mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 18:54:30 +00:00
(Refactor) Code Quality improvement - rename text_completion_codestral.py -> codestral/completion/ (#7172)
* rename files * fix codestral fim organization * fix CodestralTextCompletionConfig * fix import CodestralTextCompletion * fix BaseLLM * fix imports * fix CodestralTextCompletionConfig * fix imports CodestralTextCompletion
This commit is contained in:
Ishaan Jaff
GitHub
parent
400eb28a91
commit
78d132c1fb
10 changed files with 164 additions and 162 deletions
442
litellm/llms/codestral/completion/handler.py
Normal file
442
litellm/llms/codestral/completion/handler.py
Normal file
|
|
@ -0,0 +1,442 @@
|
|||
# What is this?
|
||||
## handler file for TextCompletionCodestral Integration - https://codestral.com/
|
||||
|
||||
import copy
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
import traceback
|
||||
import types
|
||||
from enum import Enum
|
||||
from functools import partial
|
||||
from typing import Callable, List, Literal, Optional, Union
|
||||
|
||||
import httpx # type: ignore
|
||||
import requests # type: ignore
|
||||
|
||||
import litellm
|
||||
from litellm import verbose_logger
|
||||
from litellm.litellm_core_utils.core_helpers import map_finish_reason
|
||||
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
|
||||
from litellm.litellm_core_utils.prompt_templates.factory import (
|
||||
custom_prompt,
|
||||
prompt_factory,
|
||||
)
|
||||
from litellm.llms.base import BaseLLM
|
||||
from litellm.llms.custom_httpx.http_handler import (
|
||||
AsyncHTTPHandler,
|
||||
get_async_httpx_client,
|
||||
)
|
||||
from litellm.llms.openai.completion.transformation import OpenAITextCompletionConfig
|
||||
from litellm.types.llms.databricks import GenericStreamingChunk
|
||||
from litellm.utils import (
|
||||
Choices,
|
||||
CustomStreamWrapper,
|
||||
Message,
|
||||
TextCompletionResponse,
|
||||
Usage,
|
||||
)
|
||||
|
||||
|
||||
class TextCompletionCodestralError(Exception):
|
||||
def __init__(
|
||||
self,
|
||||
status_code,
|
||||
message,
|
||||
request: Optional[httpx.Request] = None,
|
||||
response: Optional[httpx.Response] = None,
|
||||
):
|
||||
self.status_code = status_code
|
||||
self.message = message
|
||||
if request is not None:
|
||||
self.request = request
|
||||
else:
|
||||
self.request = httpx.Request(
|
||||
method="POST",
|
||||
url="https://docs.codestral.com/user-guide/inference/rest_api",
|
||||
)
|
||||
if response is not None:
|
||||
self.response = response
|
||||
else:
|
||||
self.response = httpx.Response(
|
||||
status_code=status_code, request=self.request
|
||||
)
|
||||
super().__init__(
|
||||
self.message
|
||||
) # Call the base class constructor with the parameters it needs
|
||||
|
||||
|
||||
async def make_call(
|
||||
client: AsyncHTTPHandler,
|
||||
api_base: str,
|
||||
headers: dict,
|
||||
data: str,
|
||||
model: str,
|
||||
messages: list,
|
||||
logging_obj,
|
||||
):
|
||||
response = await client.post(api_base, headers=headers, data=data, stream=True)
|
||||
|
||||
if response.status_code != 200:
|
||||
raise TextCompletionCodestralError(
|
||||
status_code=response.status_code, message=response.text
|
||||
)
|
||||
|
||||
completion_stream = response.aiter_lines()
|
||||
# LOGGING
|
||||
logging_obj.post_call(
|
||||
input=messages,
|
||||
api_key="",
|
||||
original_response=completion_stream, # Pass the completion stream for logging
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
|
||||
return completion_stream
|
||||
|
||||
|
||||
class CodestralTextCompletion(BaseLLM):
|
||||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
|
||||
def _validate_environment(
|
||||
self,
|
||||
api_key: Optional[str],
|
||||
user_headers: dict,
|
||||
) -> dict:
|
||||
if api_key is None:
|
||||
raise ValueError(
|
||||
"Missing CODESTRAL_API_Key - Please add CODESTRAL_API_Key to your environment variables"
|
||||
)
|
||||
headers = {
|
||||
"content-type": "application/json",
|
||||
"Authorization": "Bearer {}".format(api_key),
|
||||
}
|
||||
if user_headers is not None and isinstance(user_headers, dict):
|
||||
headers = {**headers, **user_headers}
|
||||
return headers
|
||||
|
||||
def output_parser(self, generated_text: str):
|
||||
"""
|
||||
Parse the output text to remove any special characters. In our current approach we just check for ChatML tokens.
