mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 18:54:30 +00:00
refactor(sagemaker/): separate chat + completion routes + make them b… (#7151)
* refactor(sagemaker/): separate chat + completion routes + make them both use base llm config Addresses https://github.com/andrewyng/aisuite/issues/113#issuecomment-2512369132 * fix(main.py): pass hf model name + custom prompt dict to litellm params
This commit is contained in:
Krish Dholakia
GitHub
parent
1e87782215
commit
e903fe6038
14 changed files with 799 additions and 534 deletions
709
litellm/llms/sagemaker/completion/handler.py
Normal file
709
litellm/llms/sagemaker/completion/handler.py
Normal file
|
|
@ -0,0 +1,709 @@
|
|||
import io
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import traceback
|
||||
import types
|
||||
from copy import deepcopy
|
||||
from enum import Enum
|
||||
from functools import partial
|
||||
from typing import Any, AsyncIterator, Callable, Dict, Iterator, List, Optional, Union
|
||||
|
||||
import httpx # type: ignore
|
||||
import requests # type: ignore
|
||||
|
||||
import litellm
|
||||
from litellm._logging import verbose_logger
|
||||
from litellm.litellm_core_utils.asyncify import asyncify
|
||||
from litellm.llms.custom_httpx.http_handler import (
|
||||
AsyncHTTPHandler,
|
||||
HTTPHandler,
|
||||
_get_httpx_client,
|
||||
get_async_httpx_client,
|
||||
)
|
||||
from litellm.types.llms.openai import AllMessageValues
|
||||
from litellm.utils import (
|
||||
CustomStreamWrapper,
|
||||
EmbeddingResponse,
|
||||
ModelResponse,
|
||||
Usage,
|
||||
get_secret,
|
||||
)
|
||||
|
||||
from ...base_aws_llm import BaseAWSLLM
|
||||
from ...prompt_templates.factory import custom_prompt, prompt_factory
|
||||
from ..common_utils import AWSEventStreamDecoder, SagemakerError
|
||||
from .transformation import SagemakerConfig
|
||||
|
||||
sagemaker_config = SagemakerConfig()
|
||||
|
||||
"""
|
||||
SAGEMAKER AUTH Keys/Vars
|
||||
os.environ['AWS_ACCESS_KEY_ID'] = ""
|
||||
os.environ['AWS_SECRET_ACCESS_KEY'] = ""
|
||||
"""
|
||||
|
||||
|
||||
# set os.environ['AWS_REGION_NAME'] = <your-region_name>
|
||||
class SagemakerLLM(BaseAWSLLM):
|
||||
|
||||
def _load_credentials(
|
||||
self,
|
||||
optional_params: dict,
|
||||
):
|
||||
try:
|
||||
from botocore.credentials import Credentials
|
||||
except ImportError:
|
||||
raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
|
||||
## CREDENTIALS ##
|
||||
# pop aws_secret_access_key, aws_access_key_id, aws_session_token, aws_region_name from kwargs, since completion calls fail with them
|
||||
aws_secret_access_key = optional_params.pop("aws_secret_access_key", None)
|
||||
aws_access_key_id = optional_params.pop("aws_access_key_id", None)
|
||||
aws_session_token = optional_params.pop("aws_session_token", None)
|
||||
aws_region_name = optional_params.pop("aws_region_name", None)
|
||||
aws_role_name = optional_params.pop("aws_role_name", None)
|
||||
aws_session_name = optional_params.pop("aws_session_name", None)
|
||||
aws_profile_name = optional_params.pop("aws_profile_name", None)
|
||||
optional_params.pop(
|
||||
"aws_bedrock_runtime_endpoint", None
|
||||
) # https://bedrock-runtime.{region_name}.amazonaws.com
|
||||
aws_web_identity_token = optional_params.pop("aws_web_identity_token", None)
|
||||
aws_sts_endpoint = optional_params.pop("aws_sts_endpoint", None)
|
||||
|
||||
### SET REGION NAME ###
|
||||
if aws_region_name is None:
|
||||
# check env #
|
||||
litellm_aws_region_name = get_secret("AWS_REGION_NAME", None)
|
||||
|
||||
