chore: initial public snapshot for github upload
This commit is contained in:
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@@ -0,0 +1,699 @@
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"""
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Handles embedding calls to Bedrock's `/invoke` endpoint
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"""
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import copy
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import json
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import urllib.parse
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from typing import Any, Callable, List, Optional, Tuple, Union, get_args
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import httpx
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import litellm
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from litellm.constants import BEDROCK_EMBEDDING_PROVIDERS_LITERAL
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from litellm.llms.cohere.embed.handler import embedding as cohere_embedding
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from litellm.llms.custom_httpx.http_handler import (
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AsyncHTTPHandler,
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HTTPHandler,
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_get_httpx_client,
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get_async_httpx_client,
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)
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from litellm.secret_managers.main import get_secret
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from litellm.types.llms.bedrock import (
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AmazonEmbeddingRequest,
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CohereEmbeddingRequest,
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)
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from litellm.types.utils import EmbeddingResponse, LlmProviders
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from ..base_aws_llm import BaseAWSLLM
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from ..common_utils import BedrockError
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from .amazon_nova_transformation import AmazonNovaEmbeddingConfig
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from .amazon_titan_g1_transformation import AmazonTitanG1Config
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from .amazon_titan_multimodal_transformation import (
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AmazonTitanMultimodalEmbeddingG1Config,
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)
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from .amazon_titan_v2_transformation import AmazonTitanV2Config
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from .cohere_transformation import BedrockCohereEmbeddingConfig
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from .twelvelabs_marengo_transformation import TwelveLabsMarengoEmbeddingConfig
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class BedrockEmbedding(BaseAWSLLM):
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def _load_credentials(
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self,
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optional_params: dict,
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) -> Tuple[Any, str]:
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try:
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from botocore.credentials import Credentials
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except ImportError:
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raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
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## CREDENTIALS ##
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# pop aws_secret_access_key, aws_access_key_id, aws_session_token, aws_region_name from kwargs, since completion calls fail with them
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aws_secret_access_key = optional_params.pop("aws_secret_access_key", None)
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aws_access_key_id = optional_params.pop("aws_access_key_id", None)
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aws_session_token = optional_params.pop("aws_session_token", None)
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aws_region_name = optional_params.pop("aws_region_name", None)
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aws_role_name = optional_params.pop("aws_role_name", None)
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aws_session_name = optional_params.pop("aws_session_name", None)
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aws_profile_name = optional_params.pop("aws_profile_name", None)
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aws_web_identity_token = optional_params.pop("aws_web_identity_token", None)
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aws_sts_endpoint = optional_params.pop("aws_sts_endpoint", None)
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### SET REGION NAME ###
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if aws_region_name is None:
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# check env #
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litellm_aws_region_name = get_secret("AWS_REGION_NAME", None)
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if litellm_aws_region_name is not None and isinstance(
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litellm_aws_region_name, str
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):
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aws_region_name = litellm_aws_region_name
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standard_aws_region_name = get_secret("AWS_REGION", None)
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if standard_aws_region_name is not None and isinstance(
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standard_aws_region_name, str
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):
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aws_region_name = standard_aws_region_name
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if aws_region_name is None:
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aws_region_name = "us-west-2"
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credentials: Credentials = self.get_credentials( # type: ignore
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aws_access_key_id=aws_access_key_id,
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aws_secret_access_key=aws_secret_access_key,
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aws_session_token=aws_session_token,
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aws_region_name=aws_region_name,
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aws_session_name=aws_session_name,
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aws_profile_name=aws_profile_name,
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aws_role_name=aws_role_name,
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aws_web_identity_token=aws_web_identity_token,
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aws_sts_endpoint=aws_sts_endpoint,
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)
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return credentials, aws_region_name
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async def async_embeddings(self):
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pass
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def _make_sync_call(
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self,
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client: Optional[HTTPHandler],
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timeout: Optional[Union[float, httpx.Timeout]],
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api_base: str,
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headers: dict,
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data: dict,
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) -> dict:
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if client is None or not isinstance(client, HTTPHandler):
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_params = {}
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if timeout is not None:
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if isinstance(timeout, float) or isinstance(timeout, int):
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timeout = httpx.Timeout(timeout)
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_params["timeout"] = timeout
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client = _get_httpx_client(_params) # type: ignore
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else:
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client = client
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try:
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response = client.post(url=api_base, headers=headers, data=json.dumps(data)) # type: ignore
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response.raise_for_status()
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except httpx.HTTPStatusError as err:
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error_code = err.response.status_code
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raise BedrockError(status_code=error_code, message=err.response.text)
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except httpx.TimeoutException:
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raise BedrockError(status_code=408, message="Timeout error occurred.")
