Update app.py

app.py

@@ -22,7 +22,29 @@ import tqdm
 import accelerate
 import re
 
+# LlamaParse import
+from llama_parse import LlamaParse
+import asyncio
+from llama_index.core.async_utils import DEFAULT_NUM_WORKERS, run_jobs
+from llama_index.core.base.response.schema import PydanticResponse
+from llama_index.core.bridge.pydantic import BaseModel, Field, ValidationError
+from llama_index.core.callbacks.base import CallbackManager
+from llama_index.core.llms.llm import LLM
+from llama_index.core.node_parser.interface import NodeParser
+from llama_index.core.schema import BaseNode, Document, IndexNode, TextNode
+from llama_index.core.utils import get_tqdm_iterable
 
+from io import StringIO
+from typing import Any, Callable, List, Optional
+
+import pandas as pd
+from llama_index.core.node_parser.relational.base_element import (
+#     BaseElementNodeParser,
+    Element,
+)
+from llama_index.core.schema import BaseNode, TextNode
+
+# Implementations
 
 # default_persist_directory = './chroma_HF/'
 list_llm = ["mistralai/Mistral-7B-Instruct-v0.3", "mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mistral-7B-Instruct-v0.1", \
@@ -36,21 +58,14 @@ list_llm_simple = [os.path.basename(llm) for llm in list_llm]
 
 # Load PDF document and create doc splits
 def load_doc(list_file_path, chunk_size, chunk_overlap):
-    # 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 = 600, chunk_overlap = 50)
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size = chunk_size, 
-        chunk_overlap = chunk_overlap)
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size = chunk_size, chunk_overlap = chunk_overlap)
     doc_splits = text_splitter.split_documents(pages)
     return doc_splits
 
-
 # Create vector database
 def create_db(splits, collection_name):
     embedding = HuggingFaceEmbeddings()
@@ -60,52 +75,25 @@ def create_db(splits, collection_name):
         embedding=embedding,
         client=new_client,
         collection_name=collection_name,
-        # persist_directory=default_persist_directory
     )
     return vectordb
 
-
 # Load vector database
 def load_db():
     embedding = HuggingFaceEmbeddings()
     vectordb = Chroma(
-        # persist_directory=default_persist_directory, 
         embedding_function=embedding)
     return vectordb
 
-
 # Initialize langchain LLM chain
 def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
     progress(0.1, desc="Initializing HF tokenizer...")
-    # HuggingFacePipeline uses local model
-    # Note: it will download model locally...
-    # tokenizer=AutoTokenizer.from_pretrained(llm_model)
-    # progress(0.5, desc="Initializing HF pipeline...")
-    # pipeline=transformers.pipeline(
-    #     "text-generation",
-    #     model=llm_model,
-    #     tokenizer=tokenizer,
-    #     torch_dtype=torch.bfloat16,
-    #     trust_remote_code=True,
-    #     device_map="auto",
-    #     # max_length=1024,
-    #     max_new_tokens=max_tokens,
-    #     do_sample=True,
-    #     top_k=top_k,
-    #     num_return_sequences=1,
-    #     eos_token_id=tokenizer.eos_token_id
-    #     )
-    # llm = HuggingFacePipeline(pipeline=pipeline, model_kwargs={'temperature': temperature})
     
-    # HuggingFaceHub uses HF inference endpoints
     progress(0.5, desc="Initializing HF Hub...")
-    # Use of trust_remote_code as model_kwargs
-    # Warning: langchain issue
-    # URL: https://github.com/langchain-ai/langchain/issues/6080
+    
     if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.3":
         llm = HuggingFaceEndpoint(
             repo_id=llm_model, 
-            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "load_in_8bit": True}
             temperature = temperature,
             max_new_tokens = max_tokens,
             top_k = top_k,
@@ -120,10 +108,8 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, pr
             top_k = top_k,
         )
     elif llm_model == "microsoft/phi-2":
-        # raise gr.Error("phi-2 model requires 'trust_remote_code=True', currently not supported by langchain HuggingFaceHub...")
         llm = HuggingFaceEndpoint(
             repo_id=llm_model, 
-            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"}
             temperature = temperature,
             max_new_tokens = max_tokens,
             top_k = top_k,
@@ -133,7 +119,6 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, pr
     elif llm_model == "TinyLlama/TinyLlama-1.1B-Chat-v1.0":
         llm = HuggingFaceEndpoint(
             repo_id=llm_model, 
-            # model_kwargs={"temperature": temperature, "max_new_tokens": 250, "top_k": top_k}
             temperature = temperature,
             max_new_tokens = 250,
             top_k = top_k,
@@ -142,7 +127,6 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, pr
         raise gr.Error("Llama-2-7b-chat-hf model requires a Pro subscription...")
         llm = HuggingFaceEndpoint(
             repo_id=llm_model, 
-            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
             temperature = temperature,
             max_new_tokens = max_tokens,
             top_k = top_k,
@@ -150,8 +134,6 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, pr
     else:
         llm = HuggingFaceEndpoint(
             repo_id=llm_model, 
-            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"}
-            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
             temperature = temperature,
             max_new_tokens = max_tokens,
             top_k = top_k,
@@ -163,7 +145,6 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, pr
         output_key='answer',
         return_messages=True
     )
-    # retriever=vector_db.as_retriever(search_type="similarity", search_kwargs={'k': 3})
     retriever=vector_db.as_retriever()
     progress(0.8, desc="Defining retrieval chain...")
     qa_chain = ConversationalRetrievalChain.from_llm(
@@ -171,34 +152,21 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, pr
         retriever=retriever,
         chain_type="stuff", 
         memory=memory,
-        # combine_docs_chain_kwargs={"prompt": your_prompt})
         return_source_documents=True,
-        #return_generated_question=False,
         verbose=False,
     )
     progress(0.9, desc="Done!")
     return qa_chain
 
