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import os |
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import json |
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from enum import Enum |
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import requests |
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import time |
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from typing import Callable, Optional |
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from litellm.utils import ModelResponse, Usage |
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from .prompt_templates.factory import prompt_factory, custom_prompt |
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class OobaboogaError(Exception): |
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def __init__(self, status_code, message): |
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self.status_code = status_code |
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self.message = message |
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super().__init__( |
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self.message |
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) |
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def validate_environment(api_key): |
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headers = { |
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"accept": "application/json", |
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"content-type": "application/json", |
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} |
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if api_key: |
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headers["Authorization"] = f"Token {api_key}" |
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return headers |
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def completion( |
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model: str, |
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messages: list, |
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api_base: Optional[str], |
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model_response: ModelResponse, |
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print_verbose: Callable, |
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encoding, |
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api_key, |
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logging_obj, |
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custom_prompt_dict={}, |
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optional_params=None, |
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litellm_params=None, |
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logger_fn=None, |
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default_max_tokens_to_sample=None, |
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): |
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headers = validate_environment(api_key) |
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if "https" in model: |
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completion_url = model |
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elif api_base: |
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completion_url = api_base |
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else: |
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raise OobaboogaError( |
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status_code=404, |
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message="API Base not set. Set one via completion(..,api_base='your-api-url')", |
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) |
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model = model |
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completion_url = completion_url + "/v1/chat/completions" |
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data = { |
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"messages": messages, |
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**optional_params, |
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} |
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logging_obj.pre_call( |
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input=messages, |
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api_key=api_key, |
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additional_args={"complete_input_dict": data}, |
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) |
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response = requests.post( |
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completion_url, |
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headers=headers, |
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data=json.dumps(data), |
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stream=optional_params["stream"] if "stream" in optional_params else False, |
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) |
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if "stream" in optional_params and optional_params["stream"] == True: |
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return response.iter_lines() |
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else: |
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logging_obj.post_call( |
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input=messages, |
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api_key=api_key, |
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original_response=response.text, |
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additional_args={"complete_input_dict": data}, |
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) |
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print_verbose(f"raw model_response: {response.text}") |
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try: |
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completion_response = response.json() |
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except: |
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raise OobaboogaError( |
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message=response.text, status_code=response.status_code |
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) |
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if "error" in completion_response: |
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raise OobaboogaError( |
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message=completion_response["error"], |
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status_code=response.status_code, |
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) |
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else: |
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try: |
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model_response["choices"][0]["message"][ |
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"content" |
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] = completion_response["choices"][0]["message"]["content"] |
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except: |
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raise OobaboogaError( |
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message=json.dumps(completion_response), |
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status_code=response.status_code, |
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) |
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model_response["created"] = int(time.time()) |
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model_response["model"] = model |
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usage = Usage( |
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prompt_tokens=completion_response["usage"]["prompt_tokens"], |
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completion_tokens=completion_response["usage"]["completion_tokens"], |
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total_tokens=completion_response["usage"]["total_tokens"], |
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) |
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model_response.usage = usage |
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return model_response |
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def embedding( |
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model: str, |
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input: list, |
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api_key: Optional[str] = None, |
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api_base: Optional[str] = None, |
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logging_obj=None, |
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model_response=None, |
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optional_params=None, |
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encoding=None, |
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): |
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if "https" in model: |
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embeddings_url = model |
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elif api_base: |
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embeddings_url = f"{api_base}/v1/embeddings" |
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else: |
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raise OobaboogaError( |
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status_code=404, |
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message="API Base not set. Set one via completion(..,api_base='your-api-url')", |
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) |
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data = {"input": input} |
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if optional_params: |
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data.update(optional_params) |
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if logging_obj: |
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logging_obj.pre_call( |
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input=input, api_key=api_key, additional_args={"complete_input_dict": data} |
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) |
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headers = validate_environment(api_key) |
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response = requests.post(embeddings_url, headers=headers, json=data) |
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if not response.ok: |
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raise OobaboogaError(message=response.text, status_code=response.status_code) |
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completion_response = response.json() |
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if "error" in completion_response: |
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raise OobaboogaError( |
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message=completion_response["error"], |
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status_code=completion_response.get("status_code", 500), |
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) |
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model_response["data"] = [ |
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{ |
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"embedding": completion_response["data"][0]["embedding"], |
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"index": 0, |
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"object": "embedding", |
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} |
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] |
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num_tokens = len(completion_response["data"][0]["embedding"]) |
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model_response.usage = Usage(prompt_tokens=num_tokens, total_tokens=num_tokens) |
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model_response["object"] = "list" |
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model_response["model"] = model |
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return model_response |
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