Update app.py

app.py

@@ -1,216 +1,159 @@
 import gradio as gr
 import os
-api_token = os.getenv("HF_TOKEN")
-
-
-from langchain_community.vectorstores import FAISS
+from langchain_community.vectorstores import FAISS, Chroma
 from langchain_community.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import Chroma
 from langchain.chains import ConversationalRetrievalChain
 from langchain_community.embeddings import HuggingFaceEmbeddings 
-from langchain_community.llms import HuggingFacePipeline
-from langchain.chains import ConversationChain
-from langchain.memory import ConversationBufferMemory
 from langchain_community.llms import HuggingFaceEndpoint
-import torch
+from langchain.memory import ConversationBufferMemory
+from pathlib import Path
+
+# Set environment variable for Hugging Face API token
+api_token = os.getenv("HF_TOKEN")
 
-list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]  
+# LLM model options
+list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]
 list_llm_simple = [os.path.basename(llm) for llm in list_llm]
 
+# Set directory for persistent storage
+default_persist_directory = './chroma_database/'  # Ensure directory exists
+
 # Load and split PDF document
 def load_doc(list_file_path):
-    # Processing for one document only
-    # loader = PyPDFLoader(file_path)
-    # pages = loader.load()
     loaders = [PyPDFLoader(x) for x in list_file_path]
     pages = []
     for loader in loaders:
         pages.extend(loader.load())
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size = 1024, 
-        chunk_overlap = 64 
-    )  
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=64)
     doc_splits = text_splitter.split_documents(pages)
     return doc_splits
 
-# Create vector database
+# Create or update vector database with Chroma and persistence
 def create_db(splits):
     embeddings = HuggingFaceEmbeddings()
-    vectordb = FAISS.from_documents(splits, embeddings)
+    vectordb = Chroma.from_documents(
+        documents=splits,
+        embedding=embeddings,
+        persist_directory=default_persist_directory  # Set persistence directory
+    )
+    vectordb.persist()  # Ensure data is saved to chroma.sqlite3
     return vectordb
 
-
 # Initialize langchain LLM chain
 def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
-    if llm_model == "meta-llama/Meta-Llama-3-8B-Instruct":
-        llm = HuggingFaceEndpoint(
-            repo_id=llm_model,
-            huggingfacehub_api_token = api_token,
-            temperature = temperature,
-            max_new_tokens = max_tokens,
-            top_k = top_k,
-        )
-    else:
-        llm = HuggingFaceEndpoint(
-            huggingfacehub_api_token = api_token,
-            repo_id=llm_model, 
-            temperature = temperature,
-            max_new_tokens = max_tokens,
-            top_k = top_k,
-        )
-    
-    memory = ConversationBufferMemory(
-        memory_key="chat_history",
-        output_key='answer',
-        return_messages=True
+    llm = HuggingFaceEndpoint(
+        repo_id=llm_model,
+        huggingfacehub_api_token=api_token,
+        temperature=temperature,
+        max_new_tokens=max_tokens,
+        top_k=top_k
     )
-
-    retriever=vector_db.as_retriever()
+    
+    memory = ConversationBufferMemory(memory_key="chat_history", output_key='answer', return_messages=True)
+    retriever = vector_db.as_retriever()
     qa_chain = ConversationalRetrievalChain.from_llm(
         llm,
         retriever=retriever,
-        chain_type="stuff", 
+        chain_type="stuff",
         memory=memory,
         return_source_documents=True,
-        verbose=False,
+        verbose=False
     )
     return qa_chain
 
-# Initialize database
+# Initialize database with persistence
 def initialize_database(list_file_obj, progress=gr.Progress()):
-    # Create a list of documents (when valid)
     list_file_path = [x.name for x in list_file_obj if x is not None]
-    # Load document and create splits
     doc_splits = load_doc(list_file_path)
-    # Create or load vector database
     vector_db = create_db(doc_splits)
-    return vector_db, "Database created!"
+    return vector_db, "Database created and persisted!"
 
 # Initialize LLM
 def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
-    # print("llm_option",llm_option)
     llm_name = list_llm[llm_option]
-    print("llm_name: ",llm_name)
     qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
     return qa_chain, "QA chain initialized. Chatbot is ready!"
 
-
 def format_chat_history(message, 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
-    
 
+# Conversation handling
 def conversation(qa_chain, message, history):
     formatted_chat_history = format_chat_history(message, history)
-    # Generate response using QA chain
     response = qa_chain.invoke({"question": message, "chat_history": formatted_chat_history})
     response_answer = response["answer"]
-    if response_answer.find("Helpful Answer:") != -1:
-        response_answer = response_answer.split("Helpful Answer:")[-1]
     response_sources = response["source_documents"]
     response_source1 = response_sources[0].page_content.strip()
     response_source2 = response_sources[1].page_content.strip()
     response_source3 = response_sources[2].page_content.strip()
-    # Langchain sources are zero-based
     response_source1_page = response_sources[0].metadata["page"] + 1
     response_source2_page = response_sources[1].metadata["page"] + 1
     response_source3_page = response_sources[2].metadata["page"] + 1
-    # Append user message and response to chat history
     new_history = history + [(message, response_answer)]
     return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
-    
-
-def upload_file(file_obj):
-    list_file_path = []
-    for idx, file in enumerate(file_obj):
-        file_path = file_obj.name
-        list_file_path.append(file_path)
-    return list_file_path
-
 
