GPT magic functions

gpt.py

import codecs
from IPython import get_ipython  # type: ignore
from IPython.core.magic import register_line_cell_magic
from IPython.display import clear_output, display, Markdown, update_display  # type: ignore
from openai import OpenAI
from openai.types.chat import ChatCompletionMessageParam
from openai.types.chat.completion_create_params import Function
import os
import re
import requests
import tiktoken
import time
from typing import Any, cast, Optional


client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))


run_description = """When you send a message containing Python code to run, it will be executed in a
stateful Jupyter notebook environment.
You can use this to execute any Python code and complete user requests.
Print, plot or display() anything you want the user to see in the notebook output.
Don't import any modules that have already been imported.
"""

functions: list[Function] = [
    {
        "name": "run",
        "description": run_description,
        "parameters": {
            "type": "object",
            "properties": {
                "code": {
                    "type": "string",
                    "description": "The Python code to run.",
                },
            },
            "required": ["code"],
        },
    },
]

system_messages: list[ChatCompletionMessageParam] = [
    {
        "role": "system",
        "content": r"Use dollar signs instead of brackets and parentheses for inline or block math expressions. For example: $ \sigma $ or $$ \sqrt{x_i} $$",
    },
    # {
    #     "role": "system",
    #     "content": f"Be sure to call run() to fulfill user requests if necessary.",
    # },
]


def get_globals() -> dict[str, Any]:
    return get_ipython().user_ns  # type: ignore


def get_plain_text(output: dict[str, Any]) -> str:
    for item in output["items"]:
        if item["mime"] == "text/plain":
            return item["data"]
    for item in output["items"]:
        if item["mime"] == "text/markdown":
            return item["data"]
    return output["items"][0]["data"]


def get_source(content: str) -> str:
    content = content.replace("with_column(", "with_columns(")
    if is_code_block(content):
        return content[10:-4]
    match = re.search(r'"code":\s*"((?:[^"\\]|\\.)*)', content)
    if match:
        return codecs.decode(match.group(1), "unicode_escape")
    else:
        return content


@register_line_cell_magic
def gpt(
    line: str,
    _cell: Optional[str] = None,
    model="gpt-3.5-turbo",
    function_name: str = "gpt",
    max_tokens: int = 4096,
) -> None:
    notebook = requests.get("http://localhost:4903/notebook").json()
    cells = notebook["cells"]
    messages = system_messages.copy()

    for cell in cells:
        if cell["kind"] == 1:
            messages.append(
                {
                    "role": "system",
                    "content": f"Markdown cell:\n{cell['document']['text']}",
                }
            )
            continue
        cell_input = cell["document"]["text"]
        cell_output = None
        if cell["outputs"]:
            cell_output = cell["outputs"][0]["items"][0]["data"]

        is_cell_magic = cell_input.startswith(f"%%{function_name}")
        is_line_magic = cell_input.startswith(f"%{function_name}")
        if is_cell_magic or is_line_magic:
            user_message = cell_input.replace(
                f"%%{function_name}" if is_cell_magic else f"%{function_name}", ""
            ).strip()
            if user_message == (_cell or line).rstrip():
                break
            messages.append({"role": "user", "content": user_message})

            if cell_output:
                messages.append({"role": "assistant", "content": cell_output.strip()})
        else:
            messages.append(
                {
                    "role": "system",
                    "content": f"```python\n{cell_input}\n```"
                    + "".join(
                        f"\nOutput:\n{get_plain_text(cell_output)}"
                        for cell_output in cell["outputs"]
                    ),
                }
            )

    messages.append({"role": "user", "content": (_cell or line).strip()})

    trim_messages(messages, max_tokens, model)

    clear_output()
    time.sleep(0.4)
    display_id = display(Markdown(""), display_id=True).display_id  # type: ignore

    content = ""
    last_update_time = time.time()
    debounce_interval = 0.1  # seconds

    chunks = client.chat.completions.create(
        messages=messages,
        model=model,
        stream=True,
        functions=functions,
        temperature=0,
    )

    is_function_call = False
    function_call_name = None

    for chunk in chunks:
        delta = chunk.choices[0].delta
        if delta.function_call:
            if not is_function_call:
                if content:
                    update_display(
                        Markdown(content),
                        display_id=display_id,
                    )
                    display_id = display(Markdown(""), display_id=True).display_id  # type: ignore
                    last_update_time = time.time()
                    content = ""
                is_function_call = True
            if function_call_name is None:
                function_call_name = delta.function_call.name
            content += delta.function_call.arguments or ""
        else:
            content += delta.content or ""
        current_time = time.time()

