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refactor: Delete dead legacy code from autogpt.llm.providers.openai
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@Pwuts
Pwuts committed Nov 7, 2023
1 parent 7a7a144 commit a2dc4b1
Showing 1 changed file with 0 additions and 193 deletions.
193 changes: 0 additions & 193 deletions autogpts/autogpt/autogpt/llm/providers/openai.py
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Original file line number Diff line number Diff line change
@@ -1,20 +1,9 @@
from __future__ import annotations

import functools
import logging
import time
from typing import Callable, Iterable, TypeVar
from unittest.mock import patch

import openai
import openai.api_resources.abstract.engine_api_resource as engine_api_resource
from colorama import Fore, Style
from openai.error import APIError, RateLimitError, ServiceUnavailableError, Timeout
from openai.openai_object import OpenAIObject

from autogpt.core.resource.model_providers import CompletionModelFunction
from autogpt.core.utils.json_schema import JSONSchema
from autogpt.logs.helpers import request_user_double_check
from autogpt.models.command import Command

logger = logging.getLogger(__name__)
Expand All @@ -23,141 +12,6 @@
T = TypeVar("T", bound=Callable)


def meter_api(func: T) -> T:
"""Adds ApiManager metering to functions which make OpenAI API calls"""
from autogpt.llm.api_manager import ApiManager

api_manager = ApiManager()

openai_obj_processor = openai.util.convert_to_openai_object

def update_usage_with_response(response: OpenAIObject):
try:
usage = response.usage
logger.debug(f"Reported usage from call to model {response.model}: {usage}")
api_manager.update_cost(
response.usage.prompt_tokens,
response.usage.completion_tokens if "completion_tokens" in usage else 0,
response.model,
)
except Exception as err:
logger.warn(f"Failed to update API costs: {err.__class__.__name__}: {err}")

def metering_wrapper(*args, **kwargs):
openai_obj = openai_obj_processor(*args, **kwargs)
if isinstance(openai_obj, OpenAIObject) and "usage" in openai_obj:
update_usage_with_response(openai_obj)
return openai_obj

@functools.wraps(func)
def metered_func(*args, **kwargs):
with patch.object(
engine_api_resource.util,
"convert_to_openai_object",
side_effect=metering_wrapper,
):
return func(*args, **kwargs)

return metered_func


def retry_api(
max_retries: int = 10,
backoff_base: float = 2.0,
warn_user: bool = True,
):
"""Retry an OpenAI API call.
Args:
num_retries int: Number of retries. Defaults to 10.
backoff_base float: Base for exponential backoff. Defaults to 2.
warn_user bool: Whether to warn the user. Defaults to True.
"""
error_messages = {
ServiceUnavailableError: "The OpenAI API engine is currently overloaded",
RateLimitError: "Reached rate limit",
}
api_key_error_msg = (
f"Please double check that you have setup a "
f"{Style.BRIGHT}PAID{Style.NORMAL} OpenAI API Account. You can "
f"read more here: {Fore.CYAN}https://docs.agpt.co/setup/#getting-an-api-key{Fore.RESET}"
)
backoff_msg = "Waiting {backoff} seconds..."

def _wrapper(func: T) -> T:
@functools.wraps(func)
def _wrapped(*args, **kwargs):
user_warned = not warn_user
max_attempts = max_retries + 1 # +1 for the first attempt
for attempt in range(1, max_attempts + 1):
try:
return func(*args, **kwargs)

except (RateLimitError, ServiceUnavailableError) as e:
if attempt >= max_attempts or (
# User's API quota exceeded
isinstance(e, RateLimitError)
and (err := getattr(e, "error", {}))
and err.get("code") == "insufficient_quota"
):
raise

error_msg = error_messages[type(e)]
logger.warn(error_msg)
if not user_warned:
request_user_double_check(api_key_error_msg)
logger.debug(f"Status: {e.http_status}")
logger.debug(f"Response body: {e.json_body}")
logger.debug(f"Response headers: {e.headers}")
user_warned = True

except (APIError, Timeout) as e:
if (e.http_status not in [429, 502]) or (attempt == max_attempts):
raise

backoff = backoff_base ** (attempt + 2)
logger.warn(backoff_msg.format(backoff=backoff))
time.sleep(backoff)

return _wrapped

return _wrapper


def format_openai_function_for_prompt(func: CompletionModelFunction) -> str:
"""Returns the function formatted similarly to the way OpenAI does it internally:
https://community.openai.com/t/how-to-calculate-the-tokens-when-using-function-call/266573/18
Example:
```ts
// Get the current weather in a given location
type get_current_weather = (_: {
// The city and state, e.g. San Francisco, CA
location: string,
unit?: "celsius" | "fahrenheit",
}) => any;
```
"""

def param_signature(name: str, spec: JSONSchema) -> str:
# TODO: enum type support
type_dec = (
spec.type if not spec.enum else " | ".join(repr(e) for e in spec.enum)
)
return (
f"// {spec.description}\n" if spec.description else ""
) + f"{name}{'' if spec.required else '?'}: {type_dec},"

return "\n".join(
[
f"// {func.description}",
f"type {func.name} = (_ :{{",
*[param_signature(name, p) for name, p in func.parameters.items()],
"}) => any;",
]
)


def get_openai_command_specs(
commands: Iterable[Command],
) -> list[CompletionModelFunction]:
Expand All @@ -172,50 +26,3 @@ def get_openai_command_specs(
)
for command in commands
]


def count_openai_functions_tokens(
functions: list[CompletionModelFunction], for_model: str
) -> int:
"""Returns the number of tokens taken up by a set of function definitions
Reference: https://community.openai.com/t/how-to-calculate-the-tokens-when-using-function-call/266573/18
"""
from autogpt.llm.utils import (
count_string_tokens, # FIXME: maybe move to OpenAIProvider?
)

return count_string_tokens(
f"# Tools\n\n## functions\n\n{format_function_specs_as_typescript_ns(functions)}",
for_model,
)


def format_function_specs_as_typescript_ns(
functions: list[CompletionModelFunction],
) -> str:
"""Returns a function signature block in the format used by OpenAI internally:
https://community.openai.com/t/how-to-calculate-the-tokens-when-using-function-call/266573/18
For use with `count_string_tokens` to determine token usage of provided functions.
Example:
```ts
namespace functions {
// Get the current weather in a given location
type get_current_weather = (_: {
// The city and state, e.g. San Francisco, CA
location: string,
unit?: "celsius" | "fahrenheit",
}) => any;
} // namespace functions
```
"""

return (
"namespace functions {\n\n"
+ "\n\n".join(format_openai_function_for_prompt(f) for f in functions)
+ "\n\n} // namespace functions"
)

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