diff --git a/.gitignore b/.gitignore index 6e5f15dbe86a..4a92cbf31c78 100644 --- a/.gitignore +++ b/.gitignore @@ -67,3 +67,4 @@ litellm/tests/langfuse.log litellm/proxy/google-cloud-sdk/* tests/llm_translation/log.txt venv/ +tests/local_testing/log.txt diff --git a/docs/my-website/docs/adding_provider/directory_structure.md b/docs/my-website/docs/adding_provider/directory_structure.md index 133aa6be0337..23b239c36471 100644 --- a/docs/my-website/docs/adding_provider/directory_structure.md +++ b/docs/my-website/docs/adding_provider/directory_structure.md @@ -5,16 +5,16 @@ When adding a new provider, you need to create a directory for the provider that ``` litellm/llms/ └── provider_name/ - ├── completion/ + ├── completion/ # use when endpoint is equivalent to openai's `/v1/completions` │ ├── handler.py │ └── transformation.py - ├── chat/ + ├── chat/ # use when endpoint is equivalent to openai's `/v1/chat/completions` │ ├── handler.py │ └── transformation.py - ├── embed/ + ├── embed/ # use when endpoint is equivalent to openai's `/v1/embeddings` │ ├── handler.py │ └── transformation.py - └── rerank/ + └── rerank/ # use when endpoint is equivalent to cohere's `/rerank` endpoint. ├── handler.py └── transformation.py ``` diff --git a/litellm/__init__.py b/litellm/__init__.py index bb844c302b6c..16a25b240f6a 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -991,6 +991,7 @@ def add_known_models(): get_api_base, get_first_chars_messages, ModelResponse, + ModelResponseStream, EmbeddingResponse, ImageResponse, TranscriptionResponse, @@ -1157,6 +1158,7 @@ def add_known_models(): from .llms.azure.chat.o1_transformation import AzureOpenAIO1Config from .llms.watsonx.completion.transformation import IBMWatsonXAIConfig from .llms.watsonx.chat.transformation import IBMWatsonXChatConfig +from .llms.watsonx.embed.transformation import IBMWatsonXEmbeddingConfig from .main import * # type: ignore from .integrations import * from .exceptions import ( diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 1e3d374e1546..d5325aff5379 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -43,6 +43,7 @@ ImageResponse, LiteLLMLoggingBaseClass, ModelResponse, + ModelResponseStream, StandardCallbackDynamicParams, StandardLoggingAdditionalHeaders, StandardLoggingHiddenParams, @@ -741,6 +742,7 @@ def _response_cost_calculator( self, result: Union[ ModelResponse, + ModelResponseStream, EmbeddingResponse, ImageResponse, TranscriptionResponse, @@ -848,6 +850,7 @@ def _success_handler_helper_fn( ): # handle streaming separately if ( isinstance(result, ModelResponse) + or isinstance(result, ModelResponseStream) or isinstance(result, EmbeddingResponse) or isinstance(result, ImageResponse) or isinstance(result, TranscriptionResponse) @@ -955,6 +958,7 @@ def success_handler( # noqa: PLR0915 if self.stream and ( isinstance(result, litellm.ModelResponse) or isinstance(result, TextCompletionResponse) + or isinstance(result, ModelResponseStream) ): complete_streaming_response: Optional[ Union[ModelResponse, TextCompletionResponse] @@ -966,9 +970,6 @@ def success_handler( # noqa: PLR0915 streaming_chunks=self.sync_streaming_chunks, is_async=False, ) - _caching_complete_streaming_response: Optional[ - Union[ModelResponse, TextCompletionResponse] - ] = None if complete_streaming_response is not None: verbose_logger.debug( "Logging Details LiteLLM-Success Call streaming complete" @@ -976,9 +977,6 @@ def success_handler( # noqa: PLR0915 self.model_call_details["complete_streaming_response"] = ( complete_streaming_response ) - _caching_complete_streaming_response = copy.deepcopy( - complete_streaming_response - ) self.model_call_details["response_cost"] = ( self._response_cost_calculator(result=complete_streaming_response) ) @@ -1474,6 +1472,7 @@ async def async_success_handler( # noqa: PLR0915 ] = None if self.stream is True and ( isinstance(result, litellm.ModelResponse) + or isinstance(result, litellm.ModelResponseStream) or isinstance(result, TextCompletionResponse) ): complete_streaming_response: Optional[ diff --git a/litellm/litellm_core_utils/logging_utils.py b/litellm/litellm_core_utils/logging_utils.py index 0a9fc8342a4a..fb8689a522f9 100644 --- a/litellm/litellm_core_utils/logging_utils.py +++ b/litellm/litellm_core_utils/logging_utils.py @@ -2,7 +2,11 @@ from typing import TYPE_CHECKING, Any, List, Optional, Union from litellm._logging import verbose_logger -from litellm.types.utils import ModelResponse, TextCompletionResponse +from litellm.types.utils import ( + ModelResponse, + ModelResponseStream, + TextCompletionResponse, +) if TYPE_CHECKING: from litellm import ModelResponse as _ModelResponse @@ -38,7 +42,7 @@ def convert_litellm_response_object_to_str( def _assemble_complete_response_from_streaming_chunks( - result: Union[ModelResponse, TextCompletionResponse], + result: Union[ModelResponse, TextCompletionResponse, ModelResponseStream], start_time: datetime, end_time: datetime, request_kwargs: dict, diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index 30a34e76a874..b285bfc4b6c3 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -5,7 +5,7 @@ import traceback import uuid from concurrent.futures import ThreadPoolExecutor -from typing import Any, Callable, List, Optional +from typing import Any, Callable, Dict, List, Optional, cast import httpx from pydantic import BaseModel @@ -611,44 +611,6 @@ def handle_ollama_chat_stream(self, chunk): except Exception as e: raise e - def handle_watsonx_stream(self, chunk): - try: - if isinstance(chunk, dict): - parsed_response = chunk - elif isinstance(chunk, (str, bytes)): - if isinstance(chunk, bytes): - chunk = chunk.decode("utf-8") - if "generated_text" in chunk: - response = chunk.replace("data: ", "").strip() - parsed_response = json.loads(response) - else: - return { - "text": "", - "is_finished": False, - "prompt_tokens": 0, - "completion_tokens": 0, - } - else: - print_verbose(f"chunk: {chunk} (Type: {type(chunk)})") - raise ValueError( - f"Unable to parse response. Original response: {chunk}" - ) - results = parsed_response.get("results", []) - if len(results) > 0: - text = results[0].get("generated_text", "") - finish_reason = results[0].get("stop_reason") - is_finished = finish_reason != "not_finished" - return { - "text": text, - "is_finished": is_finished, - "finish_reason": finish_reason, - "prompt_tokens": results[0].get("input_token_count", 0), - "completion_tokens": results[0].get("generated_token_count", 0), - } - return {"text": "", "is_finished": False} - except Exception as e: - raise e - def handle_triton_stream(self, chunk): try: if isinstance(chunk, dict): @@ -702,9 +664,18 @@ def model_response_creator( # pop model keyword chunk.pop("model", None) - model_response = ModelResponse( - stream=True, model=_model, stream_options=self.stream_options, **chunk - ) + chunk_dict = {} + for key, value in chunk.items(): + if key != "stream": + chunk_dict[key] = value + + args = { + "model": _model, + "stream_options": self.stream_options, + **chunk_dict, + } + + model_response = ModelResponseStream(**args) if self.response_id is not None: model_response.id = self.response_id else: @@ -742,9 +713,9 @@ def is_delta_empty(self, delta: Delta) -> bool: def return_processed_chunk_logic( # noqa self, - completion_obj: dict, + completion_obj: Dict[str, Any], model_response: ModelResponseStream, - response_obj: dict, + response_obj: Dict[str, Any], ): print_verbose( @@ -887,11 +858,11 @@ def return_processed_chunk_logic( # noqa def chunk_creator(self, chunk): # type: ignore # noqa: PLR0915 model_response = self.model_response_creator() - response_obj: dict = {} + response_obj: Dict[str, Any] = {} try: # return this for all models - completion_obj = {"content": ""} + completion_obj: Dict[str, Any] = {"content": ""} from litellm.types.utils import GenericStreamingChunk as GChunk if ( @@ -1089,11 +1060,6 @@ def chunk_creator(self, chunk): # type: ignore # noqa: PLR0915 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 == "watsonx": - response_obj = self.handle_watsonx_stream(chunk) - completion_obj["content"] = response_obj["text"] - 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"] @@ -1158,7 +1124,7 @@ def chunk_creator(self, chunk): # type: ignore # noqa: PLR0915 self.received_finish_reason = response_obj["finish_reason"] else: # openai / azure chat model if self.custom_llm_provider == "azure": - if hasattr(chunk, "model"): + if isinstance(chunk, BaseModel) and hasattr(chunk, "model"): # for azure, we need to pass the model from the orignal chunk self.model = chunk.model response_obj = self.handle_openai_chat_completion_chunk(chunk) @@ -1190,21 +1156,29 @@ def chunk_creator(self, chunk): # type: ignore # noqa: PLR0915 if response_obj["usage"] is not None: if isinstance(response_obj["usage"], dict): - model_response.usage = litellm.Usage( - prompt_tokens=response_obj["usage"].get( - "prompt_tokens", None - ) - or None, - completion_tokens=response_obj["usage"].get( - "completion_tokens", None - ) - or None, - total_tokens=response_obj["usage"].get("total_tokens", None) - or None, + setattr( + model_response, + "usage", + litellm.Usage( + prompt_tokens=response_obj["usage"].get( + "prompt_tokens", None + ) + or None, + completion_tokens=response_obj["usage"].get( + "completion_tokens", None + ) + or None, + total_tokens=response_obj["usage"].get( + "total_tokens", None + ) + or None, + ), ) elif isinstance(response_obj["usage"], BaseModel): - model_response.usage = litellm.Usage( - **response_obj["usage"].model_dump() + setattr( + model_response, + "usage", + litellm.Usage(**response_obj["usage"].model_dump()), ) model_response.model = self.model @@ -1337,7 +1311,7 @@ def chunk_creator(self, chunk): # type: ignore # noqa: PLR0915 raise StopIteration except Exception as e: traceback.format_exc() - e.message = str(e) + setattr(e, "message", str(e)) raise exception_type( model=self.model, custom_llm_provider=self.custom_llm_provider, @@ -1434,7 +1408,9 @@ def __next__(self): # noqa: PLR0915 print_verbose( f"PROCESSED CHUNK PRE CHUNK CREATOR: {chunk}; custom_llm_provider: {self.custom_llm_provider}" ) - response: Optional[ModelResponse] = self.chunk_creator(chunk=chunk) + response: Optional[ModelResponseStream] = self.chunk_creator( + chunk=chunk + ) print_verbose(f"PROCESSED CHUNK POST CHUNK CREATOR: {response}") if response is None: @@ -1597,7 +1573,7 @@ async def __anext__(self): # noqa: PLR0915 # __anext__ also calls async_success_handler, which does logging print_verbose(f"PROCESSED ASYNC CHUNK PRE CHUNK CREATOR: {chunk}") - processed_chunk: Optional[ModelResponse] = self.chunk_creator( + processed_chunk: Optional[ModelResponseStream] = self.chunk_creator( chunk=chunk ) print_verbose( @@ -1624,7 +1600,7 @@ async def __anext__(self): # noqa: PLR0915 if self.logging_obj._llm_caching_handler is not None: asyncio.create_task( self.logging_obj._llm_caching_handler._add_streaming_response_to_cache( - processed_chunk=processed_chunk, + processed_chunk=cast(ModelResponse, processed_chunk), ) ) @@ -1663,8 +1639,8 @@ async def __anext__(self): # noqa: PLR0915 chunk = next(self.completion_stream) if chunk is not None and chunk != b"": print_verbose(f"PROCESSED CHUNK PRE CHUNK CREATOR: {chunk}") - processed_chunk: Optional[ModelResponse] = self.chunk_creator( - chunk=chunk + processed_chunk: Optional[ModelResponseStream] = ( + self.chunk_creator(chunk=chunk) ) print_verbose( f"PROCESSED CHUNK POST CHUNK CREATOR: {processed_chunk}" diff --git a/litellm/llms/base.py b/litellm/llms/base.py index 611b0aa902c3..deced222ca8a 100644 --- a/litellm/llms/base.py +++ b/litellm/llms/base.py @@ -2,7 +2,6 @@ from typing import Any, Optional, Union import httpx -import requests import litellm from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper @@ -16,7 +15,7 @@ class BaseLLM: def process_response( self, model: str, - response: Union[requests.Response, httpx.Response], + response: httpx.Response, model_response: ModelResponse, stream: bool, logging_obj: Any, @@ -35,7 +34,7 @@ def process_response( def process_text_completion_response( self, model: str, - response: Union[requests.Response, httpx.Response], + response: httpx.Response, model_response: TextCompletionResponse, stream: bool, logging_obj: Any, diff --git a/litellm/llms/base_llm/chat/transformation.py b/litellm/llms/base_llm/chat/transformation.py index afb2b1db225e..c42d2ff20b44 100644 --- a/litellm/llms/base_llm/chat/transformation.py +++ b/litellm/llms/base_llm/chat/transformation.py @@ -107,7 +107,13 @@ def validate_environment( ) -> dict: pass - def get_complete_url(self, api_base: str, model: str) -> str: + def get_complete_url( + self, + api_base: str, + model: str, + optional_params: dict, + stream: Optional[bool] = None, + ) -> str: """ OPTIONAL diff --git a/litellm/llms/base_llm/embedding/transformation.py