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Ollama Chat - parse tool calls on streaming (#11171)

Browse source 
* fix(user_api_key_auth.py): fix else block

Fixes https://github.com/BerriAI/litellm/issues/11170

* refactor(ollama/chat): refactor to base config pattern

easier to maintain fixes

* fix(ollama/chat): support tool call parsing on streaming

Closes https://github.com/BerriAI/litellm/issues/11104

* test: update import location

* fix: cleanup unused import

* fix: fix ruff check error

* test: update import

* test: update test on ci

* ci: cleanup

* fix: fix chekc

* fix: fix api key check order

* test: fix import

* ci: fix script

* test: fix imports

* fix: fix tests
This commit is contained in:
Krish Dholakia 2025-05-27 16:14:49 -07:00 • committed by GitHub
parent 64096aeb1e
commit 4c82dd9b27
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17 changed files with 537 additions and 277 deletions
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2
.circleci/config.yml
View file

@ -931,7 +931,7 @@ jobs:
command: |
pwd
ls
python -m pytest -vv tests/litellm --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit-litellm.xml --durations=10 -n 4
python -m pytest -vv tests/test_litellm --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit-litellm.xml --durations=10 -n 4
no_output_timeout: 120m
- run:
name: Run enterprise tests

2
.github/workflows/test-litellm.yml vendored
View file

@ -1,4 +1,4 @@
name: LiteLLM Mock Tests (folder - tests/litellm)
name: LiteLLM Mock Tests (folder - tests/test_litellm)
on:
pull_request:

4
Makefile
View file

@ -26,10 +26,10 @@ test:
poetry run pytest tests/
test-unit:
poetry run pytest tests/litellm/
poetry run pytest tests/test_litellm/
test-integration:
poetry run pytest tests/ -k "not litellm"
poetry run pytest tests/ -k "not test_litellm"
test-unit-helm:
helm unittest -f 'tests/*.yaml' deploy/charts/litellm-helm

3
litellm/__init__.py
View file

@ -929,11 +929,10 @@ from .llms.vertex_ai.vertex_ai_partner_models.llama3.transformation import (
from .llms.vertex_ai.vertex_ai_partner_models.ai21.transformation import (
VertexAIAi21Config,
)
from .llms.ollama.chat.transformation import OllamaChatConfig
from .llms.ollama.completion.transformation import OllamaConfig
from .llms.sagemaker.completion.transformation import SagemakerConfig
from .llms.sagemaker.chat.transformation import SagemakerChatConfig
from .llms.ollama_chat import OllamaChatConfig
from .llms.bedrock.chat.invoke_handler import (
AmazonCohereChatConfig,
bedrock_tool_name_mappings,

44
litellm/litellm_core_utils/streaming_handler.py
View file

@ -549,41 +549,6 @@ class CustomStreamWrapper:
)
return ""
def handle_ollama_chat_stream(self, chunk):
# for ollama_chat/ provider
try:
if isinstance(chunk, dict):
json_chunk = chunk
else:
json_chunk = json.loads(chunk)
if "error" in json_chunk:
raise Exception(f"Ollama Error - {json_chunk}")
text = ""
is_finished = False
finish_reason = None
if json_chunk["done"] is True:
text = ""
is_finished = True
finish_reason = "stop"
return {
"text": text,
"is_finished": is_finished,
"finish_reason": finish_reason,
}
elif "message" in json_chunk:
print_verbose(f"delta content: {json_chunk}")
text = json_chunk["message"]["content"]
return {
"text": text,
"is_finished": is_finished,
"finish_reason": finish_reason,
}
else:
raise Exception(f"Ollama Error - {json_chunk}")
except Exception as e:
raise e
def handle_triton_stream(self, chunk):
try:
if isinstance(chunk, dict):
@ -1142,12 +1107,6 @@ class CustomStreamWrapper:
new_chunk = self.completion_stream[:chunk_size]
completion_obj["content"] = new_chunk
self.completion_stream = self.completion_stream[chunk_size:]
elif self.custom_llm_provider == "ollama_chat":
response_obj = self.handle_ollama_chat_stream(chunk)
completion_obj["content"] = response_obj["text"]
print_verbose(f"completion obj content: {completion_obj['content']}")
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
elif self.custom_llm_provider == "triton":
response_obj = self.handle_triton_stream(chunk)
completion_obj["content"] = response_obj["text"]
@ -1198,6 +1157,7 @@ class CustomStreamWrapper:
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
elif self.custom_llm_provider == "cached_response":
chunk = cast(ModelResponseStream, chunk)
response_obj = {
"text": chunk.choices[0].delta.content,
"is_finished": True,
@ -1225,7 +1185,7 @@ class CustomStreamWrapper:
if self.custom_llm_provider == "azure":
if isinstance(chunk, BaseModel) and hasattr(chunk, "model"):
# for azure, we need to pass the model from the orignal chunk
self.model = chunk.model
self.model = getattr(chunk, "model", self.model)
response_obj = self.handle_openai_chat_completion_chunk(chunk)
if response_obj is None:
return

