Langgraph's Question on Function_call in OpenAI

[147258369777]

Checked other resources

• I added a very descriptive title to this issue.
• I searched the LangChain documentation with the integrated search.
• I used the GitHub search to find a similar question and didn't find it.
• I am sure that this is a bug in LangChain rather than my code.
• The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).

Example Code

prompt = hub.pull("hwchase17/openai-functions-agent")
# Choose the LLM that will drive the agent
llm = ChatOpenAI(model="gpt-4-turbo-preview", openai_api_key=openai_api_key, openai_api_base=openai_api_base)
tools = search_tools
# 可以执行Python代码
python_repl_tool = PythonREPLTool()


def create_agent(llm, tools, system_prompt):
    # 每个工作的节点agent都有一个名字和一些工具
    prompt = ChatPromptTemplate.from_messages(
        [
            (
                "system",
                system_prompt
            ),
            MessagesPlaceholder(variable_name="messages"),
            MessagesPlaceholder(variable_name="agent_scratchpad"),
        ]
    )
    agent = create_openai_tools_agent(llm, tools, prompt)
    executor = AgentExecutor(agent=agent, tools=tools)
    return executor


def agent_node(state, agent, name):
    result = agent.invoke(state)
    return {"messages": [HumanMessage(content=result["output"], name=name)]}


# 定义agent supervisor
members = ["Researcher", "Coder"]
system_prompt = (
    "You are a supervisor tasked with managing a conversation between the"
    " following workers:  {members}. Given the following user request,"
    " respond with the worker to act next. Each worker will perform a"
    " task and respond with their results and status. When finished,"
    " respond with FINISH."
)
options = ["FINISH"] + members
# 使用openAI的function_call
function_def = {
    "name": "route",
    "description": "Select the next role.",
    "parameters": {
        "title": "routeSchema",
        "type": "object",
        "properties": {
            "next": {
                "title": "Next",
                "anyOf": [
                    {"enum": options},
                ],
            }
        },
        "required": ["next"],
    },
}
supervisor_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system_prompt),
        MessagesPlaceholder(variable_name="messages"),
        (
            "system",
            "Given the conversation above, who should act next?"
            " Or should we FINISH? Select one of: {options}",
         ),
    ]
).partial(options=str(options),members=",".join(members))
supervisor_chain=supervisor_prompt \
                 | llm.bind_functions(functions=[function_def],function_call="route") \
                 | JsonOutputFunctionsParser()
# 构建图
class AgentState(TypedDict):
    # The annotation tells the graph that new messages will always
    # be added to the current states
    messages: Annotated[Sequence[BaseMessage], operator.add]
    # The 'next' field indicates where to route to next
    next: str
researche_agent=create_agent(llm,search_tools,"You are a web researcher.")
# from functools import partial
# def power(base, exponent):
#     return base ** exponent
# # 创建一个偏函数，固定base为2
# square = partial(power, base=2)
# print(square(3))  # 输出8，相当于调用了power(2, 3)
research_node=functools.partial(agent_node,agent=researche_agent,name="Researcher")
code_agent = create_agent(
    llm,
    [python_repl_tool],
    "You may generate safe python code to analyze data and generate charts using matplotlib.",
)
code_node = functools.partial(agent_node, agent=code_agent, name="Coder")
workflow = StateGraph(AgentState)
workflow.add_node("Researcher", research_node)
workflow.add_node("Coder", code_node)
workflow.add_node("supervisor", supervisor_chain)
for member in members:
    # worker执行完之后返回supervisor节点
    workflow.add_edge(member, "supervisor")
conditional_map = {k: k for k in members}
conditional_map["FINISH"] = END
workflow.add_conditional_edges("supervisor",lambda x: x["next"],conditional_map)
workflow.set_entry_point("supervisor")
graph = workflow.compile()
for s in graph.stream(
    {
        "messages": [
            HumanMessage(content="Code hello world and print it to the terminal")
        ]
    }
):
    if "__end__" not in s:
        print(s)
        print("----")

Error Message and Stack Trace (if applicable)

Error Message and Stack Trace (if applicable)

raise OutputParserException(f"Could not parse function call: {exc}")
langchain_core.exceptions.OutputParserException: Could not parse function call: 'function_call'

Description

This is a problem that I encountered while studying Langgraph's multi-agent blog. This problem not only occurs here, but also when I was studying Langgraph's Planning Agent Examples module

System Info

Latest version

[shuqingjinse]

bind_functions 未生效？

[147258369777]

bind_functions 未生效？
就挺奇怪的刚刚能用，现在又出现这种错误了

[gw00295510]

请问有办法让supervisor不止返回next的决定，同时返回做决定谁是next的思考过程吗？

Any idea how to not only let supervior return who should act next, but return the thinking steps why the next role is chosen?

[rabader]

This example uses the JsonOutputFunctionsParser:

from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser

Is the problem with the parser, or is the example in these notebooks using the wrong type of parser? I also am having issues running those example notebooks under langgraph/examples/multi_agent. I consistently get this same error.