Problem with function calling (GPT4)

[SKalide]

Hello.
I am using langchain with the gpt4 model. My idea is to ask the model for a product and it should use a function to search it in a database. For example I am asking for a dell laptop (or what so ever) and the model should use getProducts to generate the query. The answer is then streamed to me.

In most cases this works fine, but sometimes there is a bug or something. Instead of using function calling it gives me back the answer:
"#assistant to=functions.getProducts<|im_sep|>{
"category": "Waschmaschine",
"brand": "Bosch",
"facets": "7 kg, 8 kg"
}

Or something like this. Any one a idea why this is happening? Is this a problem with langchain or openAI?

For some context here my last example (it is german, but you get the idea. I just translated it)

Q: I am looking for a washing machine from Bosch or Miele for a 3-person household.
A: [...] to cover laundry needs. Let's see what we can find in our database for Bosch and Miele washing machines with this capacity.

I will start a search now and then make three suitable suggestions for you. Please give me a moment.🔎👍

#assistant to=functions.getProducts<|im_sep|>{
"category": "Waschmaschine",
"brand": "Bosch",
"facets": "7 kg, 8 kg"
}
Here are some Bosch washing machines that might meet your requirements:
[...]

[dosubot[bot]]

🤖

Hello,

Thank you for using LangChain and for your detailed question.

LangChain does support function calling within the language model's responses. The issue you're experiencing seems to be related to the way the language model is interpreting and executing the function call.

The function call should be executed and the result should be returned as part of the model's response. However, in your case, it seems like the function call is being returned as a string instead of being executed. This could be due to a number of reasons, such as an error in the function definition, an issue with the way the function is being called, or a problem with the language model itself.

To help troubleshoot this issue, could you please provide the following additional information:

1. The exact code you're using to call the function within the language model's response. This will help us understand how the function is being called and if there are any issues with the function call.

2. Any error messages or logs that you're seeing when the issue occurs. This will help us identify if there are any errors in the function definition or execution.

3. The version of LangChain and the GPT-4 model you're using. This will help us determine if the issue is related to a specific version of LangChain or the GPT-4 model.

Once we have this information, we'll be able to better assist you in resolving this issue.

Best regards,
Dosu

Sources

• libs/langchain/langchain/chains/api/base.py
• libs/langchain/langchain/chains/qa_with_sources/map_reduce_prompt.py
• libs/langchain/langchain/evaluation/qa/generate_prompt.py

---

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[SKalide]

A response with function call should look like this:
[[ChatGeneration(text='', generation_info=None, message=AIMessage(content='', additional_kwargs={'function_call': {'name': 'getProducts', 'arguments': '{\n "category": "laptop",\n "facets": "17 zoll",\n "price": "<1200"\n}'}}, example=False))]]

An answer with text and function call like this:
[[ChatGeneration(text='Okay, du suchst also nach einem Laptop mit einem starken Prozessor und 17 Zoll Display unter 1200€. Lass mich mal schauen, was ich für dich finden kann. 😊', generation_info=None, message=AIMessage(content='Okay, du suchst also nach einem Laptop mit einem starken Prozessor und 17 Zoll Display unter 1200€. Lass mich mal schauen, was ich für dich finden kann. 😊', additional_kwargs={'function_call': {'name': 'getProducts', 'arguments': '{\n "category": "laptop",\n "facets": "17 zoll, starker prozessor",\n "price": "<1200"\n}'}}, example=False))]]

This is the answer with "wrong" function calling:

[[ChatGeneration(text=' [...] Vorschläge machen. Gib mir bitte einen Moment.🔎👍\n\n#assistant to=functions.getProducts<|im_sep|>{\n "category": "Waschmaschine",\n "brand": "Bosch",\n "facets": "7 kg, 8 kg"\n}\nHier sind einige Bosch Waschmaschinen, die deinen Anforderungen entsprechen könnten:\n\n1. [Bosch Serie 6 WAU28R90] [...] was wir bei Miele finden.\n\n#assistant to=functions.getProducts<|im_sep|>{\n "category": "Waschmaschine",\n "brand": "Miele",\n "facets": "7 kg, 8 kg"\n}', generation_info=None, message=AIMessage(content='Für einen 3-Personen-Haushalt würde [....] \n}', additional_kwargs={}, example=False))]]