483
litellm/llms/ollama/chat/transformation.py Normal file
View file

@ -0,0 +1,483 @@
import json
import time
import uuid
from typing import (
TYPE_CHECKING,
Any,
AsyncIterator,
Iterator,
List,
Optional,
Union,
cast,
)
from httpx._models import Headers, Response
from pydantic import BaseModel
import litellm
from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
from litellm.types.llms.ollama import OllamaToolCall, OllamaToolCallFunction
from litellm.types.llms.openai import (
AllMessageValues,
ChatCompletionAssistantToolCall,
ChatCompletionUsageBlock,
)
from litellm.types.utils import ModelResponse, ModelResponseStream
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 OllamaChatConfig(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_thread: Optional[int] = None
repeat_last_n: Optional[int] = None
repeat_penalty: Optional[float] = None
seed: Optional[int] = None
tfs_z: Optional[float] = None
num_predict: Optional[int] = None
top_k: Optional[int] = 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,
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().copy()
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_supported_openai_params(self, model: str):
return [
"max_tokens",
"max_completion_tokens",
"stream",
"top_p",
"temperature",
"seed",
"frequency_penalty",
"stop",
"tools",
"tool_choice",
"functions",
"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" or param == "max_completion_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)
and value.get("type") == "json_object"
):
optional_params["format"] = "json"
if (
param == "response_format"
and isinstance(value, dict)
and value.get("type") == "json_schema"
):
if value.get("json_schema") and value["json_schema"].get("schema"):
optional_params["format"] = value["json_schema"]["schema"]
### FUNCTION CALLING LOGIC ###
if param == "tools":
## CHECK IF MODEL SUPPORTS TOOL CALLING ##
try:
model_info = litellm.get_model_info(
model=model, custom_llm_provider="ollama"
)
if model_info.get("supports_function_calling") is True:
optional_params["tools"] = value
else:
raise Exception
except Exception:
optional_params["format"] = "json"
litellm.add_function_to_prompt = (
True # so that main.py adds the function call to the prompt
)
optional_params["functions_unsupported_model"] = value
if len(optional_params["functions_unsupported_model"]) == 1:
optional_params["function_name"] = optional_params[
"functions_unsupported_model"
][0]["function"]["name"]
if param == "functions":
## CHECK IF MODEL SUPPORTS TOOL CALLING ##
try:
model_info = litellm.get_model_info(
model=model, custom_llm_provider="ollama"
)
if model_info.get("supports_function_calling") is True:
optional_params["tools"] = value
else:
raise Exception
except Exception:
optional_params["format"] = "json"
litellm.add_function_to_prompt = (
True # so that main.py adds the function call to the prompt
)
optional_params[
"functions_unsupported_model"
] = non_default_params.get("functions")
non_default_params.pop("tool_choice", None) # causes ollama requests to hang
non_default_params.pop("functions", None) # causes ollama requests to hang
return optional_params
def validate_environment(
self,
headers: dict,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
) -> dict:
return headers
def get_complete_url(
self,
api_base: Optional[str],
api_key: Optional[str],
model: str,
optional_params: dict,
litellm_params: dict,
stream: Optional[bool] = None,
) -> str:
"""
OPTIONAL
Get the complete url for the request
Some providers need `model` in `api_base`
"""
if api_base is None:
api_base = "http://localhost:11434"
if api_base.endswith("/api/chat"):
url = api_base
else:
url = f"{api_base}/api/chat"
return url
def transform_request(
self,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
headers: dict,
) -> dict:
stream = optional_params.pop("stream", False)
format = optional_params.pop("format", None)
keep_alive = optional_params.pop("keep_alive", None)
function_name = optional_params.pop("function_name", None)
litellm_params["function_name"] = function_name
tools = optional_params.pop("tools", None)
new_messages = []
for m in messages:
if isinstance(
m, BaseModel
): # avoid message serialization issues - https://github.com/BerriAI/litellm/issues/5319
m = m.model_dump(exclude_none=True)
tool_calls = m.get("tool_calls")
if tool_calls is not None and isinstance(tool_calls, list):
