store_db=gr.State()
+    qa_chain=gr.State()
+    
+    gr.Markdown(
+        """
+        # PDF Knowledge Base QA using RAG
+        """
+    )
+    with gr.Accordion(label="Create Vectorstore",open=True):
+        with gr.Column():
+            file_list = gr.File(label='Upload your PDF files...', file_count='multiple', file_types=['.pdf'])
+            chunk_size=gr.Slider(minimum=100, maximum=1000, value=500, step=25, label="Chunk Size", interactive=True)
+            chunk_overlap=gr.Slider(minimum=10, maximum=200, value=30, step=10, label="Chunk Overlap", interactive=True)
+            vector_store=gr.Radio (["Chroma","FAISS","Lance"], value="Chroma", label="Vectorstore",type="index", interactive=True)
+            vectorstore_db_progress=gr.Textbox(label="Vectorstore database progress",value="Not started yet")
+            fileuploadbtn= gr.Button ("Generate Vectorstore and Move to LLM Setup Step")
+    with gr.Column(visible=False) as llm_column:
+            llm=gr.Radio(llms, label="Choose LLM Model", value=llms[0],type="index")
+            model_temp=gr.Slider(minimum=0.0, maximum=1.0,step=0.1, value=0.3, label="Temperature", interactive=True)
+            model_max_tokens=gr.Slider(minimum=100, maximum=1000,step=50, value=200, label="Maximum Tokens", interactive=True)
+            setup_llm_btn=gr.Button("Set up LLM and Start Chat")
+    with gr.Column(visible=False) as chat_column:      
+        with gr.Row():
+            chatbot=gr.Chatbot(height=300)
+        with gr.Row():
+            source=gr.Textbox(info="Source",container=False,scale=4)
+            source_page=gr.Textbox(info="Page",container=False,scale=1)
+        with gr.Row():
+            prompt=gr.Textbox(container=False, scale=4, interactive=True)
+            promptsubmit=gr.Button("Submit", scale=1, interactive=True)
+    gr.Markdown(
+        """
+        # Responsible AI Usage
+        Your documents uploaded to the system or interactions with the chatbot are not saved.
+        """
+    )
+
+    fileuploadbtn.click(fn=indexdocs, inputs = [file_list,chunk_size,chunk_overlap,vector_store], outputs=[vector_store_db,llm_column,vectorstore_db_progress])# , outputs=[rep,prompt,promptsubmit])
+    setup_llm_btn.click(fn=setup_llm, inputs=[vector_store_db,llm,model_temp,model_max_tokens], outputs=[qa_chain,chat_column])
+    promptsubmit.click(fn=chat, inputs=[qa_chain,prompt,chatbot], outputs=[qa_chain,prompt,chatbot])
+    prompt.submit(fn=chat, inputs=[qa_chain,prompt,chatbot], outputs=[qa_chain,prompt,chatbot,source,source_page],queue=False)
+
+if __name__ == "__main__":
+    demo.launch()