if litellm_aws_region_name is not None and isinstance(
|
||||
litellm_aws_region_name, str
|
||||
):
|
||||
aws_region_name = litellm_aws_region_name
|
||||
|
||||
standard_aws_region_name = get_secret("AWS_REGION", None)
|
||||
if standard_aws_region_name is not None and isinstance(
|
||||
standard_aws_region_name, str
|
||||
):
|
||||
aws_region_name = standard_aws_region_name
|
||||
|
||||
if aws_region_name is None:
|
||||
aws_region_name = "us-west-2"
|
||||
|
||||
credentials: Credentials = self.get_credentials(
|
||||
aws_access_key_id=aws_access_key_id,
|
||||
aws_secret_access_key=aws_secret_access_key,
|
||||
aws_session_token=aws_session_token,
|
||||
aws_region_name=aws_region_name,
|
||||
aws_session_name=aws_session_name,
|
||||
aws_profile_name=aws_profile_name,
|
||||
aws_role_name=aws_role_name,
|
||||
aws_web_identity_token=aws_web_identity_token,
|
||||
aws_sts_endpoint=aws_sts_endpoint,
|
||||
)
|
||||
return credentials, aws_region_name
|
||||
|
||||
def _prepare_request(
|
||||
self,
|
||||
credentials,
|
||||
model: str,
|
||||
data: dict,
|
||||
messages: List[AllMessageValues],
|
||||
optional_params: dict,
|
||||
aws_region_name: str,
|
||||
extra_headers: Optional[dict] = None,
|
||||
):
|
||||
try:
|
||||
import boto3
|
||||
from botocore.auth import SigV4Auth
|
||||
from botocore.awsrequest import AWSRequest
|
||||
from botocore.credentials import Credentials
|
||||
except ImportError:
|
||||
raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
|
||||
|
||||
sigv4 = SigV4Auth(credentials, "sagemaker", aws_region_name)
|
||||
if optional_params.get("stream") is True:
|
||||
api_base = f"https://runtime.sagemaker.{aws_region_name}.amazonaws.com/endpoints/{model}/invocations-response-stream"
|
||||
else:
|
||||
api_base = f"https://runtime.sagemaker.{aws_region_name}.amazonaws.com/endpoints/{model}/invocations"
|
||||
|
||||
sagemaker_base_url = optional_params.get("sagemaker_base_url", None)
|
||||
if sagemaker_base_url is not None:
|
||||
api_base = sagemaker_base_url
|
||||
|
||||
encoded_data = json.dumps(data).encode("utf-8")
|
||||
headers = sagemaker_config.validate_environment(
|
||||
headers=extra_headers,
|
||||
model=model,
|
||||
messages=messages,
|
||||
optional_params=optional_params,
|
||||
)
|
||||
request = AWSRequest(
|
||||
method="POST", url=api_base, data=encoded_data, headers=headers
|
||||
)
|
||||
sigv4.add_auth(request)
|
||||
if (
|
||||
extra_headers is not None and "Authorization" in extra_headers
|
||||
): # prevent sigv4 from overwriting the auth header
|
||||
request.headers["Authorization"] = extra_headers["Authorization"]
|
||||
|
||||
prepped_request = request.prepare()
|
||||
|
||||
return prepped_request
|
||||
|
||||
def completion( # noqa: PLR0915
|
||||
self,
|
||||
model: str,
|
||||
messages: list,
|
||||
model_response: ModelResponse,
|
||||
print_verbose: Callable,
|
||||
encoding,
|
||||
logging_obj,
|
||||
optional_params: dict,
|
||||
litellm_params: dict,
|
||||
timeout: Optional[Union[float, httpx.Timeout]] = None,
|
||||
custom_prompt_dict={},
|
||||
hf_model_name=None,
|
||||
logger_fn=None,
|
||||
acompletion: bool = False,
|
||||
headers: dict = {},
|
||||
):
|
||||
|
||||
# pop streaming if it's in the optional params as 'stream' raises an error with sagemaker
|
||||
credentials, aws_region_name = self._load_credentials(optional_params)
|
||||
inference_params = deepcopy(optional_params)
|
||||
stream = inference_params.pop("stream", None)
|
||||
model_id = optional_params.get("model_id", None)
|
||||
|