b/litellm/llms/base_llm/embedding/transformation.py index be2e72bc34bb..940c6bf225c2 100644 --- a/litellm/llms/base_llm/embedding/transformation.py +++ b/litellm/llms/base_llm/embedding/transformation.py @@ -1,10 +1,10 @@ from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any, List, Optional, Union +from typing import TYPE_CHECKING, Any, List, Optional import httpx from litellm.llms.base_llm.chat.transformation import BaseConfig -from litellm.types.llms.openai import AllMessageValues +from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues from litellm.types.utils import EmbeddingResponse, ModelResponse if TYPE_CHECKING: @@ -16,12 +16,11 @@ class BaseEmbeddingConfig(BaseConfig, ABC): - @abstractmethod def transform_embedding_request( self, model: str, - input: Union[str, List[str], List[float], List[List[float]]], + input: AllEmbeddingInputValues, optional_params: dict, headers: dict, ) -> dict: @@ -34,14 +33,20 @@ def transform_embedding_response( raw_response: httpx.Response, model_response: EmbeddingResponse, logging_obj: LiteLLMLoggingObj, - api_key: Optional[str] = None, - request_data: dict = {}, - optional_params: dict = {}, - litellm_params: dict = {}, + api_key: Optional[str], + request_data: dict, + optional_params: dict, + litellm_params: dict, ) -> EmbeddingResponse: return model_response - def get_complete_url(self, api_base: Optional[str], model: str) -> str: + def get_complete_url( + self, + api_base: Optional[str], + model: str, + optional_params: dict, + stream: Optional[bool] = None, + ) -> str: """ OPTIONAL diff --git a/litellm/llms/cloudflare/chat/transformation.py b/litellm/llms/cloudflare/chat/transformation.py index 8f6d5ccc1eae..59ba870de51e 100644 --- a/litellm/llms/cloudflare/chat/transformation.py +++ b/litellm/llms/cloudflare/chat/transformation.py @@ -72,7 +72,13 @@ def validate_environment( } return headers - def get_complete_url(self, api_base: str, model: str) -> str: + def get_complete_url( + self, + api_base: str, + model: str, + optional_params: dict, + stream: Optional[bool] = None, + ) -> str: return api_base + model def get_supported_openai_params(self, model: str) -> List[str]: diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index e3ccccd60dc8..f34d9ca53321 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -110,6 +110,8 @@ def completion( api_base = provider_config.get_complete_url( api_base=api_base, model=model, + optional_params=optional_params, + stream=stream, ) data = provider_config.transform_request( @@ -402,6 +404,7 @@ def embedding( logging_obj: LiteLLMLoggingObj, api_base: Optional[str], optional_params: dict, + litellm_params: dict, model_response: EmbeddingResponse, api_key: Optional[str] = None, client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, @@ -424,6 +427,7 @@ def embedding( api_base = provider_config.get_complete_url( api_base=api_base, model=model, + optional_params=optional_params, ) data = provider_config.transform_embedding_request( @@ -457,6 +461,8 @@ def embedding( api_key=api_key, timeout=timeout, client=client, + optional_params=optional_params, + litellm_params=litellm_params, ) if client is None or not isinstance(client, HTTPHandler): @@ -484,6 +490,8 @@ def embedding( logging_obj=logging_obj, api_key=api_key, request_data=data, + optional_params=optional_params, + litellm_params=litellm_params, ) async def aembedding( @@ -496,6 +504,8 @@ async def aembedding( provider_config: BaseEmbeddingConfig, model_response: EmbeddingResponse, logging_obj: LiteLLMLoggingObj, + optional_params: dict, + litellm_params: dict, api_key: Optional[str] = None, timeout: Optional[Union[float, httpx.Timeout]] = None, client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, @@ -524,6 +534,8 @@ async def aembedding( logging_obj=logging_obj, api_key=api_key, request_data=request_data, + optional_params=optional_params, + litellm_params=litellm_params, ) def rerank( diff --git a/litellm/llms/ollama/completion/transformation.py b/litellm/llms/ollama/completion/transformation.py index 52198893219f..9b4bf48e97d0 100644 --- a/litellm/llms/ollama/completion/transformation.py +++ b/litellm/llms/ollama/completion/transformation.py @@ -350,7 +350,13 @@ def validate_environment( ) -> dict: return headers - def get_complete_url(self, api_base: str, model: str) -> str: + def get_complete_url( + self, + api_base: str, + model: str, + optional_params: dict, + stream: Optional[bool] = None, + ) -> str: """ OPTIONAL diff --git a/litellm/llms/replicate/chat/handler.py b/litellm/llms/replicate/chat/handler.py index cd180e4ea6c6..31d55729b754 100644 --- a/litellm/llms/replicate/chat/handler.py +++ b/litellm/llms/replicate/chat/handler.py @@ -168,7 +168,9 @@ def completion( time.time() ) # for pricing this must remain right before calling api - prediction_url = replicate_config.get_complete_url(api_base, model) + prediction_url = replicate_config.get_complete_url( + api_base=api_base, model=model, optional_params=optional_params + ) ## COMPLETION CALL httpx_client = _get_httpx_client( @@ -235,7 +237,9 @@ async def async_completion( headers: dict, ) -> Union[ModelResponse, CustomStreamWrapper]: - prediction_url = replicate_config.get_complete_url(api_base=api_base, model=model) + prediction_url = replicate_config.get_complete_url( + api_base=api_base, model=model, optional_params=optional_params + ) async_handler = get_async_httpx_client( llm_provider=litellm.LlmProviders.REPLICATE, params={"timeout": 600.0}, diff --git a/litellm/llms/replicate/chat/transformation.py b/litellm/llms/replicate/chat/transformation.py index 0c456b8db7ce..1e8e2579ef43 100644 --- a/litellm/llms/replicate/chat/transformation.py +++ b/litellm/llms/replicate/chat/transformation.py @@ -136,7 +136,13 @@ def get_error_class( status_code=status_code, message=error_message, headers=headers ) - def get_complete_url(self, api_base: str, model: str) -> str: + def get_complete_url( + self, + api_base: str, + model: str, + optional_params: dict, + stream: Optional[bool] = None, + ) -> str: version_id = self.model_to_version_id(model) base_url = api_base if "deployments" in version_id: diff --git a/litellm/llms/triton/embedding/transformation.py b/litellm/llms/triton/embedding/transformation.py index 7f08540c7708..85857a56104b 100644 --- a/litellm/llms/triton/embedding/transformation.py +++ b/litellm/llms/triton/embedding/transformation.py @@ -7,6 +7,7 @@ BaseEmbeddingConfig, LiteLLMLoggingObj, ) +from litellm.types.llms.openai import AllEmbeddingInputValues from litellm.types.utils import EmbeddingResponse from ..common_utils import TritonError @@ -48,7 +49,7 @@ def validate_environment( def transform_embedding_request( self, model: str, - input: Union[str, List[str], List[float], List[List[float]]], + input: AllEmbeddingInputValues, optional_params: dict, headers: dict, ) -> dict: diff --git a/litellm/llms/voyage/embedding/transformation.py b/litellm/llms/voyage/embedding/transformation.py index 2a51bdde1417..3d969223a51a 100644 --- a/litellm/llms/voyage/embedding/transformation.py +++ b/litellm/llms/voyage/embedding/transformation.py @@ -6,7 +6,7 @@ from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig from litellm.secret_managers.main import get_secret_str -from litellm.types.llms.openai import AllMessageValues +from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues from litellm.types.utils import EmbeddingResponse, Usage @@ -38,7 +38,13 @@ class VoyageEmbeddingConfig(BaseEmbeddingConfig): def __init__(self) -> None: pass - def get_complete_url(self, api_base: Optional[str], model: str) -> str: + def get_complete_url( + self, + api_base: Optional[str], + model: str, + optional_params: dict, + stream: Optional[bool] = None, + ) -> str: if api_base: if not api_base.endswith("/embeddings"): api_base = f"{api_base}/embeddings" @@ -90,7 +96,7 @@ def validate_environment( def transform_embedding_request( self, model: str, - input: Union[str, List[str], List[float], List[List[float]]], + input: AllEmbeddingInputValues, optional_params: dict, headers: dict, ) -> dict: diff --git a/litellm/llms/watsonx/chat/handler.py b/litellm/llms/watsonx/chat/handler.py index 7753b929a3fb..4f2d36d7a869 100644 --- a/litellm/llms/watsonx/chat/handler.py +++ b/litellm/llms/watsonx/chat/handler.py @@ -3,57 +3,19 @@ import httpx from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler -from litellm.types.llms.watsonx import WatsonXAIEndpoint, WatsonXAPIParams from litellm.types.utils import CustomStreamingDecoder, ModelResponse from ...openai_like.chat.handler import OpenAILikeChatHandler -from ..common_utils import WatsonXAIError, _get_api_params +from ..common_utils import _get_api_params +from .transformation import IBMWatsonXChatConfig + +watsonx_chat_transformation = IBMWatsonXChatConfig() class WatsonXChatHandler(OpenAILikeChatHandler): def __init__(self, **kwargs): super().__init__(**kwargs) - def _prepare_url( - self, model: str, api_params: WatsonXAPIParams, stream: Optional[bool] - ) -> str: - if model.startswith("deployment/"): - if api_params.get("space_id") is None: - raise WatsonXAIError( - status_code=401, - message="Error: space_id is required for models called using the 'deployment/' endpoint. Pass in the space_id as a parameter or set it in the WX_SPACE_ID environment variable.", - ) - deployment_id = "/".join(model.split("/")[1:]) - endpoint = ( - WatsonXAIEndpoint.DEPLOYMENT_CHAT_STREAM.value - if stream is True - else WatsonXAIEndpoint.DEPLOYMENT_CHAT.value - ) - endpoint = endpoint.format(deployment_id=deployment_id) - else: - endpoint = ( - WatsonXAIEndpoint.CHAT_STREAM.value - if stream is True - else WatsonXAIEndpoint.CHAT.value - ) - base_url = httpx.URL(api_params["url"]) - base_url = base_url.join(endpoint) - full_url = str( - base_url.copy_add_param(key="version", value=api_params["api_version"]) - ) - - return full_url - - def _prepare_payload( - self, model: str, api_params: WatsonXAPIParams, stream: Optional[bool] - ) -> dict: - payload: dict = {} - if model.startswith("deployment/"): - return payload - payload["model_id"] = model - payload["project_id"] = api_params["project_id"] - return payload - def completion( self, *, @@ -70,32 +32,37 @@ def completion( optional_params: dict, acompletion=None, litellm_params=None, - logger_fn=None, headers: Optional[dict] = None, + logger_fn=None, timeout: Optional[Union[float, httpx.Timeout]] = None, client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, custom_endpoint: Optional[bool] = None, streaming_decoder: Optional[CustomStreamingDecoder] = None, fake_stream: bool = False, ): - api_params = _get_api_params(optional_params, print_verbose=print_verbose) + api_params = _get_api_params(params=optional_params) - if headers is None: - headers = {} - headers.update( - { - "Authorization": f"Bearer {api_params['token']}", - "Content-Type": "application/json", - "Accept": "application/json", - } + ## UPDATE HEADERS + headers = watsonx_chat_transformation.validate_environment( + headers=headers or {}, + model=model, + messages=messages, + optional_params=optional_params, + api_key=api_key, ) - stream: Optional[bool] = optional_params.get("stream", False) + ## GET API URL + api_base = watsonx_chat_transformation.get_complete_url( + api_base=api_base, + model=model, + optional_params=optional_params, + stream=optional_params.get("stream", False), + ) - ## get api url and payload - api_base = self._prepare_url(model=model, api_params=api_params, stream=stream) - watsonx_auth_payload = self._prepare_payload( - model=model, api_params=api_params, stream=stream + ## UPDATE PAYLOAD (optional params) + watsonx_auth_payload = watsonx_chat_transformation._prepare_payload( + model=model, + api_params=api_params, ) optional_params.update(watsonx_auth_payload) diff --git a/litellm/llms/watsonx/chat/transformation.py b/litellm/llms/watsonx/chat/transformation.py index 5df943005715..19e98daaaa39 100644 --- a/litellm/llms/watsonx/chat/transformation.py +++ b/litellm/llms/watsonx/chat/transformation.py @@ -7,12 +7,14 @@ from typing import List, Optional, Tuple, Union from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.watsonx import WatsonXAIEndpoint, WatsonXAPIParams from ....utils import _remove_additional_properties, _remove_strict_from_schema from ...openai.chat.gpt_transformation import OpenAIGPTConfig +from ..common_utils import IBMWatsonXMixin, WatsonXAIError -class IBMWatsonXChatConfig(OpenAIGPTConfig): +class IBMWatsonXChatConfig(IBMWatsonXMixin, OpenAIGPTConfig): def get_supported_openai_params(self, model: str) -> List: return [ @@ -75,3 +77,47 @@ def _get_openai_compatible_provider_info( api_key or get_secret_str("HOSTED_VLLM_API_KEY") or "" ) # vllm does not require an api key return api_base, dynamic_api_key + + def get_complete_url( + self, + api_base: str, + model: str, + optional_params: dict, + stream: