Update app.py

app.py

@@ -20,9 +20,16 @@ import torch
 import tqdm 
 import accelerate
 
+
+# default_persist_directory = './chroma_HF/'
+
+list_llm = ["mistralai/Mistral-7B-Instruct-v0.2"]
+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 = Py PDFLoader(file_path)
+    # loader = PyPDFLoader(file_path)
     # pages = loader.load()
     loaders = [PyPDFLoader(x) for x in list_file_path]
     pages = []
@@ -35,7 +42,6 @@ def load_doc(list_file_path, chunk_size, chunk_overlap):
     doc_splits = text_splitter.split_documents(pages)
     return doc_splits
 
-
 # Create vector database
 def create_db(splits, collection_name):
     embedding = HuggingFaceEmbeddings()
@@ -49,6 +55,7 @@ def create_db(splits, collection_name):
     )
     return vectordb
 
+
 # Load vector database
 def load_db():
     embedding = HuggingFaceEmbeddings()
@@ -57,38 +64,95 @@ def load_db():
         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...")
     
     # 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
-
-    llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1"
-    llm = HuggingFaceHub(repo_id=llm_model, model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "load_in_8bit": True})
-    progress(0.75, desc="Defining buffer memory...")
+    if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
+        llm = HuggingFaceHub(
+            repo_id=llm_model, 
+            model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "load_in_8bit": True}
+        )
+    elif llm_model == "microsoft/phi-2":
+        raise gr.Error("phi-2 model requires 'trust_remote_code=True', currently not supported by langchain HuggingFaceHub...")
+        llm = HuggingFaceHub(
+            repo_id=llm_model, 
+            model_kwargs={"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 = HuggingFaceHub(
+            repo_id=llm_model, 
+            model_kwargs={"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 = HuggingFaceHub(
+            repo_id=llm_model, 
+            model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
+        )
+    else:
+        llm = HuggingFaceHub(
+            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}
+        )
     
-    memory = ConversationBufferMemory(memory_key="chat_history",output_key='answer',return_messages=True )
-
+    progress(0.75, desc="Defining buffer memory...")
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        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(llm,retriever=retriever,chain_type="stuff", memory=memory,return_source_documents=True,verbose=False,)
+    qa_chain = ConversationalRetrievalChain.from_llm(
+        llm,
+        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
 
-# Initialize database
-def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
-    
+def start(llm_model, temperature, max_tokens, top_k, vector_db, list_file_obj, chunk_size, chunk_overlap):
+    # HuggingFaceHub uses HF inference endpoints
+    # Use of trust_remote_code as model_kwargs
+    # Warning: langchain issue
+    # URL: https://github.com/langchain-ai/langchain/issues/6080
+    llm = HuggingFaceHub(repo_id=llm_model, model_kwargs={"temperature": temperature, 
+                                                          "max_new_tokens": max_tokens, 
+                                                          "top_k": top_k, 
+                                                          "load_in_8bit": True})
+    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", 
+        memory=memory,
+        # combine_docs_chain_kwargs={"prompt": your_prompt})
+        return_source_documents=True,
+        #return_generated_question=False,
+        verbose=False,
+    )
+
     # 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 = Path(list_file_path[0]).stem
     
     # Fix potential issues from naming convention
@@ -96,112 +160,21 @@ def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Pr
     collection_name = collection_name.replace(" ","-") 
     ## Limit lenght to 50 characters
     collection_name = collection_name[:50]
-    
     ## Enforce start and end as alphanumeric character
     if not collection_name[0].isalnum():
         collection_name[0] = 'A'
     if not collection_name[-1].isalnum():
         collection_name[-1] = 'Z'
-    
     # print('list_file_path: ', list_file_path)
     print('Collection name: ', collection_name)
-    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()):
-    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:
-        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
-    # print ('chat response: ', response_answer)
-    # print('DB source', response_sources)
+
+    
+    return qa_chain, vector_db, collection_name
+
     
-    # 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 demo():
-    with gr.Blocks(theme="base") as demo:
-        vector_db = gr.State()
-        qa_chain = gr.State()
-        collection_name = gr.State()
-
-        gr.Markdown("""RAG USING MIXTRAL""")
-
-        with gr.Row():
-            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", container=True)
-            with gr.Row():
-                submit_btn = gr.Button("Submit")
-                clear_btn = gr.ClearButton([msg, chatbot])
-
-        # Chatbot events
-        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)
-
-    # Replace the placeholders with your actual functions
-    def conversation(*args, **kwargs):
-        pass
-
-
-
-