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import langchain as lc |
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from langchain.chat_models import ChatOpenAI |
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from langchain.schema import HumanMessage, SystemMessage, AIMessage |
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from langchain import PromptTemplate, LLMChain, HuggingFaceHub, OpenAI, FewShotPromptTemplate |
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from langchain.prompts.example_selector import LengthBasedExampleSelector |
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from langchain.chains import ConversationChain, MapReduceChain |
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from langchain.memory import ConversationBufferMemory, ConversationSummaryBufferMemory |
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from langchain.callbacks import get_openai_callback |
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import os |
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from langchain.chains import LLMMathChain, SQLDatabaseChain |
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from langchain.agents import Tool, load_tools, initialize_agent, AgentType |
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from langchain.agents.react.base import DocstoreExplorer |
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import gradio as gr |
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def demo8(): |
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model = OpenAI(openai_api_key=os.environ['OPENAI_API_KEY']) |
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tools = load_tools(['llm-math', 'terminal'], llm=model) |
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prompt = PromptTemplate(template="{question}", input_variables=['question']) |
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llm_chain = LLMChain(llm=model, prompt=prompt) |
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llm_tool = Tool(name="Search", func=llm_chain.run, description="general QA") |
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tools.append(llm_tool) |
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memory = ConversationBufferMemory(memory_key="chat_history") |
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conversation_agent = initialize_agent(tools=tools, |
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llm=model, |
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max_iterations=3, |
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verbose=True, |
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agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, |
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memory=memory) |
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def answer(question, history=[]): |
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history.append(question) |
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resp = conversation_agent(question) |
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print(f"resp: {resp}") |
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history.append(resp['output']) |
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dial = [(u, v) for u, v in zip(history[::2], history[1::2])] |
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return { |
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chatbot: dial, |
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state: history |
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} |
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with gr.Blocks() as demo: |
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chatbot = gr.Chatbot(elem_id="chatbot") |
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state = gr.State([]) |
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with gr.Row(): |
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text = gr.Textbox(show_label=False, placeholder="enter your prompt") |
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text.submit(answer, inputs=[text, state], outputs=[chatbot, state]) |
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demo.launch() |
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if __name__ == "__main__": |
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demo8() |
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