Optional[bool] = None, + ) -> str: + url = self._get_base_url(api_base=api_base) + if model.startswith("deployment/"): + # deployment models are passed in as 'deployment/' + if optional_params.get("space_id") is None: + raise WatsonXAIError( + status_code=401, + message="Error: space_id is required for models called using the 'deployment/' endpoint. Pass in the space_id as a parameter or set it in the WX_SPACE_ID environment variable.", + ) + deployment_id = "/".join(model.split("/")[1:]) + endpoint = ( + WatsonXAIEndpoint.DEPLOYMENT_CHAT_STREAM.value + if stream + else WatsonXAIEndpoint.DEPLOYMENT_CHAT.value + ) + endpoint = endpoint.format(deployment_id=deployment_id) + else: + endpoint = ( + WatsonXAIEndpoint.DEPLOYMENT_CHAT_STREAM.value + if stream + else WatsonXAIEndpoint.DEPLOYMENT_CHAT.value + ) + url = url.rstrip("/") + endpoint + + ## add api version + url = self._add_api_version_to_url( + url=url, api_version=optional_params.pop("api_version", None) + ) + return url + + def _prepare_payload(self, model: str, api_params: WatsonXAPIParams) -> dict: + payload: dict = {} + if model.startswith("deployment/"): + return payload + payload["model_id"] = model + payload["project_id"] = api_params["project_id"] + return payload diff --git a/litellm/llms/watsonx/common_utils.py b/litellm/llms/watsonx/common_utils.py index 82881fe796e0..ce1a16ca553f 100644 --- a/litellm/llms/watsonx/common_utils.py +++ b/litellm/llms/watsonx/common_utils.py @@ -1,13 +1,15 @@ -from typing import Callable, Dict, Optional, Union, cast +from typing import Dict, List, Optional, Union, cast import httpx import litellm from litellm import verbose_logger from litellm.caching import InMemoryCache +from litellm.litellm_core_utils.prompt_templates import factory as ptf from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.secret_managers.main import get_secret_str -from litellm.types.llms.watsonx import WatsonXAPIParams +from litellm.types.llms.openai import AllMessageValues +from litellm.types.llms.watsonx import WatsonXAPIParams, WatsonXCredentials class WatsonXAIError(BaseLLMException): @@ -65,18 +67,20 @@ def generate_iam_token(api_key=None, **params) -> str: return cast(str, result) +def _generate_watsonx_token(api_key: Optional[str], token: Optional[str]) -> str: + if token is not None: + return token + token = generate_iam_token(api_key) + return token + + def _get_api_params( params: dict, - print_verbose: Optional[Callable] = None, - generate_token: Optional[bool] = True, ) -> WatsonXAPIParams: """ Find watsonx.ai credentials in the params or environment variables and return the headers for authentication. """ # Load auth variables from params - url = params.pop("url", params.pop("api_base", params.pop("base_url", None))) - api_key = params.pop("apikey", None) - token = params.pop("token", None) project_id = params.pop( "project_id", params.pop("watsonx_project", None) ) # watsonx.ai project_id - allow 'watsonx_project' to be consistent with how vertex project implementation works -> reduce provider-specific params @@ -86,29 +90,8 @@ def _get_api_params( region_name = params.pop( "watsonx_region_name", params.pop("watsonx_region", None) ) # consistent with how vertex ai + aws regions are accepted - wx_credentials = params.pop( - "wx_credentials", - params.pop( - "watsonx_credentials", None - ), # follow {provider}_credentials, same as vertex ai - ) - api_version = params.pop("api_version", litellm.WATSONX_DEFAULT_API_VERSION) + # Load auth variables from environment variables - if url is None: - url = ( - get_secret_str("WATSONX_API_BASE") # consistent with 'AZURE_API_BASE' - or get_secret_str("WATSONX_URL") - or get_secret_str("WX_URL") - or get_secret_str("WML_URL") - ) - if api_key is None: - api_key = ( - get_secret_str("WATSONX_APIKEY") - or get_secret_str("WATSONX_API_KEY") - or get_secret_str("WX_API_KEY") - ) - if token is None: - token = get_secret_str("WATSONX_TOKEN") or get_secret_str("WX_TOKEN") if project_id is None: project_id = ( get_secret_str("WATSONX_PROJECT_ID") @@ -129,34 +112,6 @@ def _get_api_params( or get_secret_str("SPACE_ID") ) - # credentials parsing - if wx_credentials is not None: - url = wx_credentials.get("url", url) - api_key = wx_credentials.get("apikey", wx_credentials.get("api_key", api_key)) - token = wx_credentials.get( - "token", - wx_credentials.get( - "watsonx_token", token - ), # follow format of {provider}_token, same as azure - e.g. 'azure_ad_token=..' - ) - - # verify that all required credentials are present - if url is None: - raise WatsonXAIError( - status_code=401, - message="Error: Watsonx URL not set. Set WX_URL in environment variables or pass in as a parameter.", - ) - - if token is None and api_key is not None and generate_token: - # generate the auth token - if print_verbose is not None: - print_verbose("Generating IAM token for Watsonx.ai") - token = generate_iam_token(api_key) - elif token is None and api_key is None: - raise WatsonXAIError( - status_code=401, - message="Error: API key or token not found. Set WX_API_KEY or WX_TOKEN in environment variables or pass in as a parameter.", - ) if project_id is None: raise WatsonXAIError( status_code=401, @@ -164,11 +119,147 @@ def _get_api_params( ) return WatsonXAPIParams( - url=url, - api_key=api_key, - token=cast(str, token), project_id=project_id, space_id=space_id, region_name=region_name, - api_version=api_version, ) + + +def convert_watsonx_messages_to_prompt( + model: str, + messages: List[AllMessageValues], + provider: str, + custom_prompt_dict: Dict, +) -> str: + # handle anthropic prompts and amazon titan prompts + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_dict = custom_prompt_dict[model] + prompt = ptf.custom_prompt( + messages=messages, + role_dict=model_prompt_dict.get( + "role_dict", model_prompt_dict.get("roles") + ), + initial_prompt_value=model_prompt_dict.get("initial_prompt_value", ""), + final_prompt_value=model_prompt_dict.get("final_prompt_value", ""), + bos_token=model_prompt_dict.get("bos_token", ""), + eos_token=model_prompt_dict.get("eos_token", ""), + ) + return prompt + elif provider == "ibm-mistralai": + prompt = ptf.mistral_instruct_pt(messages=messages) + else: + prompt: str = ptf.prompt_factory( # type: ignore + model=model, messages=messages, custom_llm_provider="watsonx" + ) + return prompt + + +# Mixin class for shared IBM Watson X functionality +class IBMWatsonXMixin: + def validate_environment( + self, + headers: Dict, + model: str, + messages: List[AllMessageValues], + optional_params: Dict, + api_key: Optional[str] = None, + ) -> Dict: + headers = { + "Content-Type": "application/json", + "Accept": "application/json", + } + token = cast(Optional[str], optional_params.get("token")) + if token: + headers["Authorization"] = f"Bearer {token}" + else: + token = _generate_watsonx_token(api_key=api_key, token=token) + # build auth headers + headers["Authorization"] = f"Bearer {token}" + return headers + + def _get_base_url(self, api_base: Optional[str]) -> str: + url = ( + api_base + or get_secret_str("WATSONX_API_BASE") # consistent with 'AZURE_API_BASE' + or get_secret_str("WATSONX_URL") + or get_secret_str("WX_URL") + or get_secret_str("WML_URL") + ) + + if url is None: + raise WatsonXAIError( + status_code=401, + message="Error: Watsonx URL not set. Set WATSONX_API_BASE in environment variables or pass in as parameter - 'api_base='.", + ) + return url + + def _add_api_version_to_url(self, url: str, api_version: Optional[str]) -> str: + api_version = api_version or litellm.WATSONX_DEFAULT_API_VERSION + url = url + f"?version={api_version}" + + return url + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[Dict, httpx.Headers] + ) -> BaseLLMException: + return WatsonXAIError( + status_code=status_code, message=error_message, headers=headers + ) + + @staticmethod + def get_watsonx_credentials( + optional_params: dict, api_key: Optional[str], api_base: Optional[str] + ) -> WatsonXCredentials: + api_key = ( + api_key + or optional_params.pop("apikey", None) + or get_secret_str("WATSONX_APIKEY") + or get_secret_str("WATSONX_API_KEY") + or get_secret_str("WX_API_KEY") + ) + + api_base = ( + api_base + or optional_params.pop( + "url", + optional_params.pop("api_base", optional_params.pop("base_url", None)), + ) + or get_secret_str("WATSONX_API_BASE") + or get_secret_str("WATSONX_URL") + or get_secret_str("WX_URL") + or get_secret_str("WML_URL") + ) + + wx_credentials = optional_params.pop( + "wx_credentials", + optional_params.pop( + "watsonx_credentials", None + ), # follow {provider}_credentials, same as vertex ai + ) + + token: Optional[str] = None + if wx_credentials is not None: + api_base = wx_credentials.get("url", api_base) + api_key = wx_credentials.get( + "apikey", wx_credentials.get("api_key", api_key) + ) + token = wx_credentials.get( + "token", + wx_credentials.get( + "watsonx_token", None + ), # follow format of {provider}_token, same as azure - e.g. 'azure_ad_token=..' + ) + if api_key is None or not isinstance(api_key, str): + raise WatsonXAIError( + status_code=401, + message="Error: Watsonx API key not set. Set WATSONX_API_KEY in environment variables or pass in as parameter - 'api_key='.", + ) + if api_base is None or not isinstance(api_base, str): + raise WatsonXAIError( + status_code=401, + message="Error: Watsonx API base not set. Set WATSONX_API_BASE in environment variables or pass in as parameter - 'api_base='.", + ) + return WatsonXCredentials( + api_key=api_key, api_base=api_base, token=cast(Optional[str], token) + ) diff --git a/litellm/llms/watsonx/completion/handler.py b/litellm/llms/watsonx/completion/handler.py index 2d3760e8814f..2a57ddcf468b 100644 --- a/litellm/llms/watsonx/completion/handler.py +++ b/litellm/llms/watsonx/completion/handler.py @@ -1,551 +1,3 @@ -import asyncio -import json # noqa: E401 -import time -from contextlib import asynccontextmanager, contextmanager -from datetime import datetime -from typing import ( - Any, - AsyncGenerator, - AsyncIterator, - Callable, - Generator, - Iterator, - List, - Optional, - Union, -) - -import httpx # type: ignore -import requests # type: ignore - -import litellm -from litellm.litellm_core_utils.prompt_templates import factory as ptf -from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper -from litellm.llms.custom_httpx.http_handler import get_async_httpx_client -from litellm.types.llms.openai import AllMessageValues -from litellm.types.llms.watsonx import WatsonXAIEndpoint -from litellm.utils import EmbeddingResponse, ModelResponse, Usage, map_finish_reason - -from ...base import BaseLLM -from ..common_utils import WatsonXAIError, _get_api_params -from .transformation import IBMWatsonXAIConfig - - -def convert_messages_to_prompt(model, messages, provider, custom_prompt_dict) -> str: - # handle anthropic prompts and amazon titan prompts - if model in custom_prompt_dict: - # check if the model has a registered custom prompt - model_prompt_dict = custom_prompt_dict[model] - prompt = ptf.custom_prompt( - messages=messages, - role_dict=model_prompt_dict.get( - "role_dict", model_prompt_dict.get("roles") - ), - initial_prompt_value=model_prompt_dict.get("initial_prompt_value", ""), - final_prompt_value=model_prompt_dict.get("final_prompt_value", ""), - bos_token=model_prompt_dict.get("bos_token", ""), - eos_token=model_prompt_dict.get("eos_token", ""), - ) - return prompt - elif provider == "ibm-mistralai": - prompt = ptf.mistral_instruct_pt(messages=messages) - else: - prompt: str = ptf.prompt_factory( # type: ignore - model=model, messages=messages, custom_llm_provider="watsonx" - ) - return prompt - - -class IBMWatsonXAI(BaseLLM): - """ - Class to interface with IBM watsonx.ai API for text generation and embeddings. - - Reference: https://cloud.ibm.com/apidocs/watsonx-ai - """ - - api_version = "2024-03-13" - - def __init__(self) -> None: - super().