new_tools: List[OllamaToolCall] = []
for tool in tool_calls:
typed_tool = ChatCompletionAssistantToolCall(**tool) # type: ignore
if typed_tool["type"] == "function":
arguments = {}
if "arguments" in typed_tool["function"]:
arguments = json.loads(typed_tool["function"]["arguments"])
ollama_tool_call = OllamaToolCall(
function=OllamaToolCallFunction(
name=typed_tool["function"].get("name") or "",
arguments=arguments,
)
)
new_tools.append(ollama_tool_call)
cast(dict, m)["tool_calls"] = new_tools
new_messages.append(m)
data = {
"model": model,
"messages": new_messages,
"options": optional_params,
"stream": stream,
}
if format is not None:
data["format"] = format
if tools is not None:
data["tools"] = tools
if keep_alive is not None:
data["keep_alive"] = keep_alive
return data
def transform_response(
self,
model: str,
raw_response: Response,
model_response: ModelResponse,
logging_obj: LiteLLMLoggingObj,
request_data: dict,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
encoding: str,
api_key: Optional[str] = None,
json_mode: Optional[bool] = None,
) -> ModelResponse:
## LOGGING
logging_obj.post_call(
input=messages,
api_key="",
original_response=raw_response.text,
additional_args={
"headers": None,
"api_base": litellm_params.get("api_base"),
},
)
response_json = raw_response.json()
## RESPONSE OBJECT
model_response.choices[0].finish_reason = "stop"
if (
request_data.get("format", "") == "json"
and litellm_params.get("function_name") is not None
):
function_call = json.loads(response_json["message"]["content"])
message = litellm.Message(
content=None,
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
"function": {
"name": function_call.get(
"name", litellm_params.get("function_name")
),
"arguments": json.dumps(
function_call.get("arguments", function_call)
),
},
"type": "function",
}
],
)
model_response.choices[0].message = message # type: ignore
model_response.choices[0].finish_reason = "tool_calls"
else:
_message = litellm.Message(**response_json["message"])
model_response.choices[0].message = _message # type: ignore
model_response.created = int(time.time())
model_response.model = "ollama_chat/" + model
prompt_tokens = response_json.get("prompt_eval_count", litellm.token_counter(messages=messages)) # type: ignore
completion_tokens = response_json.get(
"eval_count",
litellm.token_counter(text=response_json["message"]["content"]),
)
setattr(
model_response,
"usage",
litellm.Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
),
)
return model_response
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 get_model_response_iterator(
self,
streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
sync_stream: bool,
json_mode: Optional[bool] = False,
):
return OllamaChatCompletionResponseIterator(
streaming_response=streaming_response,
sync_stream=sync_stream,
json_mode=json_mode,
)
class OllamaChatCompletionResponseIterator(BaseModelResponseIterator):
def chunk_parser(self, chunk: dict) -> ModelResponseStream:
try:
"""
Expected chunk format:
{
"model": "llama3.1",
"created_at": "2025-05-24T02:12:05.859654Z",
"message": {
"role": "assistant",
"content": "",
"tool_calls": [{
"function": {
"name": "get_latest_album_ratings",
"arguments": {
"artist_name": "Taylor Swift"
}
}
}]
},
"done_reason": "stop",
"done": true,
...
}
Need to:
- convert 'message' to 'delta'
- return finish_reason when done is true
- return usage when done is true
"""
from litellm.types.utils import Delta, StreamingChoices
delta = Delta(
content=chunk["message"].get("content", ""),
tool_calls=chunk["message"].get("tool_calls"),
)
if chunk["done"] is True:
finish_reason = chunk.get("done_reason", "stop")
choices = [
StreamingChoices(
delta=delta,
finish_reason=finish_reason,
)
]
else:
choices = [
StreamingChoices(
delta=delta,
)
]
usage = ChatCompletionUsageBlock(
prompt_tokens=chunk.get("prompt_eval_count", 0),
completion_tokens=chunk.get("eval_count", 0),
total_tokens=chunk.get("prompt_eval_count", 0)
+ chunk.get("eval_count", 0),
)
return ModelResponseStream(
id=str(uuid.uuid4()),
object="chat.completion.chunk",
created=int(time.time()), # ollama created_at is in UTC
usage=usage,
model=chunk["model"],
choices=choices,
)
except KeyError as e:
raise OllamaError(
message=f"KeyError: {e}, Got unexpected response from Ollama: {chunk}",
status_code=400,
headers={"Content-Type": "application/json"},
)
except Exception as e:
raise e

