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Revert "LiteLLM Common Base LLM Config (pt.4): Move Ollama to Base LLM Config…" (#7160)

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This reverts commit 40a22eb4c6.
This commit is contained in:
Krish Dholakia 2024-12-10 21:44:54 -08:00 • committed by GitHub
parent 1d8956a3d4
commit 488913c69f
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11 changed files with 234 additions and 322 deletions
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1
litellm/__init__.py
View file

@ -1103,7 +1103,6 @@ from .llms.vertex_ai_and_google_ai_studio.vertex_ai_partner_models.ai21.transfor
VertexAIAi21Config,
)
from .llms.ollama.completion.handler import OllamaConfig
from .llms.sagemaker.completion.transformation import SagemakerConfig
from .llms.sagemaker.chat.transformation import SagemakerChatConfig
from .llms.ollama import OllamaConfig

2
litellm/constants.py
View file

@ -33,7 +33,7 @@ LITELLM_CHAT_PROVIDERS = [
# "nlp_cloud",
# "petals",
# "oobabooga",
"ollama",
# "ollama",
# "ollama_chat",
# "deepinfra",
# "perplexity",

2
litellm/litellm_core_utils/get_supported_openai_params.py
View file

@ -29,7 +29,7 @@ def get_supported_openai_params( # noqa: PLR0915
if custom_llm_provider == "bedrock":
return litellm.AmazonConverseConfig().get_supported_openai_params(model=model)
elif custom_llm_provider == "ollama":
return litellm.OllamaConfig().get_supported_openai_params(model=model)
return litellm.OllamaConfig().get_supported_openai_params()
elif custom_llm_provider == "ollama_chat":
return litellm.OllamaChatConfig().get_supported_openai_params()
elif custom_llm_provider == "anthropic":

