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feat(bedrock): add amazon.titan-embed-g1-text-02 embedding model support

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The model amazon.titan-embed-g1-text-02 is a Bedrock Titan G1 text
embedding model that uses the same request/response format as the
existing amazon.titan-embed-text-v1 (AmazonTitanG1Config). Without this
change litellm raises 'Unable to map Bedrock request to provider'.

Add the model to:
1. The provider routing check (model list)
2. The per-model request transformation branch
3. The per-model response transformation branch

Fixes #29786
This commit is contained in:
PRABHU KIRAN VANDRANKI 2026-06-05 12:18:27 -04:00
parent a72414a061
commit 0514680914
1 changed files with 10 additions and 0 deletions
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10
litellm/llms/bedrock/embed/embedding.py
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@ -224,6 +224,10 @@ class BedrockEmbedding(BaseAWSLLM):
returned_response = AmazonTitanV2Config()._transform_response(
response_list=response_list, model=model
)
elif model == "amazon.titan-embed-g1-text-02":
returned_response = AmazonTitanG1Config()._transform_response(
response_list=response_list, model=model
)
elif provider == "twelvelabs":
returned_response = (
TwelveLabsMarengoEmbeddingConfig()._transform_response(
@ -447,6 +451,7 @@ class BedrockEmbedding(BaseAWSLLM):
"amazon.titan-embed-image-v1",
"amazon.titan-embed-text-v1",
"amazon.titan-embed-text-v2:0",
"amazon.titan-embed-g1-text-02",
]:
batch_data = []
for i in input:
@ -464,6 +469,10 @@ class BedrockEmbedding(BaseAWSLLM):
transformed_request = AmazonTitanV2Config()._transform_request(
input=i, inference_params=inference_params
)
elif model == "amazon.titan-embed-g1-text-02":
transformed_request = AmazonTitanG1Config()._transform_request(
input=i, inference_params=inference_params
)
else:
raise Exception(
"Unmapped model. Received={}. Expected={}".format(
@ -472,6 +481,7 @@ class BedrockEmbedding(BaseAWSLLM):
"amazon.titan-embed-image-v1",
"amazon.titan-embed-text-v1",
"amazon.titan-embed-text-v2:0",
"amazon.titan-embed-g1-text-02",
],
)
)