185
litellm/llms/ollama_chat.py
View file

@ -14,7 +14,6 @@ from litellm.llms.custom_httpx.http_handler import (
HTTPHandler,
get_async_httpx_client,
)
from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
from litellm.types.llms.ollama import OllamaToolCall, OllamaToolCallFunction
from litellm.types.llms.openai import ChatCompletionAssistantToolCall
from litellm.types.utils import ModelResponse, StreamingChoices
@ -31,190 +30,6 @@ class OllamaError(Exception):
) # Call the base class constructor with the parameters it needs
class OllamaChatConfig(OpenAIGPTConfig):
"""
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_thread: Optional[int] = None
repeat_last_n: Optional[int] = None
repeat_penalty: Optional[float] = None
seed: Optional[int] = None
tfs_z: Optional[float] = None
num_predict: Optional[int] = None
top_k: Optional[int] = 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,
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().copy()
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_supported_openai_params(self, model: str):
return [
"max_tokens",
"max_completion_tokens",
"stream",
"top_p",
"temperature",
"seed",
"frequency_penalty",
"stop",
"tools",
"tool_choice",
"functions",
"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" or param == "max_completion_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)
and value.get("type") == "json_object"
):
optional_params["format"] = "json"
if (
param == "response_format"
and isinstance(value, dict)
and value.get("type") == "json_schema"
):
if value.get("json_schema") and value["json_schema"].get("schema"):
optional_params["format"] = value["json_schema"]["schema"]
### FUNCTION CALLING LOGIC ###
if param == "tools":
## CHECK IF MODEL SUPPORTS TOOL CALLING ##
try:
model_info = litellm.get_model_info(
model=model, custom_llm_provider="ollama"
)
if model_info.get("supports_function_calling") is True:
optional_params["tools"] = value
else:
raise Exception
except Exception:
optional_params["format"] = "json"
litellm.add_function_to_prompt = (
True # so that main.py adds the function call to the prompt
)
optional_params["functions_unsupported_model"] = value
if len(optional_params["functions_unsupported_model"]) == 1:
optional_params["function_name"] = optional_params[
"functions_unsupported_model"
][0]["function"]["name"]
if param == "functions":
## CHECK IF MODEL SUPPORTS TOOL CALLING ##
try:
model_info = litellm.get_model_info(
model=model, custom_llm_provider="ollama"
)
if model_info.get("supports_function_calling") is True:
optional_params["tools"] = value
else:
raise Exception
except Exception:
optional_params["format"] = "json"
litellm.add_function_to_prompt = (
True # so that main.py adds the function call to the prompt
)
optional_params["functions_unsupported_model"] = (
non_default_params.get("functions")
)
non_default_params.pop("tool_choice", None) # causes ollama requests to hang
non_default_params.pop("functions", None) # causes ollama requests to hang
return optional_params
# ollama implementation
def get_ollama_response( # noqa: PLR0915
model_response: ModelResponse,