251
litellm/llms/ollama/completion/handler.py → litellm/llms/ollama.py
View file

@ -18,9 +18,228 @@ from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
from litellm.secret_managers.main import get_secret_str
from litellm.types.utils import ModelInfo, ProviderField, StreamingChoices
from ...prompt_templates.factory import custom_prompt, prompt_factory
from ..common_utils import OllamaError
from .transformation import OllamaConfig
from .prompt_templates.factory import custom_prompt, prompt_factory
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/ollama/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
- `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
- `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_gpu: Optional[int] = None
num_thread: Optional[int] = None
repeat_last_n: Optional[int] = None
repeat_penalty: Optional[float] = None
temperature: Optional[float] = None
seed: Optional[int] = None
stop: Optional[list] = (
None # stop is a list based on this - https://github.com/ollama/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_gpu: Optional[int] = None,
num_thread: Optional[int] = None,
repeat_last_n: Optional[int] = None,
repeat_penalty: Optional[float] = None,
temperature: Optional[float] = None,
seed: Optional[int] = 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
}
def get_required_params(self) -> List[ProviderField]:
"""For a given provider, return it's required fields with a description"""
return [
ProviderField(
field_name="base_url",
field_type="string",
field_description="Your Ollama API Base",
field_value="http://10.10.11.249:11434",
)
]
def get_supported_openai_params(
self,
):
return [
"max_tokens",
"stream",
"top_p",
"temperature",
"seed",
"frequency_penalty",
"stop",
"response_format",
]
def map_openai_params(
self, optional_params: dict, non_default_params: dict
) -> dict:
for param, value in non_default_params.items():
if param == "max_tokens":
optional_params["num_predict"] = value
if param == "stream":
optional_params["stream"] = value
if param == "temperature":
optional_params["temperature"] = value
if param == "seed":
optional_params["seed"] = value
if param == "top_p":
optional_params["top_p"] = value
if param == "frequency_penalty":
optional_params["repeat_penalty"] = value
if param == "stop":
optional_params["stop"] = value
if param == "response_format" and isinstance(value, dict):
if value["type"] == "json_object":
optional_params["format"] = "json"
return optional_params
def _supports_function_calling(self, ollama_model_info: dict) -> bool:
"""
Check if the 'template' field in the ollama_model_info contains a 'tools' or 'function' key.
"""
_template: str = str(ollama_model_info.get("template", "") or "")
return "tools" in _template.lower()
def _get_max_tokens(self, ollama_model_info: dict) -> Optional[int]:
_model_info: dict = ollama_model_info.get("model_info", {})
for k, v in _model_info.items():
if "context_length" in k:
return v
return None
def get_model_info(self, model: str) -> ModelInfo:
"""
curl http://localhost:11434/api/show -d '{
"name": "mistral"
}'
"""
if model.startswith("ollama/") or model.startswith("ollama_chat/"):
model = model.split("/", 1)[1]
api_base = get_secret_str("OLLAMA_API_BASE") or "http://localhost:11434"
try:
response = litellm.module_level_client.post(
url=f"{api_base}/api/show",
json={"name": model},
)
except Exception as e:
raise Exception(
f"OllamaError: Error getting model info for {model}. Set Ollama API Base via `OLLAMA_API_BASE` environment variable. Error: {e}"
)
model_info = response.json()
_max_tokens: Optional[int] = self._get_max_tokens(model_info)
return ModelInfo(
key=model,
litellm_provider="ollama",
mode="chat",
supported_openai_params=self.get_supported_openai_params(),
supports_function_calling=self._supports_function_calling(model_info),
input_cost_per_token=0.0,
output_cost_per_token=0.0,
max_tokens=_max_tokens,
max_input_tokens=_max_tokens,
max_output_tokens=_max_tokens,
)
# ollama wants plain base64 jpeg/png files as images. strip any leading dataURI
@ -125,11 +344,7 @@ def get_ollama_response(
url=f"{url}", json={**data, "stream": stream}, timeout=litellm.request_timeout
)
if response.status_code != 200:
raise OllamaError(
status_code=response.status_code,
message=response.text,
headers=dict(response.headers),
)
raise OllamaError(status_code=response.status_code, message=response.text)
## LOGGING
logging_obj.post_call(
@ -190,9 +405,7 @@ def ollama_completion_stream(url, data, logging_obj):
try:
if response.status_code != 200:
raise OllamaError(
status_code=response.status_code,
message=str(response.read()),
headers=response.headers,
status_code=response.status_code, message=response.read()
)
streamwrapper = litellm.CustomStreamWrapper(
@ -253,9 +466,7 @@ async def ollama_async_streaming(url, data, model_response, encoding, logging_ob
) as response:
if response.status_code != 200:
raise OllamaError(
status_code=response.status_code,
message=str(await response.aread()),
headers=dict(response.headers),
status_code=response.status_code, message=await response.aread()
)
streamwrapper = litellm.CustomStreamWrapper(
@ -325,11 +536,7 @@ async def ollama_acompletion(
if resp.status != 200:
text = await resp.text()
raise OllamaError(
status_code=resp.status,
message=text,
headers=dict(resp.headers),
)
raise OllamaError(status_code=resp.status, message=text)
## LOGGING
logging_obj.post_call(
@ -440,11 +647,7 @@ async def ollama_aembeddings(
if response.status != 200:
text = await response.text()
raise OllamaError(
status_code=response.status,
message=text,
headers=dict(response.headers),
)
raise OllamaError(status_code=response.status, message=text)
response_json = await response.json()

12
litellm/llms/ollama/common_utils.py
View file

@ -1,12 +0,0 @@
from typing import Union
import httpx
from litellm.llms.base_llm.transformation import BaseLLMException
class OllamaError(BaseLLMException):
def __init__(
self, status_code: int, message: str, headers: Union[dict, httpx.Headers]
):
super().__init__(status_code=status_code, message=message, headers=headers)