||||
## Load Config
|
||||
config = litellm.SagemakerConfig.get_config()
|
||||
for k, v in config.items():
|
||||
if (
|
||||
k not in inference_params
|
||||
): # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in
|
||||
inference_params[k] = v
|
||||
|
||||
if stream is True:
|
||||
if acompletion is True:
|
||||
response = self.async_streaming(
|
||||
messages=messages,
|
||||
model=model,
|
||||
custom_prompt_dict=custom_prompt_dict,
|
||||
hf_model_name=hf_model_name,
|
||||
optional_params=optional_params,
|
||||
encoding=encoding,
|
||||
model_response=model_response,
|
||||
logging_obj=logging_obj,
|
||||
model_id=model_id,
|
||||
aws_region_name=aws_region_name,
|
||||
credentials=credentials,
|
||||
headers=headers,
|
||||
litellm_params=litellm_params,
|
||||
)
|
||||
return response
|
||||
else:
|
||||
data = sagemaker_config.transform_request(
|
||||
model=model,
|
||||
messages=messages,
|
||||
optional_params=optional_params,
|
||||
litellm_params=litellm_params,
|
||||
headers=headers,
|
||||
)
|
||||
prepared_request = self._prepare_request(
|
||||
model=model,
|
||||
data=data,
|
||||
messages=messages,
|
||||
optional_params=optional_params,
|
||||
credentials=credentials,
|
||||
aws_region_name=aws_region_name,
|
||||
)
|
||||
if model_id is not None:
|
||||
# Add model_id as InferenceComponentName header
|
||||
# boto3 doc: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
|
||||
prepared_request.headers.update(
|
||||
{"X-Amzn-SageMaker-Inference-Component": model_id}
|
||||
)
|
||||
sync_handler = _get_httpx_client()
|
||||
sync_response = sync_handler.post(
|
||||
url=prepared_request.url,
|
||||
headers=prepared_request.headers, # type: ignore
|
||||
json=data,
|
||||
stream=stream,
|
||||
)
|
||||
|
||||
if sync_response.status_code != 200:
|
||||
raise SagemakerError(
|
||||
status_code=sync_response.status_code,
|
||||
message=str(sync_response.read()),
|
||||
)
|
||||
|
||||
decoder = AWSEventStreamDecoder(model="")
|
||||
|
||||
completion_stream = decoder.iter_bytes(
|
||||
sync_response.iter_bytes(chunk_size=1024)
|
||||
)
|
||||
streaming_response = CustomStreamWrapper(
|
||||
completion_stream=completion_stream,
|
||||
model=model,
|
||||
custom_llm_provider="sagemaker",
|
||||
logging_obj=logging_obj,
|
||||
)
|
||||
|
||||
## LOGGING
|
||||
logging_obj.post_call(
|
||||
input=messages,
|
||||
api_key="",
|
||||
original_response=streaming_response,
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
return streaming_response
|
||||
|
||||
# Non-Streaming Requests
|
||||
|
||||
# Async completion
|
||||
if acompletion is True:
|
||||
return self.async_completion(
|
||||
messages=messages,
|
||||
model=model,
|
||||
custom_prompt_dict=custom_prompt_dict,
|
||||
hf_model_name=hf_model_name,
|
||||
model_response=model_response,
|
||||
encoding=encoding,
|
||||
logging_obj=logging_obj,
|
||||
model_id=model_id,
|
||||
optional_params=optional_params,
|
||||
credentials=credentials,
|
||||
aws_region_name=aws_region_name,
|
||||
headers=headers,
|
||||
litellm_params=litellm_params,
|
||||
)
|
||||
|
||||
## Non-Streaming completion CALL
|
||||
_data = sagemaker_config.transform_request(
|
||||
model=model,
|
||||
messages=messages,
|
||||
optional_params=optional_params,
|
||||
litellm_params=litellm_params,
|
||||
headers=headers,
|
||||
)
|
||||
prepared_request_args = {
|
||||
"model": model,
|
||||
"data": _data,
|
||||
"optional_params": optional_params,
|
||||
"credentials": credentials,
|
||||
"aws_region_name": aws_region_name,
|
||||
