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| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.vectorstores import Chroma | |
| from langchain.agents import AgentExecutor, Tool | |
| from langchain.chains import RetrievalQAWithSourcesChain | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.memory import ConversationBufferMemory, ConversationSummaryBufferMemory | |
| from langchain.utilities import WikipediaAPIWrapper | |
| from langchain.agents import initialize_agent, AgentType | |
| import gradio as gr | |
| # Add presets for Gradio theme | |
| from app_modules.presets import * | |
| import os | |
| os.environ["OPENAI_API_KEY"] = os.environ["OPENAI_TOKEN"] | |
| # Define the LLM chat model | |
| #model = 'gpt-3.5-turbo' | |
| model = 'gpt-3.5-turbo-16k' | |
| #model = 'gpt-4' | |
| token_limit = 4000 if model == 'gpt-3.5-turbo' else 16000 | |
| memory_token_limit = token_limit//2 | |
| temperature = 0 | |
| llm = ChatOpenAI(temperature=temperature, model=model) | |
| # Load existing vectorstore | |
| persist_dir = "./chroma" | |
| embeddings = OpenAIEmbeddings() | |
| vectorstore = Chroma(persist_directory=persist_dir, embedding_function=embeddings) | |
| vectorstore.persist() | |
| # Create Retrieval Chain with sources | |
| ## It returns a dictionary with at least the 'answer' and the 'sources' as metadata if return_source_documents=True | |
| qa = RetrievalQAWithSourcesChain.from_chain_type( | |
| llm=llm, | |
| chain_type="stuff", | |
| retriever=vectorstore.as_retriever(search_type="mmr"), | |
| #retriever=vectorstore.as_retriever(), | |
| #return_source_documents=True, | |
| max_tokens_limit=token_limit | |
| ) | |
| # Define tools | |
| wikipedia = WikipediaAPIWrapper() | |
| tools = [ | |
| Tool( | |
| name="GBV Q&A Bot System", | |
| #func=qa, | |
| func=lambda question: qa({"question": question}, return_only_outputs=True), | |
| description="Useful for when you need to answer questions about the aspects asked. Input may be a partial or fully formed question.", | |
| #return_direct=True, # use the agent as a router and directly return the result | |
| ), | |
| Tool( | |
| name='Wikipedia', | |
| func=wikipedia.run, | |
| description='You must only use this tool if you cannot find answers with the other tools. Useful for when you need to look for answers in the Wikipedia.' | |
| ) | |
| ] | |
| # Create Conversational Buffer Memory | |
| #memory = ConversationBufferMemory(memory_key="chat_history", input_key='input', output_key="output", return_messages=True) | |
| # Create Conversational Summary Buffer Memory | |
| memory = ConversationSummaryBufferMemory(llm=llm, memory_key="chat_history", input_key='input', output_key="output", return_messages=True, max_token_limit=memory_token_limit) | |
| # Initialize Re-Act agent and create Agent Executor Chain | |
| react = initialize_agent(tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, max_iterations=3, early_stopping_method='generate', memory=memory) | |
| agent_chain = AgentExecutor.from_agent_and_tools( | |
| agent=react.agent, tools=tools, verbose=True, memory=memory, return_intermediate_steps=True, return_source_documents=False, handle_parsing_errors=True) | |
| # Add custom CSS | |
| with open("assets/custom.css", "r", encoding="utf-8") as f: | |
| customCSS = f.read() | |
| # split sources string in a source per line | |
| def split_sources(sources): | |
| split_sources = sources.split(",") | |
| new_sources= "\n".join(["* " + s.strip() for s in split_sources]) | |
| return new_sources | |
| # extract sources when applicable | |
| def get_sources(result): | |
| if result['intermediate_steps']: | |
| if result['intermediate_steps'][0][0].tool == "Wikipedia": | |
| sources = "\n\nSources: Wikipedia" | |
| elif result['intermediate_steps'][0][0].tool == '_Exception': | |
| sources = None | |
| else: | |
| sources = "\n\nSources:\n" + split_sources(result['intermediate_steps'][0][1]['sources']) | |
| else: | |
| sources = None | |
| return sources | |
| with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo: | |
| gr.Markdown( | |
| """ | |
| # 🦜🔗 Ask the GBV in Emergencies Q&A Bot! | |
| This generative model has been trained on various sources covering themes on Gender-Based Violence response in Humanitarian Settings. This AI agent might complement the replies with additional information retrieved from Wikipedia sources. You can maintain a natural language conversation with it in order to retrieve information on this area of knowledge. | |
| Example questions: | |
| - What are the GBV guiding principles? | |
| - Which UN agency leads the GBV response in emergencies? | |
| - How can we engage men and boys in GBV prevention and response? | |
| - Please outline a strategy to minimize GBV risks in a temporary settlement | |
| - What is the integration factor between GBV and SRH? | |
| """ | |
| ) | |
| # Start chatbot with welcome from bot | |
| chatbot = gr.Chatbot([(None,'How can I help you?')]).style(height=400) | |
| msg = gr.Textbox() | |
| clear = gr.ClearButton([msg, chatbot]) | |
| def user(user_message, history): | |
| return gr.update(value="", interactive=False), history + [[user_message, None]] | |
| def bot(history): | |
| user_message = history[-1][0] # get if from most recent history element | |
| #bot_message = conversation.run(user_message) | |
| #user_message = user_message + " Please provide the source documents" # to alter the prompt and provide sources | |
| response = agent_chain(user_message) | |
| sources = get_sources(response) | |
| bot_message = response['output'] if not sources else response['output'] + sources | |
| history[-1][1] = "" | |
| for character in bot_message: | |
| history[-1][1] += character | |
| #time.sleep(0.05) | |
| yield history | |
| response = msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then( | |
| bot, chatbot, chatbot | |
| ) | |
| response.then(lambda: gr.update(interactive=True), None, [msg], queue=False) | |
| demo.queue() | |
| demo.launch() |