269
litellm/llms/ollama/completion/transformation.py
View file

@ -1,269 +0,0 @@
import types
from typing import TYPE_CHECKING, Any, List, Optional, Union
from httpx._models import Headers, Response
import litellm
from litellm.llms.base_llm.transformation import BaseConfig, BaseLLMException
from litellm.secret_managers.main import get_secret_str
from litellm.types.llms.openai import AllMessageValues
from litellm.types.utils import (
ModelInfo,
ModelResponse,
ProviderField,
StreamingChoices,
)
from ..common_utils import OllamaError
if TYPE_CHECKING:
from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
LiteLLMLoggingObj = _LiteLLMLoggingObj
else:
LiteLLMLoggingObj = Any
class OllamaConfig(BaseConfig):
"""
Reference: https://github.com/ollama/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
- `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
- `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_gpu: Optional[int] = None
num_thread: Optional[int] = None
repeat_last_n: Optional[int] = None
repeat_penalty: Optional[float] = None
temperature: Optional[float] = None
seed: Optional[int] = None
stop: Optional[list] = (
None # stop is a list based on this - https://github.com/ollama/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_gpu: Optional[int] = None,
num_thread: Optional[int] = None,
repeat_last_n: Optional[int] = None,
repeat_penalty: Optional[float] = None,
temperature: Optional[float] = None,
seed: Optional[int] = 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 super().get_config()
def get_required_params(self) -> List[ProviderField]:
"""For a given provider, return it's required fields with a description"""
return [
ProviderField(
field_name="base_url",
field_type="string",
field_description="Your Ollama API Base",
field_value="http://10.10.11.249:11434",
)
]
def get_supported_openai_params(self, model: str):
return [
"max_tokens",
"stream",
"top_p",
"temperature",
"seed",
"frequency_penalty",
"stop",
"response_format",
]
def map_openai_params(
self,
non_default_params: dict,
optional_params: dict,
model: str,
drop_params: bool,
) -> dict:
for param, value in non_default_params.items():
if param == "max_tokens":
optional_params["num_predict"] = value
if param == "stream":
optional_params["stream"] = value
if param == "temperature":
optional_params["temperature"] = value
if param == "seed":
optional_params["seed"] = value
if param == "top_p":
optional_params["top_p"] = value
if param == "frequency_penalty":
optional_params["repeat_penalty"] = value
if param == "stop":
optional_params["stop"] = value
if param == "response_format" and isinstance(value, dict):
if value["type"] == "json_object":
optional_params["format"] = "json"
return optional_params
def _supports_function_calling(self, ollama_model_info: dict) -> bool:
"""
Check if the 'template' field in the ollama_model_info contains a 'tools' or 'function' key.
"""
_template: str = str(ollama_model_info.get("template", "") or "")
return "tools" in _template.lower()
def _get_max_tokens(self, ollama_model_info: dict) -> Optional[int]:
_model_info: dict = ollama_model_info.get("model_info", {})
for k, v in _model_info.items():
if "context_length" in k:
return v
return None
def get_model_info(self, model: str) -> ModelInfo:
"""
curl http://localhost:11434/api/show -d '{
"name": "mistral"
}'
"""
if model.startswith("ollama/") or model.startswith("ollama_chat/"):
model = model.split("/", 1)[1]
api_base = get_secret_str("OLLAMA_API_BASE") or "http://localhost:11434"
try:
response = litellm.module_level_client.post(
url=f"{api_base}/api/show",
json={"name": model},
)
except Exception as e:
raise Exception(
f"OllamaError: Error getting model info for {model}. Set Ollama API Base via `OLLAMA_API_BASE` environment variable. Error: {e}"
)
model_info = response.json()
_max_tokens: Optional[int] = self._get_max_tokens(model_info)
return ModelInfo(
key=model,
litellm_provider="ollama",
mode="chat",
supported_openai_params=self.get_supported_openai_params(model=model),
supports_function_calling=self._supports_function_calling(model_info),
input_cost_per_token=0.0,
output_cost_per_token=0.0,
max_tokens=_max_tokens,
max_input_tokens=_max_tokens,
max_output_tokens=_max_tokens,
)
def _transform_messages(
self, messages: List[AllMessageValues]
) -> List[AllMessageValues]:
return messages
def get_error_class(
self, error_message: str, status_code: int, headers: Union[dict, Headers]
) -> BaseLLMException:
return OllamaError(
status_code=status_code, message=error_message, headers=headers
)
def transform_response(
self,
model: str,
raw_response: Response,
model_response: ModelResponse,
logging_obj: LiteLLMLoggingObj,
request_data: dict,
messages: List[AllMessageValues],
optional_params: dict,
encoding: str,
api_key: Optional[str] = None,
json_mode: Optional[bool] = None,
) -> ModelResponse:
raise NotImplementedError("transformation currently done in handler.py")
def transform_request(
self,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
headers: dict,
) -> dict:
raise NotImplementedError("transformation currently done in handler.py")
def validate_environment(
self,
headers: dict,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
api_key: Optional[str] = None,
) -> dict:
raise NotImplementedError("validation currently done in handler.py")

