mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 18:54:30 +00:00
139 lines
3.8 KiB
Python
139 lines
3.8 KiB
Python
import os
|
|
import json
|
|
from enum import Enum
|
|
import requests
|
|
import time
|
|
from typing import Callable
|
|
from litellm.utils import ModelResponse, get_secret
|
|
import sys
|
|
|
|
class BedrockError(Exception):
|
|
def __init__(self, status_code, message):
|
|
self.status_code = status_code
|
|
self.message = message
|
|
super().__init__(
|
|
self.message
|
|
) # Call the base class constructor with the parameters it needs
|
|
|
|
"""
|
|
BEDROCK AUTH Keys/Vars
|
|
os.environ['AWS_ACCESS_KEY_ID'] = ""
|
|
os.environ['AWS_SECRET_ACCESS_KEY'] = ""
|
|
"""
|
|
|
|
# set os.environ['AWS_REGION_NAME'] = <your-region_name>
|
|
|
|
def completion(
|
|
model: str,
|
|
messages: list,
|
|
model_response: ModelResponse,
|
|
print_verbose: Callable,
|
|
encoding,
|
|
logging_obj,
|
|
optional_params=None,
|
|
litellm_params=None,
|
|
logger_fn=None,
|
|
):
|
|
import sys
|
|
if 'boto3' not in sys.modules:
|
|
import boto3
|
|
|
|
region_name = (
|
|
get_secret("AWS_REGION_NAME") or
|
|
"us-west-2" # default to us-west-2
|
|
)
|
|
|
|
client = boto3.client(
|
|
service_name="bedrock",
|
|
region_name=region_name
|
|
)
|
|
|
|
model = model
|
|
prompt = ""
|
|
for message in messages:
|
|
if "role" in message:
|
|
if message["role"] == "user":
|
|
prompt += (
|
|
f"{message['content']}"
|
|
)
|
|
else:
|
|
prompt += (
|
|
f"{message['content']}"
|
|
)
|
|
else:
|
|
prompt += f"{message['content']}"
|
|
|
|
|
|
data = json.dumps({
|
|
"inputText": prompt,
|
|
"textGenerationConfig":{
|
|
"maxTokenCount":4096,
|
|
"stopSequences":[],
|
|
"temperature":0,
|
|
"topP":0.9
|
|
}
|
|
})
|
|
|
|
## LOGGING
|
|
logging_obj.pre_call(
|
|
input=prompt,
|
|
api_key="",
|
|
additional_args={"complete_input_dict": data},
|
|
)
|
|
|
|
## COMPLETION CALL
|
|
accept = 'application/json'
|
|
contentType = 'application/json'
|
|
|
|
response = client.invoke_model(
|
|
body=data,
|
|
modelId=model,
|
|
accept=accept,
|
|
contentType=contentType
|
|
)
|
|
response_body = json.loads(response.get('body').read())
|
|
if "stream" in optional_params and optional_params["stream"] == True:
|
|
return response.iter_lines()
|
|
else:
|
|
## LOGGING
|
|
logging_obj.post_call(
|
|
input=prompt,
|
|
api_key="",
|
|
original_response=response,
|
|
additional_args={"complete_input_dict": data},
|
|
)
|
|
print_verbose(f"raw model_response: {response}")
|
|
## RESPONSE OBJECT
|
|
outputText = response_body.get('results')[0].get('outputText')
|
|
print(outputText)
|
|
if "error" in outputText:
|
|
raise BedrockError(
|
|
message=outputText["error"],
|
|
status_code=response.status_code,
|
|
)
|
|
else:
|
|
try:
|
|
model_response["choices"][0]["message"]["content"] = outputText
|
|
except:
|
|
raise BedrockError(message=json.dumps(outputText), status_code=response.status_code)
|
|
|
|
## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here.
|
|
prompt_tokens = len(
|
|
encoding.encode(prompt)
|
|
)
|
|
completion_tokens = len(
|
|
encoding.encode(model_response["choices"][0]["message"]["content"])
|
|
)
|
|
|
|
model_response["created"] = time.time()
|
|
model_response["model"] = model
|
|
model_response["usage"] = {
|
|
"prompt_tokens": prompt_tokens,
|
|
"completion_tokens": completion_tokens,
|
|
"total_tokens": prompt_tokens + completion_tokens,
|
|
}
|
|
return model_response
|
|
|
|
def embedding():
|
|
# logic for parsing in - calling - parsing out model embedding calls
|
|
pass
|