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return response.json()
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async def _make_async_call(
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self,
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client: Optional[AsyncHTTPHandler],
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timeout: Optional[Union[float, httpx.Timeout]],
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api_base: str,
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headers: dict,
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data: dict,
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) -> dict:
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if client is None or not isinstance(client, AsyncHTTPHandler):
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_params = {}
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if timeout is not None:
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if isinstance(timeout, float) or isinstance(timeout, int):
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timeout = httpx.Timeout(timeout)
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_params["timeout"] = timeout
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client = get_async_httpx_client(
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params=_params, llm_provider=litellm.LlmProviders.BEDROCK
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)
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else:
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client = client
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try:
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response = await client.post(url=api_base, headers=headers, data=json.dumps(data)) # type: ignore
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response.raise_for_status()
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except httpx.HTTPStatusError as err:
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error_code = err.response.status_code
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raise BedrockError(status_code=error_code, message=err.response.text)
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except httpx.TimeoutException:
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raise BedrockError(status_code=408, message="Timeout error occurred.")
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return response.json()
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def _transform_response(
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self,
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response_list: List[dict],
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model: str,
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provider: BEDROCK_EMBEDDING_PROVIDERS_LITERAL,
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is_async_invoke: Optional[bool] = False,
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batch_data: Optional[List[dict]] = None,
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) -> Optional[EmbeddingResponse]:
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"""
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Transforms the response from the Bedrock embedding provider to the OpenAI format.
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"""
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returned_response: Optional[EmbeddingResponse] = None
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# Handle async invoke responses (single response with invocationArn)
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if (
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is_async_invoke
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and len(response_list) == 1
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and "invocationArn" in response_list[0]
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):
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if provider == "twelvelabs":
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returned_response = (
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TwelveLabsMarengoEmbeddingConfig()._transform_async_invoke_response(
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response=response_list[0], model=model
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)
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)
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elif provider == "nova":
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returned_response = (
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AmazonNovaEmbeddingConfig()._transform_async_invoke_response(
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response=response_list[0], model=model
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)
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)
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else:
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# For other providers, create a generic async response
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invocation_arn = response_list[0].get("invocationArn", "")
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from litellm.types.utils import Embedding, Usage
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embedding = Embedding(
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embedding=[],
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index=0,
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object="embedding", # Must be literal "embedding"
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)
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usage = Usage(prompt_tokens=0, total_tokens=0)
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# Create hidden params with job ID
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from litellm.types.llms.base import HiddenParams
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hidden_params = HiddenParams()
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setattr(hidden_params, "_invocation_arn", invocation_arn)
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returned_response = EmbeddingResponse(
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data=[embedding],
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model=model,
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usage=usage,
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hidden_params=hidden_params,
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)
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else:
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# Handle regular invoke responses
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if model == "amazon.titan-embed-image-v1":
