lcel_funcall.py

prompt_system = open("C:\\Users\\koduki\\git\\ai-tuber\\src\\backend\\prompt_system.txt", "r", encoding='utf-8').read()
prompt_for_chat = ChatPromptTemplate.from_messages([
    ("system", prompt_system),
    ("user", "{input}"),
    MessagesPlaceholder(variable_name="agent_scratchpad"),
]).partial(format_instructions=parser.get_format_instructions())

prompt_for_tools = ChatPromptTemplate.from_messages([
    ("system", "You are agentai"),
    ("user", "{input}"),
])

tools = [weather_tool.weather_api, short_talk_tool.talk]

llm = ChatOpenAI(temperature=0, model='gpt-4-0613')
llm_with_tools = ChatOpenAI(temperature=0, model='gpt-3.5-turbo').bind(functions=[format_tool_to_openai_function(t) for t in tools])

agent = {
    "input": lambda x: x["input"],
    "agent_scratchpad": lambda x: format_to_openai_functions(x['intermediate_steps'])
} | prompt_for_chat | llm | OpenAIFunctionsAgentOutputParser()

agent_with_tools = {
    "input": lambda x: x["input"],
} | prompt_for_tools | llm_with_tools | OpenAIFunctionsAgentOutputParser()

# Agent Loop 1
intermediate_steps = []
output = agent_with_tools.invoke({
    "input": "今日の東京の天気は？",
})
print(output.tool, output.tool_input)

tool = next(x for x in tools if x.name == output.tool)
observation = tool.run(output.tool_input)
intermediate_steps.append((output, observation))

# Agent Loop 2
output = agent.invoke({
    "input": "今日の東京の天気は？",
    "intermediate_steps": intermediate_steps
})
output