__init__() - - def _prepare_text_generation_req( - self, - model_id: str, - messages: List[AllMessageValues], - prompt: str, - stream: bool, - optional_params: dict, - print_verbose: Optional[Callable] = None, - ) -> dict: - """ - Get the request parameters for text generation. - """ - api_params = _get_api_params(optional_params, print_verbose=print_verbose) - # build auth headers - api_token = api_params.get("token") - self.token = api_token - headers = IBMWatsonXAIConfig().validate_environment( - headers={}, - model=model_id, - messages=messages, - optional_params=optional_params, - api_key=api_token, - ) - extra_body_params = optional_params.pop("extra_body", {}) - optional_params.update(extra_body_params) - # init the payload to the text generation call - payload = { - "input": prompt, - "moderations": optional_params.pop("moderations", {}), - "parameters": optional_params, - } - request_params = dict(version=api_params["api_version"]) - # text generation endpoint deployment or model / stream or not - if model_id.startswith("deployment/"): - # deployment models are passed in as 'deployment/' - if api_params.get("space_id") is None: - raise WatsonXAIError( - status_code=401, - message="Error: space_id is required for models called using the 'deployment/' endpoint. Pass in the space_id as a parameter or set it in the WX_SPACE_ID environment variable.", - ) - deployment_id = "/".join(model_id.split("/")[1:]) - endpoint = ( - WatsonXAIEndpoint.DEPLOYMENT_TEXT_GENERATION_STREAM.value - if stream - else WatsonXAIEndpoint.DEPLOYMENT_TEXT_GENERATION.value - ) - endpoint = endpoint.format(deployment_id=deployment_id) - else: - payload["model_id"] = model_id - payload["project_id"] = api_params["project_id"] - endpoint = ( - WatsonXAIEndpoint.TEXT_GENERATION_STREAM - if stream - else WatsonXAIEndpoint.TEXT_GENERATION - ) - url = api_params["url"].rstrip("/") + endpoint - return dict( - method="POST", url=url, headers=headers, json=payload, params=request_params - ) - - def _process_text_gen_response( - self, json_resp: dict, model_response: Union[ModelResponse, None] = None - ) -> ModelResponse: - if "results" not in json_resp: - raise WatsonXAIError( - status_code=500, - message=f"Error: Invalid response from Watsonx.ai API: {json_resp}", - ) - if model_response is None: - model_response = ModelResponse(model=json_resp.get("model_id", None)) - generated_text = json_resp["results"][0]["generated_text"] - prompt_tokens = json_resp["results"][0]["input_token_count"] - completion_tokens = json_resp["results"][0]["generated_token_count"] - model_response.choices[0].message.content = generated_text # type: ignore - model_response.choices[0].finish_reason = map_finish_reason( - json_resp["results"][0]["stop_reason"] - ) - if json_resp.get("created_at"): - model_response.created = int( - datetime.fromisoformat(json_resp["created_at"]).timestamp() - ) - else: - model_response.created = int(time.time()) - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - ) - setattr(model_response, "usage", usage) - return model_response - - def completion( - self, - model: str, - messages: list, - custom_prompt_dict: dict, - model_response: ModelResponse, - print_verbose: Callable, - encoding, - logging_obj: Any, - optional_params: dict, - acompletion=None, - litellm_params=None, - logger_fn=None, - timeout=None, - ): - """ - Send a text generation request to the IBM Watsonx.ai API. - Reference: https://cloud.ibm.com/apidocs/watsonx-ai#text-generation - """ - stream = optional_params.pop("stream", False) - - # Load default configs - config = IBMWatsonXAIConfig.get_config() - for k, v in config.items(): - if k not in optional_params: - optional_params[k] = v - - # Make prompt to send to model - provider = model.split("/")[0] - # model_name = "/".join(model.split("/")[1:]) - prompt = convert_messages_to_prompt( - model, messages, provider, custom_prompt_dict - ) - model_response.model = model - - def process_stream_response( - stream_resp: Union[Iterator[str], AsyncIterator], - ) -> CustomStreamWrapper: - streamwrapper = litellm.CustomStreamWrapper( - stream_resp, - model=model, - custom_llm_provider="watsonx", - logging_obj=logging_obj, - ) - return streamwrapper - - # create the function to manage the request to watsonx.ai - self.request_manager = RequestManager(logging_obj) - - def handle_text_request(request_params: dict) -> ModelResponse: - with self.request_manager.request( - request_params, - input=prompt, - timeout=timeout, - ) as resp: - json_resp = resp.json() - - return self._process_text_gen_response(json_resp, model_response) - - async def handle_text_request_async(request_params: dict) -> ModelResponse: - async with self.request_manager.async_request( - request_params, - input=prompt, - timeout=timeout, - ) as resp: - json_resp = resp.json() - return self._process_text_gen_response(json_resp, model_response) - - def handle_stream_request(request_params: dict) -> CustomStreamWrapper: - # stream the response - generated chunks will be handled - # by litellm.utils.CustomStreamWrapper.handle_watsonx_stream - with self.request_manager.request( - request_params, - stream=True, - input=prompt, - timeout=timeout, - ) as resp: - streamwrapper = process_stream_response(resp.iter_lines()) - return streamwrapper - - async def handle_stream_request_async( - request_params: dict, - ) -> CustomStreamWrapper: - # stream the response - generated chunks will be handled - # by litellm.utils.CustomStreamWrapper.handle_watsonx_stream - async with self.request_manager.async_request( - request_params, - stream=True, - input=prompt, - timeout=timeout, - ) as resp: - streamwrapper = process_stream_response(resp.aiter_lines()) - return streamwrapper - - try: - ## Get the response from the model - req_params = self._prepare_text_generation_req( - model_id=model, - prompt=prompt, - messages=messages, - stream=stream, - optional_params=optional_params, - print_verbose=print_verbose, - ) - if stream and (acompletion is True): - # stream and async text generation - return handle_stream_request_async(req_params) - elif stream: - # streaming text generation - return handle_stream_request(req_params) - elif acompletion is True: - # async text generation - return handle_text_request_async(req_params) - else: - # regular text generation - return handle_text_request(req_params) - except WatsonXAIError as e: - raise e - except Exception as e: - raise WatsonXAIError(status_code=500, message=str(e)) - - def _process_embedding_response( - self, json_resp: dict, model_response: Optional[EmbeddingResponse] = None - ) -> EmbeddingResponse: - if model_response is None: - model_response = EmbeddingResponse(model=json_resp.get("model_id", None)) - results = json_resp.get("results", []) - embedding_response = [] - for idx, result in enumerate(results): - embedding_response.append( - { - "object": "embedding", - "index": idx, - "embedding": result["embedding"], - } - ) - model_response.object = "list" - model_response.data = embedding_response - input_tokens = json_resp.get("input_token_count", 0) - setattr( - model_response, - "usage", - Usage( - prompt_tokens=input_tokens, - completion_tokens=0, - total_tokens=input_tokens, - ), - ) - return model_response - - def embedding( - self, - model: str, - input: Union[list, str], - model_response: EmbeddingResponse, - api_key: Optional[str], - logging_obj: Any, - optional_params: dict, - encoding=None, - print_verbose=None, - aembedding=None, - ) -> EmbeddingResponse: - """ - Send a text embedding request to the IBM Watsonx.ai API. - """ - if optional_params is None: - optional_params = {} - # Load default configs - config = IBMWatsonXAIConfig.get_config() - for k, v in config.items(): - if k not in optional_params: - optional_params[k] = v - - model_response.model = model - - # Load auth variables from environment variables - if isinstance(input, str): - input = [input] - if api_key is not None: - optional_params["api_key"] = api_key - api_params = _get_api_params(optional_params) - # build auth headers - api_token = api_params.get("token") - self.token = api_token - headers = { - "Authorization": f"Bearer {api_token}", - "Content-Type": "application/json", - "Accept": "application/json", - } - # init the payload to the text generation call - payload = { - "inputs": input, - "model_id": model, - "project_id": api_params["project_id"], - "parameters": optional_params, - } - request_params = dict(version=api_params["api_version"]) - url = api_params["url"].rstrip("/") + WatsonXAIEndpoint.EMBEDDINGS - req_params = { - "method": "POST", - "url": url, - "headers": headers, - "json": payload, - "params": request_params, - } - request_manager = RequestManager(logging_obj) - - def handle_embedding(request_params: dict) -> EmbeddingResponse: - with request_manager.request(request_params, input=input) as resp: - json_resp = resp.json() - return self._process_embedding_response(json_resp, model_response) - - async def handle_aembedding(request_params: dict) -> EmbeddingResponse: - async with request_manager.async_request( - request_params, input=input - ) as resp: - json_resp = resp.json() - return self._process_embedding_response(json_resp, model_response) - - try: - if aembedding is True: - return handle_aembedding(req_params) # type: ignore - else: - return handle_embedding(req_params) - except WatsonXAIError as e: - raise e - except Exception as e: - raise WatsonXAIError(status_code=500, message=str(e)) - - def get_available_models(self, *, ids_only: bool = True, **params): - api_params = _get_api_params(params) - self.token = api_params["token"] - headers = { - "Authorization": f"Bearer {api_params['token']}", - "Content-Type": "application/json", - "Accept": "application/json", - } - request_params = dict(version=api_params["api_version"]) - url = api_params["url"].rstrip("/") + WatsonXAIEndpoint.AVAILABLE_MODELS - req_params = dict(method="GET", url=url, headers=headers, params=request_params) - with RequestManager(logging_obj=None).request(req_params) as resp: - json_resp = resp.json() - if not ids_only: - return json_resp - return [res["model_id"] for res in json_resp["resources"]] - - -class RequestManager: - """ - A class to handle sync/async HTTP requests to the IBM Watsonx.ai API. - - Usage: - ```python - request_params = dict(method="POST", url="https://api.example.com", headers={"Authorization" : "Bearer token"}, json={"key": "value"}) - request_manager = RequestManager(logging_obj=logging_obj) - with request_manager.request(request_params) as resp: - ... - # or - async with request_manager.async_request(request_params) as resp: - ... - ``` - """ - - def __init__(self, logging_obj=None): - self.logging_obj = logging_obj - - def pre_call( - self, - request_params: dict, - input: Optional[Any] = None, - is_async: Optional[bool] = False, - ): - if self.logging_obj is None: - return - request_str = ( - f"response = {'await ' if is_async else ''}{request_params['method']}(\n" - f"\turl={request_params['url']},\n" - f"\tjson={request_params.get('json')},\n" - f")" - ) - self.logging_obj.pre_call( - input=input, - api_key=request_params["headers"].get("Authorization"), - additional_args={ - "complete_input_dict": request_params.get("json"), - "request_str": request_str, - }, - ) - - def post_call(self, resp, request_params): - if self.logging_obj is None: - return - self.logging_obj.post_call( - input=input, - api_key=request_params["headers"].get("Authorization"), - original_response=json.dumps(resp.json()), - additional_args={ - "status_code": resp.status_code, - "complete_input_dict": request_params.get( - "data", request_params.get("json") - ), - }, - ) - - @contextmanager - def request( - self, - request_params: dict, - stream: bool = False, - input: Optional[Any] = None, - timeout=None, - ) -> Generator[requests.Response, None, None]: - """ - Returns a context manager that yields the response from the request. - """ - self.pre_call(request_params, input) - if timeout: - request_params["timeout"] = timeout - if stream: - request_params["stream"] = stream - try: - resp = requests.request(**request_params) - if not resp.ok: - raise WatsonXAIError( - status_code=resp.status_code, - message=f"Error {resp.status_code} ({resp.reason}): {resp.text}", - ) - yield resp - except Exception as e: - raise WatsonXAIError(status_code=500, message=str(e)) - if not stream: - self.post_call(resp, request_params) - - @asynccontextmanager - async def async_request( - self, - request_params: dict, - stream: bool = False, - input: Optional[Any] = None, - timeout=None, - ) -> AsyncGenerator[httpx.Response, None]: - self.pre_call(request_params, input, is_async=True) - if timeout: - request_params["timeout"] = timeout - if stream: - request_params["stream"] = stream - try: - self.async_handler = get_async_httpx_client( - llm_provider=litellm.LlmProviders.WATSONX, - params={ - "timeout": httpx.Timeout( - timeout=request_params.pop("timeout", 600.0), connect=5.0 - ), - }, - ) - if "json" in request_params: - request_params["data"] = json.dumps(request_params.pop("json", {})) - method = request_params.pop("method") - retries = 0 - resp: Optional[httpx.Response] = None - while retries < 3: - if method.upper() == "POST": - resp = await self.async_handler.post(**request_params) - else: - resp = await self.async_handler.get(**request_params) - if resp is not None and resp.status_code in [429, 503, 504, 520]: - # to handle rate limiting and service unavailable errors - # see: ibm_watsonx_ai.foundation_models.inference.base_model_inference.BaseModelInference._send_inference_payload - await asyncio.sleep(2**retries) - retries += 1 - else: - break - if resp is None: - raise WatsonXAIError( - status_code=500, - message="No response from the server", - ) - if resp.is_error: - error_reason = getattr(resp, "reason", "") - raise WatsonXAIError( - status_code=resp.status_code, - message=f"Error {resp.status_code} ({error_reason}): {resp.text}", - ) - yield resp - # await async_handler.close() - except Exception as e: - raise e - raise WatsonXAIError(status_code=500, message=str(e)) - if not stream: - self.post_call(resp, request_params) +""" +Watsonx uses the llm_http_handler.py to handle the requests. +""" diff --git a/litellm/llms/watsonx/completion/transformation.py b/litellm/llms/watsonx/completion/transformation.py index e1706291d5a8..e214a945d243 100644 --- a/litellm/llms/watsonx/completion/transformation.py +++ b/litellm/llms/watsonx/completion/transformation.py @@ -1,13 +1,31 @@ -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union +import time +from datetime import datetime +from typing import ( + TYPE_CHECKING, + Any, + AsyncIterator, + Dict, + Iterator, + List, + Optional, + Union, +) import httpx -from litellm.llms.base_llm.chat.transformation import BaseLLMException -from litellm.types.llms.openai import AllMessageValues -from litellm.utils import ModelResponse +from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator +from litellm.types.llms.openai import AllMessageValues, ChatCompletionUsageBlock +from litellm.types.llms.watsonx import WatsonXAIEndpoint +from litellm.types.utils import GenericStreamingChunk, ModelResponse, Usage +from litellm.utils import map_finish_reason from ...base_llm.chat.transformation import BaseConfig -from ..common_utils import WatsonXAIError +from ..common_utils import ( + IBMWatsonXMixin, + WatsonXAIError, + _get_api_params, + convert_watsonx_messages_to_prompt, +) if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj @@ -17,7 +35,7 @@ LiteLLMLoggingObj = Any -class IBMWatsonXAIConfig(BaseConfig): +class IBMWatsonXAIConfig(IBMWatsonXMixin, BaseConfig): """ Reference: https://cloud.ibm.com/apidocs/watsonx-ai#text-generation (See ibm_watsonx_ai.metanames.GenTextParamsMetaNames for a list of all available params) @@ -210,13 +228,6 @@ def get_us_regions(self) -> List[str]: "us-south", ] - def get_error_class( - self, error_message: str, status_code: int, headers: Union[Dict, httpx.Headers] - ) -> BaseLLMException: - return WatsonXAIError( - status_code=status_code, message=error_message, headers=headers - ) - def transform_request( self, model: str, @@ -225,9 +236,28 @@ def transform_request( litellm_params: Dict, headers: Dict, ) -> Dict: - raise NotImplementedError( - "transform_request not implemented. Done in watsonx/completion handler.py" + provider = model.split("/")[0] + prompt = convert_watsonx_messages_to_prompt( + model=model, + messages=messages, + provider=provider, + custom_prompt_dict={}, ) + extra_body_params = optional_params.pop("extra_body", {}) + optional_params.update(extra_body_params) + watsonx_api_params = _get_api_params(params=optional_params) + # init the payload to the text generation call + payload = { + "input": prompt, + "moderations": optional_params.pop("moderations", {}), + "parameters": optional_params, + } + + if not model.startswith("deployment/"): + payload["model_id"] = model + payload["project_id"] = watsonx_api_params["project_id"] + + return payload def transform_response( self, @@ -243,22 +273,120 @@ def transform_response( api_key: Optional[str] = None, json_mode: Optional[bool] = None, ) -> ModelResponse: - raise NotImplementedError( - "transform_response not implemented. Done in watsonx/completion handler.py" + ## LOGGING + logging_obj.post_call( + input=messages, + api_key="", + original_response=raw_response.text, ) - def validate_environment( + json_resp = raw_response.json() + + if "results" not in json_resp: + raise WatsonXAIError( + status_code=500, + message=f"Error: Invalid response from Watsonx.ai API: {json_resp}", + ) + if model_response is None: + model_response = ModelResponse(model=json_resp.get("model_id", None)) + generated_text = json_resp["results"][0]["generated_text"] + prompt_tokens = json_resp["results"][0]["input_token_count"] + completion_tokens = json_resp["results"][0]["generated_token_count"] + model_response.choices[0].message.content = generated_text # type: ignore + model_response.choices[0].finish_reason = map_finish_reason( + json_resp["results"][0]["stop_reason"] + ) + if json_resp.get("created_at"): + model_response.created = int( + datetime.fromisoformat(json_resp["created_at"]).timestamp() + ) + else: + model_response.created = int(time.time()) + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + setattr(model_response, "usage", usage) + return model_response + + def get_complete_url( self, - headers: Dict, + api_base: str, model: str, - messages: List[AllMessageValues], - optional_params: Dict, - api_key: Optional[str] = None, - ) -> Dict: - headers = { - "Content-Type": "application/json", - "Accept": "application/json", - } - if api_key: - headers["Authorization"] = f"Bearer {api_key}" - return headers + optional_params: dict, + stream: Optional[bool] = None, + ) -> str: + url = self._get_base_url(api_base=api_base) + if model.startswith("deployment/"): + # deployment models are passed in as 'deployment/' + if optional_params.get("space_id") is None: + raise WatsonXAIError( + status_code=401, + message="Error: space_id is required for models called using the 'deployment/' endpoint. Pass in the space_id as a parameter or set it in the WX_SPACE_ID environment variable.", + ) + deployment_id = "/".join(model.split("/")[1:]) + endpoint = ( + WatsonXAIEndpoint.DEPLOYMENT_TEXT_GENERATION_STREAM.value + if stream + else WatsonXAIEndpoint.DEPLOYMENT_TEXT_GENERATION.value + ) + endpoint = endpoint.format(deployment_id=deployment_id) + else: + endpoint = ( + WatsonXAIEndpoint.TEXT_GENERATION_STREAM + if stream + else WatsonXAIEndpoint.TEXT_GENERATION + ) + url = url.rstrip("/") + endpoint + + ## add api version + url = self._add_api_version_to_url( + url=url, api_version=optional_params.pop("api_version", None) + ) + 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 WatsonxTextCompletionResponseIterator( + streaming_response=streaming_response, + sync_stream=sync_stream, + json_mode=json_mode, + ) + + +class WatsonxTextCompletionResponseIterator(BaseModelResponseIterator): + # def _handle_string_chunk(self, str_line: str) -> GenericStreamingChunk: + # return self.chunk_parser(json.loads(str_line)) + + def chunk_parser(self, chunk: dict) -> GenericStreamingChunk: + try: + results = chunk.get("results", []) + if len(results) > 0: + text = results[0].get("generated_text", "") + finish_reason = results[0].get("stop_reason") + is_finished = finish_reason != "not_finished" + + return GenericStreamingChunk( + text=text, + is_finished=is_finished, + finish_reason=finish_reason, + usage=ChatCompletionUsageBlock( + prompt_tokens=results[0].get("input_token_count", 0), + completion_tokens=results[0].get("generated_token_count", 0), + total_tokens=results[0].get("input_token_count", 0) + + results[0].get("generated_token_count", 0), + ), + ) + return GenericStreamingChunk( + text="", + is_finished=False, + finish_reason="stop", + usage=None, + ) + except Exception as e: + raise e diff --git a/litellm/llms/watsonx/embed/transformation.py b/litellm/llms/watsonx/embed/transformation.py new file mode 100644 index 000000000000..a41a0d6f2a16 --- /dev/null +++ b/litellm/llms/watsonx/embed/transformation.py @@ -0,0 +1,116 @@ +""" +Translates from OpenAI's `/v1/embeddings` to IBM's `/text/embeddings` route. +""" + +from typing import Optional + +import httpx + +from litellm.llms.base_llm.embedding.transformation import ( + BaseEmbeddingConfig, + LiteLLMLoggingObj, +) +from litellm.types.llms.openai import AllEmbeddingInputValues +from litellm.types.llms.watsonx import WatsonXAIEndpoint +from litellm.types.utils import EmbeddingResponse, Usage + +from ..common_utils import IBMWatsonXMixin, WatsonXAIError, _get_api_params + + +class IBMWatsonXEmbeddingConfig(IBMWatsonXMixin, BaseEmbeddingConfig): + def get_supported_openai_params(self, model: str) -> list: + return [] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + return optional_params + + def transform_embedding_request( + self, + model: str, + input: AllEmbeddingInputValues, + optional_params: dict, + headers: dict, + ) -> dict: + watsonx_api_params = _get_api_params(params=optional_params) + project_id = watsonx_api_params["project_id"] + if not project_id: + raise ValueError("project_id is required") + return { + "inputs": input, + "model_id": model, + "project_id": project_id, + "parameters": optional_params, + } + + def get_complete_url( + self, + api_base: Optional[str], + model: str, + optional_params: dict, + stream: Optional[bool] = None, + ) -> str: + url = self._get_base_url(api_base=api_base) + endpoint = WatsonXAIEndpoint.EMBEDDINGS.value + if model.startswith("deployment/"): + # deployment models are passed in as 'deployment/' + if optional_params.get("space_id") is None: + raise WatsonXAIError( + status_code=401, + message="Error: space_id is required for models called using the 'deployment/' endpoint. Pass in the space_id as a parameter or set it in the WX_SPACE_ID environment variable.", + ) + deployment_id = "/".join(model.split("/")[1:]) + endpoint = endpoint.format(deployment_id=deployment_id) + url = url.rstrip("/") + endpoint + + ## add api version + url = self._add_api_version_to_url( + url=url, api_version=optional_params.pop("api_version", None) + ) + return url + + def transform_embedding_response( + self, + model: str, + raw_response: httpx.Response, + model_response: EmbeddingResponse, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str], + request_data: dict, + optional_params: dict, + litellm_params: dict, + ) -> EmbeddingResponse: + logging_obj.post_call( + original_response=raw_response.text, + ) + json_resp = raw_response.json() + if model_response is None: + model_response = EmbeddingResponse(model=json_resp.get("model_id", None)) + results = json_resp.get("results", []) + embedding_response = [] + for idx, result in enumerate(results): + embedding_response.append( + { + "object": "embedding", + "index": idx, + "embedding": result["embedding"], + } + ) + model_response.object = "list" + model_response.data = embedding_response + input_tokens = json_resp.get("input_token_count", 0) + setattr( + model_response, + "usage", + Usage( + prompt_tokens=input_tokens, + completion_tokens=0, + total_tokens=input_tokens, + ), + ) + return model_response diff --git a/litellm/main.py b/litellm/main.py index 1991644c472f..532abd93fcb3 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -145,7 +145,7 @@ from .llms.vertex_ai.vertex_model_garden.main import VertexAIModelGardenModels from .llms.vllm.completion import handler as vllm_handler from .llms.watsonx.chat.handler import WatsonXChatHandler -from .llms.watsonx.completion.handler import IBMWatsonXAI +from .llms.watsonx.common_utils import IBMWatsonXMixin from .types.llms.openai import ( ChatCompletionAssistantMessage, ChatCompletionAudioParam, @@ -205,7 +205,6 @@ vertex_partner_models_chat_completion = VertexAIPartnerModels() vertex_model_garden_chat_completion = VertexAIModelGardenModels() vertex_text_to_speech = VertexTextToSpeechAPI() -watsonxai = IBMWatsonXAI() sagemaker_llm = SagemakerLLM() watsonx_chat_completion = WatsonXChatHandler() openai_like_embedding = OpenAILikeEmbeddingHandler() @@ -2585,43 +2584,68 @@ def completion( # type: ignore # noqa: PLR0915 custom_llm_provider="watsonx", ) elif custom_llm_provider == "watsonx_text": - custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict - response = watsonxai.completion( + api_key = ( + api_key + or optional_params.pop("apikey", None) + or get_secret_str("WATSONX_APIKEY") + or get_secret_str("WATSONX_API_KEY") + or get_secret_str("WX_API_KEY") + ) + + api_base = ( + api_base + or optional_params.pop( + "url", + optional_params.pop( + "api_base", optional_params.pop("base_url", None) + ), + ) + or get_secret_str("WATSONX_API_BASE") + or get_secret_str("WATSONX_URL") + or get_secret_str("WX_URL") + or get_secret_str("WML_URL") + ) + + wx_credentials = optional_params.pop( + "wx_credentials", + optional_params.pop( + "watsonx_credentials", None + ), # follow {provider}_credentials, same as vertex ai + ) + + token: Optional[str] = None + if wx_credentials is not None: + api_base = wx_credentials.get("url", api_base) + api_key = wx_credentials.get( + "apikey", wx_credentials.get("api_key", api_key) + ) + token = wx_credentials.get( + "token", + wx_credentials.get( + "watsonx_token", None + ), # follow format of {provider}_token, same as azure - e.g. 