scriptoriumadmin/litellm
1
0
Fork 0
mirror of https://github.com/BerriAI/litellm.git synced 2026-09-09 22:31:41 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions 133

Fix Bedrock Titan V2 encoding_format parameter support

Browse source 
- Add encoding_format to supported OpenAI parameters list
- Implement encoding_format to embeddingTypes parameter mapping
- Map 'float' to ['float'] and 'base64' to ['binary'] formats
- Handle response with proper fallback: binary > float > embedding field
- Support both float and binary response formats per AWS documentation

Fixes #14685 - UnsupportedParamsError when using encoding_format with Titan V2
This commit is contained in:
Tim Elfrink 2025-09-18 20:23:45 +02:00
parent 0e096d7883
commit 72b492c761
1 changed files with 37 additions and 15 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

52
litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py
View file

@ -10,7 +10,7 @@ Docs - https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-tit
"""
import types
from typing import List, Optional
from typing import List, Optional, Union
from litellm.types.llms.bedrock import (
AmazonTitanV2EmbeddingRequest,
@ -30,9 +30,7 @@ class AmazonTitanV2Config:
normalize: Optional[bool] = None
dimensions: Optional[int] = None
def __init__(
self, normalize: Optional[bool] = None, dimensions: Optional[int] = None
) -> None:
def __init__(self, normalize: Optional[bool] = None, dimensions: Optional[int] = None) -> None:
locals_ = locals().copy()
for key, value in locals_.items():
if key != "self" and value is not None:
@ -57,32 +55,56 @@ class AmazonTitanV2Config:
}
def get_supported_openai_params(self) -> List[str]:
return ["dimensions"]
return ["dimensions", "encoding_format"]
def map_openai_params(
self, non_default_params: dict, optional_params: dict
) -> dict:
def map_openai_params(self, non_default_params: dict, optional_params: dict) -> dict:
for k, v in non_default_params.items():
if k == "dimensions":
optional_params["dimensions"] = v
elif k == "encoding_format":
# Map OpenAI encoding_format to AWS embeddingTypes
if v == "float":
optional_params["embeddingTypes"] = ["float"]
elif v == "base64":
# base64 maps to binary format in AWS
optional_params["embeddingTypes"] = ["binary"]
else:
# For any other encoding format, default to float
optional_params["embeddingTypes"] = ["float"]
return optional_params
def _transform_request(
self, input: str, inference_params: dict
) -> AmazonTitanV2EmbeddingRequest:
def _transform_request(self, input: str, inference_params: dict) -> AmazonTitanV2EmbeddingRequest:
return AmazonTitanV2EmbeddingRequest(inputText=input, **inference_params) # type: ignore
def _transform_response(
self, response_list: List[dict], model: str
) -> EmbeddingResponse:
def _transform_response(self, response_list: List[dict], model: str) -> EmbeddingResponse:
total_prompt_tokens = 0
transformed_responses: List[Embedding] = []
for index, response in enumerate(response_list):
_parsed_response = AmazonTitanV2EmbeddingResponse(**response) # type: ignore
# According to AWS docs, embeddingsByType is always present
# If binary was requested (encoding_format="base64"), use binary data
# Otherwise, use float data from embeddingsByType or fallback to embedding field
embedding_data: Union[List[float], List[int]]
if ("embeddingsByType" in _parsed_response and
"binary" in _parsed_response["embeddingsByType"]):
# Use binary data if available (for encoding_format="base64")
embedding_data = _parsed_response["embeddingsByType"]["binary"]
elif ("embeddingsByType" in _parsed_response and
"float" in _parsed_response["embeddingsByType"]):
# Use float data from embeddingsByType
embedding_data = _parsed_response["embeddingsByType"]["float"]
elif "embedding" in _parsed_response:
# Fallback to legacy embedding field
embedding_data = _parsed_response["embedding"]
else:
raise ValueError(f"No embedding data found in response: {response}")
transformed_responses.append(
Embedding(
embedding=_parsed_response["embedding"],
embedding=embedding_data,
index=index,
object="embedding",
)