Update app.py

app.py

@@ -294,6 +294,34 @@ def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Pr
     progress(0.9, desc="Done!")
     return vector_db, collection_name, "Complete!"
 
+# Initialize LlamaIndex parsing
+def initialize_llama_index(file_obj):
+    documents = LlamaParse(result_type="markdown", api_key=api_token).load_data(file_obj[0].name)
+    node_parser = MarkdownElementNodeParser(llm=None, num_workers=8)
+    nodes = node_parser.get_nodes_from_documents(documents)
+    base_nodes, objects = node_parser.get_nodes_and_objects(nodes)
+    
+    # Usando SimpleVectorStore para criar um índice vetorial
+    vector_store = SimpleVectorStore()
+    for node in base_nodes + objects:
+        vector_store.add(node)
+
+    # Criando um recuperador a partir do índice vetorial
+    index_ret = VectorIndexRetriever(vector_store=vector_store, top_k=15)
+
+    # Configurando o motor de consulta
+    reranker = FlagEmbeddingReranker(
+        top_n=5,
+        model="BAAI/bge-reranker-large"
+    )
+    recursive_query_engine = RetrieverQueryEngine(
+        retriever=index_ret,
+        node_postprocessors=[reranker],
+        verbose=False
+    )
+
+    return recursive_query_engine, "LlamaIndex parsing complete"
+
 def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
     llm_name = list_llm[llm_option]
     print("llm_name: ",llm_name)
@@ -305,4 +333,133 @@ def format_chat_history(message, 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
+    return formatted_chat_history
+
+def conversation(qa_chain, message, history):
+    formatted_chat_history = format_chat_history(message, history)
+   
+    response = qa_chain({"question": message, "chat_history": formatted_chat_history})
+    response_answer = response["answer"]
+    if "Helpful Answer:" in response_answer:
+        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()
+    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
+    
+    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 file in file_obj:
+        list_file_path.append(file.name)
+    return list_file_path
+
+def demo():
+    with gr.Blocks(theme="base") as demo:
+        vector_db = gr.State()
+        qa_chain = gr.State()
+        collection_name = gr.State()
+        llama_index_engine = gr.State()
+        
+        gr.Markdown(
+        """<center><h2>PDF-based chatbot</center></h2>
+        <h3>Ask any questions about your PDF documents</h3>""")
+        gr.Markdown(
+        """<b>Note:</b> Esta é a lucIAna, primeira Versão da IA para seus PDF documentos. 
+        Este chatbot leva em consideração perguntas anteriores ao gerar respostas (por meio de memória conversacional) e inclui referências a documentos para fins de clareza.
+        """)
+        
+        with gr.Tab("Step 1 - Upload PDF"):
+            with gr.Row():
+                document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)")
+        
+        with gr.Tab("Step 2 - Process document"):
+            with gr.Row():
+                db_btn = gr.Radio(["ChromaDB"], label="Vector database type", value="ChromaDB", type="index", info="Choose your vector database")
+            with gr.Accordion("Advanced options - Document text splitter", open=False):
+                with gr.Row():
+                    slider_chunk_size = gr.Slider(minimum=100, maximum=1000, value=600, step=20, label="Chunk size", info="Chunk size", interactive=True)
+                with gr.Row():
+                    slider_chunk_overlap = gr.Slider(minimum=10, maximum=200, value=40, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True)
+            with gr.Row():
+                db_progress = gr.Textbox(label="Vector database initialization", value="None")
+            with gr.Row():
+                db_btn = gr.Button("Generate vector database")
+        
+        with gr.Tab("Step 3 - Initialize QA chain"):
+            with gr.Row():
+                llm_btn = gr.Radio(list_llm_simple, 
+                    label="LLM models", value=list_llm_simple[0], type="index", info="Choose your LLM model")
+            with gr.Accordion("Advanced options - LLM model", open=False):
+                with gr.Row():
+                    slider_temperature = gr.Slider(minimum=0.01, maximum=1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True)
+                with gr.Row():
+                    slider_maxtokens = gr.Slider(minimum=224, maximum=4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True)
+                with gr.Row():
+                    slider_topk = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True)
+            with gr.Row():
+                llm_progress = gr.Textbox(value="None", label="QA chain initialization")
+            with gr.Row():
+                qachain_btn = gr.Button("Initialize Question Answering chain")
+
+        with gr.Tab("Step 4 - LlamaIndex parsing"):
+            with gr.Row():
+                llama_index_btn = gr.Button("Parse with LlamaIndex")
+            with gr.Row():
+                llama_index_progress = gr.Textbox(label="LlamaIndex parsing status", value="None")
+
+        with gr.Tab("Step 5 - Chatbot"):
+            chatbot = gr.Chatbot(height=300)
+            with gr.Accordion("Advanced - Document references", 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="Type message (e.g. 'What is this document about?')", container=True)
+            with gr.Row():
+                submit_btn = gr.Button("Submit message")
+                clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation")
+            
+        # Preprocessing events
+        db_btn.click(initialize_database, 
+            inputs=[document, slider_chunk_size, slider_chunk_overlap], 
+            outputs=[vector_db, collection_name, 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)
+        llama_index_btn.click(initialize_llama_index, 
+            inputs=[document], 
+            outputs=[llama_index_engine, llama_index_progress])
+
+        # 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)
+    demo.queue().launch(debug=True)
+
+
+if __name__ == "__main__":
+    demo()