import json
import time
import types
from typing import Callable, Optional
import httpx # type: ignore
import litellm
from litellm.utils import Choices, Message, ModelResponse, Usage
class AlephAlphaError(Exception):
def __init__(self, status_code, message):
self.status_code = status_code
self.message = message
self.request = httpx.Request(
method="POST", url="https://api.aleph-alpha.com/complete"
)
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
class AlephAlphaConfig:
"""
Reference: https://docs.aleph-alpha.com/api/complete/
The `AlephAlphaConfig` class represents the configuration for the Aleph Alpha API. Here are the properties:
- `maximum_tokens` (integer, required): The maximum number of tokens to be generated by the completion. The sum of input tokens and maximum tokens may not exceed 2048.
- `minimum_tokens` (integer, optional; default value: 0): Generate at least this number of tokens before an end-of-text token is generated.
- `echo` (boolean, optional; default value: false): Whether to echo the prompt in the completion.
- `temperature` (number, nullable; default value: 0): Adjusts how creatively the model generates outputs. Use combinations of temperature, top_k, and top_p sensibly.
- `top_k` (integer, nullable; default value: 0): Introduces randomness into token generation by considering the top k most likely options.
- `top_p` (number, nullable; default value: 0): Adds randomness by considering the smallest set of tokens whose cumulative probability exceeds top_p.
- `presence_penalty`, `frequency_penalty`, `sequence_penalty` (number, nullable; default value: 0): Various penalties that can reduce repetition.
- `sequence_penalty_min_length` (integer; default value: 2): Minimum number of tokens to be considered as a sequence.
- `repetition_penalties_include_prompt`, `repetition_penalties_include_completion`, `use_multiplicative_presence_penalty`,`use_multiplicative_frequency_penalty`,`use_multiplicative_sequence_penalty` (boolean, nullable; default value: false): Various settings that adjust how the repetition penalties are applied.
- `penalty_bias` (string, nullable): Text used in addition to the penalized tokens for repetition penalties.
- `penalty_exceptions` (string[], nullable): Strings that may be generated without penalty.
- `penalty_exceptions_include_stop_sequences` (boolean, nullable; default value: true): Include all stop_sequences in penalty_exceptions.
- `best_of` (integer, nullable; default value: 1): The number of completions will be generated on the server side.
- `n` (integer, nullable; default value: 1): The number of completions to return.
- `logit_bias` (object, nullable): Adjust the logit scores before sampling.
- `log_probs` (integer, nullable): Number of top log probabilities for each token generated.
- `stop_sequences` (string[], nullable): List of strings that will stop generation if they're generated.
- `tokens` (boolean, nullable; default value: false): Flag indicating whether individual tokens of the completion should be returned or not.
- `raw_completion` (boolean; default value: false): if True, the raw completion of the model will be returned.
- `disable_optimizations` (boolean, nullable; default value: false): Disables any applied optimizations to both your prompt and completion.
- `completion_bias_inclusion`, `completion_bias_exclusion` (string[], default value: []): Set of strings to bias the generation of tokens.
- `completion_bias_inclusion_first_token_only`, `completion_bias_exclusion_first_token_only` (boolean; default value: false): Consider only the first token for the completion_bias_inclusion/exclusion.
- `contextual_control_threshold` (number, nullable): Control over how similar tokens are controlled.
- `control_log_additive` (boolean; default value: true): Method of applying control to attention scores.
"""
maximum_tokens: Optional[int] = (
litellm.max_tokens
) # aleph alpha requires max tokens
minimum_tokens: Optional[int] = None
echo: Optional[bool] = None
temperature: Optional[int] = None
top_k: Optional[int] = None
top_p: Optional[int] = None
presence_penalty: Optional[int] = None
frequency_penalty: Optional[int] = None
sequence_penalty: Optional[int] = None
sequence_penalty_min_length: Optional[int] = None
repetition_penalties_include_prompt: Optional[bool] = None
repetition_penalties_include_completion: Optional[bool] = None
use_multiplicative_presence_penalty: Optional[bool] = None
use_multiplicative_frequency_penalty: Optional[bool] = None
use_multiplicative_sequence_penalty: Optional[bool] = None
penalty_bias: Optional[str] = None
penalty_exceptions_include_stop_sequences: Optional[bool] = None
best_of: Optional[int] = None
n: Optional[int] = None
logit_bias: Optional[dict] = None
log_probs: Optional[int] = None
stop_sequences: Optional[list] = None
tokens: Optional[bool] = None
raw_completion: Optional[bool] = None
disable_optimizations: Optional[bool] = None
completion_bias_inclusion: Optional[list] = None
completion_bias_exclusion: Optional[list] = None
completion_bias_inclusion_first_token_only: Optional[bool] = None
completion_bias_exclusion_first_token_only: Optional[bool] = None
