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litellm/litellm/llms/ollama.py
Ishaan Jaff eabe323eaa fix ollama
2024-11-22 17:34:08 -08:00

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import asyncio
import json
import time
import traceback
import types
import uuid
from copy import deepcopy
from itertools import chain
from typing import Any, Dict, List, Optional
import aiohttp
import httpx # type: ignore
import requests # type: ignore
import litellm
from litellm import verbose_logger
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
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
# and convert to jpeg if necessary.
def _convert_image(image):
import base64
import io
try:
from PIL import Image
except Exception:
raise Exception(
"ollama image conversion failed please run `pip install Pillow`"
)
orig = image
if image.startswith("data:"):
image = image.split(",")[-1]
try:
image_data = Image.open(io.BytesIO(base64.b64decode(image)))
if image_data.format in ["JPEG", "PNG"]:
return image
except Exception:
return orig
jpeg_image = io.BytesIO()
image_data.convert("RGB").save(jpeg_image, "JPEG")
jpeg_image.seek(0)
return base64.b64encode(jpeg_image.getvalue()).decode("utf-8")
# ollama implementation
def get_ollama_response(
model_response: litellm.ModelResponse,
model: str,
prompt: str,
optional_params: dict,
logging_obj: Any,
encoding: Any,
acompletion: bool = False,
api_base="http://localhost:11434",
):
if api_base.endswith("/api/generate"):
url = api_base
else:
url = f"{api_base}/api/generate"
## Load Config
config = litellm.OllamaConfig.get_config()
for k, v in config.items():
if (
k not in optional_params
): # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in
optional_params[k] = v
stream = optional_params.pop("stream", False)
format = optional_params.pop("format", None)
images = optional_params.pop("images", None)
data = {
"model": model,
"prompt": prompt,
"options": optional_params,
"stream": stream,
}
if format is not None:
data["format"] = format
if images is not None:
data["images"] = [_convert_image(image) for image in images]
## LOGGING
logging_obj.pre_call(
input=None,
api_key=None,
additional_args={
"api_base": url,
"complete_input_dict": data,
"headers": {},
"acompletion": acompletion,
},
)
if acompletion is True:
if stream is True:
response = ollama_async_streaming(
url=url,
data=data,
model_response=model_response,
encoding=encoding,
logging_obj=logging_obj,
)
else:
response = ollama_acompletion(
url=url,
data=data,
model_response=model_response,
encoding=encoding,
logging_obj=logging_obj,
)
return response
elif stream is True:
return ollama_completion_stream(url=url, data=data, logging_obj=logging_obj)
response = requests.post(
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)
## LOGGING
logging_obj.post_call(
input=prompt,
api_key="",
original_response=response.text,
additional_args={
"headers": None,
"api_base": api_base,
},
)
response_json = response.json()
## RESPONSE OBJECT
model_response.choices[0].finish_reason = "stop"
if data.get("format", "") == "json":
function_call = json.loads(response_json["response"])
message = litellm.Message(
content=None,
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
"function": {
"name": function_call["name"],
"arguments": json.dumps(function_call["arguments"]),
},
"type": "function",
}
],
)
model_response.choices[0].message = message # type: ignore
model_response.choices[0].finish_reason = "tool_calls"
else:
model_response.choices[0].message.content = response_json["response"] # type: ignore
model_response.created = int(time.time())
model_response.model = "ollama/" + model
prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(prompt, disallowed_special=()))) # type: ignore
completion_tokens = response_json.get(
"eval_count", len(response_json.get("message", dict()).get("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 ollama_completion_stream(url, data, logging_obj):
with httpx.stream(
url=url, json=data, method="POST", timeout=litellm.request_timeout
) as response:
try:
if response.status_code != 200:
raise OllamaError(
status_code=response.status_code, message=response.read()
)
streamwrapper = litellm.CustomStreamWrapper(
completion_stream=response.iter_lines(),
model=data["model"],
custom_llm_provider="ollama",
logging_obj=logging_obj,
)
# If format is JSON, this was a function call
# Gather all chunks and return the function call as one delta to simplify parsing
if data.get("format", "") == "json":
first_chunk = next(streamwrapper)
content_chunks = []
for chunk in chain([first_chunk], streamwrapper):
content_chunk = chunk.choices[0]
if (
isinstance(content_chunk, StreamingChoices)
and hasattr(content_chunk, "delta")
and hasattr(content_chunk.delta, "content")
and content_chunk.delta.content is not None
):
content_chunks.append(content_chunk.delta.content)
response_content = "".join(content_chunks)
function_call = json.loads(response_content)
delta = litellm.utils.Delta(
content=None,
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
"function": {
"name": function_call["name"],
"arguments": json.dumps(function_call["arguments"]),
},
"type": "function",
}
],
)
model_response = first_chunk
model_response.choices[0].delta = delta # type: ignore
model_response.choices[0].finish_reason = "tool_calls"
yield model_response
else:
for transformed_chunk in streamwrapper:
yield transformed_chunk
except Exception as e:
raise e
async def ollama_async_streaming(url, data, model_response, encoding, logging_obj):
try:
_async_http_client = get_async_httpx_client(
llm_provider=litellm.LlmProviders.OLLAMA
)
client = _async_http_client.client
async with client.stream(
url=f"{url}", json=data, method="POST", timeout=litellm.request_timeout
) as response:
if response.status_code != 200:
raise OllamaError(
