|
import os, types |
|
import json |
|
from enum import Enum |
|
import requests |
|
import time |
|
from typing import Callable, Optional |
|
import litellm |
|
from litellm.utils import ModelResponse, Choices, Message, Usage |
|
import httpx |
|
|
|
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 |
|
) |
|
|
|
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 |
|
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() |
|
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=None, |
|
litellm_params=None, |
|
logger_fn=None, |
|
default_max_tokens_to_sample=None, |
|
): |
|
headers = validate_environment(api_key) |
|
|
|
|
|
config = litellm.AlephAlphaConfig.get_config() |
|
for k, v in config.items(): |
|
if k not in optional_params: |
|
optional_params[k] = v |
|
|
|
completion_url = api_base |
|
model = model |
|
prompt = "" |
|
if "control" in model: |
|
for idx, message in enumerate(messages): |
|
if "role" in message: |
|
if idx == 0: |
|
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_obj.pre_call( |
|
input=prompt, |
|
api_key=api_key, |
|
additional_args={"complete_input_dict": data}, |
|
) |
|
|
|
response = requests.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"] == True: |
|
return response.iter_lines() |
|
else: |
|
|
|
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}") |
|
|
|
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 |
|
except: |
|
raise AlephAlphaError(message=json.dumps(completion_response), status_code=response.status_code) |
|
|
|
|
|
prompt_tokens = len( |
|
encoding.encode(prompt) |
|
) |
|
completion_tokens = len( |
|
encoding.encode(model_response["choices"][0]["message"]["content"]) |
|
) |
|
|
|
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 |
|
) |
|
model_response.usage = usage |
|
return model_response |
|
|
|
def embedding(): |
|
|
|
pass |
|
|
