update
Browse files
app.py
CHANGED
@@ -11,45 +11,7 @@ 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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def conversation_agent():
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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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resp = conversation_agent("what is (4.5*2.1)^2.2?")
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print(resp)
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# resp = conversation_agent("if Mary has four apples and Giorgio brings two and a half apple "
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# "boxes (apple box contains eight apples), how many apples do we "
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# "have?")
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# print(resp)
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resp = conversation_agent("what is the capital of Norway?")
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print(resp)
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resp = conversation_agent("what's the most famous landmark of this city")
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print(resp)
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resp = conversation_agent("free -h")
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print(resp)
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print("--------")
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print(conversation_agent.agent.llm_chain.prompt.template)
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from langchain.agents.react.base import DocstoreExplorer
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from langchain.docstore import Wikipedia
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import gradio as gr
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@@ -77,7 +39,10 @@ def demo8():
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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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with gr.Blocks() as demo:
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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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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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