613 lines
25 KiB
Python
613 lines
25 KiB
Python
import json
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import time
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from litellm._uuid import uuid
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from typing import TYPE_CHECKING, Any, AsyncIterator, Iterator, List, Optional, Union
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from httpx._models import Headers, Response
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import litellm
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from litellm._logging import verbose_logger, verbose_proxy_logger
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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get_str_from_messages,
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)
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from litellm.litellm_core_utils.prompt_templates.factory import (
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convert_to_ollama_image,
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custom_prompt,
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ollama_pt,
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)
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from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
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from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.llms.openai import AllMessageValues, ChatCompletionUsageBlock
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from litellm.types.utils import (
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Delta,
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GenericStreamingChunk,
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ModelInfoBase,
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ModelResponse,
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ModelResponseStream,
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ProviderField,
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StreamingChoices,
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)
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from ..common_utils import OllamaError, _convert_image
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if TYPE_CHECKING:
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from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
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LiteLLMLoggingObj = _LiteLLMLoggingObj
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else:
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LiteLLMLoggingObj = Any
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class OllamaConfig(BaseConfig):
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"""
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Reference: https://github.com/ollama/ollama/blob/main/docs/api.md#parameters
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The class `OllamaConfig` provides the configuration for the Ollama's API interface. Below are the parameters:
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- `mirostat` (int): Enable Mirostat sampling for controlling perplexity. Default is 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0. Example usage: mirostat 0
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- `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
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- `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
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- `num_ctx` (int): Sets the size of the context window used to generate the next token. Default: 2048. Example usage: num_ctx 4096
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- `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
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- `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
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- `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
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- `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
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- `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
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- `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
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- `seed` (int): Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. Example usage: seed 42
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- `stop` (string[]): Sets the stop sequences to use. Example usage: stop "AI assistant:"
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- `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
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- `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
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- `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
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- `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
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- `system` (string): system prompt for model (overrides what is defined in the Modelfile)
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- `template` (string): the full prompt or prompt template (overrides what is defined in the Modelfile)
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"""
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mirostat: Optional[int] = None
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mirostat_eta: Optional[float] = None
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mirostat_tau: Optional[float] = None
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num_ctx: Optional[int] = None
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num_gqa: Optional[int] = None
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num_gpu: Optional[int] = None
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num_thread: Optional[int] = None
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repeat_last_n: Optional[int] = None
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repeat_penalty: Optional[float] = None
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temperature: Optional[float] = None
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seed: Optional[int] = None
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stop: Optional[
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list
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] = None # stop is a list based on this - https://github.com/ollama/ollama/pull/442
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tfs_z: Optional[float] = None
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num_predict: Optional[int] = None
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top_k: Optional[int] = None
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top_p: Optional[float] = None
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system: Optional[str] = None
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template: Optional[str] = None
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def __init__(
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self,
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mirostat: Optional[int] = None,
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mirostat_eta: Optional[float] = None,
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mirostat_tau: Optional[float] = None,
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num_ctx: Optional[int] = None,
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num_gqa: Optional[int] = None,
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num_gpu: Optional[int] = None,
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num_thread: Optional[int] = None,
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repeat_last_n: Optional[int] = None,
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repeat_penalty: Optional[float] = None,
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temperature: Optional[float] = None,
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seed: Optional[int] = None,
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stop: Optional[list] = None,
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tfs_z: Optional[float] = None,
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num_predict: Optional[int] = None,
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top_k: Optional[int] = None,
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top_p: Optional[float] = None,
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system: Optional[str] = None,
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template: Optional[str] = None,
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) -> None:
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locals_ = locals().copy()
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for key, value in locals_.items():
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if key != "self" and value is not None:
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setattr(self.__class__, key, value)
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@classmethod
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def get_config(cls):
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return super().get_config()
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def get_required_params(self) -> List[ProviderField]:
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"""For a given provider, return it's required fields with a description"""
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return [
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ProviderField(
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field_name="base_url",
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field_type="string",
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field_description="Your Ollama API Base",
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field_value="http://10.10.11.249:11434",
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)
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]
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def get_supported_openai_params(self, model: str):
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return [
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"max_tokens",
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"stream",
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"top_p",
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"temperature",
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"seed",
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"frequency_penalty",
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"stop",
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"response_format",
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"max_completion_tokens",
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"reasoning_effort",
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]
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def map_openai_params(
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self,
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non_default_params: dict,
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optional_params: dict,
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model: str,
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drop_params: bool,
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) -> dict:
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for param, value in non_default_params.items():
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if param == "max_tokens" or param == "max_completion_tokens":
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optional_params["num_predict"] = value
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elif param == "stream":
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optional_params["stream"] = value
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elif param == "temperature":
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optional_params["temperature"] = value
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elif param == "seed":
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optional_params["seed"] = value
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elif param == "top_p":
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optional_params["top_p"] = value
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elif param == "frequency_penalty":
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optional_params["frequency_penalty"] = value
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elif param == "stop":
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optional_params["stop"] = value
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elif param == "reasoning_effort" and value is not None:
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if model.startswith("gpt-oss"):
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optional_params["think"] = value
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else:
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optional_params["think"] = value in {"low", "medium", "high"}
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elif param == "response_format" and isinstance(value, dict):
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if value["type"] == "json_object":
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optional_params["format"] = "json"
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elif value["type"] == "json_schema":
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optional_params["format"] = value["json_schema"]["schema"]
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return optional_params
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def _supports_function_calling(self, ollama_model_info: dict) -> bool:
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"""
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Check if the 'template' field in the ollama_model_info contains a 'tools' or 'function' key.