'azure_ad_token=..' + ) + + if token is not None: + optional_params["token"] = token + + response = base_llm_http_handler.completion( model=model, + stream=stream, messages=messages, - custom_prompt_dict=custom_prompt_dict, + acompletion=acompletion, + api_base=api_base, model_response=model_response, - print_verbose=print_verbose, optional_params=optional_params, - litellm_params=litellm_params, # type: ignore - logger_fn=logger_fn, + litellm_params=litellm_params, + custom_llm_provider="watsonx_text", + timeout=timeout, + headers=headers, encoding=encoding, - logging_obj=logging, - timeout=timeout, # type: ignore - acompletion=acompletion, + 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 ( - "stream" in optional_params - and optional_params["stream"] is True - and not isinstance(response, CustomStreamWrapper) - ): - # don't try to access stream object, - response = CustomStreamWrapper( - iter(response), - model, - custom_llm_provider="watsonx", - logging_obj=logging, - ) - - if optional_params.get("stream", False): - ## LOGGING - logging.post_call( - input=messages, - api_key=None, - original_response=response, - ) - ## RESPONSE OBJECT - response = response elif custom_llm_provider == "vllm": custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict model_response = vllm_handler.completion( @@ -3485,6 +3509,7 @@ def embedding( # noqa: PLR0915 optional_params=optional_params, client=client, aembedding=aembedding, + litellm_params={}, ) elif custom_llm_provider == "gemini": gemini_api_key = ( @@ -3661,6 +3686,32 @@ def embedding( # noqa: PLR0915 optional_params=optional_params, client=client, aembedding=aembedding, + litellm_params={}, + ) + elif custom_llm_provider == "watsonx": + credentials = IBMWatsonXMixin.get_watsonx_credentials( + optional_params=optional_params, api_key=api_key, api_base=api_base + ) + + api_key = credentials["api_key"] + api_base = credentials["api_base"] + + if "token" in credentials: + optional_params["token"] = credentials["token"] + + response = base_llm_http_handler.embedding( + model=model, + input=input, + custom_llm_provider=custom_llm_provider, + api_base=api_base, + api_key=api_key, + logging_obj=logging, + timeout=timeout, + model_response=EmbeddingResponse(), + optional_params=optional_params, + litellm_params={}, + client=client, + aembedding=aembedding, ) elif custom_llm_provider == "xinference": api_key = ( @@ -3687,17 +3738,6 @@ def embedding( # noqa: PLR0915 client=client, aembedding=aembedding, ) - elif custom_llm_provider == "watsonx": - response = watsonxai.embedding( - model=model, - input=input, - encoding=encoding, - logging_obj=logging, - optional_params=optional_params, - model_response=EmbeddingResponse(), - aembedding=aembedding, - api_key=api_key, - ) elif custom_llm_provider == "azure_ai": api_base = ( api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py index e544cf24d227..78d99db613dc 100644 --- a/litellm/types/llms/openai.py +++ b/litellm/types/llms/openai.py @@ -611,3 +611,6 @@ class FineTuningJobCreate(BaseModel): class LiteLLMFineTuningJobCreate(FineTuningJobCreate): custom_llm_provider: Literal["openai", "azure", "vertex_ai"] + + +AllEmbeddingInputValues = Union[str, List[str], List[int], List[List[int]]] diff --git a/litellm/types/llms/watsonx.py b/litellm/types/llms/watsonx.py index f3b9c5d0b20b..7dee28365270 100644 --- a/litellm/types/llms/watsonx.py +++ b/litellm/types/llms/watsonx.py @@ -6,13 +6,15 @@ class WatsonXAPIParams(TypedDict): - url: str - api_key: Optional[str] - token: str project_id: str space_id: Optional[str] region_name: Optional[str] - api_version: str + + +class WatsonXCredentials(TypedDict): + api_key: str + api_base: str + token: Optional[str] class WatsonXAIEndpoint(str, Enum): diff --git a/litellm/types/utils.py b/litellm/types/utils.py index 1d66c86d629e..405cc9b1757e 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -808,6 +808,8 @@ class ModelResponseStream(ModelResponseBase): def __init__( self, choices: Optional[List[Union[StreamingChoices, dict, BaseModel]]] = None, + id: Optional[str] = None, + created: Optional[int] = None, **kwargs, ): if choices is not None and isinstance(choices, list): @@ -824,6 +826,20 @@ def __init__( kwargs["choices"] = new_choices else: kwargs["choices"] = [StreamingChoices()] + + if id is None: + id = _generate_id() + else: + id = id + if created is None: + created = int(time.time()) + else: + created = created + + kwargs["id"] = id + kwargs["created"] = created + kwargs["object"] = "chat.completion.chunk" + super().__init__(**kwargs) def __contains__(self, key): diff --git a/litellm/utils.py b/litellm/utils.py index 239a5e8bb9a0..c6d852b4ef44 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -6244,7 +6244,9 @@ def get_provider_embedding_config( return litellm.VoyageEmbeddingConfig() elif litellm.LlmProviders.TRITON == provider: return litellm.TritonEmbeddingConfig() - raise ValueError(f"Provider {provider} does not support embedding config") + elif litellm.LlmProviders.WATSONX == provider: + return litellm.IBMWatsonXEmbeddingConfig() + raise ValueError(f"Provider {provider.value} does not support embedding config") @staticmethod def get_provider_rerank_config( diff --git a/poetry.lock b/poetry.lock index cca2f49a757b..6ea4301b87dd 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,120 +1,120 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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b/tests/llm_translation/test_xai.py @@ -133,7 +133,7 @@ def test_completion_xai(stream): for chunk in response: print(chunk) assert chunk is not None - assert isinstance(chunk, litellm.ModelResponse) + assert isinstance(chunk, litellm.ModelResponseStream) assert isinstance(chunk.choices[0], litellm.utils.StreamingChoices) else: diff --git a/tests/local_testing/log.txt b/tests/local_testing/log.txt deleted file mode 100644 index 9b8654df00c5..000000000000 --- a/tests/local_testing/log.txt +++ /dev/null @@ -1,104 +0,0 @@ -============================= test session starts ============================== -platform darwin -- Python 3.11.4, pytest-8.3.2, pluggy-1.5.0 -- /Users/krrishdholakia/Documents/litellm/myenv/bin/python3.11 -cachedir: .pytest_cache -rootdir: /Users/krrishdholakia/Documents/litellm -configfile: pyproject.toml -plugins: asyncio-0.23.8, respx-0.21.1, anyio-4.6.0 -asyncio: mode=Mode.STRICT -collecting ... collected 1 item - 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'description': 'The city and state'}, 'unit': {'type': 'string', 'enum': ['celsius', 'fahrenheit']}}, 'required': ['location']}}}], 'tool_choice': {'type': 'auto'}} -optional_params: {'tools': [{'type': 'function', 'function': {'name': 'get_current_weather', 'description': 'Get the current weather in a given location', 'parameters': {'type': 'object', 'properties': {'location': {'type': 'string', 'description': 'The city and state'}, 'unit': {'type': 'string', 'enum': ['celsius', 'fahrenheit']}}, 'required': ['location']}}}], 'tool_choice': {'type': 'auto'}} -SENT optional_params: {'tools': [{'type': 'function', 'function': {'name': 'get_current_weather', 'description': 'Get the current weather in a given location', 'parameters': {'type': 'object', 'properties': {'location': {'type': 'string', 'description': 'The city and state'}, 'unit': {'type': 'string', 'enum': ['celsius', 'fahrenheit']}}, 'required': ['location']}}}], 'tool_choice': {'type': 'auto'}, 'max_tokens': 4096} -tool: {'type': 'function', 'function': {'name': 'get_current_weather', 'description': 'Get the current weather in a given location', 'parameters': {'type': 'object', 'properties': {'location': {'type': 'string', 'description': 'The city and state'}, 'unit': {'type': 'string', 'enum': ['celsius', 'fahrenheit']}}, 'required': ['location']}}} - - -POST Request Sent from LiteLLM: -curl -X POST \ -https://api.anthropic.com/v1/messages \ --H 'accept: *****' -H 'anthropic-version: *****' -H 'content-type: *****' -H 'x-api-key: sk-ant-api03-bJf1M8qp-JDptRcZRE5ve5efAfSIaL5u-SZ9vItIkvuFcV5cUsd********************************************' -H 'anthropic-beta: *****' \ --d '{'messages': [{'role': 'user', 'content': [{'type': 'text', 'text': "What's the weather like in San Francisco, Tokyo, and Paris? - give me 3 responses"}]}], 'tools': [{'name': 'get_current_weather', 'description': 'Get the current weather in a given location', 'input_schema': {'type': 'object', 'properties': {'location': {'type': 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cities:"},{"type":"tool_use","id":"toolu_016U6G3kpxjHSiJLwVCrrScz","name":"get_current_weather","input":{"location":"San Francisco","unit":"celsius"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":379,"output_tokens":87}} -Logging Details LiteLLM-Success Call: Cache_hit=None -Looking up model=claude-3-haiku-20240307 in model_cost_map -Looking up model=claude-3-haiku-20240307 in model_cost_map -Response - ModelResponse(id='chatcmpl-7222f6c2-962a-4776-8639-576723466cb7', choices=[Choices(finish_reason='tool_calls', index=0, message=Message(content="Okay, let's check the current weather in those three cities:", role='assistant', tool_calls=[ChatCompletionMessageToolCall(index=1, function=Function(arguments='{"location": "San Francisco", "unit": "celsius"}', name='get_current_weather'), id='toolu_016U6G3kpxjHSiJLwVCrrScz', type='function')], function_call=None))], created=1727897483, model='claude-3-haiku-20240307', object='chat.completion', system_fingerprint=None, usage=Usage(completion_tokens=87, prompt_tokens=379, total_tokens=466, completion_tokens_details=None)) -length of tool calls 1 -Expecting there to be 3 tool calls -tool_calls: [ChatCompletionMessageToolCall(index=1, function=Function(arguments='{"location": "San Francisco", "unit": "celsius"}', name='get_current_weather'), id='toolu_016U6G3kpxjHSiJLwVCrrScz', type='function')] -Response message - Message(content="Okay, let's check the current weather in those three cities:", role='assistant', tool_calls=[ChatCompletionMessageToolCall(index=1, function=Function(arguments='{"location": "San Francisco", "unit": "celsius"}', name='get_current_weather'), id='toolu_016U6G3kpxjHSiJLwVCrrScz', type='function')], function_call=None) -messages: [{'role': 'user', 'content': "What's the weather like in San Francisco, Tokyo, and Paris? - give me 3 responses"}, Message(content="Okay, let's check the current weather in those three cities:", role='assistant', tool_calls=[ChatCompletionMessageToolCall(index=1, function=Function(arguments='{"location": "San Francisco", "unit": "celsius"}', name='get_current_weather'), id='toolu_016U6G3kpxjHSiJLwVCrrScz', type='function')], function_call=None), {'tool_call_id': 'toolu_016U6G3kpxjHSiJLwVCrrScz', 'role': 'tool', 'name': 'get_current_weather', 'content': '{"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}'}] - - -Request to litellm: -litellm.completion(model='claude-3-haiku-20240307', messages=[{'role': 'user', 'content': "What's the weather like in San Francisco, Tokyo, and Paris? - give me 3 responses"}, Message(content="Okay, let's check the current weather in those three cities:", role='assistant', tool_calls=[ChatCompletionMessageToolCall(index=1, function=Function(arguments='{"location": "San Francisco", "unit": "celsius"}', name='get_current_weather'), id='toolu_016U6G3kpxjHSiJLwVCrrScz', type='function')], function_call=None), {'tool_call_id': 'toolu_016U6G3kpxjHSiJLwVCrrScz', 'role': 'tool', 'name': 'get_current_weather', 'content': '{"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}'}], temperature=0.2, seed=22, drop_params=True) - - -SYNC kwargs[caching]: False; litellm.cache: None; kwargs.get('cache')['no-cache']: False -Final returned optional params: {'temperature': 0.2, 'tools': [{'type': 'function', 'function': {'name': 'dummy-tool', 'description': '', 'parameters': {'type': 'object', 'properties': {}}}}]} -optional_params: {'temperature': 0.2, 'tools': [{'type': 'function', 'function': {'name': 'dummy-tool', 'description': '', 'parameters': {'type': 'object', 'properties': {}}}}]} -SENT optional_params: {'temperature': 0.2, 'tools': [{'type': 'function', 'function': {'name': 'dummy-tool', 'description': '', 'parameters': {'type': 'object', 'properties': {}}}}], 'max_tokens': 4096} -tool: {'type': 'function', 'function': {'name': 'dummy-tool', 'description': '', 'parameters': {'type': 'object', 'properties': {}}}} - - -POST Request Sent from LiteLLM: -curl -X POST \ -https://api.anthropic.com/v1/messages \ --H 'accept: *****' -H 'anthropic-version: *****' -H 'content-type: *****' -H 'x-api-key: sk-ant-api03-bJf1M8qp-JDptRcZRE5ve5efAfSIaL5u-SZ9vItIkvuFcV5cUsd********************************************' -H 'anthropic-beta: *****' \ --d '{'messages': [{'role': 'user', 'content': [{'type': 'text', 'text': "What's the weather like in San Francisco, Tokyo, and Paris? - give me 3 responses"}]}, {'role': 'assistant', 'content': [{'type': 'tool_use', 'id': 'toolu_016U6G3kpxjHSiJLwVCrrScz', 'name': 'get_current_weather', 'input': {'location': 'San Francisco', 'unit': 'celsius'}}]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_016U6G3kpxjHSiJLwVCrrScz', 'content': '{"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}'}]}], 'temperature': 0.2, 'tools': [{'name': 'dummy-tool', 'description': '', 'input_schema': {'type': 'object', 'properties': {}}}], 'max_tokens': 4096, 'model': 'claude-3-haiku-20240307'}' - - -_is_function_call: False -RAW RESPONSE: -{"id":"msg_01Wp8NVScugz6yAGsmB5trpZ","type":"message","role":"assistant","model":"claude-3-haiku-20240307","content":[{"type":"text","text":"The current weather in San Francisco is 72°F (22°C)."