"messages": messages,
|
||||
}
|
||||
prepared_request = self._prepare_request(**prepared_request_args)
|
||||
try:
|
||||
if model_id is not None:
|
||||
# Add model_id as InferenceComponentName header
|
||||
# boto3 doc: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
|
||||
prepared_request.headers.update(
|
||||
{"X-Amzn-SageMaker-Inference-Component": model_id}
|
||||
)
|
||||
|
||||
## LOGGING
|
||||
timeout = 300.0
|
||||
sync_handler = _get_httpx_client()
|
||||
## LOGGING
|
||||
logging_obj.pre_call(
|
||||
input=[],
|
||||
api_key="",
|
||||
additional_args={
|
||||
"complete_input_dict": _data,
|
||||
"api_base": prepared_request.url,
|
||||
"headers": prepared_request.headers,
|
||||
},
|
||||
)
|
||||
|
||||
# make sync httpx post request here
|
||||
try:
|
||||
sync_response = sync_handler.post(
|
||||
url=prepared_request.url,
|
||||
headers=prepared_request.headers, # type: ignore
|
||||
json=_data,
|
||||
timeout=timeout,
|
||||
)
|
||||
|
||||
if sync_response.status_code != 200:
|
||||
raise SagemakerError(
|
||||
status_code=sync_response.status_code,
|
||||
message=sync_response.text,
|
||||
)
|
||||
except Exception as e:
|
||||
## LOGGING
|
||||
logging_obj.post_call(
|
||||
input=[],
|
||||
api_key="",
|
||||
original_response=str(e),
|
||||
additional_args={"complete_input_dict": _data},
|
||||
)
|
||||
raise e
|
||||
except Exception as e:
|
||||
verbose_logger.error("Sagemaker error %s", str(e))
|
||||
status_code = (
|
||||
getattr(e, "response", {})
|
||||
.get("ResponseMetadata", {})
|
||||
.get("HTTPStatusCode", 500)
|
||||
)
|
||||
error_message = (
|
||||
getattr(e, "response", {}).get("Error", {}).get("Message", str(e))
|
||||
)
|
||||
if "Inference Component Name header is required" in error_message:
|
||||
error_message += "\n pass in via `litellm.completion(..., model_id={InferenceComponentName})`"
|
||||
raise SagemakerError(status_code=status_code, message=error_message)
|
||||
|
||||
return sagemaker_config.transform_response(
|
||||
model=model,
|
||||
raw_response=sync_response,
|
||||
model_response=model_response,
|
||||
logging_obj=logging_obj,
|
||||
request_data=_data,
|
||||
messages=messages,
|
||||
optional_params=optional_params,
|
||||
encoding=encoding,
|
||||
)
|
||||
|
||||
async def make_async_call(
|
||||
self,
|
||||
api_base: str,
|
||||
headers: dict,
|
||||
data: dict,
|
||||
logging_obj,
|
||||
client=None,
|
||||
):
|
||||
try:
|
||||
if client is None:
|
||||
client = get_async_httpx_client(
|
||||
llm_provider=litellm.LlmProviders.SAGEMAKER
|
||||
) # Create a new client if none provided
|
||||
response = await client.post(
|
||||
api_base,
|
||||
headers=headers,
|
||||
json=data,
|
||||
stream=True,
|
||||
)
|
||||
|
||||
if response.status_code != 200:
|
||||
raise SagemakerError(
|
||||
status_code=response.status_code, message=response.text
|
||||
)
|
||||
|
||||
decoder = AWSEventStreamDecoder(model="")
|
||||
completion_stream = decoder.aiter_bytes(
|
||||
response.aiter_bytes(chunk_size=1024)
|
||||
)
|
||||
|
||||
return completion_stream
|
||||
|
||||
# LOGGING
|
||||
logging_obj.post_call(
|
||||
input=[],
|
||||
api_key="",
|
||||
original_response="first stream response received",
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
|
||||
except httpx.HTTPStatusError as err:
|
||||
error_code = err.response.status_code
|
||||
raise SagemakerError(status_code=error_code, message=err.response.text)
|
||||
except httpx.TimeoutException:
|
||||
raise SagemakerError(status_code=408, message="Timeout error occurred.")
