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