|
||||
|
||||
Initial issue that prompted this - https://github.com/BerriAI/litellm/issues/763
|
||||
"""
|
||||
chat_template_tokens = [
|
||||
"<|assistant|>",
|
||||
"<|system|>",
|
||||
"<|user|>",
|
||||
"<s>",
|
||||
"</s>",
|
||||
]
|
||||
for token in chat_template_tokens:
|
||||
if generated_text.strip().startswith(token):
|
||||
generated_text = generated_text.replace(token, "", 1)
|
||||
if generated_text.endswith(token):
|
||||
generated_text = generated_text[::-1].replace(token[::-1], "", 1)[::-1]
|
||||
return generated_text
|
||||
|
||||
def process_text_completion_response(
|
||||
self,
|
||||
model: str,
|
||||
response: Union[requests.Response, httpx.Response],
|
||||
model_response: TextCompletionResponse,
|
||||
stream: bool,
|
||||
logging_obj: LiteLLMLogging,
|
||||
optional_params: dict,
|
||||
api_key: str,
|
||||
data: Union[dict, str],
|
||||
messages: list,
|
||||
print_verbose,
|
||||
encoding,
|
||||
) -> TextCompletionResponse:
|
||||
## LOGGING
|
||||
logging_obj.post_call(
|
||||
input=messages,
|
||||
api_key=api_key,
|
||||
original_response=response.text,
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
print_verbose(f"codestral api: raw model_response: {response.text}")
|
||||
## RESPONSE OBJECT
|
||||
if response.status_code != 200:
|
||||
raise TextCompletionCodestralError(
|
||||
message=str(response.text),
|
||||
status_code=response.status_code,
|
||||
)
|
||||
try:
|
||||
completion_response = response.json()
|
||||
except Exception:
|
||||
raise TextCompletionCodestralError(message=response.text, status_code=422)
|
||||
|
||||
_original_choices = completion_response.get("choices", [])
|
||||
_choices: List[litellm.utils.TextChoices] = []
|
||||
for choice in _original_choices:
|
||||
# This is what 1 choice looks like from codestral API
|
||||
# {
|
||||
# "index": 0,
|
||||
# "message": {
|
||||
# "role": "assistant",
|
||||
# "content": "\n assert is_odd(1)\n assert",
|
||||
# "tool_calls": null
|
||||
# },
|
||||
# "finish_reason": "length",
|
||||
# "logprobs": null
|
||||
# }
|
||||
_finish_reason = None
|
||||
_index = 0
|
||||
_text = None
|
||||
_logprobs = None
|
||||
|
||||
_choice_message = choice.get("message", {})
|
||||
_choice = litellm.utils.TextChoices(
|
||||
finish_reason=choice.get("finish_reason"),
|
||||
index=choice.get("index"),
|
||||
text=_choice_message.get("content"),
|
||||
logprobs=choice.get("logprobs"),
|
||||
)
|
||||
|
||||
_choices.append(_choice)
|
||||
|
||||
_response = litellm.TextCompletionResponse(
|
||||
id=completion_response.get("id"),
|
||||
choices=_choices,
|
||||
created=completion_response.get("created"),
|
||||
model=completion_response.get("model"),
|
||||
usage=completion_response.get("usage"),
|
||||
stream=False,
|
||||
object=completion_response.get("object"),
|
||||
)
|
||||
return _response
|
||||
|
||||
def completion(
|
||||
self,
|
||||
model: str,
|
||||
messages: list,
|
||||
api_base: str,
|
||||
custom_prompt_dict: dict,
|
||||
model_response: TextCompletionResponse,
|
||||
print_verbose: Callable,
|
||||
encoding,
|
||||
api_key: str,
|
||||
logging_obj,
|
||||
optional_params: dict,
|
||||