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returned_response = (
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AmazonTitanMultimodalEmbeddingG1Config()._transform_response(
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response_list=response_list, model=model, batch_data=batch_data
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)
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)
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elif model == "amazon.titan-embed-text-v1":
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returned_response = AmazonTitanG1Config()._transform_response(
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response_list=response_list, model=model
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)
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elif model == "amazon.titan-embed-text-v2:0":
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returned_response = AmazonTitanV2Config()._transform_response(
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response_list=response_list, model=model
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)
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elif provider == "twelvelabs":
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returned_response = (
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TwelveLabsMarengoEmbeddingConfig()._transform_response(
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response_list=response_list, model=model
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)
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)
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elif provider == "nova":
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returned_response = AmazonNovaEmbeddingConfig()._transform_response(
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response_list=response_list, model=model, batch_data=batch_data
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)
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##########################################################
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# Validate returned response
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##########################################################
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if returned_response is None:
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raise Exception(
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"Unable to map model response to known provider format. model={}".format(
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model
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)
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)
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return returned_response
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def _single_func_embeddings(
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self,
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client: Optional[HTTPHandler],
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timeout: Optional[Union[float, httpx.Timeout]],
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batch_data: List[dict],
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credentials: Any,
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extra_headers: Optional[dict],
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endpoint_url: str,
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aws_region_name: str,
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model: str,
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logging_obj: Any,
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provider: BEDROCK_EMBEDDING_PROVIDERS_LITERAL,
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api_key: Optional[str] = None,
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is_async_invoke: Optional[bool] = False,
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):
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responses: List[dict] = []
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for data in batch_data:
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headers = {"Content-Type": "application/json"}
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if extra_headers is not None:
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headers = {"Content-Type": "application/json", **extra_headers}
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prepped = self.get_request_headers( # type: ignore # type: ignore
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credentials=credentials,
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aws_region_name=aws_region_name,
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extra_headers=extra_headers,
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endpoint_url=endpoint_url,
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data=json.dumps(data),
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headers=headers,
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api_key=api_key,
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)
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## LOGGING
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logging_obj.pre_call(
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input=data,
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api_key="",
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additional_args={
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"complete_input_dict": data,
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"api_base": prepped.url,
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"headers": prepped.headers,
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},
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)
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headers_for_request = (
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dict(prepped.headers) if hasattr(prepped, "headers") else {}
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)
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response = self._make_sync_call(
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client=client,
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timeout=timeout,
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api_base=prepped.url,
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headers=headers_for_request,
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data=data,
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)
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## LOGGING
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logging_obj.post_call(
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input=data,
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api_key="",