        if current_time - last_update_time >= debounce_interval:
            update_display(
                Markdown(
                    f"```python\n{get_source(content)}\n```"
                    if is_function_call
                    else content
                ),
                display_id=display_id,
            )
            last_update_time = current_time

    if is_function_call or is_code_block(content):
        source = get_source(content)
        time.sleep(0.1)
        update_display(
            Markdown(f"```python\n{source}\n```"),
            display_id=display_id,
        )
        time.sleep(0.1)
        exec_source = "\n".join(source.splitlines()[:-1])
        eval_source = source.splitlines()[-1]
        try:
            exec(exec_source, get_globals())
        except SyntaxError:
            return exec(source, get_globals())
        try:
            eval_result = eval(eval_source, get_globals())
            if eval_result is not None:
                display(eval_result)
        except SyntaxError:
            exec(eval_source, get_globals())
    else:
        update_display(Markdown(content), display_id=display_id)


@register_line_cell_magic
def gpt3(line: str, _cell: Optional[str] = None) -> None:
    return gpt(
        line, _cell, model="gpt-3.5-turbo-0613", function_name="gpt3", max_tokens=4096
    )


@register_line_cell_magic
def gpt4(line: str, _cell: Optional[str] = None) -> None:
    return gpt(
        line,
        _cell,
        model="gpt-4-1106-preview",
        function_name="gpt4",
        max_tokens=16384,  # May be up to 131072
    )
    return gpt(line, _cell, model="gpt-4-0613", function_name="gpt4", max_tokens=8192)


@register_line_cell_magic
def gpt16(line: str, _cell: Optional[str] = None) -> None:
    return gpt(
        line,
        _cell,
        model="gpt-3.5-turbo-16k-0613",
        function_name="gpt16",
        max_tokens=16384,
    )


def is_code_block(content: str) -> bool:
    return content.startswith("```python\n") and content.endswith("\n```")


def trim_messages(
    messages: list[ChatCompletionMessageParam], max_tokens: int, model: str
) -> None:
    messages_len = len(messages)
    while num_tokens_from_messages(messages, model=model) > max_tokens - 1_000:
        messages.pop(len(system_messages))
    if len(messages) < messages_len:
        messages.insert(
            len(system_messages),
            {
                "role": "system",
                "content": "Note: Some older messages have been removed for brevity.",
            },
        )


def num_tokens_from_messages(
    messages: list[ChatCompletionMessageParam], model: str
) -> int:
    """
    Return the number of tokens used by a list of messages.

    Source:
    https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
    """
    try:
        encoding = tiktoken.encoding_for_model(model)
    except KeyError:
        print("Warning: model not found. Using cl100k_base encoding.")
        encoding = tiktoken.get_encoding("cl100k_base")
    if model in {
        "gpt-3.5-turbo-0613",
        "gpt-3.5-turbo-16k-0613",
        "gpt-4-0314",
        "gpt-4-32k-0314",
        "gpt-4-0613",
        "gpt-4-32k-0613",
        "gpt-4-1106-preview",
    }:
        tokens_per_message = 3
        tokens_per_name = 1
    elif model == "gpt-3.5-turbo-0301":
        tokens_per_message = (
            4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
        )
        tokens_per_name = -1  # if there's a name, the role is omitted
    elif "gpt-3.5-turbo" in model:
        print(
            "Warning: gpt-3.5-turbo may update over time. Returning num tokens assuming gpt-3.5-turbo-0613."
        )
        return num_tokens_from_messages(messages, model="gpt-3.5-turbo-0613")
    elif "gpt-4" in model:
        print(
            "Warning: gpt-4 may update over time. Returning num tokens assuming gpt-4-0613."
        )
        return num_tokens_from_messages(messages, model="gpt-4-0613")
    else:
        raise NotImplementedError(
            f"""num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens."""
        )
    num_tokens = 0
    for message in messages:
        num_tokens += tokens_per_message
        for key, value in message.items():
            num_tokens += len(encoding.encode(cast(str, value)))
            if key == "name":
                num_tokens += tokens_per_name
    num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
    return num_tokens

Updated the gist today to include logic for handling token overflow by removing older messages.

Updated the gist today to support function calling 😁

More updates