|
||||
except Exception as e:
|
||||
raise SagemakerError(status_code=500, message=str(e))
|
||||
|
||||
async def async_streaming(
|
||||
self,
|
||||
messages: List[AllMessageValues],
|
||||
model: str,
|
||||
custom_prompt_dict: dict,
|
||||
hf_model_name: Optional[str],
|
||||
credentials,
|
||||
aws_region_name: str,
|
||||
optional_params,
|
||||
encoding,
|
||||
model_response: ModelResponse,
|
||||
model_id: Optional[str],
|
||||
logging_obj: Any,
|
||||
litellm_params: dict,
|
||||
headers: dict,
|
||||
):
|
||||
data = await sagemaker_config.async_transform_request(
|
||||
model=model,
|
||||
messages=messages,
|
||||
optional_params={**optional_params, "stream": True},
|
||||
litellm_params=litellm_params,
|
||||
headers=headers,
|
||||
)
|
||||
asyncified_prepare_request = asyncify(self._prepare_request)
|
||||
prepared_request_args = {
|
||||
"model": model,
|
||||
"data": data,
|
||||
"optional_params": optional_params,
|
||||
"credentials": credentials,
|
||||
"aws_region_name": aws_region_name,
|
||||
"messages": messages,
|
||||
}
|
||||
prepared_request = await asyncified_prepare_request(**prepared_request_args)
|
||||
completion_stream = await self.make_async_call(
|
||||
api_base=prepared_request.url,
|
||||
headers=prepared_request.headers, # type: ignore
|
||||
data=data,
|
||||
logging_obj=logging_obj,
|
||||
)
|
||||
streaming_response = CustomStreamWrapper(
|
||||
completion_stream=completion_stream,
|
||||
model=model,
|
||||
custom_llm_provider="sagemaker",
|
||||
logging_obj=logging_obj,
|
||||
)
|
||||
|
||||
# LOGGING
|
||||
logging_obj.post_call(
|
||||
input=[],
|
||||
api_key="",
|
||||
original_response="first stream response received",
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
|
||||
return streaming_response
|
||||
|
||||
async def async_completion(
|
||||
self,
|
||||
messages: List[AllMessageValues],
|
||||
model: str,
|
||||
custom_prompt_dict: dict,
|
||||
hf_model_name: Optional[str],
|
||||
credentials,
|
||||
aws_region_name: str,
|
||||
encoding,
|
||||
model_response: ModelResponse,
|
||||
optional_params: dict,
|
||||
logging_obj: Any,
|
||||
model_id: Optional[str],
|
||||
headers: dict,
|
||||
litellm_params: dict,
|
||||
):
|
||||
timeout = 300.0
|
||||
async_handler = get_async_httpx_client(
|
||||
llm_provider=litellm.LlmProviders.SAGEMAKER
|
||||
)
|
||||
|
||||
data = await sagemaker_config.async_transform_request(
|
||||
model=model,
|
||||
messages=messages,
|
||||
optional_params=optional_params,
|
||||
litellm_params=litellm_params,
|
||||
headers=headers,
|
||||
)
|
||||
|
||||
asyncified_prepare_request = asyncify(self._prepare_request)
|
||||
prepared_request_args = {
|
||||
"model": model,
|
||||
"data": data,
|
||||
"optional_params": optional_params,
|
||||
"credentials": credentials,
|
||||
"aws_region_name": aws_region_name,
|
||||
"messages": messages,
|
||||
}
|
||||
|
||||
prepared_request = await asyncified_prepare_request(**prepared_request_args)
|
||||
## LOGGING
|
||||
logging_obj.pre_call(
|
||||
input=[],
|
||||
api_key="",
|
||||
additional_args={
|
||||
"complete_input_dict": data,