contextual_control_threshold: Optional[int] = None
control_log_additive: Optional[bool] = None
def __init__(
self,
maximum_tokens: Optional[int] = None,
minimum_tokens: Optional[int] = None,
echo: Optional[bool] = None,
temperature: Optional[int] = None,
top_k: Optional[int] = None,
top_p: Optional[int] = None,
presence_penalty: Optional[int] = None,
frequency_penalty: Optional[int] = None,
sequence_penalty: Optional[int] = None,
sequence_penalty_min_length: Optional[int] = None,
repetition_penalties_include_prompt: Optional[bool] = None,
repetition_penalties_include_completion: Optional[bool] = None,
use_multiplicative_presence_penalty: Optional[bool] = None,
use_multiplicative_frequency_penalty: Optional[bool] = None,
use_multiplicative_sequence_penalty: Optional[bool] = None,
penalty_bias: Optional[str] = None,
penalty_exceptions_include_stop_sequences: Optional[bool] = None,
best_of: Optional[int] = None,
n: Optional[int] = None,
logit_bias: Optional[dict] = None,
log_probs: Optional[int] = None,
stop_sequences: Optional[list] = None,
tokens: Optional[bool] = None,
raw_completion: Optional[bool] = None,
disable_optimizations: Optional[bool] = None,
completion_bias_inclusion: Optional[list] = None,
completion_bias_exclusion: Optional[list] = None,
completion_bias_inclusion_first_token_only: Optional[bool] = None,
completion_bias_exclusion_first_token_only: Optional[bool] = None,
contextual_control_threshold: Optional[int] = None,
control_log_additive: Optional[bool] = None,
) -> None:
locals_ = locals().copy()
for key, value in locals_.items():
if key != "self" and value is not None:
setattr(self.__class__, key, value)
@classmethod
def get_config(cls):
return {
k: v
for k, v in cls.__dict__.items()
if not k.startswith("__")
and not isinstance(
v,
(
types.FunctionType,
types.BuiltinFunctionType,
classmethod,
staticmethod,
),
)
and v is not None
}
def validate_environment(api_key):
headers = {
"accept": "application/json",
"content-type": "application/json",
}
if api_key:
headers["Authorization"] = f"Bearer {api_key}"
return headers
def completion(
model: str,
messages: list,
api_base: str,
model_response: ModelResponse,
print_verbose: Callable,
encoding,
api_key,
logging_obj,
optional_params: dict,
litellm_params=None,
logger_fn=None,
default_max_tokens_to_sample=None,
):
headers = validate_environment(api_key)
## Load Config
config = litellm.AlephAlphaConfig.get_config()
for k, v in config.items():
if (
k not in optional_params
): # completion(top_k=3) > aleph_alpha_config(top_k=3) <- allows for dynamic variables to be passed in
optional_params[k] = v
completion_url = api_base
model = model
prompt = ""
if "control" in model: # follow the ###Instruction / ###Response format
for idx, message in enumerate(messages):
if "role" in message:
if (
idx == 0
): # set first message as instruction (required), let later user messages be input
prompt += f"###Instruction: {message['content']}"
else:
if message["role"] == "system":
prompt += f"###Instruction: {message['content']}"
elif message["role"] == "user":
prompt += f"###Input: {message['content']}"
else:
prompt += f"###Response: {message['content']}"
else:
prompt += f"{message['content']}"
else:
prompt = " ".join(message["content"] for message in messages)
data = {
"model": model,
"prompt": prompt,
**optional_params,
}
## LOGGING
logging_obj.pre_call(
input=prompt,
api_key=api_key,
additional_args={"complete_input_dict": data},
)
## COMPLETION CALL
response = litellm.module_level_client.post(
completion_url,
headers=headers,
data=json.dumps(data),
stream=optional_params["stream"] if "stream" in optional_params else False,
)
if "stream" in optional_params and optional_params["stream"] is True:
return response.iter_lines()
else:
## LOGGING
logging_obj.post_call(
input=prompt,
api_key=api_key,
original_response=response.text,
additional_args={"complete_input_dict": data},
)
print_verbose(f"raw model_response: {response.text}")
## RESPONSE OBJECT
completion_response = response.json()
if "error" in completion_response:
raise AlephAlphaError(
message=completion_response["error"],
status_code=response.status_code,
)
else:
try:
choices_list = []
for idx, item in enumerate(completion_response["completions"]):
if len(item["completion"]) > 0:
message_obj = Message(content=item["completion"])
else:
message_obj = Message(content=None)
choice_obj = Choices(
finish_reason=item["finish_reason"],
index=idx + 1,
message=message_obj,
)
choices_list.append(choice_obj)
model_response.choices = choices_list # type: ignore
except Exception:
raise AlephAlphaError(
message=json.dumps(completion_response),
status_code=response.status_code,
)
## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here.
prompt_tokens = len(encoding.encode(prompt))
completion_tokens = len(
encoding.encode(
model_response["choices"][0]["message"]["content"],
disallowed_special=(),
)
)
model_response.created = int(time.time())
model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
setattr(model_response, "usage", usage)
return model_response
def embedding():
# logic for parsing in - calling - parsing out model embedding calls
pass
