import requests, types import json import traceback from typing import Optional import litellm import httpx try: from async_generator import async_generator, yield_ # optional dependency async_generator_imported = True except ImportError: async_generator_imported = False # this should not throw an error, it will impact the 'import litellm' statement class OllamaError(Exception): def __init__(self, status_code, message): self.status_code = status_code self.message = message self.request = httpx.Request(method="POST", url="http://localhost:11434") self.response = httpx.Response(status_code=status_code, request=self.request) super().__init__( self.message ) # Call the base class constructor with the parameters it needs class OllamaConfig(): """ Reference: https://github.com/jmorganca/ollama/blob/main/docs/api.md#parameters The class `OllamaConfig` provides the configuration for the Ollama's API interface. Below are the parameters: - `mirostat` (int): Enable Mirostat sampling for controlling perplexity. Default is 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0. Example usage: mirostat 0 - `mirostat_eta` (float): Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. Default: 0.1. Example usage: mirostat_eta 0.1 - `mirostat_tau` (float): Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. Default: 5.0. Example usage: mirostat_tau 5.0 - `num_ctx` (int): Sets the size of the context window used to generate the next token. Default: 2048. Example usage: num_ctx 4096 - `num_gqa` (int): The number of GQA groups in the transformer layer. Required for some models, for example it is 8 for llama2:70b. Example usage: num_gqa 1 - `num_gpu` (int): The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support, 0 to disable. Example usage: num_gpu 0 - `num_thread` (int): Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). Example usage: num_thread 8 - `repeat_last_n` (int): Sets how far back for the model to look back to prevent repetition. Default: 64, 0 = disabled, -1 = num_ctx. Example usage: repeat_last_n 64 - `repeat_penalty` (float): Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. Default: 1.1. Example usage: repeat_penalty 1.1 - `temperature` (float): The temperature of the model. Increasing the temperature will make the model answer more creatively. Default: 0.8. Example usage: temperature 0.7 - `stop` (string[]): Sets the stop sequences to use. Example usage: stop "AI assistant:" - `tfs_z` (float): Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. Default: 1. Example usage: tfs_z 1 - `num_predict` (int): Maximum number of tokens to predict when generating text. Default: 128, -1 = infinite generation, -2 = fill context. Example usage: num_predict 42 - `top_k` (int): Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. Default: 40. Example usage: top_k 40 - `top_p` (float): Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. Default: 0.9. Example usage: top_p 0.9 - `system` (string): system prompt for model (overrides what is defined in the Modelfile) - `template` (string): the full prompt or prompt template (overrides what is defined in the Modelfile) """ mirostat: Optional[int]=None mirostat_eta: Optional[float]=None mirostat_tau: Optional[float]=None num_ctx: Optional[int]=None num_gqa: Optional[int]=None num_thread: Optional[int]=None repeat_last_n: Optional[int]=None repeat_penalty: Optional[float]=None temperature: Optional[float]=None stop: Optional[list]=None # stop is a list based on this - https://github.com/jmorganca/ollama/pull/442 tfs_z: Optional[float]=None num_predict: Optional[int]=None top_k: Optional[int]=None top_p: Optional[float]=None system: Optional[str]=None template: Optional[str]=None def __init__(self, mirostat: Optional[int]=None, mirostat_eta: Optional[float]=None, mirostat_tau: Optional[float]=None, num_ctx: Optional[int]=None, num_gqa: Optional[int]=None, num_thread: Optional[int]=None, repeat_last_n: Optional[int]=None, repeat_penalty: Optional[float]=None, temperature: Optional[float]=None, stop: Optional[list]=None, tfs_z: Optional[float]=None, num_predict: Optional[int]=None, top_k: Optional[int]=None, top_p: Optional[float]=None, system: Optional[str]=None, template: Optional[str]=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} # ollama implementation def get_ollama_response_stream( api_base="http://localhost:11434", model="llama2", prompt="Why is the sky blue?", optional_params=None, logging_obj=None, ): if api_base.endswith("/api/generate"): url = api_base else: url = f"{api_base}/api/generate" ## Load Config config=litellm.OllamaConfig.get_config() for k, v in config.items(): if k not in optional_params: # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in optional_params[k] = v data = { "model": model, "prompt": prompt, **optional_params } ## LOGGING logging_obj.pre_call( input=None, api_key=None, additional_args={"api_base": url, "complete_input_dict": data}, ) session = requests.Session() with session.post(url, json=data, stream=True) as resp: if resp.status_code != 200: raise OllamaError(status_code=resp.status_code, message=resp.text) for line in resp.iter_lines(): if line: try: json_chunk = line.decode("utf-8") chunks = json_chunk.split("\n") for chunk in chunks: if chunk.strip() != "": j = json.loads(chunk) if "error" in j: completion_obj = { "role": "assistant", "content": "", "error": j } yield completion_obj if "response" in j: completion_obj = { "role": "assistant", "content": "", } completion_obj["content"] = j["response"] yield completion_obj except Exception as e: traceback.print_exc() session.close() if async_generator_imported: # ollama implementation @async_generator async def async_get_ollama_response_stream( api_base="http://localhost:11434", model="llama2", prompt="Why is the sky blue?", optional_params=None, logging_obj=None, ): url = f"{api_base}/api/generate" ## Load Config config=litellm.OllamaConfig.get_config() for k, v in config.items(): if k not in optional_params: # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in optional_params[k] = v data = { "model": model, "prompt": prompt, **optional_params } ## LOGGING logging_obj.pre_call( input=None, api_key=None, additional_args={"api_base": url, "complete_input_dict": data}, ) session = requests.Session() with session.post(url, json=data, stream=True) as resp: if resp.status_code != 200: raise OllamaError(status_code=resp.status_code, message=resp.text) for line in resp.iter_lines(): if line: try: json_chunk = line.decode("utf-8") chunks = json_chunk.split("\n") for chunk in chunks: if chunk.strip() != "": j = json.loads(chunk) if "error" in j: completion_obj = { "role": "assistant", "content": "", "error": j } await yield_({"choices": [{"delta": completion_obj}]}) if "response" in j: completion_obj = { "role": "assistant", "content": "", } completion_obj["content"] = j["response"] await yield_({"choices": [{"delta": completion_obj}]}) except Exception as e: import logging logging.debug(f"Error decoding JSON: {e}") session.close()