daveebbelaar / langchain-experiments Goto Github PK

import json
from openai import OpenAI
from datetime import datetime, timedelta

client = OpenAI()

# --------------------------------------------------------------
# Ask ChatGPT a Question
# --------------------------------------------------------------

completion = client.chat.completions.create(
    model="gpt-3.5-turbo-0613",
    messages=[
        {
            "role": "user",
            "content": "When's the next flight from Amsterdam to New York?",
        },
    ],
)

output = completion.choices[0].message.content
print("\n")
print(output)
print("\n")

# --------------------------------------------------------------
# Use OpenAI’s Function Calling Feature
# --------------------------------------------------------------

tools = [
  {
    "type": "function",
    "function": {
      "name": "get_flight_info",
      "description": "Get flight information between two locations",
      "parameters": {
        "type": "object",
        "properties": {
          "loc_origin": {
            "type": "string",
            "description":  "The departure airport, e.g. DUS",
          },
          "loc_destination": {
                    "type": "string",
                    "description": "The destination airport, e.g. HAM",
          }
        },
        "required": ["loc_origin", "loc_destination"],
      },
    }
  }
]

user_prompt = "When's the next flight from Amsterdam to New York?"

completion = client.chat.completions.create(
    model="gpt-3.5-turbo-0613",
    messages=[{"role": "user", "content": user_prompt}],
    # Add function calling
    tools=tools,
    tool_choice="auto",  # specify the function call
)

# It automatically fills the arguments with correct info based on the prompt
# Note: the function does not exist yet

output = completion.choices[0].message.tool_calls[0]
print("\n")
print(output)
print("\n")

# --------------------------------------------------------------
# Add a Function
# --------------------------------------------------------------


def get_flight_info(loc_origin, loc_destination):
    """Get flight information between two locations."""

    # Example output returned from an API or database
    flight_info = {
        "loc_origin": loc_origin,
        "loc_destination": loc_destination,
        "datetime": str(datetime.now() + timedelta(hours=2)),
        "airline": "KLM",
        "flight": "KL643",
    }

    return json.dumps(flight_info)


# Use the LLM output to manually call the function
# The json.loads function converts the string to a Python object

origin = json.loads(output.function.arguments).get("loc_origin")
destination = json.loads(output.function.arguments).get("loc_destination")
params = json.loads(output.function.arguments)
type(params)

print("\n")
print(origin)
print(destination)
print(params)
print("\n")

# Call the function with arguments

chosen_function = eval(output.function.name)
flight = chosen_function(**params)

print("\n")
print(flight)
print("\n")

# --------------------------------------------------------------
# Add function result to the prompt for a final answer
# --------------------------------------------------------------

# The key is to add the function output back to the messages with role: function
second_completion = client.chat.completions.create(
    model="gpt-3.5-turbo-0613",
    messages=[
        {"role": "user", "content": user_prompt},
        {"role": "tool", "tool_call_id": output.id, "content": flight}, # this line seems to be the problem
    ],
    tools=tools,
)
response = second_completion.choices[0].message.content
print("\n")
print(response)
print("\n")

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/Mac/openai-env/lib/python3.12/site-packages/openai/_utils/_utils.py", line 272, in wrapper
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/Users/Mac/openai-env/lib/python3.12/site-packages/openai/resources/chat/completions.py", line 645, in create
    return self._post(
           ^^^^^^^^^^^
  File "/Users/Mac/openai-env/lib/python3.12/site-packages/openai/_base_client.py", line 1088, in post
    return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/Mac/openai-env/lib/python3.12/site-packages/openai/_base_client.py", line 853, in request
    return self._request(
           ^^^^^^^^^^^^^^
  File "/Users/Mac/openai-env/lib/python3.12/site-packages/openai/_base_client.py", line 930, in _request
    raise self._make_status_error_from_response(err.response) from None
openai.BadRequestError: Error code: 400 - {'error': {'message': "Invalid parameter: messages with role 'tool' must be a response to a preceeding message with 'tool_calls'.", 'type': 'invalid_request_error', 'param': 'messages.[1].role', 'code': None}}

Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised APIError: HTTP code 413 from API (<html>
<head><title>413 Request Entity Too Large<[/title](https://file+.vscode-resource.vscode-cdn.net/title)><[/head](https://file+.vscode-resource.vscode-cdn.net/head)>
<body>
<center><h1>413 Request Entity Too Large<[/h1](https://file+.vscode-resource.vscode-cdn.net/h1)><[/center](https://file+.vscode-resource.vscode-cdn.net/center)>
<hr><center>openresty<[/center](https://file+.vscode-resource.vscode-cdn.net/center)>
<[/body](https://file+.vscode-resource.vscode-cdn.net/body)>
<[/html](https://file+.vscode-resource.vscode-cdn.net/html)>
).