timeout: Union[float, httpx.Timeout],
|
||||
acompletion=None,
|
||||
litellm_params=None,
|
||||
logger_fn=None,
|
||||
headers: dict = {},
|
||||
) -> Union[TextCompletionResponse, CustomStreamWrapper]:
|
||||
headers = self._validate_environment(api_key, headers)
|
||||
|
||||
if optional_params.pop("custom_endpoint", None) is True:
|
||||
completion_url = api_base
|
||||
else:
|
||||
completion_url = (
|
||||
api_base or "https://codestral.mistral.ai/v1/fim/completions"
|
||||
)
|
||||
|
||||
if model in custom_prompt_dict:
|
||||
# check if the model has a registered custom prompt
|
||||
model_prompt_details = custom_prompt_dict[model]
|
||||
prompt = custom_prompt(
|
||||
role_dict=model_prompt_details["roles"],
|
||||
initial_prompt_value=model_prompt_details["initial_prompt_value"],
|
||||
final_prompt_value=model_prompt_details["final_prompt_value"],
|
||||
messages=messages,
|
||||
)
|
||||
else:
|
||||
prompt = prompt_factory(model=model, messages=messages)
|
||||
|
||||
## Load Config
|
||||
config = litellm.CodestralTextCompletionConfig.get_config()
|
||||
for k, v in config.items():
|
||||
if (
|
||||
k not in optional_params
|
||||
): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
|
||||
optional_params[k] = v
|
||||
|
||||
stream = optional_params.pop("stream", False)
|
||||
|
||||
data = {
|
||||
"model": model,
|
||||
"prompt": prompt,
|
||||
**optional_params,
|
||||
}
|
||||
input_text = prompt
|
||||
## LOGGING
|
||||
logging_obj.pre_call(
|
||||
input=input_text,
|
||||
api_key=api_key,
|
||||
additional_args={
|
||||
"complete_input_dict": data,
|
||||
"headers": headers,
|
||||
"api_base": completion_url,
|
||||
"acompletion": acompletion,
|
||||
},
|
||||
)
|
||||
## COMPLETION CALL
|
||||
if acompletion is True:
|
||||
### ASYNC STREAMING
|
||||
if stream is True:
|
||||
return self.async_streaming(
|
||||
model=model,
|
||||
messages=messages,
|
||||
data=data,
|
||||
api_base=completion_url,
|
||||
model_response=model_response,
|
||||
print_verbose=print_verbose,
|
||||
encoding=encoding,
|
||||
api_key=api_key,
|
||||
logging_obj=logging_obj,
|
||||
optional_params=optional_params,
|
||||
litellm_params=litellm_params,
|
||||
logger_fn=logger_fn,
|
||||
headers=headers,
|
||||
timeout=timeout,
|
||||
) # type: ignore
|
||||
else:
|
||||
### ASYNC COMPLETION
|
||||
return self.async_completion(
|
||||
model=model,
|
||||
messages=messages,
|
||||
data=data,
|
||||
api_base=completion_url,
|
||||
model_response=model_response,
|
||||
print_verbose=print_verbose,
|
||||
encoding=encoding,
|
||||
api_key=api_key,
|
||||
logging_obj=logging_obj,
|
||||
optional_params=optional_params,
|
||||
stream=False,
|
||||
litellm_params=litellm_params,
|
||||
logger_fn=logger_fn,
|
||||
headers=headers,
|
||||
timeout=timeout,
|
||||
) # type: ignore
|
||||
|
||||
### SYNC STREAMING
|
||||
if stream is True:
|
||||
response = requests.post(
|
||||
completion_url,
|
||||
headers=headers,
|
||||
data=json.dumps(data),
|
||||
stream=stream,
|
||||
)
|
||||
_response = CustomStreamWrapper(
|
||||
response.iter_lines(),
|
||||
model,
|
||||
custom_llm_provider="codestral",
|
||||
logging_obj=logging_obj,
|
||||
)