23
litellm/main.py
View file

@ -127,7 +127,7 @@ from .litellm_core_utils.prompt_templates.factory import (
stringify_json_tool_call_content,
)
from .litellm_core_utils.streaming_chunk_builder_utils import ChunkProcessor
from .llms import baseten, maritalk, ollama_chat
from .llms import baseten
from .llms.anthropic.chat import AnthropicChatCompletion
from .llms.azure.audio_transcriptions import AzureAudioTranscription
from .llms.azure.azure import AzureChatCompletion, _check_dynamic_azure_params
@ -2957,23 +2957,24 @@ def completion( # type: ignore # noqa: PLR0915
or os.environ.get("OLLAMA_API_KEY")
or litellm.api_key
)
## LOGGING
generator = ollama_chat.get_ollama_response(
api_base=api_base,
api_key=api_key,
response = base_llm_http_handler.completion(
model=model,
stream=stream,
messages=messages,
optional_params=optional_params,
logging_obj=logging,
acompletion=acompletion,
api_base=api_base,
model_response=model_response,
optional_params=optional_params,
litellm_params=litellm_params,
custom_llm_provider="ollama_chat",
timeout=timeout,
headers=headers,
encoding=encoding,
api_key=api_key,
logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements
client=client,
)
if acompletion is True or optional_params.get("stream", False) is True:
return generator
response = generator
elif custom_llm_provider == "triton":
api_base = litellm.api_base or api_base

3
litellm/proxy/_new_secret_config.yaml
View file

@ -4,8 +4,9 @@ model_list:
model: openai/fake
api_key: fake-key
api_base: https://exampleopenaiendpoint-production.up.railway.app/
litellm_settings:
cache: true
success_callback: ["langfuse"]
failure_callback: ["langfuse"]
alerting: ["slack"]
alerting: ["slack"]

35
litellm/proxy/auth/user_api_key_auth.py
View file

@ -725,20 +725,6 @@ async def _user_api_key_auth_builder( # noqa: PLR0915
)
## Check DB
if (
isinstance(api_key, str) and valid_token is None
): # if generated token, make sure it starts with sk-.
assert api_key.startswith(
"sk-"
), "LiteLLM Virtual Key expected. Received={}, expected to start with 'sk-'.".format(
api_key
) # prevent token hashes from being used
else:
verbose_logger.warning(
"litellm.proxy.proxy_server.user_api_key_auth(): Warning - Key={} is not a string.".format(
api_key
)
)
if (
prisma_client is None
@ -752,11 +738,26 @@ async def _user_api_key_auth_builder( # noqa: PLR0915
## check for cache hit (In-Memory Cache)
_user_role = None
abbreviated_api_key = abbreviate_api_key(api_key=api_key)
if api_key.startswith("sk-"):
api_key = hash_token(token=api_key)
if valid_token is None:
if isinstance(
api_key, str
): # if generated token, make sure it starts with sk-.
assert api_key.startswith(
"sk-"
), "LiteLLM Virtual Key expected. Received={}, expected to start with 'sk-'.".format(
api_key
) # prevent token hashes from being used
else:
verbose_logger.warning(
"litellm.proxy.proxy_server.user_api_key_auth(): Warning - Key={} is not a string.".format(
api_key
)
)
abbreviated_api_key = abbreviate_api_key(api_key=api_key)
if api_key.startswith("sk-"):
api_key = hash_token(token=api_key)
try:
valid_token = await get_key_object(
hashed_token=api_key,

