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import os
import json
from enum import Enum
import requests
import time
from typing import Callable, Optional
from litellm.utils import ModelResponse, Usage
from .prompt_templates.factory import prompt_factory, custom_prompt
class OobaboogaError(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
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: Optional[str],
model_response: ModelResponse,
print_verbose: Callable,
encoding,
api_key,
logging_obj,
custom_prompt_dict={},
optional_params=None,
litellm_params=None,
logger_fn=None,
default_max_tokens_to_sample=None,
):
headers = validate_environment(api_key)
if "https" in model:
completion_url = model
elif api_base:
completion_url = api_base
else:
raise OobaboogaError(
status_code=404,
message="API Base not set. Set one via completion(..,api_base='your-api-url')",
)
model = model
completion_url = completion_url + "/v1/chat/completions"
data = {
"messages": messages,
**optional_params,
}
## LOGGING
logging_obj.pre_call(
input=messages,
api_key=api_key,
additional_args={"complete_input_dict": data},
)
## 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 response.iter_lines()
else:
## LOGGING
logging_obj.post_call(
input=messages,
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 OobaboogaError(
message=response.text, status_code=response.status_code
)
if "error" in completion_response:
raise OobaboogaError(
message=completion_response["error"],
status_code=response.status_code,
)
else:
try:
model_response["choices"][0]["message"][
"content"
] = completion_response["choices"][0]["message"]["content"]
except:
raise OobaboogaError(
message=json.dumps(completion_response),
status_code=response.status_code,
)
model_response["created"] = int(time.time())
model_response["model"] = model
usage = Usage(
prompt_tokens=completion_response["usage"]["prompt_tokens"],
completion_tokens=completion_response["usage"]["completion_tokens"],
total_tokens=completion_response["usage"]["total_tokens"],
)
model_response.usage = usage
return model_response
def embedding(
model: str,
input: list,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
logging_obj=None,
model_response=None,
optional_params=None,
encoding=None,
):
# Create completion URL
if "https" in model:
embeddings_url = model
elif api_base:
embeddings_url = f"{api_base}/v1/embeddings"
else:
raise OobaboogaError(
status_code=404,
message="API Base not set. Set one via completion(..,api_base='your-api-url')",
)
# Prepare request data
data = {"input": input}
if optional_params:
data.update(optional_params)
# Logging before API call
if logging_obj:
logging_obj.pre_call(
input=input, api_key=api_key, additional_args={"complete_input_dict": data}
)
# Send POST request
headers = validate_environment(api_key)
response = requests.post(embeddings_url, headers=headers, json=data)
if not response.ok:
raise OobaboogaError(message=response.text, status_code=response.status_code)
completion_response = response.json()
# Check for errors in response
if "error" in completion_response:
raise OobaboogaError(
message=completion_response["error"],
status_code=completion_response.get("status_code", 500),
)
# Process response data
model_response["data"] = [
{
"embedding": completion_response["data"][0]["embedding"],
"index": 0,
"object": "embedding",
}
]
num_tokens = len(completion_response["data"][0]["embedding"])
# Adding metadata to response
model_response.usage = Usage(prompt_tokens=num_tokens, total_tokens=num_tokens)
model_response["object"] = "list"
model_response["model"] = model
return model_response
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