},{"type":"tool_use","id":"toolu_01HTXEYDX4MspM76STtJqs1n","name":"get_current_weather","input":{"location":"Tokyo","unit":"celsius"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":426,"output_tokens":90}} - - -raw model_response: {"id":"msg_01Wp8NVScugz6yAGsmB5trpZ","type":"message","role":"assistant","model":"claude-3-haiku-20240307","content":[{"type":"text","text":"The current weather in San Francisco is 72°F (22°C)."},{"type":"tool_use","id":"toolu_01HTXEYDX4MspM76STtJqs1n","name":"get_current_weather","input":{"location":"Tokyo","unit":"celsius"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":426,"output_tokens":90}} -Logging Details LiteLLM-Success Call: Cache_hit=None -Looking up model=claude-3-haiku-20240307 in model_cost_map -Looking up model=claude-3-haiku-20240307 in model_cost_map -second response - ModelResponse(id='chatcmpl-c4ed5c25-ba7c-49e5-a6be-5720ab25fff0', choices=[Choices(finish_reason='tool_calls', index=0, message=Message(content='The current weather in San Francisco is 72°F (22°C).', role='assistant', tool_calls=[ChatCompletionMessageToolCall(index=1, function=Function(arguments='{"location": "Tokyo", "unit": "celsius"}', name='get_current_weather'), id='toolu_01HTXEYDX4MspM76STtJqs1n', type='function')], function_call=None))], created=1727897484, model='claude-3-haiku-20240307', object='chat.completion', system_fingerprint=None, usage=Usage(completion_tokens=90, prompt_tokens=426, total_tokens=516, completion_tokens_details=None)) -PASSED - -=============================== warnings summary =============================== -../../myenv/lib/python3.11/site-packages/pydantic/_internal/_config.py:284 - /Users/krrishdholakia/Documents/litellm/myenv/lib/python3.11/site-packages/pydantic/_internal/_config.py:284: PydanticDeprecatedSince20: Support for class-based `config` is deprecated, use ConfigDict instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/ - warnings.warn(DEPRECATION_MESSAGE, DeprecationWarning) - -../../litellm/utils.py:17 - /Users/krrishdholakia/Documents/litellm/litellm/utils.py:17: DeprecationWarning: 'imghdr' is deprecated and slated for removal in Python 3.13 - import imghdr - -../../litellm/utils.py:124 - /Users/krrishdholakia/Documents/litellm/litellm/utils.py:124: DeprecationWarning: open_text is deprecated. Use files() instead. Refer to https://importlib-resources.readthedocs.io/en/latest/using.html#migrating-from-legacy for migration advice. - with resources.open_text("litellm.llms.tokenizers", "anthropic_tokenizer.json") as f: - -test_function_calling.py:56 - /Users/krrishdholakia/Documents/litellm/tests/local_testing/test_function_calling.py:56: PytestUnknownMarkWarning: Unknown pytest.mark.flaky - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html - @pytest.mark.flaky(retries=3, delay=1) - -tests/local_testing/test_function_calling.py::test_aaparallel_function_call[claude-3-haiku-20240307] -tests/local_testing/test_function_calling.py::test_aaparallel_function_call[claude-3-haiku-20240307] - /Users/krrishdholakia/Documents/litellm/myenv/lib/python3.11/site-packages/httpx/_content.py:202: DeprecationWarning: Use 'content=<...>' to upload raw bytes/text content. - warnings.warn(message, DeprecationWarning) - --- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html -======================== 1 passed, 6 warnings in 1.89s ========================= diff --git a/tests/local_testing/test_amazing_vertex_completion.py b/tests/local_testing/test_amazing_vertex_completion.py index eadb9e093b0c..9fa95437db56 100644 --- a/tests/local_testing/test_amazing_vertex_completion.py +++ b/tests/local_testing/test_amazing_vertex_completion.py @@ -1805,7 +1805,7 @@ async def test_gemini_pro_function_calling_streaming(sync_mode): for chunk in response: chunks.append(chunk) - assert isinstance(chunk, litellm.ModelResponse) + assert isinstance(chunk, litellm.ModelResponseStream) else: response = await litellm.acompletion(**data) print(f"completion: {response}") @@ -1815,7 +1815,7 @@ async def test_gemini_pro_function_calling_streaming(sync_mode): async for chunk in response: print(f"chunk: {chunk}") chunks.append(chunk) - assert isinstance(chunk, litellm.ModelResponse) + assert isinstance(chunk, litellm.ModelResponseStream) complete_response = litellm.stream_chunk_builder(chunks=chunks) assert ( diff --git a/tests/local_testing/test_completion.py b/tests/local_testing/test_completion.py index 5902047b70ca..097274b04e34 100644 --- a/tests/local_testing/test_completion.py +++ b/tests/local_testing/test_completion.py @@ -4019,19 +4019,20 @@ def test_completion_deepseek(): @pytest.mark.skip(reason="Account deleted by IBM.") def test_completion_watsonx_error(): litellm.set_verbose = True - model_name = "watsonx/ibm/granite-13b-chat-v2" + model_name = "watsonx_text/ibm/granite-13b-chat-v2" - with pytest.raises(litellm.BadRequestError) as e: - response = completion( - model=model_name, - messages=messages, - stop=["stop"], - max_tokens=20, - ) - # Add any assertions here to check the response - print(response) + response = completion( + model=model_name, + messages=messages, + stop=["stop"], + max_tokens=20, + stream=True, + ) - assert "use 'watsonx_text' route instead" in str(e).lower() + for chunk in response: + print(chunk) + # Add any assertions here to check the response + print(response) @pytest.mark.skip(reason="Skip test. account deleted.") diff --git a/tests/local_testing/test_custom_callback_input.py b/tests/local_testing/test_custom_callback_input.py index 9b7b6d5329f0..747174279f8b 100644 --- a/tests/local_testing/test_custom_callback_input.py +++ b/tests/local_testing/test_custom_callback_input.py @@ -135,7 +135,7 @@ async def async_log_stream_event(self, kwargs, response_obj, start_time, end_tim ## END TIME assert isinstance(end_time, datetime) ## RESPONSE OBJECT - assert isinstance(response_obj, litellm.ModelResponse) + assert isinstance(response_obj, litellm.ModelResponseStream) ## KWARGS assert isinstance(kwargs["model"], str) assert isinstance(kwargs["messages"], list) and isinstance( diff --git a/tests/local_testing/test_custom_callback_router.py b/tests/local_testing/test_custom_callback_router.py index cee77d516c1d..3e9ac39edabf 100644 --- a/tests/local_testing/test_custom_callback_router.py +++ b/tests/local_testing/test_custom_callback_router.py @@ -153,7 +153,7 @@ async def async_log_stream_event(self, kwargs, response_obj, start_time, end_tim ## END TIME assert isinstance(end_time, datetime) ## RESPONSE OBJECT - assert isinstance(response_obj, litellm.ModelResponse) + assert isinstance(response_obj, litellm.ModelResponseStream) ## KWARGS assert isinstance(kwargs["model"], str) assert isinstance(kwargs["messages"], list) and isinstance( diff --git a/tests/local_testing/test_custom_logger.py b/tests/local_testing/test_custom_logger.py index 3565466b1574..4058a9aa03a6 100644 --- a/tests/local_testing/test_custom_logger.py +++ b/tests/local_testing/test_custom_logger.py @@ -122,8 +122,13 @@ async def test_async_chat_openai_stream(): complete_streaming_response = complete_streaming_response.strip("'") + print(f"complete_streaming_response: {complete_streaming_response}") + await asyncio.sleep(3) + print( + f"tmp_function.complete_streaming_response_in_callback: {tmp_function.complete_streaming_response_in_callback}" + ) # problematic line response1 = tmp_function.complete_streaming_response_in_callback["choices"][0][ "message" diff --git a/tests/local_testing/test_embedding.py b/tests/local_testing/test_embedding.py index 668a9fe3842f..6886f4892d93 100644 --- a/tests/local_testing/test_embedding.py +++ b/tests/local_testing/test_embedding.py @@ -801,8 +801,11 @@ def test_fireworks_embeddings(): def test_watsonx_embeddings(): + from litellm.llms.custom_httpx.http_handler import HTTPHandler + + client = HTTPHandler() - def mock_wx_embed_request(method: str, url: str, **kwargs): + def mock_wx_embed_request(url: str, **kwargs): mock_response = MagicMock() mock_response.status_code = 200 mock_response.headers = {"Content-Type": "application/json"} @@ -816,12 +819,14 @@ def mock_wx_embed_request(method: str, url: str, **kwargs): try: litellm.set_verbose = True - with patch("requests.request", side_effect=mock_wx_embed_request): + with patch.object(client, "post", side_effect=mock_wx_embed_request): response = litellm.embedding( model="watsonx/ibm/slate-30m-english-rtrvr", input=["good morning from litellm"], token="secret-token", + client=client, ) + print(f"response: {response}") assert isinstance(response.usage, litellm.Usage) except litellm.RateLimitError as e: @@ -832,6 +837,9 @@ def mock_wx_embed_request(method: str, url: str, **kwargs): @pytest.mark.asyncio async def test_watsonx_aembeddings(): + from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler + + client = AsyncHTTPHandler() def mock_async_client(*args, **kwargs): @@ -856,12 +864,14 @@ async def mock_send(request, *args, stream: bool = False, **kwags): try: litellm.set_verbose = True - with patch("httpx.AsyncClient", side_effect=mock_async_client): + with patch.object(client, "post", side_effect=mock_async_client) as mock_client: response = await litellm.aembedding( model="watsonx/ibm/slate-30m-english-rtrvr", input=["good morning from litellm"], token="secret-token", + client=client, ) + mock_client.assert_called_once() print(f"response: {response}") assert isinstance(response.usage, litellm.Usage) except litellm.RateLimitError as e: diff --git a/tests/local_testing/test_streaming.py b/tests/local_testing/test_streaming.py index eaf62cd78b6c..5d553cae40ba 100644 --- a/tests/local_testing/test_streaming.py +++ b/tests/local_testing/test_streaming.py @@ -17,6 +17,7 @@ import litellm.litellm_core_utils import litellm.litellm_core_utils.litellm_logging from litellm.utils import ModelResponseListIterator +from litellm.types.utils import ModelResponseStream sys.path.insert( 0, os.path.abspath("../..") @@ -69,7 +70,7 @@ def logger_fn(model_call_object: dict): def validate_first_format(chunk): # write a test to make sure chunk follows the same format as first_openai_chunk_example - assert isinstance(chunk, ModelResponse), "Chunk should be a dictionary." + assert isinstance(chunk, ModelResponseStream), "Chunk should be a dictionary." assert isinstance(chunk["id"], str), "'id' should be a string." assert isinstance(chunk["object"], str), "'object' should be a string." assert isinstance(chunk["created"], int), "'created' should be an integer." @@ -99,7 +100,7 @@ def validate_first_format(chunk): def validate_second_format(chunk): - assert isinstance(chunk, ModelResponse), "Chunk should be a dictionary." + assert isinstance(chunk, ModelResponseStream), "Chunk should be a dictionary." assert isinstance(chunk["id"], str), "'id' should be a string." assert isinstance(chunk["object"], str), "'object' should be a string." assert isinstance(chunk["created"], int), "'created' should be an integer." @@ -137,7 +138,7 @@ def validate_last_format(chunk): """ Ensure last chunk has no remaining content or tools """ - assert isinstance(chunk, ModelResponse), "Chunk should be a dictionary." + assert isinstance(chunk, ModelResponseStream), "Chunk should be a dictionary." assert isinstance(chunk["id"], str), "'id' should be a string." assert isinstance(chunk["object"], str), "'object' should be a string." assert isinstance(chunk["created"], int), "'created' should be an integer." @@ -1523,7 +1524,7 @@ def sync_test_streaming(): num_finish_reason = 0 for chunk in response: print(f"chunk: {chunk}") - if isinstance(chunk, ModelResponse): + if isinstance(chunk, ModelResponseStream): if chunk.choices[0].finish_reason is not None: num_finish_reason += 1 assert num_finish_reason == 1 @@ -1541,7 +1542,7 @@ async def test_streaming(): num_finish_reason = 0 async for chunk in response: print(f"type of chunk: {type(chunk)}") - if isinstance(chunk, ModelResponse): + if isinstance(chunk, ModelResponseStream): print(f"OUTSIDE CHUNK: {chunk.choices[0]}") if chunk.choices[0].finish_reason is not None: num_finish_reason += 1 diff --git a/tests/proxy_unit_tests/log.txt b/tests/proxy_unit_tests/log.txt deleted file mode 100644 index 9b8654df00c5..000000000000 --- a/tests/proxy_unit_tests/log.txt +++ /dev/null @@ -1,104 +0,0 @@ -============================= test