2
litellm/main.py
View file

@ -88,6 +88,7 @@ from .llms import (
baseten,
maritalk,
nlp_cloud,
ollama,
ollama_chat,
oobabooga,
openrouter,
@ -114,7 +115,6 @@ from .llms.databricks.chat.handler import DatabricksChatCompletion
from .llms.databricks.embed.handler import DatabricksEmbeddingHandler
from .llms.groq.chat.handler import GroqChatCompletion
from .llms.huggingface_restapi import Huggingface
from .llms.ollama.completion import handler as ollama
from .llms.openai.transcriptions.handler import OpenAIAudioTranscription
from .llms.openai.chat.o1_handler import OpenAIO1ChatCompletion
from .llms.openai.completion.handler import OpenAITextCompletion

8
litellm/utils.py
View file

@ -3308,12 +3308,6 @@ def get_optional_params( # noqa: PLR0915
optional_params = litellm.OllamaConfig().map_openai_params(
non_default_params=non_default_params,
optional_params=optional_params,
model=model,
drop_params=(
drop_params
if drop_params is not None and isinstance(drop_params, bool)
else False
),
)
elif custom_llm_provider == "ollama_chat":
supported_params = get_supported_openai_params(
@ -6319,8 +6313,6 @@ class ProviderConfigManager:
return litellm.LMStudioChatConfig()
elif litellm.LlmProviders.GALADRIEL == provider:
return litellm.GaladrielChatConfig()
elif litellm.LlmProviders.OLLAMA == provider:
return litellm.OllamaConfig()
return litellm.OpenAIGPTConfig()

2
tests/local_testing/test_get_model_info.py
View file

@ -78,7 +78,7 @@ def test_get_model_info_gemini_pro():
def test_get_model_info_ollama_chat():
from litellm.llms.ollama.completion.transformation import OllamaConfig
from litellm.llms.ollama import OllamaConfig
with patch.object(
litellm.module_level_client,

4
tests/local_testing/test_ollama.py
View file

@ -80,7 +80,7 @@ mock_ollama_embedding_response = EmbeddingResponse(model="ollama/nomic-embed-tex
@mock.patch(
"litellm.llms.ollama.completion.handler.ollama_embeddings",
"litellm.llms.ollama.ollama_embeddings",
return_value=mock_ollama_embedding_response,
)
def test_ollama_embeddings(mock_embeddings):
@ -107,7 +107,7 @@ def test_ollama_embeddings(mock_embeddings):
@mock.patch(
"litellm.llms.ollama.completion.handler.ollama_aembeddings",
"litellm.llms.ollama.ollama_aembeddings",
return_value=mock_ollama_embedding_response,
)
def test_ollama_aembeddings(mock_aembeddings):

3
tests/local_testing/test_secret_manager.py
View file

@ -198,7 +198,6 @@ def test_oidc_env_path():
del os.environ[env_var_name]
@pytest.mark.flaky(retries=6, delay=1)
def test_google_secret_manager():
"""
Test that we can get a secret from Google Secret Manager
@ -208,7 +207,7 @@ def test_google_secret_manager():
from litellm.secret_managers.google_secret_manager import GoogleSecretManager
load_vertex_ai_credentials()
# load_vertex_ai_credentials()
secret_manager = GoogleSecretManager()
secret_val = secret_manager.get_secret_from_google_secret_manager(