update

app.py

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+import langchain as lc
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import HumanMessage, SystemMessage, AIMessage
+from langchain import PromptTemplate, LLMChain, HuggingFaceHub, OpenAI, FewShotPromptTemplate
+from langchain.prompts.example_selector import LengthBasedExampleSelector
+from langchain.chains import ConversationChain, MapReduceChain
+from langchain.memory import ConversationBufferMemory, ConversationSummaryBufferMemory
+from langchain.callbacks import get_openai_callback
+
+import os
+
+from langchain.chains import LLMMathChain, SQLDatabaseChain
+from langchain.agents import Tool, load_tools, initialize_agent, AgentType
+
+def conversation_agent():
+    model = OpenAI(openai_api_key=os.environ['OPENAI_API_KEY'])
+    tools = load_tools(['llm-math', 'terminal'], llm=model)
+
+    prompt = PromptTemplate(template="{question}", input_variables=['question'])
+    llm_chain = LLMChain(llm=model, prompt=prompt)
+    llm_tool = Tool(name="Search", func=llm_chain.run, description="general QA")
+    tools.append(llm_tool)
+
+    memory = ConversationBufferMemory(memory_key="chat_history")
+    conversation_agent = initialize_agent(tools=tools,
+                                          llm=model,
+                                       max_iterations=3,
+                                       verbose=True,
+                                       agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
+                                       memory=memory)
+    resp = conversation_agent("what is (4.5*2.1)^2.2?")
+    print(resp)
+
+    # resp = conversation_agent("if Mary has four apples and Giorgio brings two and a half apple "
+    #             "boxes (apple box contains eight apples), how many apples do we "
+    #             "have?")
+    # print(resp)
+
+    resp = conversation_agent("what is the capital of Norway?")
+    print(resp)
+
+    resp = conversation_agent("what's the most famous landmark of this city")
+    print(resp)
+
+    resp = conversation_agent("free -h")
+    print(resp)
+
+    print("--------")
+    print(conversation_agent.agent.llm_chain.prompt.template)
+
+from langchain.agents.react.base import DocstoreExplorer
+from langchain.docstore import Wikipedia
+
+import gradio as gr 
+
+def demo8():
+
+    model = OpenAI(openai_api_key=os.environ['OPENAI_API_KEY'])
+    tools = load_tools(['llm-math', 'terminal'], llm=model)
+
+    prompt = PromptTemplate(template="{question}", input_variables=['question'])
+    llm_chain = LLMChain(llm=model, prompt=prompt)
+    llm_tool = Tool(name="Search", func=llm_chain.run, description="general QA")
+    tools.append(llm_tool)
+
+    memory = ConversationBufferMemory(memory_key="chat_history")
+    conversation_agent = initialize_agent(tools=tools,
+                                          llm=model,
+                                       max_iterations=3,
+                                       verbose=True,
+                                       agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
+                                       memory=memory)
+    # resp = conversation_agent("what is (4.5*2.1)^2.2?")
+    def answer(question, history=[]):
+        history.append(question)
+        resp = conversation_agent(question)
+        print(f"resp: {resp}")
+        history.append(resp)
+
+
+    with gr.Blocks() as demo:
+        chatbot = gr.Chatbot(elem_id="chatbot")
+        state = gr.State([])
+
+        with gr.Row():
+            text = gr.Textbox(show_label=False, placeholder="enter your prompt")
+        text.submit(answer, inputs=[text, state], outputs=[chatbot, state])
+    
+    demo.launch()
+
+if __name__ == "__main__":
+    demo8()

requirements.txt

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+openai
+langchain
+gradio