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LiteLLM Minor Fixes & Improvements (10/24/2024) (#6421)

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* fix(utils.py): support passing dynamic api base to validate_environment

Returns True if just api base is required and api base is passed

* fix(litellm_pre_call_utils.py): feature flag sending client headers to llm api

Fixes https://github.com/BerriAI/litellm/issues/6410

* fix(anthropic/chat/transformation.py): return correct error message

* fix(http_handler.py): add error response text in places where we expect it

* fix(factory.py): handle base case of no non-system messages to bedrock

Fixes https://github.com/BerriAI/litellm/issues/6411

* feat(cohere/embed): Support cohere image embeddings

Closes https://github.com/BerriAI/litellm/issues/6413

* fix(__init__.py): fix linting error

* docs(supported_embedding.md): add image embedding example to docs

* feat(cohere/embed): use cohere embedding returned usage for cost calc

* build(model_prices_and_context_window.json): add embed-english-v3.0 details (image cost + 'supports_image_input' flag)

* fix(cohere_transformation.py): fix linting error

* test(test_proxy_server.py): cleanup test

* test: cleanup test

* fix: fix linting errors
This commit is contained in:
Krish Dholakia 2024-10-25 15:55:56 -07:00 • committed by GitHub
parent 17082d5e7d
commit 197655bf2a
23 changed files with 417 additions and 150 deletions
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4
litellm/llms/bedrock/embed/embedding.py
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@ -11,7 +11,7 @@ from typing import Any, Callable, List, Literal, Optional, Tuple, Union
import httpx
import litellm
from litellm.llms.cohere.embed import embedding as cohere_embedding
from litellm.llms.cohere.embed.handler import embedding as cohere_embedding
from litellm.llms.custom_httpx.http_handler import (
AsyncHTTPHandler,
HTTPHandler,
@ -369,7 +369,7 @@ class BedrockEmbedding(BaseAWSLLM):
batch_data: Optional[List] = None
if provider == "cohere":
data = BedrockCohereEmbeddingConfig()._transform_request(
input=input, inference_params=inference_params
model=model, input=input, inference_params=inference_params
)
elif provider == "amazon" and model in [
"amazon.titan-embed-image-v1",