status_code=response.status_code, message=await response.aread()
)
streamwrapper = litellm.CustomStreamWrapper(
completion_stream=response.aiter_lines(),
model=data["model"],
custom_llm_provider="ollama",
logging_obj=logging_obj,
)
# If format is JSON, this was a function call
# Gather all chunks and return the function call as one delta to simplify parsing
if data.get("format", "") == "json":
first_chunk = await anext(streamwrapper) # noqa F821
chunk_choice = first_chunk.choices[0]
if (
isinstance(chunk_choice, StreamingChoices)
and hasattr(chunk_choice, "delta")
and hasattr(chunk_choice.delta, "content")
):
first_chunk_content = chunk_choice.delta.content or ""
else:
first_chunk_content = ""
content_chunks = []
async for chunk in streamwrapper:
chunk_choice = chunk.choices[0]
if (
isinstance(chunk_choice, StreamingChoices)
and hasattr(chunk_choice, "delta")
and hasattr(chunk_choice.delta, "content")
):
content_chunks.append(chunk_choice.delta.content)
response_content = first_chunk_content + "".join(content_chunks)
function_call = json.loads(response_content)
delta = litellm.utils.Delta(
content=None,
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
"function": {
"name": function_call["name"],
"arguments": json.dumps(function_call["arguments"]),
},
"type": "function",
}
],
)
model_response = first_chunk
model_response.choices[0].delta = delta # type: ignore
model_response.choices[0].finish_reason = "tool_calls"
yield model_response
else:
async for transformed_chunk in streamwrapper:
yield transformed_chunk
except Exception as e:
raise e # don't use verbose_logger.exception, if exception is raised
async def ollama_acompletion(
url, data, model_response: litellm.ModelResponse, encoding, logging_obj
):
data["stream"] = False
try:
timeout = aiohttp.ClientTimeout(total=litellm.request_timeout) # 10 minutes
async with aiohttp.ClientSession(timeout=timeout) as session:
resp = await session.post(url, json=data)
if resp.status != 200:
text = await resp.text()
raise OllamaError(status_code=resp.status, message=text)
## LOGGING
logging_obj.post_call(
input=data["prompt"],
api_key="",
original_response=resp.text,
additional_args={
"headers": None,
"api_base": url,
},
)
response_json = await resp.json()
## RESPONSE OBJECT
model_response.choices[0].finish_reason = "stop"
if data.get("format", "") == "json":
function_call = json.loads(response_json["response"])
message = litellm.Message(
content=None,
tool_calls=[
{
"id": f"call_{str(uuid.uuid4())}",
"function": {
"name": function_call.get(
"name", function_call.get("function", None)
),
"arguments": json.dumps(function_call["arguments"]),
},
"type": "function",
}
],
)
model_response.choices[0].message = message # type: ignore
model_response.choices[0].finish_reason = "tool_calls"
else:
model_response.choices[0].message.content = response_json["response"] # type: ignore
model_response.created = int(time.time())
model_response.model = "ollama/" + data["model"]
prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(data["prompt"], disallowed_special=()))) # type: ignore
completion_tokens = response_json.get(
"eval_count",
len(response_json.get("message", dict()).get("content", "")),
)
setattr(
model_response,
"usage",
litellm.Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
),
)
return model_response
except Exception as e:
raise e # don't use verbose_logger.exception, if exception is raised
async def ollama_aembeddings(
api_base: str,
model: str,
prompts: List[str],
model_response: litellm.EmbeddingResponse,
optional_params: dict,
logging_obj: Any,
encoding: Any,
):
if api_base.endswith("/api/embed"):
url = api_base
else:
url = f"{api_base}/api/embed"
## Load Config
config = litellm.OllamaConfig.get_config()
for k, v in config.items():
if (
k not in optional_params
): # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in
optional_params[k] = v
data: Dict[str, Any] = {"model": model, "input": prompts}
special_optional_params = ["truncate", "options", "keep_alive"]
for k, v in optional_params.items():
if k in special_optional_params:
data[k] = v
else:
# Ensure "options" is a dictionary before updating it
data.setdefault("options", {})
if isinstance(data["options"], dict):
data["options"].update({k: v})
total_input_tokens = 0
output_data = []
timeout = aiohttp.ClientTimeout(total=litellm.request_timeout) # 10 minutes
async with aiohttp.ClientSession(timeout=timeout) as session:
## LOGGING
logging_obj.pre_call(
input=None,
api_key=None,
additional_args={
"api_base": url,
"complete_input_dict": data,
"headers": {},
},
)
response = await session.post(url, json=data)
if response.status != 200:
text = await response.text()
raise OllamaError(status_code=response.status, message=text)
response_json = await response.json()
embeddings: List[List[float]] = response_json["embeddings"]
for idx, emb in enumerate(embeddings):
output_data.append({"object": "embedding", "index": idx, "embedding": emb})
input_tokens = response_json.get("prompt_eval_count") or len(
encoding.encode("".join(prompt for prompt in prompts))
)
total_input_tokens += input_tokens
model_response.object = "list"
model_response.data = output_data
model_response.model = "ollama/" + model
setattr(
model_response,
"usage",
litellm.Usage(
prompt_tokens=total_input_tokens,
completion_tokens=total_input_tokens,
total_tokens=total_input_tokens,
prompt_tokens_details=None,
completion_tokens_details=None,
),
)
return model_response
def ollama_embeddings(
api_base: str,
model: str,
prompts: list,
optional_params: dict,
model_response: litellm.EmbeddingResponse,
logging_obj: Any,
encoding=None,
):
return asyncio.run(
ollama_aembeddings(
api_base=api_base,
model=model,
prompts=prompts,
model_response=model_response,
optional_params=optional_params,
logging_obj=logging_obj,
encoding=encoding,
)
)
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