Update app.py

app.py

@@ -5,12 +5,10 @@ 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_huggingface import HuggingFaceEmbeddings
 from langchain_community.llms import HuggingFacePipeline
 from langchain.chains import ConversationChain
 from langchain.memory import ConversationBufferMemory
-#from langchain_community.llms import HuggingFaceEndpoint
 from langchain_huggingface import HuggingFaceEndpoint
 
 from pathlib import Path
@@ -24,28 +22,16 @@ import tqdm
 import accelerate
 import re
 
-# default_persist_directory = './chroma_HF/'
-list_llm = ["mistralai/Mistral-7B-Instruct-v0.2", "mistralai/Mixtral-8x7B-Instruct-v0.1",
-            "mistralai/Mistral-7B-Instruct-v0.1", \
-            "google/gemma-7b-it", "google/gemma-2b-it", \
-            "HuggingFaceH4/zephyr-7b-beta", "HuggingFaceH4/zephyr-7b-gemma-v0.1", \
-            "meta-llama/Llama-2-7b-chat-hf", "microsoft/phi-2", \
-            "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "mosaicml/mpt-7b-instruct", "tiiuae/falcon-7b-instruct", \
-            "google/flan-t5-xxl"
-            ]
-list_llm_simple = [os.path.basename(llm) for llm in list_llm]
+# LLM model to use
+llm_model = "mistralai/Mistral-7B-Instruct-v0.2"
 
 
 # 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)
@@ -56,14 +42,12 @@ def load_doc(list_file_path, chunk_size, chunk_overlap):
 # Create vector database
 def create_db(splits, collection_name):
     embedding = HuggingFaceEmbeddings()
-    #new_client = chromadb.EphemeralClient()
     new_client = chromadb.PersistentClient()
     vectordb = Chroma.from_documents(
         documents=splits,
         embedding=embedding,
         client=new_client,
         collection_name=collection_name,
-        # persist_directory=default_persist_directory
     )
     return vectordb
 
@@ -72,93 +56,20 @@ def create_db(splits, collection_name):
 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-v0.1":
-        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,
-            load_in_8bit=True,
-        )
-    elif llm_model in ["HuggingFaceH4/zephyr-7b-gemma-v0.1", "mosaicml/mpt-7b-instruct"]:
-        raise gr.Error("LLM model is too large to be loaded automatically on free inference endpoint")
-        llm = HuggingFaceEndpoint(
-            repo_id=llm_model,
-            temperature=temperature,
-            max_new_tokens=max_tokens,
-            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,
-            trust_remote_code=True,
-            torch_dtype="auto",
-        )
-    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,
-        )
-    elif llm_model == "meta-llama/Llama-2-7b-chat-hf":
-        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,
-        )
-    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,
-        )
+    llm = HuggingFaceEndpoint(
+        repo_id=llm_model,
+        temperature=temperature,
+        max_new_tokens=max_tokens,
+        top_k=top_k,
+        load_in_8bit=True,
+    )
 
     progress(0.75, desc="Defining buffer memory...")
     memory = ConversationBufferMemory(
@@ -166,7 +77,6 @@ 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(
@@ -174,9 +84,7 @@ 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!")
@@ -184,24 +92,14 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, pr
 
 
 # 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():
@@ -213,27 +111,20 @@ def create_collection_name(filepath):
 
 # 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)
+def initialize_LLM(llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
+    print("LLM model: ", llm_model)
+    qa_chain = initialize_llmchain(llm_model, llm_temperature, max_tokens, top_k, vector_db, progress)
     return qa_chain, "Complete!"
 
 
@@ -247,9 +138,6 @@ def format_chat_history(message, 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:
@@ -258,16 +146,10 @@ 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
 
@@ -277,8 +159,6 @@ def upload_file(file_obj):
     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
 
 
@@ -293,7 +173,7 @@ def demo():
             <h3>Ask any questions about your PDF documents</h3>""")
         gr.Markdown(
             """<b>Note:</b> This AI assistant, using Langchain and open-source LLMs, performs retrieval-augmented generation (RAG) from your PDF documents. \
-            The user interface explicitely shows multiple steps to help understand the RAG workflow. 
+            The user interface explicitly shows multiple steps to help understand the RAG workflow. 
             This chatbot takes past questions into account when generating answers (via conversational memory), and includes document references for clarity purposes.<br>
             <br><b>Warning:</b> This space uses the free CPU Basic hardware from Hugging Face. Some steps and LLM models used below (free inference endpoints) can take some time to generate a reply.
             """)
@@ -302,7 +182,6 @@ def demo():
             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():
@@ -322,19 +201,14 @@ def demo():
 
         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)
+                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():
@@ -358,13 +232,11 @@ def demo():
                 submit_btn = gr.Button("Submit message")
                 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])
         qachain_btn.click(initialize_LLM, \
-                          inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], \
+                          inputs=[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,
@@ -372,7 +244,6 @@ def demo():
                                                                           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,
@@ -392,4 +263,4 @@ def demo():
 
 
 if __name__ == "__main__":
-    demo()
+    demo()