mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-04-25 16:36:21 +02:00
65cda7b276
* Add system prompt to LLM invocation * Added system parameter to LLMs * Added to Bedrock and VertexAI
336 lines
11 KiB
Python
Executable file
336 lines
11 KiB
Python
Executable file
|
|
"""
|
|
Simple LLM service, performs text prompt completion using AWS Bedrock.
|
|
Input is prompt, output is response. Mistral is default.
|
|
"""
|
|
|
|
import boto3
|
|
import json
|
|
from prometheus_client import Histogram
|
|
import os
|
|
|
|
from .... schema import TextCompletionRequest, TextCompletionResponse, Error
|
|
from .... schema import text_completion_request_queue
|
|
from .... schema import text_completion_response_queue
|
|
from .... log_level import LogLevel
|
|
from .... base import ConsumerProducer
|
|
from .... exceptions import TooManyRequests
|
|
|
|
module = ".".join(__name__.split(".")[1:-1])
|
|
|
|
default_input_queue = text_completion_request_queue
|
|
default_output_queue = text_completion_response_queue
|
|
default_subscriber = module
|
|
default_model = 'mistral.mistral-large-2407-v1:0'
|
|
default_temperature = 0.0
|
|
default_max_output = 2048
|
|
default_aws_id_key = os.getenv("AWS_ID_KEY", None)
|
|
default_aws_secret = os.getenv("AWS_SECRET", None)
|
|
default_aws_region = os.getenv("AWS_REGION", 'us-west-2')
|
|
|
|
class Processor(ConsumerProducer):
|
|
|
|
def __init__(self, **params):
|
|
|
|
input_queue = params.get("input_queue", default_input_queue)
|
|
output_queue = params.get("output_queue", default_output_queue)
|
|
subscriber = params.get("subscriber", default_subscriber)
|
|
model = params.get("model", default_model)
|
|
aws_id_key = params.get("aws_id_key", default_aws_id_key)
|
|
aws_secret = params.get("aws_secret", default_aws_secret)
|
|
aws_region = params.get("aws_region", default_aws_region)
|
|
temperature = params.get("temperature", default_temperature)
|
|
max_output = params.get("max_output", default_max_output)
|
|
|
|
if aws_id_key is None:
|
|
raise RuntimeError("AWS ID not specified")
|
|
|
|
if aws_secret is None:
|
|
raise RuntimeError("AWS secret not specified")
|
|
|
|
if aws_region is None:
|
|
raise RuntimeError("AWS region not specified")
|
|
|
|
super(Processor, self).__init__(
|
|
**params | {
|
|
"input_queue": input_queue,
|
|
"output_queue": output_queue,
|
|
"subscriber": subscriber,
|
|
"input_schema": TextCompletionRequest,
|
|
"output_schema": TextCompletionResponse,
|
|
"model": model,
|
|
"temperature": temperature,
|
|
"max_output": max_output,
|
|
}
|
|
)
|
|
|
|
if not hasattr(__class__, "text_completion_metric"):
|
|
__class__.text_completion_metric = Histogram(
|
|
'text_completion_duration',
|
|
'Text completion duration (seconds)',
|
|
buckets=[
|
|
0.25, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0,
|
|
8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
|
|
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0,
|
|
30.0, 35.0, 40.0, 45.0, 50.0, 60.0, 80.0, 100.0,
|
|
120.0
|
|
]
|
|
)
|
|
|
|
self.model = model
|
|
self.temperature = temperature
|
|
self.max_output = max_output
|
|
|
|
self.session = boto3.Session(
|
|
aws_access_key_id=aws_id_key,
|
|
aws_secret_access_key=aws_secret,
|
|
region_name=aws_region
|
|
)
|
|
|
|
self.bedrock = self.session.client(service_name='bedrock-runtime')
|
|
|
|
print("Initialised", flush=True)
|
|
|
|
def handle(self, msg):
|
|
|
|
v = msg.value()
|
|
|
|
# Sender-produced ID
|
|
|
|
id = msg.properties()["id"]
|
|
|
|
print(f"Handling prompt {id}...", flush=True)
|
|
|