+# Gradio UI setup
 def demo():
-    # with gr.Blocks(theme=gr.themes.Default(primary_hue="sky")) as demo:
-    with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue = "sky")) as demo:
+    with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue="sky")) as demo:
         vector_db = gr.State()
         qa_chain = gr.State()
         gr.HTML("<center><h1>RAG PDF chatbot</h1><center>")
-        gr.Markdown("""<b>Query your PDF documents!</b> This AI agent is designed to perform retrieval augmented generation (RAG) on PDF documents. The app is hosted on Hugging Face Hub for the sole purpose of demonstration. \
-        <b>Please do not upload confidential documents.</b>
-        """)
+        gr.Markdown("""<b>Query your PDF documents!</b> This AI agent performs retrieval augmented generation (RAG) on PDF documents. \
+                       Please do not upload confidential documents.""")
+
         with gr.Row():
-            with gr.Column(scale = 86):
+            with gr.Column(scale=86):
                 gr.Markdown("<b>Step 1 - Upload PDF documents and Initialize RAG pipeline</b>")
-                with gr.Row():
-                    document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents")
-                with gr.Row():
-                    db_btn = gr.Button("Create vector database")
-                with gr.Row():
-                        db_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Vector database status", 
-                gr.Markdown("<style>body { font-size: 16px; }</style><b>Select Large Language Model (LLM) and input parameters</b>")
-                with gr.Row():
-                    llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value = list_llm_simple[0], type="index") # info="Select LLM", show_label=False
-                with gr.Row():
-                    with gr.Accordion("LLM input parameters", open=False):
-                        with gr.Row():
-                            slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.5, step=0.1, label="Temperature", info="Controls randomness in token generation", interactive=True)
-                        with gr.Row():
-                            slider_maxtokens = gr.Slider(minimum = 128, maximum = 9192, value=4096, step=128, label="Max New Tokens", info="Maximum number of tokens to be generated",interactive=True)
-                        with gr.Row():
-                                slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k", info="Number of tokens to select the next token from", interactive=True)
-                with gr.Row():
-                    qachain_btn = gr.Button("Initialize Question Answering Chatbot")
-                with gr.Row():
-                        llm_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Chatbot status", 
-
-            with gr.Column(scale = 200):
+                document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents")
+                db_btn = gr.Button("Create vector database")
+                db_progress = gr.Textbox(value="Not initialized", show_label=False)
+
+                gr.Markdown("<style>body { font-size: 16px; }</style><b>Select LLM and input parameters</b>")
+                llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value=list_llm_simple[0], type="index")
+                with gr.Accordion("LLM input parameters", open=False):
+                    slider_temperature = gr.Slider(minimum=0.01, maximum=1.0, value=0.5, step=0.1, label="Temperature")
+                    slider_maxtokens = gr.Slider(minimum=128, maximum=9192, value=4096, step=128, label="Max New Tokens")
+                    slider_topk = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="top-k")
+
+                qachain_btn = gr.Button("Initialize Question Answering Chatbot")
+                llm_progress = gr.Textbox(value="Not initialized", show_label=False)
+
+            with gr.Column(scale=200):
                 gr.Markdown("<b>Step 2 - Chat with your Document</b>")
                 chatbot = gr.Chatbot(height=505)
-                with gr.Accordion("Relevent context from the source document", open=False):
-                    with gr.Row():
-                        doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
-                        source1_page = gr.Number(label="Page", scale=1)
-                    with gr.Row():
-                        doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
-                        source2_page = gr.Number(label="Page", scale=1)
-                    with gr.Row():
-                        doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
-                        source3_page = gr.Number(label="Page", scale=1)
-                with gr.Row():
-                    msg = gr.Textbox(placeholder="Ask a question", container=True)
-                with gr.Row():
-                    submit_btn = gr.Button("Submit")
-                    clear_btn = gr.ClearButton([msg, chatbot], value="Clear")
-            
-        # Preprocessing events
-        db_btn.click(initialize_database, \
-            inputs=[document], \
-            outputs=[vector_db, db_progress])
-        qachain_btn.click(initialize_LLM, \
-            inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], \
-            outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], \
-            inputs=None, \
-            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
-            queue=False)
-
-        # Chatbot events
-        msg.submit(conversation, \
-            inputs=[qa_chain, msg, chatbot], \
-            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
-            queue=False)
-        submit_btn.click(conversation, \
-            inputs=[qa_chain, msg, chatbot], \
-            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
-            queue=False)
-        clear_btn.click(lambda:[None,"",0,"",0,"",0], \
-            inputs=None, \
-            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
-            queue=False)
+                with gr.Accordion("Relevant context from the source document", open=False):
+                    doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
+                    source1_page = gr.Number(label="Page", scale=1)
+                    doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
+                    source2_page = gr.Number(label="Page", scale=1)
+                    doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
+                    source3_page = gr.Number(label="Page", scale=1)
+
+                msg = gr.Textbox(placeholder="Ask a question", container=True)
+                submit_btn = gr.Button("Submit")
+                clear_btn = gr.ClearButton([msg, chatbot], value="Clear")
+
+        # Set up Gradio events
+        db_btn.click(initialize_database, inputs=[document], outputs=[vector_db, db_progress])
+        qachain_btn.click(initialize_LLM, inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db],
+                          outputs=[qa_chain, llm_progress]).then(lambda: [None, "", 0, "", 0, "", 0],
+                                                                 inputs=None,
+                                                                 outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
+                                                                 queue=False)
+
+        msg.submit(conversation, inputs=[qa_chain, msg, chatbot],
+                   outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], queue=False)
+        submit_btn.click(conversation, inputs=[qa_chain, msg, chatbot],
+                         outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], queue=False)
+        clear_btn.click(lambda: [None, "", 0, "", 0, "", 0],
+                        inputs=None,
+                        outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], queue=False)
     demo.queue().launch(debug=True)
 
-
 if __name__ == "__main__":
-    demo()
+    demo()