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"""
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_template: str = str(ollama_model_info.get("template", "") or "")
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return "tools" in _template.lower()
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def _get_max_tokens(self, ollama_model_info: dict) -> Optional[int]:
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_model_info: dict = ollama_model_info.get("model_info", {})
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for k, v in _model_info.items():
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if "context_length" in k:
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return v
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return None
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@staticmethod
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def get_api_key() -> Optional[str]:
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"""Get API key from environment variables or litellm configuration"""
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import os
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import litellm
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from litellm.secret_managers.main import get_secret_str
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return (
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os.environ.get("OLLAMA_API_KEY")
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or litellm.api_key
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or litellm.openai_key
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or get_secret_str("OLLAMA_API_KEY")
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)
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def get_model_info(
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self, model: str, api_base: Optional[str] = None
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) -> ModelInfoBase:
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"""
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curl http://localhost:11434/api/show -d '{
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"name": "mistral"
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}'
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"""
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if model.startswith("ollama/") or model.startswith("ollama_chat/"):
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model = model.split("/", 1)[1]
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api_base = (
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api_base or get_secret_str("OLLAMA_API_BASE") or "http://localhost:11434"
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)
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api_key = self.get_api_key()
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headers = {"Authorization": f"Bearer {api_key}"} if api_key else {}
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try:
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response = litellm.module_level_client.post(
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url=f"{api_base}/api/show",
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json={"name": model},
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headers=headers,
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)
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except Exception as e:
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verbose_logger.debug(
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"OllamaError: Could not get model info for %s from %s. Error: %s",
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model,
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api_base,
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e,
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)
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return ModelInfoBase(
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key=model,
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litellm_provider="ollama",
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mode="chat",
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input_cost_per_token=0.0,
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output_cost_per_token=0.0,
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max_tokens=None,
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max_input_tokens=None,
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max_output_tokens=None,
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)
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model_info = response.json()
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_max_tokens: Optional[int] = self._get_max_tokens(model_info)
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return ModelInfoBase(
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key=model,
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litellm_provider="ollama",
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mode="chat",
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supports_function_calling=self._supports_function_calling(model_info),
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input_cost_per_token=0.0,
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output_cost_per_token=0.0,
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max_tokens=_max_tokens,
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max_input_tokens=_max_tokens,
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max_output_tokens=_max_tokens,
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)
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def get_error_class(
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self, error_message: str, status_code: int, headers: Union[dict, Headers]
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) -> BaseLLMException:
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return OllamaError(
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status_code=status_code, message=error_message, headers=headers
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)
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def transform_response(
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self,
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model: str,
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raw_response: Response,
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model_response: ModelResponse,
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logging_obj: LiteLLMLoggingObj,
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request_data: dict,
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messages: List[AllMessageValues],
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optional_params: dict,
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litellm_params: dict,
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encoding: str,
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api_key: Optional[str] = None,
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json_mode: Optional[bool] = None,
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) -> ModelResponse:
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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_parse_content_for_reasoning,
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)
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response_json = raw_response.json()
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## RESPONSE OBJECT
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model_response.choices[0].finish_reason = "stop"
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if request_data.get("format", "") == "json":
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# Check if response field exists and is not empty before parsing JSON
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response_text = response_json.get("response", "")
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if not response_text or not response_text.strip():
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# Handle empty response gracefully - set empty content