-
 # Generate collection name for vector database
-#  - Use filepath as input, ensuring unicode text
 def create_collection_name(filepath):
-    # Extract filename without extension
     collection_name = Path(filepath).stem
-    # Fix potential issues from naming convention
-    ## Remove space
     collection_name = collection_name.replace(" ","-") 
-    ## ASCII transliterations of Unicode text
     collection_name = unidecode(collection_name)
-    ## Remove special characters
-    #collection_name = re.findall("[\dA-Za-z]*", collection_name)[0]
     collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
-    ## Limit length to 50 characters
     collection_name = collection_name[:50]
-    ## Minimum length of 3 characters
     if len(collection_name) < 3:
         collection_name = collection_name + 'xyz'
-    ## Enforce start and end as alphanumeric character
     if not collection_name[0].isalnum():
         collection_name = 'A' + collection_name[1:]
     if not collection_name[-1].isalnum():
@@ -207,46 +175,34 @@ def create_collection_name(filepath):
     print('Collection name: ', collection_name)
     return collection_name
 
-
 # Initialize database
 def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
-    # Create list of documents (when valid)
     list_file_path = [x.name for x in list_file_obj if x is not None]
-    # Create collection_name for vector database
     progress(0.1, desc="Creating collection name...")
     collection_name = create_collection_name(list_file_path[0])
     progress(0.25, desc="Loading document...")
-    # Load document and create splits
     doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
-    # Create or load vector database
     progress(0.5, desc="Generating vector database...")
-    # global vector_db
     vector_db = create_db(doc_splits, collection_name)
     progress(0.9, desc="Done!")
     return vector_db, collection_name, "Complete!"
 
-
 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, "Complete!"
 
-
 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
-    
 
 def conversation(qa_chain, message, history):
     formatted_chat_history = format_chat_history(message, history)
-    #print("formatted_chat_history",formatted_chat_history)
    
-    # Generate response using QA chain
     response = qa_chain({"question": message, "chat_history": formatted_chat_history})
     response_answer = response["answer"]
     if response_answer.find("Helpful Answer:") != -1:
@@ -255,34 +211,40 @@ def conversation(qa_chain, message, history):
     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
-    # print ('chat response: ', response_answer)
-    # print('DB source', response_sources)
     
-    # Append user message and response to chat history
     new_history = history + [(message, response_answer)]
-    # return gr.update(value=""), new_history, response_sources[0], response_sources[1] 
     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)
-    # print(file_path)
-    # initialize_database(file_path, progress)
     return list_file_path
 
+# Initialize LlamaIndex parsing
+def initialize_llama_index(file_obj):
+    documents = LlamaParse(result_type="markdown",api_key=secret_value_0).load_data(file_obj.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)
+    index_with_obj = VectorStoreIndex(nodes=base_nodes+objects)
+    index_ret = index_with_obj.as_retriever(top_k=15)
+    recursive_query_engine = RetrieverQueryEngine.from_args(index_ret, node_postprocessors=[FlagEmbeddingReranker(
+        top_n=5,
+        model="BAAI/bge-reranker-large",
+    )], verbose=False)
+    return recursive_query_engine, "LlamaIndex parsing complete"
 
 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>
@@ -295,7 +257,6 @@ def demo():
         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)")
-                # upload_btn = gr.UploadButton("Loading document...", height=100, file_count="multiple", file_types=["pdf"], scale=1)
         
         with gr.Tab("Step 2 - Process document"):
             with gr.Row():
@@ -309,7 +270,7 @@ def demo():
                 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, \
@@ -326,7 +287,13 @@ def demo():
             with gr.Row():
                 qachain_btn = gr.Button("Initialize Question Answering chain")
 
-        with gr.Tab("Step 4 - Chatbot"):
+        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():
@@ -345,7 +312,6 @@ def demo():
                 clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation")
             
         # Preprocessing events
-        #upload_btn.upload(upload_file, inputs=[upload_btn], outputs=[document])
         db_btn.click(initialize_database, \
             inputs=[document, slider_chunk_size, slider_chunk_overlap], \
             outputs=[vector_db, collection_name, db_progress])
@@ -355,6 +321,9 @@ def demo():
             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, \