2
litellm/proxy/hooks/__init__.py
View file

@ -23,7 +23,7 @@ PROXY_HOOKS = {
## FEATURE FLAG HOOKS ##
if os.getenv("EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING", "false").lower() == "true":
PROXY_HOOKS["max_parallel_requests"] = _PROXY_MaxParallelRequestsHandler_v2
PROXY_HOOKS["parallel_request_limiter"] = _PROXY_MaxParallelRequestsHandler_v2
### update PROXY_HOOKS with ENTERPRISE_PROXY_HOOKS ###

14
litellm/router_strategy/base_routing_strategy.py
View file

@ -124,10 +124,10 @@ class BaseRoutingStrategy(ABC):
if not self.dual_cache.redis_cache:
return # Redis is not initialized
verbose_router_logger.debug(
"Pushing Redis Increment Pipeline for queue: %s",
self.redis_increment_operation_queue,
)
# verbose_router_logger.debug(
# "Pushing Redis Increment Pipeline for queue: %s",
# self.redis_increment_operation_queue,
# )
if len(self.redis_increment_operation_queue) > 0:
await self.dual_cache.redis_cache.async_increment_pipeline(
increment_list=self.redis_increment_operation_queue,
@ -207,9 +207,9 @@ class BaseRoutingStrategy(ABC):
await self.dual_cache.in_memory_cache.async_set_cache(
key=key, value=float(value)
)
verbose_router_logger.debug(
f"Updated in-memory cache for {key}: {value}"
)
# verbose_router_logger.debug(
# f"Updated in-memory cache for {key}: {value}"
# )
self.reset_in_memory_keys_to_update()
except Exception as e:

2
tests/code_coverage_tests/router_code_coverage.py
View file

@ -31,7 +31,7 @@ def get_all_functions_called_in_tests(base_dir):
specifically in files containing the word 'router'.
"""
called_functions = set()
test_dirs = ["local_testing", "router_unit_tests", "litellm"]
test_dirs = ["local_testing", "router_unit_tests", "test_litellm"]
for test_dir in test_dirs:
dir_path = os.path.join(base_dir, test_dir)

2
tests/test_fallbacks.py
View file

@ -3,7 +3,7 @@
import pytest
import asyncio
import aiohttp
from large_text import text
from tests.large_text import text
import time
from typing import Optional

2
tests/test_litellm/llms/ollama/test_ollama_chat_transformation.py
View file

@ -9,7 +9,7 @@ sys.path.insert(
0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../../.."))
)
from litellm.llms.ollama_chat import OllamaChatConfig
from litellm.llms.ollama.chat.transformation import OllamaChatConfig
from litellm.utils import get_optional_params

2
tests/test_litellm/test_utils.py
View file

@ -85,7 +85,7 @@ def test_all_model_configs():
drop_params=False,
) == {"max_tokens": 10}
from litellm.llms.ollama_chat import OllamaChatConfig
from litellm.llms.ollama.chat.transformation import OllamaChatConfig
assert "max_completion_tokens" in OllamaChatConfig().get_supported_openai_params(
model="llama3"

6
tests/test_models.py
View file

@ -451,7 +451,7 @@ async def test_get_personal_models_for_user():
"""
Test /models endpoint with team
"""
from test_users import new_user
from tests.test_users import new_user
async with aiohttp.ClientSession() as session:
# Creat a user
user_data = await new_user(session=session, i=0, models=["gpt-3.5-turbo"])
@ -504,8 +504,8 @@ async def test_team_model_e2e():
- update model
- delete model
"""
from test_users import new_user
from test_team import new_team
from tests.test_users import new_user
from tests.test_team import new_team
import uuid
async with aiohttp.ClientSession() as session:
# Creat a user