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message = litellm.Message(content="")
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model_response.choices[0].message = message # type: ignore
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model_response.choices[0].finish_reason = "stop"
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else:
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try:
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response_content = json.loads(response_text)
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# Check if this is a function call format with name/arguments structure
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if (
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isinstance(response_content, dict)
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and "name" in response_content
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and "arguments" in response_content
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):
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# Handle as function call (original behavior)
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function_call = response_content
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message = litellm.Message(
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content=None,
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tool_calls=[
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{
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"id": f"call_{str(uuid.uuid4())}",
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"function": {
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"name": function_call["name"],
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"arguments": json.dumps(
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function_call["arguments"]
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),
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},
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"type": "function",
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}
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],
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)
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model_response.choices[0].message = message # type: ignore
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model_response.choices[0].finish_reason = "tool_calls"
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else:
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# Handle as regular JSON (new behavior)
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message = litellm.Message(
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content=json.dumps(response_content),
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)
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model_response.choices[0].message = message # type: ignore
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model_response.choices[0].finish_reason = "stop"
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except json.JSONDecodeError:
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# If JSON parsing fails, treat as regular text response
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## output parse reasoning content from response_text
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reasoning_content: Optional[str] = None
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content: Optional[str] = None
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if response_text is not None:
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reasoning_content, content = _parse_content_for_reasoning(
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response_text
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)
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message = litellm.Message(
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content=content, reasoning_content=reasoning_content
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)
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model_response.choices[0].message = message # type: ignore
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model_response.choices[0].finish_reason = "stop"
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else:
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response_text = response_json.get("response", "")
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content = None
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reasoning_content = None
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if response_text is not None and isinstance(response_text, str):
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reasoning_content, content = _parse_content_for_reasoning(response_text)
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else:
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content = response_text # type: ignore
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model_response.choices[0].message.content = content # type: ignore
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model_response.choices[0].message.reasoning_content = reasoning_content # type: ignore
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model_response.created = int(time.time())
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model_response.model = "ollama/" + model
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_prompt = request_data.get("prompt", "")
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prompt_tokens = response_json.get(
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"prompt_eval_count", len(encoding.encode(_prompt, disallowed_special=())) # type: ignore
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)
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completion_tokens = response_json.get(
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"eval_count", len(response_json.get("message", dict()).get("content", ""))
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)
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setattr(
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model_response,
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"usage",
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litellm.Usage(
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=prompt_tokens + completion_tokens,
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),
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)
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return model_response
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def transform_request(
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self,
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model: str,
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messages: List[AllMessageValues],
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optional_params: dict,
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litellm_params: dict,
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headers: dict,
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) -> dict:
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custom_prompt_dict = (
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litellm_params.get("custom_prompt_dict") or litellm.custom_prompt_dict
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)
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text_completion_request = litellm_params.get("text_completion")
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if model in custom_prompt_dict:
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# check if the model has a registered custom prompt
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model_prompt_details = custom_prompt_dict[model]
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ollama_prompt = custom_prompt(
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role_dict=model_prompt_details["roles"],
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initial_prompt_value=model_prompt_details["initial_prompt_value"],
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final_prompt_value=model_prompt_details["final_prompt_value"],