session starts ============================== -platform darwin -- Python 3.11.4, pytest-8.3.2, pluggy-1.5.0 -- /Users/krrishdholakia/Documents/litellm/myenv/bin/python3.11 -cachedir: .pytest_cache -rootdir: /Users/krrishdholakia/Documents/litellm -configfile: pyproject.toml -plugins: asyncio-0.23.8, respx-0.21.1, anyio-4.6.0 -asyncio: mode=Mode.STRICT -collecting ... collected 1 item - -test_function_calling.py::test_aaparallel_function_call[claude-3-haiku-20240307] - - -Request to litellm: -litellm.completion(model='claude-3-haiku-20240307', messages=[{'role': 'user', 'content': "What's the weather like in San Francisco, Tokyo, and Paris? - give me 3 responses"}], tools=[{'type': 'function', 'function': {'name': 'get_current_weather', 'description': 'Get the current weather in a given location', 'parameters': {'type': 'object', 'properties': {'location': {'type': 'string', 'description': 'The city and state'}, 'unit': {'type': 'string', 'enum': ['celsius', 'fahrenheit']}}, 'required': ['location']}}}], tool_choice='auto') - - -SYNC kwargs[caching]: False; litellm.cache: None; kwargs.get('cache')['no-cache']: False -Final returned optional params: {'tools': [{'type': 'function', 'function': {'name': 'get_current_weather', 'description': 'Get the current weather in a given location', 'parameters': {'type': 'object', 'properties': {'location': {'type': 'string', 'description': 'The city and state'}, 'unit': {'type': 'string', 'enum': ['celsius', 'fahrenheit']}}, 'required': ['location']}}}], 'tool_choice': {'type': 'auto'}} -optional_params: {'tools': [{'type': 'function', 'function': {'name': 'get_current_weather', 'description': 'Get the current weather in a given location', 'parameters': {'type': 'object', 'properties': {'location': {'type': 'string', 'description': 'The city and state'}, 'unit': {'type': 'string', 'enum': ['celsius', 'fahrenheit']}}, 'required': ['location']}}}], 'tool_choice': {'type': 'auto'}} -SENT optional_params: {'tools': [{'type': 'function', 'function': {'name': 'get_current_weather', 'description': 'Get the current weather in a given location', 'parameters': {'type': 'object', 'properties': {'location': {'type': 'string', 'description': 'The city and state'}, 'unit': {'type': 'string', 'enum': ['celsius', 'fahrenheit']}}, 'required': ['location']}}}], 'tool_choice': {'type': 'auto'}, 'max_tokens': 4096} -tool: {'type': 'function', 'function': {'name': 'get_current_weather', 'description': 'Get the current weather in a given location', 'parameters': {'type': 'object', 'properties': {'location': {'type': 'string', 'description': 'The city and state'}, 'unit': {'type': 'string', 'enum': ['celsius', 'fahrenheit']}}, 'required': ['location']}}} - - -POST Request Sent from LiteLLM: -curl -X POST \ -https://api.anthropic.com/v1/messages \ --H 'accept: *****' -H 'anthropic-version: *****' -H 'content-type: *****' -H 'x-api-key: sk-ant-api03-bJf1M8qp-JDptRcZRE5ve5efAfSIaL5u-SZ9vItIkvuFcV5cUsd********************************************' -H 'anthropic-beta: *****' \ --d '{'messages': [{'role': 'user', 'content': [{'type': 'text', 'text': "What's the weather like in San Francisco, Tokyo, and Paris? - give me 3 responses"}]}], 'tools': [{'name': 'get_current_weather', 'description': 'Get the current weather in a given location', 'input_schema': {'type': 'object', 'properties': {'location': {'type': 'string', 'description': 'The city and state'}, 'unit': {'type': 'string', 'enum': ['celsius', 'fahrenheit']}}, 'required': ['location']}}], 'tool_choice': {'type': 'auto'}, 'max_tokens': 4096, 'model': 'claude-3-haiku-20240307'}' - - -_is_function_call: False -RAW RESPONSE: -{"id":"msg_01HRugqzL4WmcxMmbvDheTph","type":"message","role":"assistant","model":"claude-3-haiku-20240307","content":[{"type":"text","text":"Okay, let's check the current weather in those three cities:"},{"type":"tool_use","id":"toolu_016U6G3kpxjHSiJLwVCrrScz","name":"get_current_weather","input":{"location":"San Francisco","unit":"celsius"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":379,"output_tokens":87}} - - -raw model_response: {"id":"msg_01HRugqzL4WmcxMmbvDheTph","type":"message","role":"assistant","model":"claude-3-haiku-20240307","content":[{"type":"text","text":"Okay, let's check the current weather in those three cities:"},{"type":"tool_use","id":"toolu_016U6G3kpxjHSiJLwVCrrScz","name":"get_current_weather","input":{"location":"San Francisco","unit":"celsius"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":379,"output_tokens":87}} -Logging Details LiteLLM-Success Call: Cache_hit=None -Looking up model=claude-3-haiku-20240307 in model_cost_map -Looking up model=claude-3-haiku-20240307 in model_cost_map -Response - ModelResponse(id='chatcmpl-7222f6c2-962a-4776-8639-576723466cb7', choices=[Choices(finish_reason='tool_calls', index=0, message=Message(content="Okay, let's check the current weather in those three cities:", role='assistant', tool_calls=[ChatCompletionMessageToolCall(index=1, function=Function(arguments='{"location": "San Francisco", "unit": "celsius"}', name='get_current_weather'), id='toolu_016U6G3kpxjHSiJLwVCrrScz', type='function')], function_call=None))], created=1727897483, model='claude-3-haiku-20240307', object='chat.completion', system_fingerprint=None, usage=Usage(completion_tokens=87, prompt_tokens=379, total_tokens=466, completion_tokens_details=None)) -length of tool calls 1 -Expecting there to be 3 tool calls -tool_calls: [ChatCompletionMessageToolCall(index=1, function=Function(arguments='{"location": "San Francisco", "unit": "celsius"}', name='get_current_weather'), id='toolu_016U6G3kpxjHSiJLwVCrrScz', type='function')] -Response message - Message(content="Okay, let's check the current weather in those three cities:", role='assistant', tool_calls=[ChatCompletionMessageToolCall(index=1, function=Function(arguments='{"location": "San Francisco", "unit": "celsius"}', name='get_current_weather'), id='toolu_016U6G3kpxjHSiJLwVCrrScz', type='function')], function_call=None) -messages: [{'role': 'user', 'content': "What's the weather like in San Francisco, Tokyo, and Paris? - give me 3 responses"}, Message(content="Okay, let's check the current weather in those three cities:", role='assistant', tool_calls=[ChatCompletionMessageToolCall(index=1, function=Function(arguments='{"location": "San Francisco", "unit": "celsius"}', name='get_current_weather'), id='toolu_016U6G3kpxjHSiJLwVCrrScz', type='function')], function_call=None), {'tool_call_id': 'toolu_016U6G3kpxjHSiJLwVCrrScz', 'role': 'tool', 'name': 'get_current_weather', 'content': '{"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}'}] - - -Request to litellm: -litellm.completion(model='claude-3-haiku-20240307', messages=[{'role': 'user', 'content': "What's the weather like in San Francisco, Tokyo, and Paris? - give me 3 responses"}, Message(content="Okay, let's check the current weather in those three cities:", role='assistant', tool_calls=[ChatCompletionMessageToolCall(index=1, function=Function(arguments='{"location": "San Francisco", "unit": "celsius"}', name='get_current_weather'), id='toolu_016U6G3kpxjHSiJLwVCrrScz', type='function')], function_call=None), {'tool_call_id': 'toolu_016U6G3kpxjHSiJLwVCrrScz', 'role': 'tool', 'name': 'get_current_weather', 'content': '{"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}'}], temperature=0.2, seed=22, drop_params=True) - - -SYNC kwargs[caching]: False; litellm.cache: None; kwargs.get('cache')['no-cache']: False -Final returned optional params: {'temperature': 0.2, 'tools': [{'type': 'function', 'function': {'name': 'dummy-tool', 'description': '', 'parameters': {'type': 'object', 'properties': {}}}}]} -optional_params: {'temperature': 0.2, 'tools': [{'type': 'function', 'function': {'name': 'dummy-tool', 'description': '', 'parameters': {'type': 'object', 'properties': {}}}}]} -SENT optional_params: {'temperature': 0.2, 'tools': [{'type': 'function', 'function': {'name': 'dummy-tool', 'description': '', 'parameters': {'type': 'object', 'properties': {}}}}], 'max_tokens': 4096} -tool: {'type': 'function', 'function': {'name': 'dummy-tool', 'description': '', 'parameters': {'type': 'object', 'properties': {}}}} - - -POST Request Sent from LiteLLM: -curl -X POST \ -https://api.anthropic.com/v1/messages \ --H 'accept: *****' -H 'anthropic-version: *****' -H 'content-type: *****' -H 'x-api-key: sk-ant-api03-bJf1M8qp-JDptRcZRE5ve5efAfSIaL5u-SZ9vItIkvuFcV5cUsd********************************************' -H 'anthropic-beta: *****' \ --d '{'messages': [{'role': 'user', 'content': [{'type': 'text', 'text': "What's the weather like in San Francisco, Tokyo, and Paris? - give me 3 responses"}]}, {'role': 'assistant', 'content': [{'type': 'tool_use', 'id': 'toolu_016U6G3kpxjHSiJLwVCrrScz', 'name': 'get_current_weather', 'input': {'location': 'San Francisco', 'unit': 'celsius'}}]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_016U6G3kpxjHSiJLwVCrrScz', 'content': '{"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}'}]}], 'temperature': 0.2, 'tools': [{'name': 'dummy-tool', 'description': '', 'input_schema': {'type': 'object', 'properties': {}}}], 'max_tokens': 4096, 'model': 'claude-3-haiku-20240307'}' - - -_is_function_call: False -RAW RESPONSE: -{"id":"msg_01Wp8NVScugz6yAGsmB5trpZ","type":"message","role":"assistant","model":"claude-3-haiku-20240307","content":[{"type":"text","text":"The current weather in San Francisco is 72°F (22°C)."},{"type":"tool_use","id":"toolu_01HTXEYDX4MspM76STtJqs1n","name":"get_current_weather","input":{"location":"Tokyo","unit":"celsius"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":426,"output_tokens":90}} - - -raw model_response: {"id":"msg_01Wp8NVScugz6yAGsmB5trpZ","type":"message","role":"assistant","model":"claude-3-haiku-20240307","content":[{"type":"text","text":"The current weather in San Francisco is 72°F (22°C)."},{"type":"tool_use","id":"toolu_01HTXEYDX4MspM76STtJqs1n","name":"get_current_weather","input":{"location":"Tokyo","unit":"celsius"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":426,"output_tokens":90}} -Logging Details LiteLLM-Success Call: Cache_hit=None -Looking up model=claude-3-haiku-20240307 in model_cost_map -Looking up model=claude-3-haiku-20240307 in model_cost_map -second response - ModelResponse(id='chatcmpl-c4ed5c25-ba7c-49e5-a6be-5720ab25fff0', choices=[Choices(finish_reason='tool_calls', index=0, message=Message(content='The current weather in San Francisco is 72°F (22°C).', role='assistant', tool_calls=[ChatCompletionMessageToolCall(index=1, function=Function(arguments='{"location": "Tokyo", "unit": "celsius"}', name='get_current_weather'), id='toolu_01HTXEYDX4MspM76STtJqs1n', type='function')], function_call=None))], created=1727897484, model='claude-3-haiku-20240307', object='chat.completion', system_fingerprint=None, usage=Usage(completion_tokens=90, prompt_tokens=426, total_tokens=516, completion_tokens_details=None)) -PASSED - -=============================== warnings summary =============================== -../../myenv/lib/python3.11/site-packages/pydantic/_internal/_config.py:284 - /Users/krrishdholakia/Documents/litellm/myenv/lib/python3.11/site-packages/pydantic/_internal/_config.py:284: PydanticDeprecatedSince20: Support for class-based `config` is deprecated, use ConfigDict instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.7/migration/ - warnings.warn(DEPRECATION_MESSAGE, DeprecationWarning) - -../../litellm/utils.py:17 - /Users/krrishdholakia/Documents/litellm/litellm/utils.py:17: DeprecationWarning: 'imghdr' is deprecated and slated for removal in Python 3.13 - import imghdr - -../../litellm/utils.py:124 - /Users/krrishdholakia/Documents/litellm/litellm/utils.py:124: DeprecationWarning: open_text is deprecated. Use files() instead. Refer to https://importlib-resources.readthedocs.io/en/latest/using.html#migrating-from-legacy for migration advice. - with resources.open_text("litellm.llms.tokenizers", "anthropic_tokenizer.json") as f: - -test_function_calling.py:56 - /Users/krrishdholakia/Documents/litellm/tests/local_testing/test_function_calling.py:56: PytestUnknownMarkWarning: Unknown pytest.mark.flaky - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html - @pytest.mark.flaky(retries=3, delay=1) - -tests/local_testing/test_function_calling.py::test_aaparallel_function_call[claude-3-haiku-20240307] -tests/local_testing/test_function_calling.py::test_aaparallel_function_call[claude-3-haiku-20240307] - /Users/krrishdholakia/Documents/litellm/myenv/lib/python3.11/site-packages/httpx/_content.py:202: DeprecationWarning: Use 'content=<...>' to upload raw bytes/text content. - warnings.warn(message, DeprecationWarning) - --- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html -======================== 1 passed, 6 warnings in 1.89s =========================