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original_response=response,
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additional_args={"complete_input_dict": data},
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)
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responses.append(response)
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return self._transform_response(
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response_list=responses,
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model=model,
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provider=provider,
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is_async_invoke=is_async_invoke,
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batch_data=batch_data,
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)
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async def _async_single_func_embeddings(
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self,
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client: Optional[AsyncHTTPHandler],
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timeout: Optional[Union[float, httpx.Timeout]],
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batch_data: List[dict],
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credentials: Any,
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extra_headers: Optional[dict],
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endpoint_url: str,
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aws_region_name: str,
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model: str,
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logging_obj: Any,
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provider: BEDROCK_EMBEDDING_PROVIDERS_LITERAL,
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api_key: Optional[str] = None,
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is_async_invoke: Optional[bool] = False,
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):
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responses: List[dict] = []
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for data in batch_data:
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headers = {"Content-Type": "application/json"}
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if extra_headers is not None:
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headers = {"Content-Type": "application/json", **extra_headers}
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prepped = self.get_request_headers( # type: ignore # type: ignore
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credentials=credentials,
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aws_region_name=aws_region_name,
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extra_headers=extra_headers,
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endpoint_url=endpoint_url,
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data=json.dumps(data),
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headers=headers,
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api_key=api_key,
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)
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## LOGGING
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logging_obj.pre_call(
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input=data,
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api_key="",
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additional_args={
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"complete_input_dict": data,
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"api_base": prepped.url,
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"headers": prepped.headers,
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},
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)
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# Convert CaseInsensitiveDict to regular dict for httpx compatibility
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# This ensures custom headers are properly forwarded, especially with IAM roles and custom api_base
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headers_for_request = (
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dict(prepped.headers) if hasattr(prepped, "headers") else {}
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)
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response = await self._make_async_call(
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client=client,
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timeout=timeout,
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api_base=prepped.url,
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headers=headers_for_request,
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data=data,
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||||
)
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## LOGGING
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logging_obj.post_call(
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input=data,
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api_key="",
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original_response=response,
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additional_args={"complete_input_dict": data},
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)
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responses.append(response)
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## TRANSFORM RESPONSE ##
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return self._transform_response(
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response_list=responses,
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model=model,
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provider=provider,
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is_async_invoke=is_async_invoke,
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batch_data=batch_data,
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||||
)
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def embeddings( # noqa: PLR0915
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||||
self,
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model: str,
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input: List[str],
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api_base: Optional[str],
|
||||
model_response: EmbeddingResponse,
|
||||
print_verbose: Callable,
|
||||
encoding,
|
||||
logging_obj,
|
||||
client: Optional[Union[HTTPHandler, AsyncHTTPHandler]],
|
||||
timeout: Optional[Union[float, httpx.Timeout]],
|
||||
aembedding: Optional[bool],
|
||||
extra_headers: Optional[dict],
|
||||
optional_params: dict,
|
||||
litellm_params: dict,