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messages=messages,
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)
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elif text_completion_request: # handle `/completions` requests
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ollama_prompt = get_str_from_messages(messages=messages)
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else: # handle `/chat/completions` requests
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modified_prompt = ollama_pt(model=model, messages=messages)
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if isinstance(modified_prompt, dict):
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ollama_prompt, images = (
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modified_prompt["prompt"],
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modified_prompt["images"],
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)
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optional_params["images"] = images
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else:
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ollama_prompt = modified_prompt
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stream = optional_params.pop("stream", False)
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format = optional_params.pop("format", None)
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images = optional_params.pop("images", None)
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think = optional_params.pop("think", None)
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data = {
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"model": model,
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"prompt": ollama_prompt,
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"options": optional_params,
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"stream": stream,
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}
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if format is not None:
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data["format"] = format
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if images is not None:
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data["images"] = [
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_convert_image(convert_to_ollama_image(image)) for image in images
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]
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if think is not None:
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data["think"] = think
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return data
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def validate_environment(
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self,
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headers: dict,
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model: str,
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messages: List[AllMessageValues],
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optional_params: dict,
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litellm_params: dict,
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api_key: Optional[str] = None,
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api_base: Optional[str] = None,
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) -> dict:
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return headers
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def get_complete_url(
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self,
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api_base: Optional[str],
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api_key: Optional[str],
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model: str,
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optional_params: dict,
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litellm_params: dict,
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stream: Optional[bool] = None,
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) -> str:
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"""
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OPTIONAL
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Get the complete url for the request
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Some providers need `model` in `api_base`
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"""
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|
if api_base is None:
|
|
api_base = "http://localhost:11434"
|
|
if api_base.endswith("/api/generate"):
|
|
url = api_base
|
|
else:
|
|
url = f"{api_base}/api/generate"
|
|
|
|
return url
|
|
|
|
def get_model_response_iterator(
|
|
self,
|
|
streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
|
|
sync_stream: bool,
|
|
json_mode: Optional[bool] = False,
|
|
):
|
|
return OllamaTextCompletionResponseIterator(
|
|
streaming_response=streaming_response,
|
|
sync_stream=sync_stream,
|
|
json_mode=json_mode,
|
|
)
|
|
|
|
|
|
class OllamaTextCompletionResponseIterator(BaseModelResponseIterator):
|
|
def __init__(
|
|
self, streaming_response, sync_stream: bool, json_mode: Optional[bool] = False
|
|
):
|
|
super().__init__(streaming_response, sync_stream, json_mode)
|
|
self.started_reasoning_content: bool = False
|
|
self.finished_reasoning_content: bool = False
|
|
|
|
def _handle_string_chunk(
|
|
self, str_line: str
|
|
) -> Union[GenericStreamingChunk, ModelResponseStream]:
|
|
return self.chunk_parser(json.loads(str_line))
|
|
|
|
def chunk_parser(
|
|
self, chunk: dict
|
|
) -> Union[GenericStreamingChunk, ModelResponseStream]:
|
|
try:
|
|
if "error" in chunk:
|
|
raise Exception(f"Ollama Error - {chunk}")
|
|
|
|
text = ""
|
|
is_finished = False
|
|
finish_reason = None
|
|
if chunk["done"] is True:
|
|
text = ""
|
|
is_finished = True
|
|
finish_reason = "stop"
|
|
prompt_eval_count: Optional[int] = chunk.get("prompt_eval_count", None)
|
|
eval_count: Optional[int] = chunk.get("eval_count", None)
|
|
|
|
usage: Optional[ChatCompletionUsageBlock] = None
|
|
if prompt_eval_count is not None and eval_count is not None:
|
|
usage = ChatCompletionUsageBlock(
|
|
prompt_tokens=prompt_eval_count,
|
|
completion_tokens=eval_count,
|
|
total_tokens=prompt_eval_count + eval_count,
|
|
)
|
|
return GenericStreamingChunk(
|
|
text=text,
|
|
is_finished=is_finished,
|
|
finish_reason=finish_reason,
|
|
usage=usage,
|
|
)
|
|
elif chunk["response"]:
|
|
text = chunk["response"]
|
|
reasoning_content: Optional[str] = None
|
|
content: Optional[str] = None
|
|
if text is not None:
|
|
if "<think>" in text:
|
|
text = text.replace("<think>", "")
|
|
self.started_reasoning_content = True
|
|
elif "</think>" in text:
|
|
text = text.replace("</think>", "")
|
|
self.finished_reasoning_content = True
|
|
|
|
if (
|
|
self.started_reasoning_content
|
|
and not self.finished_reasoning_content
|
|
):
|
|
reasoning_content = text
|
|
else:
|
|
content = text
|
|
|
|
return ModelResponseStream(
|
|
choices=[
|
|
StreamingChoices(
|
|
index=0,
|
|
delta=Delta(
|
|
reasoning_content=reasoning_content, content=content
|
|
),
|
|
)
|
|
],
|
|
finish_reason=finish_reason,
|
|
usage=None,
|
|
)
|
|
# return GenericStreamingChunk(
|
|
# text=text,
|
|
# is_finished=is_finished,
|
|
# finish_reason="stop",
|
|
# usage=None,
|
|
# )
|
|
elif "thinking" in chunk and not chunk["response"]:
|
|
# Return reasoning content as ModelResponseStream so UIs can render it
|
|
thinking_content = chunk.get("thinking") or ""
|
|
return ModelResponseStream(
|
|
choices=[
|
|
StreamingChoices(
|
|
index=0,
|
|
delta=Delta(reasoning_content=thinking_content),
|
|
)
|
|
]
|
|
)
|
|
else:
|
|
# In this case, 'thinking' is not present in the chunk, chunk["done"] is false,
|
|
# and chunk["response"] is falsy (None or empty string),
|
|
# but Ollama is just starting to stream, so it should be processed as a normal dict
|
|
return ModelResponseStream(
|
|
choices=[
|
|
StreamingChoices(
|
|
index=0,
|
|
delta=Delta(reasoning_content=""),
|
|
)
|
|
]
|
|
)
|
|
# raise Exception(f"Unable to parse ollama chunk - {chunk}")
|
|
except Exception as e:
|
|
verbose_proxy_logger.error(f"Unable to parse ollama chunk - {chunk}")
|
|
raise e
|