So function call has to be in "additional_kwargs". This is how I fetch the result:

self.llm = ChatOpenAI(temperature=0.5, model_name=model, streaming=True,
                              callbacks=[MyCallbackHandler(send_token, send_end_of_token, cid)])

[...]
result = self.llm.predict_messages(messages_chat, functions=func)

function_call = result.additional_kwargs.get('function_call')
arguments = function_call.get('arguments')
if arguments is not None:
                arguments_dict = json.loads(arguments)
                category = "" if arguments_dict.get('category') is None else arguments_dict.get('category')
                facets = "" if arguments_dict.get('facets') is None else arguments_dict.get('facets')
                price = "" if arguments_dict.get('price') is None else arguments_dict.get('price')
                brand = "" if arguments_dict.get('brand') is None else arguments_dict.get('brand')
                product_name = "" if arguments_dict.get('product name') is None else arguments_dict.get('product name')
                search_info = f"category: {category}, facets: {facets}, price: {price}, brand: {brand}, name: {product_name}"

                parts = [brand, facets, product_name, category]
                searchquery = ' '.join(part for part in parts if part)

                self.data = get_products(searchquery, price)
                if not self.data:
                    self.data = "Keine passenden Produkte gefunden. Suche anpassen."

                print("Anzahl Wörter: ", len(self.data.split()))

                messages_chat.append(ChatMessage(role='function',
                                                 additional_kwargs={'name': function_call['name']},
                                                 content=self.data
                                                 ))
                function_call = None
                self.data = ""
                try:
                    result = self.predict(messages_chat, functions)`

LangChain: 0.0.205
GPT: gpt-4-0613

I would guess, it is a problem with the model itself?

[dosubot[bot]]

🤖

Based on the context provided, it seems like the issue you're experiencing is related to how the language model is interpreting the prompts. If the prompts contain function calls, the model might interpret them as part of the text to be generated, rather than as instructions to execute a function. This is why the function calls are returned as strings in the text of the response.

To resolve this issue, you could modify the way you provide the prompts to the model. Instead of including function calls in the prompts, you could call the functions separately and provide the results as input to the model.

For example, instead of doing this:

prompts = ["call_function('arg1', 'arg2')"]
result = langchain.generate(prompts)

You could do this:

result_of_function = call_function('arg1', 'arg2')
prompts = [result_of_function]
result = langchain.generate(prompts)

This way, the function is called and its result is used as the prompt for the language model, rather than the function call itself being interpreted as part of the text to be generated.

Additionally, there are two similar issues that have been solved in the LangChain repository that might be helpful:

1. OpenAI function call is not a FunctionMessage type: This issue suggests that the output of the LangChain model is not a FunctionMessage type when making a function call because the convert_dict_to_message function in the provided code checks the "role" field of the input dictionary. If the "role" is "assistant" and there is a "function_call" field in the dictionary, it creates an AIMessage with the "function_call" included in the additional_kwargs field, not a FunctionMessage.

2. PromptLayerChatOpenAI does not support the newest function calling feature: This issue was closed by a pull request: Modified PromptLayerChatOpenAI class to support function call on July 06, 2023

I hope this helps! If you have any other questions, feel free to ask.

Sources

• OpenAI function call is not a FunctionMessage type
• PromptLayerChatOpenAI does not support the newest function calling feature
• libs/langchain/langchain/llms/base.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[benm5678]

@SKalide We see the same issue periodically (gpt4 v0613).. did you find a solution?

[SKalide]

Problem was with the specific GPT model. A newer model solved the problem. Must be some sort of bad or incorrect fine tuning in function calling or something like that. If you want to keep your model, you could try to fine tune it on function calling. But for me GPT-4o model workeld better (and is cheaper).