|
||||
return _response
|
||||
### SYNC COMPLETION
|
||||
else:
|
||||
|
||||
response = requests.post(
|
||||
url=completion_url,
|
||||
headers=headers,
|
||||
data=json.dumps(data),
|
||||
)
|
||||
return self.process_text_completion_response(
|
||||
model=model,
|
||||
response=response,
|
||||
model_response=model_response,
|
||||
stream=optional_params.get("stream", False),
|
||||
logging_obj=logging_obj, # type: ignore
|
||||
optional_params=optional_params,
|
||||
api_key=api_key,
|
||||
data=data,
|
||||
messages=messages,
|
||||
print_verbose=print_verbose,
|
||||
encoding=encoding,
|
||||
)
|
||||
|
||||
async def async_completion(
|
||||
self,
|
||||
model: str,
|
||||
messages: list,
|
||||
api_base: str,
|
||||
model_response: TextCompletionResponse,
|
||||
print_verbose: Callable,
|
||||
encoding,
|
||||
api_key,
|
||||
logging_obj,
|
||||
stream,
|
||||
data: dict,
|
||||
optional_params: dict,
|
||||
timeout: Union[float, httpx.Timeout],
|
||||
litellm_params=None,
|
||||
logger_fn=None,
|
||||
headers={},
|
||||
) -> TextCompletionResponse:
|
||||
|
||||
async_handler = get_async_httpx_client(
|
||||
llm_provider=litellm.LlmProviders.TEXT_COMPLETION_CODESTRAL,
|
||||
params={"timeout": timeout},
|
||||
)
|
||||
try:
|
||||
|
||||
response = await async_handler.post(
|
||||
api_base, headers=headers, data=json.dumps(data)
|
||||
)
|
||||
except httpx.HTTPStatusError as e:
|
||||
raise TextCompletionCodestralError(
|
||||
status_code=e.response.status_code,
|
||||
message="HTTPStatusError - {}".format(e.response.text),
|
||||
)
|
||||
except Exception as e:
|
||||
raise TextCompletionCodestralError(
|
||||
status_code=500, message="{}".format(str(e))
|
||||
) # don't use verbose_logger.exception, if exception is raised
|
||||
return self.process_text_completion_response(
|
||||
model=model,
|
||||
response=response,
|
||||
model_response=model_response,
|
||||
stream=stream,
|
||||
logging_obj=logging_obj,
|
||||
api_key=api_key,
|
||||
data=data,
|
||||
messages=messages,
|
||||
print_verbose=print_verbose,
|
||||
optional_params=optional_params,
|
||||
encoding=encoding,
|
||||
)
|
||||
|
||||
async def async_streaming(
|
||||
self,
|
||||
model: str,
|
||||
messages: list,
|
||||
api_base: str,
|
||||
model_response: TextCompletionResponse,
|
||||
print_verbose: Callable,
|
||||
encoding,
|
||||
api_key,
|
||||
logging_obj,
|
||||
data: dict,
|
||||
timeout: Union[float, httpx.Timeout],
|
||||
optional_params=None,
|
||||
litellm_params=None,
|
||||
logger_fn=None,
|
||||
headers={},
|
||||
) -> CustomStreamWrapper:
|
||||
data["stream"] = True
|
||||
|
||||
streamwrapper = CustomStreamWrapper(
|
||||
completion_stream=None,
|
||||
make_call=partial(
|
||||
make_call,
|
||||
api_base=api_base,
|
||||
headers=headers,
|
||||
data=json.dumps(data),
|
||||
model=model,
|
||||
messages=messages,
|
||||
logging_obj=logging_obj,
|
||||
),
|
||||
model=model,
|
||||
custom_llm_provider="text-completion-codestral",
|
||||
logging_obj=logging_obj,
|
||||
)
|
||||
return streamwrapper
|
||||
|
||||
def embedding(self, *args, **kwargs):
|
||||
pass
|
||||
Loading…
Add table
Add a link
Reference in a new issue