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Extract lf.llms.OpenAICompatible for OpenAI-compatible LLMs. #404

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Jan 9, 2025
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Extract lf.llms.OpenAICompatible for OpenAI-compatible LLMs. #404

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160 changes: 8 additions & 152 deletions langfun/core/llms/deepseek.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,8 +17,7 @@
from typing import Annotated, Any

import langfun.core as lf
from langfun.core import modalities as lf_modalities
from langfun.core.llms import rest
from langfun.core.llms import openai_compatible
import pyglove as pg

SUPPORTED_MODELS_AND_SETTINGS = {
Expand All @@ -39,7 +38,7 @@
# DeepSeek API uses an API format compatible with OpenAI.
# Reference: https://api-docs.deepseek.com/
@lf.use_init_args(['model'])
class DeepSeek(rest.REST):
class DeepSeek(openai_compatible.OpenAICompatible):
"""DeepSeek model."""

model: pg.typing.Annotated[
Expand All @@ -51,10 +50,6 @@ class DeepSeek(rest.REST):

api_endpoint: str = 'https://api.deepseek.com/chat/completions'

multimodal: Annotated[bool, 'Whether this model has multimodal support.'] = (
False
)

api_key: Annotated[
str | None,
(
Expand All @@ -63,25 +58,18 @@ class DeepSeek(rest.REST):
),
] = None

def _on_bound(self):
super()._on_bound()
self._api_key = None

def _initialize(self):
@property
def headers(self) -> dict[str, Any]:
api_key = self.api_key or os.environ.get('DEEPSEEK_API_KEY', None)
if not api_key:
raise ValueError(
'Please specify `api_key` during `__init__` or set environment '
'variable `DEEPSEEK_API_KEY` with your DeepSeek API key.'
)
self._api_key = api_key

@property
def headers(self) -> dict[str, Any]:
headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {self._api_key}',
}
headers = super().headers
headers.update({
'Authorization': f'Bearer {api_key}',
})
return headers

@property
Expand Down Expand Up @@ -118,138 +106,6 @@ def estimate_cost(
def dir(cls):
return [k for k, v in SUPPORTED_MODELS_AND_SETTINGS.items() if v.in_service]

def _request_args(self, options: lf.LMSamplingOptions) -> dict[str, Any]:
# Reference:
# https://platform.openai.com/docs/api-reference/completions/create
# NOTE(daiyip): options.top_k is not applicable.
args = dict(
model=self.model,
n=options.n,
top_logprobs=options.top_logprobs,
)
if options.logprobs:
args['logprobs'] = options.logprobs

if options.temperature is not None:
args['temperature'] = options.temperature
if options.max_tokens is not None:
args['max_completion_tokens'] = options.max_tokens
if options.top_p is not None:
args['top_p'] = options.top_p
if options.stop:
args['stop'] = options.stop
if options.random_seed is not None:
args['seed'] = options.random_seed
return args

def _content_from_message(self, message: lf.Message):
"""Returns a OpenAI content object from a Langfun message."""

def _uri_from(chunk: lf.Modality) -> str:
if chunk.uri and chunk.uri.lower().startswith(
('http:', 'https:', 'ftp:')
):
return chunk.uri
return chunk.content_uri

content = []
for chunk in message.chunk():
if isinstance(chunk, str):
item = dict(type='text', text=chunk)
elif isinstance(chunk, lf_modalities.Image) and self.multimodal:
item = dict(type='image_url', image_url=dict(url=_uri_from(chunk)))
else:
raise ValueError(f'Unsupported modality: {chunk!r}.')
content.append(item)
return content

def request(
self, prompt: lf.Message, sampling_options: lf.LMSamplingOptions
) -> dict[str, Any]:
"""Returns the JSON input for a message."""
request_args = self._request_args(sampling_options)

# Users could use `metadata_json_schema` to pass additional
# request arguments.
json_schema = prompt.metadata.get('json_schema')
if json_schema is not None:
if not isinstance(json_schema, dict):
raise ValueError(f'`json_schema` must be a dict, got {json_schema!r}.')
if 'title' not in json_schema:
raise ValueError(
'The root of `json_schema` must have a `title` field, '
f'got {json_schema!r}.'
)
request_args.update(
response_format=dict(
type='json_schema',
json_schema=dict(
schema=json_schema,
name=json_schema['title'],
strict=True,
),
)
)
prompt.metadata.formatted_text = (
prompt.text
+ '\n\n [RESPONSE FORMAT (not part of prompt)]\n'
+ pg.to_json_str(request_args['response_format'], json_indent=2)
)

# Prepare messages.
messages = []
# Users could use `metadata_system_message` to pass system message.
system_message = prompt.metadata.get('system_message')
if system_message:
system_message = lf.SystemMessage.from_value(system_message)
messages.append(
dict(
role='system', content=self._content_from_message(system_message)
)
)
messages.append(
dict(role='user', content=self._content_from_message(prompt))
)
request = dict()
request.update(request_args)
request['messages'] = messages
return request

def _parse_choice(self, choice: dict[str, Any]) -> lf.LMSample:
# Reference:
# https://platform.openai.com/docs/api-reference/chat/object
logprobs = None
choice_logprobs = choice.get('logprobs')
if choice_logprobs:
logprobs = [
(
t['token'],
t['logprob'],
[(tt['token'], tt['logprob']) for tt in t['top_logprobs']],
)
for t in choice_logprobs['content']
]
return lf.LMSample(
choice['message']['content'],
score=0.0,
logprobs=logprobs,
)

def result(self, json: dict[str, Any]) -> lf.LMSamplingResult:
usage = json['usage']
return lf.LMSamplingResult(
samples=[self._parse_choice(choice) for choice in json['choices']],
usage=lf.LMSamplingUsage(
prompt_tokens=usage['prompt_tokens'],
completion_tokens=usage['completion_tokens'],
total_tokens=usage['total_tokens'],
estimated_cost=self.estimate_cost(
num_input_tokens=usage['prompt_tokens'],
num_output_tokens=usage['completion_tokens'],
),
),
)


class DeepSeekChat(DeepSeek):
"""DeepSeek Chat model.
Expand Down
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