litellm-mirror/litellm/llms/azure_ai/rerank/handler.py
Krish Dholakia bd17424c4b
LiteLLM Minor Fixes & Improvements (09/26/2024) (#5925) (#5937)
* LiteLLM Minor Fixes & Improvements (09/26/2024)  (#5925)

* fix(litellm_logging.py): don't initialize prometheus_logger if non premium user

Prevents bad error messages in logs

Fixes https://github.com/BerriAI/litellm/issues/5897

* Add Support for Custom Providers in Vision and Function Call Utils (#5688)

* Add Support for Custom Providers in Vision and Function Call Utils Lookup

* Remove parallel function call due to missing model info param

* Add Unit Tests for Vision and Function Call Changes

* fix-#5920: set header value to string to fix "'int' object has no att… (#5922)

* LiteLLM Minor Fixes & Improvements (09/24/2024) (#5880)

* LiteLLM Minor Fixes & Improvements (09/23/2024)  (#5842)

* feat(auth_utils.py): enable admin to allow client-side credentials to be passed

Makes it easier for devs to experiment with finetuned fireworks ai models

* feat(router.py): allow setting configurable_clientside_auth_params for a model

Closes https://github.com/BerriAI/litellm/issues/5843

* build(model_prices_and_context_window.json): fix anthropic claude-3-5-sonnet max output token limit

Fixes https://github.com/BerriAI/litellm/issues/5850

* fix(azure_ai/): support content list for azure ai

Fixes https://github.com/BerriAI/litellm/issues/4237

* fix(litellm_logging.py): always set saved_cache_cost

Set to 0 by default

* fix(fireworks_ai/cost_calculator.py): add fireworks ai default pricing

handles calling 405b+ size models

* fix(slack_alerting.py): fix error alerting for failed spend tracking

Fixes regression with slack alerting error monitoring

* fix(vertex_and_google_ai_studio_gemini.py): handle gemini no candidates in streaming chunk error

* docs(bedrock.md): add llama3-1 models

* test: fix tests

* fix(azure_ai/chat): fix transformation for azure ai calls

* feat(azure_ai/embed): Add azure ai embeddings support

Closes https://github.com/BerriAI/litellm/issues/5861

* fix(azure_ai/embed): enable async embedding

* feat(azure_ai/embed): support azure ai multimodal embeddings

* fix(azure_ai/embed): support async multi modal embeddings

* feat(together_ai/embed): support together ai embedding calls

* feat(rerank/main.py): log source documents for rerank endpoints to langfuse

improves rerank endpoint logging

* fix(langfuse.py): support logging `/audio/speech` input to langfuse

* test(test_embedding.py): fix test

* test(test_completion_cost.py): fix helper util

* fix-#5920: set header value to string to fix "'int' object has no attribute 'encode'"

---------

Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>

* Revert "fix-#5920: set header value to string to fix "'int' object has no att…" (#5926)

This reverts commit a554ae2695.

* build(model_prices_and_context_window.json): add azure ai cohere rerank model pricing

Enables cost tracking for azure ai cohere rerank models

* fix(litellm_logging.py): fix debug log to be clearer

Closes https://github.com/BerriAI/litellm/issues/5909

* test(test_utils.py): fix test name

* fix(azure_ai/cost_calculator.py): support cost tracking for azure ai rerank models

* fix(azure_ai): fix azure ai base model cost tracking for rerank endpoints

* fix(converse_handler.py): support new llama 3-2 models

Fixes https://github.com/BerriAI/litellm/issues/5901

* fix(litellm_logging.py): ensure response is redacted for standard message logging

Fixes https://github.com/BerriAI/litellm/issues/5890#issuecomment-2378242360

* fix(cost_calculator.py): use 'get_model_info' for cohere rerank cost calculation

allows user to set custom cost for model

* fix(config.yml): fix docker hub auht

* build(config.yml): add docker auth to all tests

* fix(db/create_views.py): fix linting error

* fix(main.py): fix circular import

* fix(azure_ai/__init__.py): fix circular import

* fix(main.py): fix import

* fix: fix linting errors

* test: fix test

* fix(proxy_server.py): pass premium user value on startup

used for prometheus init

---------

Co-authored-by: Cole Murray <colemurray.cs@gmail.com>
Co-authored-by: bravomark <62681807+bravomark@users.noreply.github.com>

