ValueError: {'code': None, 'message': 'ModelMetaclass object argument after ** must be a mapping, not str'

Intermediate
1 
class Agent:

    def __init__(self, model, tools, system=""):
        self.system = system
        graph = StateGraph(AgentState)
        graph.add_node("llm", self.call_openai)
        graph.add_node("action", self.take_action)
        graph.add_conditional_edges(
            "llm",
            self.exists_action,
            {True: "action", False: END}
        )
        graph.add_edge("action", "llm")
        graph.set_entry_point("llm")
        self.graph = graph.compile()
        self.tools = {t.name: t for t in tools}
        self.model = model.bind_tools(tools)

    def exists_action(self, state: AgentState):
        result = state['messages'][-1]
        return len(result.tool_calls) > 0

    def call_openai(self, state: AgentState):
        messages = state['messages']
        if self.system:
            messages = [SystemMessage(content=self.system)] + messages
        message = self.model.invoke(messages)
        return {'messages': [message]}

    def take_action(self, state: AgentState):
        tool_calls = state['messages'][-1].tool_calls
        results = []
        for t in tool_calls:
            print(f"Calling: {t}")
            print("TOOL ARG" , t['args'])
            if not t['name'] in self.tools:      # check for bad tool name from LLM
                print("\n ....bad tool name....")
                result = "bad tool name, retry"  # instruct LLM to retry if bad
            else:
                search  = t['args']['query']
                print('QUERY:' , search)
                print(type(search))
                print(type(t['args']))
                result = self.tools[t['name']].invoke({'query': search})  
            results.append(ToolMessage(tool_call_id=t['id'], name=t['name'], content=str(result)))
        print("Back to the model!")
        return {'messages': results}
prompt = """You are a smart research assistant. Use the search engine to look up information. \
You are allowed to make multiple calls (either together or in sequence). \
Only look up information when you are sure of what you want. \
If you need to look up some information before asking a follow up question, you are allowed to do that!
"""

model = ChatOpenAI(model="Meta-Llama-3.3-70B-Instruct", base_url="https://api.sambanova.ai/v1")  #reduce inference cost
abot = Agent(model, [tool], system=prompt)
query = "Who won the super bowl in 2024? What is the longest river"
messages = [HumanMessage(content = query)]
result = abot.graph.invoke({"messages": messages})

I run this code. But there is an error in the final line. The error means that in part I invoke the tool, the input is not dict, str. How can I solve it? Help me!

ValueError: {‘code’: None, ‘message’: ‘ModelMetaclass object argument
after ** must be a mapping, not str’, ‘model_output’: ‘{“name”:
“tavily_search_results_json”, “parameters”: “{\“query\”: \“longest
river\”}”}’, ‘param’: None, ‘type’: ‘Invalid function calling
output.’}

1 Like
2

hi @Alanturner2

Can you please share the import part as well :slight_smile: ?

2 Likes
3 
from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated
import operator
from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage, ToolMessage
from langchain_openai import ChatOpenAI
from langchain_community.tools.tavily_search import TavilySearchResults

# from tavily import TavilyClient

# # Initialize the Tavily client with your API key
# client = TavilyClient(api_key="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")
import os

os.environ["TAVILY_API_KEY"] = "api"
os.environ["OPENAI_API_KEY"] = "api"
tool = TavilySearchResults(max_results=4)
print(type(tool))
print(tool.name)
class AgentState(TypedDict):
    messages: Annotated[list[AnyMessage], operator.add]
2 Likes
4

There seems to be a quirk in the way to use OpenAI’s FunctionCalling.

1 Like
5

I appreciate your help but I am afraid this is totally different problem.
I so sorry.

1 Like