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||||
api_key: Optional[str] = None,
|
||||
) -> EmbeddingResponse:
|
||||
credentials, aws_region_name = self._load_credentials(optional_params)
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||||
|
||||
### TRANSFORMATION ###
|
||||
unencoded_model_id = (
|
||||
optional_params.pop("model_id", None) or model
|
||||
) # default to model if not passed
|
||||
modelId = urllib.parse.quote(unencoded_model_id, safe="")
|
||||
aws_region_name = self._get_aws_region_name(
|
||||
optional_params={"aws_region_name": aws_region_name},
|
||||
model=model,
|
||||
model_id=unencoded_model_id,
|
||||
)
|
||||
# Check async invoke needs to be used
|
||||
has_async_invoke = "async_invoke/" in model
|
||||
if has_async_invoke:
|
||||
model = model.replace("async_invoke/", "", 1)
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||||
provider = self.get_bedrock_embedding_provider(model)
|
||||
if provider is None:
|
||||
raise Exception(
|
||||
f"Unable to determine bedrock embedding provider for model: {model}. "
|
||||
f"Supported providers: {list(get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL))}"
|
||||
)
|
||||
inference_params = copy.deepcopy(optional_params)
|
||||
inference_params = {
|
||||
k: v
|
||||
for k, v in inference_params.items()
|
||||
if k.lower() not in self.aws_authentication_params
|
||||
}
|
||||
inference_params.pop(
|
||||
"user", None
|
||||
) # make sure user is not passed in for bedrock call
|
||||
|
||||
data: Optional[CohereEmbeddingRequest] = None
|
||||
batch_data: Optional[List] = None
|
||||
if provider == "cohere":
|
||||
data = BedrockCohereEmbeddingConfig()._transform_request(
|
||||
model=model, input=input, inference_params=inference_params
|
||||
)
|
||||
elif provider == "amazon" and model in [
|
||||
"amazon.titan-embed-image-v1",
|
||||
"amazon.titan-embed-text-v1",
|
||||
"amazon.titan-embed-text-v2:0",
|
||||
]:
|
||||
batch_data = []
|
||||
for i in input:
|
||||
if model == "amazon.titan-embed-image-v1":
|
||||
transformed_request: (
|
||||
AmazonEmbeddingRequest
|
||||
) = AmazonTitanMultimodalEmbeddingG1Config()._transform_request(
|
||||
input=i, inference_params=inference_params
|
||||
)
|
||||
elif model == "amazon.titan-embed-text-v1":
|
||||
transformed_request = AmazonTitanG1Config()._transform_request(
|
||||
input=i, inference_params=inference_params
|
||||
)
|
||||
elif model == "amazon.titan-embed-text-v2:0":
|
||||
transformed_request = AmazonTitanV2Config()._transform_request(
|
||||
input=i, inference_params=inference_params
|
||||
)
|
||||
else:
|
||||
raise Exception(
|
||||
"Unmapped model. Received={}. Expected={}".format(
|
||||
model,
|
||||
[
|
||||
"amazon.titan-embed-image-v1",
|
||||
"amazon.titan-embed-text-v1",
|
||||
"amazon.titan-embed-text-v2:0",
|
||||
],
|
||||
)
|
||||
)
|
||||
batch_data.append(transformed_request)
|
||||
elif provider == "twelvelabs":
|
||||
batch_data = []
|
||||
for i in input:
|
||||
twelvelabs_request = (
|
||||
TwelveLabsMarengoEmbeddingConfig()._transform_request(
|
||||
input=i,
|
||||
inference_params=inference_params,
|
||||
async_invoke_route=has_async_invoke,
|
||||
model_id=modelId,
|
||||
output_s3_uri=inference_params.get("output_s3_uri"),
|
||||
)
|
||||
)
|
||||
batch_data.append(twelvelabs_request)
|
||||
elif provider == "nova":
|
||||
batch_data = []
|
||||
for i in input:
|
||||
nova_request = AmazonNovaEmbeddingConfig()._transform_request(
|
||||
input=i,
|
||||
inference_params=inference_params,
|
||||
async_invoke_route=has_async_invoke,
|
||||
model_id=modelId,
|
||||
output_s3_uri=inference_params.get("output_s3_uri"),
|
||||
)
|
||||
batch_data.append(nova_request)
|
||||
|
||||
### SET RUNTIME ENDPOINT ###
|
||||
endpoint_url, proxy_endpoint_url = self.get_runtime_endpoint(
|
||||
api_base=api_base,
|
||||
aws_bedrock_runtime_endpoint=optional_params.pop(
|
||||
"aws_bedrock_runtime_endpoint", None
|
||||
),
|
||||
aws_region_name=aws_region_name,
|
||||
)
|
||||
if has_async_invoke:
|
||||
endpoint_url = f"{endpoint_url}/async-invoke"
|
||||
else:
|
||||
endpoint_url = f"{endpoint_url}/model/{modelId}/invoke"
|
||||
|
||||
if batch_data is not None:
|
||||
if aembedding:
|
||||
return self._async_single_func_embeddings( # type: ignore
|
||||
client=(
|
||||
client
|
||||
if client is not None and isinstance(client, AsyncHTTPHandler)
|
||||
else None
|
||||
),
|
||||
timeout=timeout,
|
||||
batch_data=batch_data,
|
||||
credentials=credentials,
|
||||
extra_headers=extra_headers,
|
||||
endpoint_url=endpoint_url,
|
||||
aws_region_name=aws_region_name,
|
||||
model=model,
|
||||
logging_obj=logging_obj,
|
||||
api_key=api_key,
|
||||
provider=provider,
|
||||
is_async_invoke=has_async_invoke,
|
||||
)
|
||||
returned_response = self._single_func_embeddings(
|
||||
client=(
|
||||
client
|
||||
if client is not None and isinstance(client, HTTPHandler)
|
||||
else None
|
||||
),
|
||||
timeout=timeout,
|
||||
batch_data=batch_data,
|
||||
credentials=credentials,
|
||||
extra_headers=extra_headers,
|
||||
endpoint_url=endpoint_url,
|
||||
aws_region_name=aws_region_name,
|
||||
model=model,
|
||||
logging_obj=logging_obj,
|
||||
api_key=api_key,
|
||||
provider=provider,
|
||||
is_async_invoke=has_async_invoke,
|
||||
)
|
||||
if returned_response is None:
|
||||
raise Exception("Unable to map Bedrock request to provider")
|
||||
return returned_response
|
||||
elif data is None:
|
||||
raise Exception("Unable to map Bedrock request to provider")
|
||||
|
||||
headers = {"Content-Type": "application/json"}
|
||||
if extra_headers is not None:
|
||||
headers = {"Content-Type": "application/json", **extra_headers}
|
||||
|
||||
prepped = self.get_request_headers( # type: ignore
|
||||
credentials=credentials,
|
||||
aws_region_name=aws_region_name,
|
||||
extra_headers=extra_headers,
|
||||
endpoint_url=endpoint_url,
|
||||
data=json.dumps(data),
|
||||
headers=headers,
|
||||
api_key=api_key,
|
||||
)
|
||||
|
||||
## ROUTING ##
|
||||
# Convert CaseInsensitiveDict to regular dict for httpx compatibility
|
||||
headers_for_request = (
|
||||
dict(prepped.headers) if hasattr(prepped, "headers") else {}
|
||||
)
|
||||
return cohere_embedding(
|
||||
model=model,
|
||||
input=input,
|
||||
model_response=model_response,
|
||||
logging_obj=logging_obj,
|
||||
optional_params=optional_params,
|
||||
encoding=encoding,
|
||||
data=data, # type: ignore
|
||||
complete_api_base=prepped.url,
|
||||
api_key=None,
|
||||
aembedding=aembedding,
|
||||
timeout=timeout,
|
||||
client=client,
|
||||
headers=headers_for_request,
|
||||
)
|
||||
|
||||
async def _get_async_invoke_status(
|
||||
self, invocation_arn: str, aws_region_name: str, logging_obj=None, **kwargs
|
||||
) -> dict:
|
||||
"""
|
||||
Get the status of an async invoke job using the GetAsyncInvoke operation.
