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https://github.com/BerriAI/litellm.git
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197655bf2a
* 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
47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
"""
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Transformation logic from OpenAI /v1/embeddings format to Bedrock Cohere /invoke format.
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Why separate file? Make it easy to see how transformation works
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"""
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from typing import List
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import litellm
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from litellm.llms.cohere.embed.transformation import CohereEmbeddingConfig
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from litellm.types.llms.bedrock import CohereEmbeddingRequest, CohereEmbeddingResponse
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from litellm.types.utils import Embedding, EmbeddingResponse
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class BedrockCohereEmbeddingConfig:
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def __init__(self) -> None:
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pass
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def get_supported_openai_params(self) -> List[str]:
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return ["encoding_format"]
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def map_openai_params(
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self, non_default_params: dict, optional_params: dict
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) -> dict:
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for k, v in non_default_params.items():
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if k == "encoding_format":
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optional_params["embedding_types"] = v
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return optional_params
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def _is_v3_model(self, model: str) -> bool:
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return "3" in model
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def _transform_request(
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self, model: str, input: List[str], inference_params: dict
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) -> CohereEmbeddingRequest:
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transformed_request = CohereEmbeddingConfig()._transform_request(
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model, input, inference_params
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)
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new_transformed_request = CohereEmbeddingRequest(
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input_type=transformed_request["input_type"],
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)
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for k in CohereEmbeddingRequest.__annotations__.keys():
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if k in transformed_request:
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new_transformed_request[k] = transformed_request[k] # type: ignore
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return new_transformed_request
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