|
||||
"api_base": prepared_request.url,
|
||||
"headers": prepared_request.headers,
|
||||
},
|
||||
)
|
||||
try:
|
||||
if model_id is not None:
|
||||
# Add model_id as InferenceComponentName header
|
||||
# boto3 doc: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
|
||||
prepared_request.headers.update(
|
||||
{"X-Amzn-SageMaker-Inference-Componen": model_id}
|
||||
)
|
||||
# make async httpx post request here
|
||||
try:
|
||||
response = await async_handler.post(
|
||||
url=prepared_request.url,
|
||||
headers=prepared_request.headers, # type: ignore
|
||||
json=data,
|
||||
timeout=timeout,
|
||||
)
|
||||
|
||||
if response.status_code != 200:
|
||||
raise SagemakerError(
|
||||
status_code=response.status_code, message=response.text
|
||||
)
|
||||
except Exception as e:
|
||||
## LOGGING
|
||||
logging_obj.post_call(
|
||||
input=data["inputs"],
|
||||
api_key="",
|
||||
original_response=str(e),
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
raise e
|
||||
except Exception as e:
|
||||
error_message = f"{str(e)}"
|
||||
if "Inference Component Name header is required" in error_message:
|
||||
error_message += "\n pass in via `litellm.completion(..., model_id={InferenceComponentName})`"
|
||||
raise SagemakerError(status_code=500, message=error_message)
|
||||
return sagemaker_config.transform_response(
|
||||
model=model,
|
||||
raw_response=response,
|
||||
model_response=model_response,
|
||||
logging_obj=logging_obj,
|
||||
request_data=data,
|
||||
messages=messages,
|
||||
optional_params=optional_params,
|
||||
encoding=encoding,
|
||||
)
|
||||
|
||||
def embedding(
|
||||
self,
|
||||
model: str,
|
||||
input: list,
|
||||
model_response: EmbeddingResponse,
|
||||
print_verbose: Callable,
|
||||
encoding,
|
||||
logging_obj,
|
||||
optional_params: dict,
|
||||
custom_prompt_dict={},
|
||||
litellm_params=None,
|
||||
logger_fn=None,
|
||||
):
|
||||
"""
|
||||
Supports Huggingface Jumpstart embeddings like GPT-6B
|
||||
"""
|
||||
### BOTO3 INIT
|
||||
import boto3
|
||||
|
||||
# pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them
|
||||
aws_secret_access_key = optional_params.pop("aws_secret_access_key", None)
|
||||
aws_access_key_id = optional_params.pop("aws_access_key_id", None)
|
||||
aws_region_name = optional_params.pop("aws_region_name", None)
|
||||
|
||||
if aws_access_key_id is not None:
|
||||
# uses auth params passed to completion
|
||||
# aws_access_key_id is not None, assume user is trying to auth using litellm.completion
|
||||
client = boto3.client(
|
||||
service_name="sagemaker-runtime",
|
||||
aws_access_key_id=aws_access_key_id,
|
||||
aws_secret_access_key=aws_secret_access_key,
|
||||
region_name=aws_region_name,
|
||||
)
|
||||
else:
|
||||
# aws_access_key_id is None, assume user is trying to auth using env variables
|
||||
# boto3 automaticaly reads env variables
|
||||
|
||||
# we need to read region name from env
|
||||
# I assume majority of users use .env for auth
|
||||
region_name = (
|
||||
get_secret("AWS_REGION_NAME")
|
||||
or aws_region_name # get region from config file if specified
