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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, Usage
class NLPCloudError(Exception):
def __init__(self, status_code, message):
self.status_code = status_code
self.message = message
super().__init__(
self.message
) # Call the base class constructor with the parameters it needs
class NLPCloudConfig:
"""
Reference: https://docs.nlpcloud.com/#generation
- `max_length` (int): Optional. The maximum number of tokens that the generated text should contain.
- `length_no_input` (boolean): Optional. Whether `min_length` and `max_length` should not include the length of the input text.
- `end_sequence` (string): Optional. A specific token that should be the end of the generated sequence.
- `remove_end_sequence` (boolean): Optional. Whether to remove the `end_sequence` string from the result.
- `remove_input` (boolean): Optional. Whether to remove the input text from the result.
- `bad_words` (list of strings): Optional. List of tokens that are not allowed to be generated.
- `temperature` (float): Optional. Temperature sampling. It modulates the next token probabilities.
- `top_p` (float): Optional. Top P sampling. Below 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.
- `top_k` (int): Optional. Top K sampling. The number of highest probability vocabulary tokens to keep for top k filtering.
- `repetition_penalty` (float): Optional. Prevents the same word from being repeated too many times.
- `num_beams` (int): Optional. Number of beams for beam search.
- `num_return_sequences` (int): Optional. The number of independently computed returned sequences.
"""
max_length: Optional[int] = None
length_no_input: Optional[bool] = None
end_sequence: Optional[str] = None
remove_end_sequence: Optional[bool] = None
remove_input: Optional[bool] = None
bad_words: Optional[list] = None
temperature: Optional[float] = None
top_p: Optional[float] = None
top_k: Optional[int] = None
repetition_penalty: Optional[float] = None
num_beams: Optional[int] = None
num_return_sequences: Optional[int] = None
def __init__(
self,
max_length: Optional[int] = None,
length_no_input: Optional[bool] = None,
end_sequence: Optional[str] = None,
remove_end_sequence: Optional[bool] = None,
remove_input: Optional[bool] = None,
bad_words: Optional[list] = None,
temperature: Optional[float] = None,
top_p: Optional[float] = None,
top_k: Optional[int] = None,
repetition_penalty: Optional[float] = None,
num_beams: Optional[int] = None,
num_return_sequences: Optional[int] = 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"Token {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)
## Load Config
config = litellm.NLPCloudConfig.get_config()
for k, v in config.items():
if (
k not in optional_params
): # completion(top_k=3) > togetherai_config(top_k=3) <- allows for dynamic variables to be passed in
optional_params[k] = v
completion_url_fragment_1 = api_base
completion_url_fragment_2 = "/generation"
model = model
text = " ".join(message["content"] for message in messages)
data = {
"text": text,
**optional_params,
}
completion_url = completion_url_fragment_1 + model + completion_url_fragment_2
## LOGGING
logging_obj.pre_call(
input=text,
api_key=api_key,
additional_args={
"complete_input_dict": data,
"headers": headers,
"api_base": completion_url,
},
)
## COMPLETION CALL
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 clean_and_iterate_chunks(response)
else:
## LOGGING
logging_obj.post_call(
input=text,
api_key=api_key,
original_response=response.text,
additional_args={"complete_input_dict": data},
)
print_verbose(f"raw model_response: {response.text}")
## RESPONSE OBJECT
try:
completion_response = response.json()
except:
raise NLPCloudError(message=response.text, status_code=response.status_code)
if "error" in completion_response:
raise NLPCloudError(
message=completion_response["error"],
status_code=response.status_code,
)
else:
try:
if len(completion_response["generated_text"]) > 0:
model_response["choices"][0]["message"][
"content"
] = completion_response["generated_text"]
except:
raise NLPCloudError(
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 = completion_response["nb_input_tokens"]
completion_tokens = completion_response["nb_generated_tokens"]
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 clean_and_iterate_chunks(response):
# def process_chunk(chunk):
# print(f"received chunk: {chunk}")
# cleaned_chunk = chunk.decode("utf-8")
# # Perform further processing based on your needs
# return cleaned_chunk
# for line in response.iter_lines():
# if line:
# yield process_chunk(line)
def clean_and_iterate_chunks(response):
buffer = b""
for chunk in response.iter_content(chunk_size=1024):
if not chunk:
break
buffer += chunk
while b"\x00" in buffer:
buffer = buffer.replace(b"\x00", b"")
yield buffer.decode("utf-8")
buffer = b""
# No more data expected, yield any remaining data in the buffer
if buffer:
yield buffer.decode("utf-8")
def embedding():
# logic for parsing in - calling - parsing out model embedding calls
pass
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