|
||||
|
||||
Args:
|
||||
invocation_arn: The invocation ARN from the async invoke response
|
||||
aws_region_name: AWS region name
|
||||
**kwargs: Additional parameters (credentials, etc.)
|
||||
|
||||
Returns:
|
||||
dict: Status response from AWS Bedrock
|
||||
"""
|
||||
|
||||
# Get AWS credentials using the same method as other Bedrock methods
|
||||
credentials, _ = self._load_credentials(kwargs)
|
||||
|
||||
# Get the runtime endpoint
|
||||
endpoint_url, _ = self.get_runtime_endpoint(
|
||||
api_base=None,
|
||||
aws_bedrock_runtime_endpoint=kwargs.get("aws_bedrock_runtime_endpoint"),
|
||||
aws_region_name=aws_region_name,
|
||||
)
|
||||
|
||||
from urllib.parse import quote
|
||||
|
||||
# Encode the ARN for use in URL path
|
||||
encoded_arn = quote(invocation_arn, safe="")
|
||||
status_url = f"{endpoint_url.rstrip('/')}/async-invoke/{encoded_arn}"
|
||||
|
||||
# Prepare headers for GET request
|
||||
headers = {"Content-Type": "application/json"}
|
||||
|
||||
# Use AWSRequest directly for GET requests (get_request_headers hardcodes POST)
|
||||
try:
|
||||
from botocore.auth import SigV4Auth
|
||||
from botocore.awsrequest import AWSRequest
|
||||
except ImportError:
|
||||
raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
|
||||
|
||||
# Create AWSRequest with GET method and encoded URL
|
||||
request = AWSRequest(
|
||||
method="GET",
|
||||
url=status_url,
|
||||
data=None, # GET request, no body
|
||||
headers=headers,
|
||||
)
|
||||
|
||||
# Sign the request - SigV4Auth will create canonical string from request URL
|
||||
sigv4 = SigV4Auth(credentials, "bedrock", aws_region_name)
|
||||
sigv4.add_auth(request)
|
||||
|
||||
# Prepare the request
|
||||
prepped = request.prepare()
|
||||
|
||||
# LOGGING
|
||||
if logging_obj is not None:
|
||||
# Create custom curl command for GET request
|
||||
masked_headers = logging_obj._get_masked_headers(prepped.headers)
|
||||
formatted_headers = " ".join(
|
||||
[f"-H '{k}: {v}'" for k, v in masked_headers.items()]
|
||||
)
|
||||
custom_curl = "\n\nGET Request Sent from LiteLLM:\n"
|
||||
custom_curl += "curl -X GET \\\n"
|
||||
custom_curl += f"{prepped.url} \\\n"
|
||||
custom_curl += f"{formatted_headers}\n"
|
||||
|
||||
logging_obj.pre_call(
|
||||
input=invocation_arn,
|
||||
api_key="",
|
||||
additional_args={
|
||||
"complete_input_dict": {"invocation_arn": invocation_arn},
|
||||
"api_base": prepped.url,
|
||||
"headers": prepped.headers,
|
||||
"request_str": custom_curl, # Override with custom GET curl command
|
||||
},
|
||||
)
|
||||
|
||||
# Make the GET request
|
||||
client = get_async_httpx_client(llm_provider=LlmProviders.BEDROCK)
|
||||
response = await client.get(
|
||||
url=prepped.url,
|
||||
headers=prepped.headers,
|
||||
)
|
||||
|
||||
# LOGGING
|
||||
if logging_obj is not None:
|
||||
logging_obj.post_call(
|
||||
input=invocation_arn,
|
||||
api_key="",
|
||||
original_response=response,
|
||||
additional_args={
|
||||
"complete_input_dict": {"invocation_arn": invocation_arn}
|
||||
},
|
||||
)
|
||||
|
||||
# Parse response
|
||||
if response.status_code == 200:
|
||||
return response.json()
|
||||
else:
|
||||
raise Exception(
|
||||
f"Failed to get async invoke status: {response.status_code} - {response.text}"
|
||||
)
|
||||
Reference in New Issue
Block a user