import os import openai from dotenv import load_dotenv _ = load_dotenv() # read local .env file import gradio as gr from langchain_chroma import Chroma from langchain.chains import ConversationalRetrievalChain from langchain_openai import OpenAIEmbeddings, ChatOpenAI # Custom class to handle API routing for different models class ChatOpenRouter(ChatOpenAI): openai_api_base: str openai_api_key: str model_name: str def __init__(self, model_name: str, openai_api_key: str = None, openai_api_base: str = "https://openrouter.ai/api/v1", **kwargs): openai_api_key = openai_api_key or os.getenv('OPENROUTER_API_KEY') super().__init__(openai_api_base=openai_api_base, openai_api_key=openai_api_key, model_name=model_name, **kwargs) # Initialize embedding function here embedding_function = OpenAIEmbeddings() # Updated cbfs class with dynamic database and model selection class cbfs: def __init__(self, persist_directory, model_name): self.chat_history = [] self.answer = "" self.db_query = "" self.db_response = [] self.panels = [] # Initialize Chroma and the ConversationalRetrievalChain with the chosen database and model db = Chroma(persist_directory=persist_directory, embedding_function=embedding_function) retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 3}) # Select model dynamically if model_name == "GPT-4": chosen_llm = ChatOpenAI(model_name="gpt-4-1106-preview", temperature=0) # elif model_name == "GPT-3.5": # chosen_llm = ChatOpenAI(model_name="gpt-3.5-turbo-0125", temperature=0) # elif model_name == "Llama-3 8B": # chosen_llm = ChatOpenRouter(model_name="meta-llama/llama-3-8b-instruct", temperature=0) # elif model_name == "Gemini-1.5 Pro": # chosen_llm = ChatOpenRouter(model_name="google/gemini-pro-1.5", temperature=0) # elif model_name == "Claude 3 Sonnet": # chosen_llm = ChatOpenRouter(model_name='anthropic/claude-3-sonnet', temperature=0) # elif model_name == "Claude 3.5 Sonnet": # chosen_llm = ChatOpenRouter(model_name='anthropic/claude-3.5-sonnet', temperature=0) else: # Default model # chosen_llm = ChatOpenRouter(model_name="meta-llama/llama-3-70b-instruct", temperature=0) chosen_llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0) self.qa = ConversationalRetrievalChain.from_llm( llm=chosen_llm, retriever=retriever, return_source_documents=True, return_generated_question=True, ) def convchain(self, query): if not query: return [("User", ""), ("ChatBot", "")] result = self.qa.invoke({"question": query, "chat_history": self.chat_history}) self.chat_history.append((query, result["answer"])) self.db_query = result["generated_question"] self.db_response = result["source_documents"] self.answer = result['answer'] self.panels.append(["User", query]) # Ensure this is a list of two strings self.panels.append(["ChatBot", self.answer]) # Ensure this is a list of two strings return self.panels def clr_history(self): self.chat_history = [] self.panels = [] return self.panels # Clear the chatbot display # Create Gradio interface functions def initialize_cbfs(db_choice, model_choice): """Initialize cbfs object based on the database and model selection and clear history.""" if db_choice == "Covenants": return cbfs(persist_directory='docs/doc_cov/', model_name=model_choice) elif db_choice == "Bylaws": return cbfs(persist_directory='docs/doc_byl/', model_name=model_choice) else: return None def chat_history(query, db_choice, model_choice, cb): """Handles chat submissions. Reminds the user to select a document if none is selected.""" # cb = initialize_cbfs(db_choice, model_choice) # Reinitialize cbfs if cb is None: # If cb is not initialized, remind to select a document return [("ChatBot", "Please select a document from the dropdown menu before submitting your query.")], "" else: return cb.convchain(query), "" # Clear input box by returning empty string def clear_history(cb): # cb = initialize_cbfs(db_choice, model_choice) # Reinitialize cbfs to clear history if cb is None: # Check if cbfs instance is None return [], "" # No error message, simply clear the UI components else: cb.clr_history() return [], "" # Create Gradio UI layout with gr.Blocks() as demo: # Full-width image at the top with gr.Row(): gr.Image("prloa.jpg", elem_id="full_width_image", show_label=False) # Full-width text below the image with gr.Row(): gr.Markdown("

Painted Rocks Lot-owners Association

") gr.Markdown("# PRLOA Covenants and Bylaws ChatBot") with gr.Row(): db_choice = gr.Dropdown(["Covenants", "Bylaws"], label="Select Document", scale=1) # model_choice = gr.Dropdown(["GPT-3.5", "GPT-4", "Llama-3 70B", "Llama-3 8B", "Gemini-1.5 Pro", "Claude 3 Sonnet", "Claude 3.5 Sonnet"], # label="Select Model", scale=1, value = "Llama-3 70B") model_choice = gr.Dropdown(["GPT-3.5", "GPT-4"], label="Select Model", scale=1, value = "GPT-3.5") button_clearhistory = gr.Button("Clear History", scale=1) with gr.Row(): inp = gr.Textbox(placeholder="Enter text here…", scale=8) button_submit = gr.Button("Submit", scale=1) output = gr.Chatbot() # Initialize cbfs instance cbfs_instance = gr.State(initialize_cbfs(db_choice.value, model_choice.value)) # Update cbfs_instance and clear chat history when the dropdown values change def update_cbfs_and_clear_history(db_choice, model_choice): new_cbfs = initialize_cbfs(db_choice, model_choice) if new_cbfs: new_cbfs.clr_history() return new_cbfs, [], "" # Clear the chatbot display and input box db_choice.change( fn=update_cbfs_and_clear_history, inputs=[db_choice, model_choice], outputs=[cbfs_instance, output, inp] ) model_choice.change( fn=update_cbfs_and_clear_history, inputs=[db_choice, model_choice], outputs=[cbfs_instance, output, inp] ) # Define interactions for both submit button and Enter key inp.submit(fn=chat_history, inputs=[inp, db_choice, model_choice, cbfs_instance], outputs=[output, inp]) button_submit.click(fn=chat_history, inputs=[inp, db_choice, model_choice, cbfs_instance], outputs=[output, inp]) button_clearhistory.click(fn=clear_history, inputs=cbfs_instance, outputs=[output, inp]) # Launch the Gradio app demo.launch()