|
||||
or "us-west-2" # default to us-west-2 if region not specified
|
||||
)
|
||||
client = boto3.client(
|
||||
service_name="sagemaker-runtime",
|
||||
region_name=region_name,
|
||||
)
|
||||
|
||||
# pop streaming if it's in the optional params as 'stream' raises an error with sagemaker
|
||||
inference_params = deepcopy(optional_params)
|
||||
inference_params.pop("stream", None)
|
||||
|
||||
## Load Config
|
||||
config = litellm.SagemakerConfig.get_config()
|
||||
for k, v in config.items():
|
||||
if (
|
||||
k not in inference_params
|
||||
): # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in
|
||||
inference_params[k] = v
|
||||
|
||||
#### HF EMBEDDING LOGIC
|
||||
data = json.dumps({"text_inputs": input}).encode("utf-8")
|
||||
|
||||
## LOGGING
|
||||
request_str = f"""
|
||||
response = client.invoke_endpoint(
|
||||
EndpointName={model},
|
||||
ContentType="application/json",
|
||||
Body={data}, # type: ignore
|
||||
CustomAttributes="accept_eula=true",
|
||||
)""" # type: ignore
|
||||
logging_obj.pre_call(
|
||||
input=input,
|
||||
api_key="",
|
||||
additional_args={"complete_input_dict": data, "request_str": request_str},
|
||||
)
|
||||
## EMBEDDING CALL
|
||||
try:
|
||||
response = client.invoke_endpoint(
|
||||
EndpointName=model,
|
||||
ContentType="application/json",
|
||||
Body=data,
|
||||
CustomAttributes="accept_eula=true",
|
||||
)
|
||||
except Exception as e:
|
||||
status_code = (
|
||||
getattr(e, "response", {})
|
||||
.get("ResponseMetadata", {})
|
||||
.get("HTTPStatusCode", 500)
|
||||
)
|
||||
error_message = (
|
||||
getattr(e, "response", {}).get("Error", {}).get("Message", str(e))
|
||||
)
|
||||
raise SagemakerError(status_code=status_code, message=error_message)
|
||||
|
||||
response = json.loads(response["Body"].read().decode("utf8"))
|
||||
## LOGGING
|
||||
logging_obj.post_call(
|
||||
input=input,
|
||||
api_key="",
|
||||
original_response=response,
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
|
||||
print_verbose(f"raw model_response: {response}")
|
||||
if "embedding" not in response:
|
||||
raise SagemakerError(
|
||||
status_code=500, message="embedding not found in response"
|
||||
)
|
||||
embeddings = response["embedding"]
|
||||
|
||||
if not isinstance(embeddings, list):
|
||||
raise SagemakerError(
|
||||
status_code=422,
|
||||
message=f"Response not in expected format - {embeddings}",
|
||||
)
|
||||
|
||||
output_data = []
|
||||
for idx, embedding in enumerate(embeddings):
|
||||
output_data.append(
|
||||
{"object": "embedding", "index": idx, "embedding": embedding}
|
||||
)
|
||||
|
||||
model_response.object = "list"
|
||||
model_response.data = output_data
|
||||
model_response.model = model
|
||||
|
||||
input_tokens = 0
|
||||
for text in input:
|
||||
input_tokens += len(encoding.encode(text))
|
||||
|
||||
setattr(
|
||||
model_response,
|
||||
"usage",
|
||||
Usage(
|
||||
prompt_tokens=input_tokens,
|
||||
completion_tokens=0,
|
||||
total_tokens=input_tokens,
|
||||
),
|
||||
)
|
||||
|
||||
return model_response
|
||||
Loading…
Add table
Add a link
Reference in a new issue