* handle streaming for azure ai studio error

* [Perf Proxy] parallel request limiter - use one cache update call (#5932)

* fix parallel request limiter - use one cache update call

* ci/cd run again

* run ci/cd again

* use docker username password

* fix config.yml

* fix config

* fix config

* fix config.yml

* ci/cd run again

* use correct typing for batch set cache

* fix async_set_cache_pipeline

* fix only check user id tpm / rpm limits when limits set

* fix test_openai_azure_embedding_with_oidc_and_cf

* test: fix test

* test(test_rerank.py): fix test

---------

Co-authored-by: Cole Murray <colemurray.cs@gmail.com>
Co-authored-by: bravomark <62681807+bravomark@users.noreply.github.com>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
2024-09-27 17:54:13 -07:00

125 lines
4.3 KiB
Python

from typing import Any, Dict, List, Optional, Union
import httpx
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.cohere.rerank import CohereRerank
from litellm.rerank_api.types import RerankResponse
class AzureAIRerank(CohereRerank):
def get_base_model(self, azure_model_group: Optional[str]) -> Optional[str]:
if azure_model_group is None:
return None
if azure_model_group == "offer-cohere-rerank-mul-paygo":
return "azure_ai/cohere-rerank-v3-multilingual"
if azure_model_group == "offer-cohere-rerank-eng-paygo":
return "azure_ai/cohere-rerank-v3-english"
return azure_model_group
async def async_azure_rerank(
self,
model: str,
api_key: str,
api_base: str,
query: str,
documents: List[Union[str, Dict[str, Any]]],
headers: Optional[dict],
litellm_logging_obj: LiteLLMLoggingObj,
top_n: Optional[int] = None,
rank_fields: Optional[List[str]] = None,
return_documents: Optional[bool] = True,
max_chunks_per_doc: Optional[int] = None,
):
returned_response: RerankResponse = await super().rerank( # type: ignore
model=model,
api_key=api_key,
api_base=api_base,
query=query,
documents=documents,
top_n=top_n,
rank_fields=rank_fields,
return_documents=return_documents,
max_chunks_per_doc=max_chunks_per_doc,
_is_async=True,
headers=headers,
litellm_logging_obj=litellm_logging_obj,
)
# get base model
additional_headers = (
returned_response._hidden_params.get("additional_headers") or {}
)
base_model = self.get_base_model(
additional_headers.get("llm_provider-azureml-model-group")
)
returned_response._hidden_params["model"] = base_model
return returned_response
def rerank(
self,
model: str,
api_key: str,
api_base: str,
query: str,
documents: List[Union[str, Dict[str, Any]]],
headers: Optional[dict],
litellm_logging_obj: LiteLLMLoggingObj,
top_n: Optional[int] = None,
rank_fields: Optional[List[str]] = None,
return_documents: Optional[bool] = True,
max_chunks_per_doc: Optional[int] = None,
_is_async: Optional[bool] = False,
) -> RerankResponse:
if headers is None:
headers = {"Authorization": "Bearer {}".format(api_key)}
else:
headers = {**headers, "Authorization": "Bearer {}".format(api_key)}
# Assuming api_base is a string representing the base URL
api_base_url = httpx.URL(api_base)
# Replace the path with '/v1/rerank' if it doesn't already end with it
if not api_base_url.path.endswith("/v1/rerank"):
api_base = str(api_base_url.copy_with(path="/v1/rerank"))
if _is_async:
return self.async_azure_rerank( # type: ignore
model=model,
api_key=api_key,
api_base=api_base,
query=query,
documents=documents,
top_n=top_n,
rank_fields=rank_fields,
return_documents=return_documents,
max_chunks_per_doc=max_chunks_per_doc,
headers=headers,
litellm_logging_obj=litellm_logging_obj,
)
else:
returned_response = super().rerank(
model=model,
api_key=api_key,
api_base=api_base,
query=query,
documents=documents,
top_n=top_n,
rank_fields=rank_fields,
return_documents=return_documents,
max_chunks_per_doc=max_chunks_per_doc,
_is_async=_is_async,
headers=headers,
litellm_logging_obj=litellm_logging_obj,
)
# get base model
base_model = self.get_base_model(
returned_response._hidden_params.get("llm_provider-azureml-model-group")
)
returned_response._hidden_params["model"] = base_model
return returned_response