|
prompt = v.system + "\n\n" + v.prompt
|
|
|
|
try:
|
|
|
|
# Mistral Input Format
|
|
if self.model.startswith("mistral"):
|
|
promptbody = json.dumps({
|
|
"prompt": prompt,
|
|
"max_tokens": self.max_output,
|
|
"temperature": self.temperature,
|
|
"top_p": 0.99,
|
|
"top_k": 40
|
|
})
|
|
|
|
# Llama 3.1 Input Format
|
|
elif self.model.startswith("meta"):
|
|
promptbody = json.dumps({
|
|
"prompt": prompt,
|
|
"max_gen_len": self.max_output,
|
|
"temperature": self.temperature,
|
|
"top_p": 0.95,
|
|
})
|
|
|
|
# Anthropic Input Format
|
|
elif self.model.startswith("anthropic"):
|
|
promptbody = json.dumps({
|
|
"anthropic_version": "bedrock-2023-05-31",
|
|
"max_tokens": self.max_output,
|
|
"temperature": self.temperature,
|
|
"top_p": 0.999,
|
|
"messages": [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": prompt
|
|
}
|
|
]
|
|
}
|
|
]
|
|
})
|
|
|
|
# Jamba Input Format
|
|
elif self.model.startswith("ai21"):
|
|
promptbody = json.dumps({
|
|
"max_tokens": self.max_output,
|
|
"temperature": self.temperature,
|
|
"top_p": 0.9,
|
|
"messages": [
|
|
{
|
|
"role": "user",
|
|
"content": prompt
|
|
}
|
|
]
|
|
})
|
|
|
|
# Cohere Input Format
|
|
elif self.model.startswith("cohere"):
|
|
promptbody = json.dumps({
|
|
"max_tokens": self.max_output,
|
|
"temperature": self.temperature,
|
|
"message": prompt
|
|
})
|
|
|
|
# Use Mistral format as defualt
|
|
else:
|
|
promptbody = json.dumps({
|
|
"prompt": prompt,
|
|
"max_tokens": self.max_output,
|
|
"temperature": self.temperature,
|
|
"top_p": 0.99,
|
|
"top_k": 40
|
|
})
|
|
|
|
accept = 'application/json'
|
|
contentType = 'application/json'
|
|
|
|
# FIXME: Consider catching request limits and raise TooManyRequests
|
|
# See https://boto3.amazonaws.com/v1/documentation/api/latest/guide/retries.html
|
|
|
|
with __class__.text_completion_metric.time():
|
|
response = self.bedrock.invoke_model(
|
|
body=promptbody, modelId=self.model, accept=accept,
|
|
contentType=contentType
|
|
)
|
|
|
|
# Mistral Response Structure
|
|
if self.model.startswith("mistral"):
|
|
response_body = json.loads(response.get("body").read())
|
|
outputtext = response_body['outputs'][0]['text']
|
|
|
|
# Claude Response Structure
|
|
elif self.model.startswith("anthropic"):
|
|
model_response = json.loads(response["body"].read())
|
|
outputtext = model_response['content'][0]['text']
|
|
|
|
# Llama 3.1 Response Structure
|
|
elif self.model.startswith("meta"):
|
|
model_response = json.loads(response["body"].read())
|
|
outputtext = model_response["generation"]
|
|
|
|
# Jamba Response Structure
|
|
elif self.model.startswith("ai21"):
|
|
content = response['body'].read()
|
|
content_str = content.decode('utf-8')
|
|
content_json = json.loads(content_str)
|
|
outputtext = content_json['choices'][0]['message']['content']
|
|
|
|
# Cohere Input Format
|
|
elif self.model.startswith("cohere"):
|
|
content = response['body'].read()
|
|
content_str = content.decode('utf-8')
|
|
content_json = json.loads(content_str)
|
|
outputtext = content_json['text']
|
|
|
|
# Use Mistral as default
|
|
else:
|
|
response_body = json.loads(response.get("body").read())
|
|
outputtext = response_body['outputs'][0]['text']
|
|
|
|
metadata = response['ResponseMetadata']['HTTPHeaders']
|
|
inputtokens = int(metadata['x-amzn-bedrock-input-token-count'])
|
|
outputtokens = int(metadata['x-amzn-bedrock-output-token-count'])
|
|
|
|
print(outputtext, flush=True)
|
|
print(f"Input Tokens: {inputtokens}", flush=True)
|
|
print(f"Output Tokens: {outputtokens}", flush=True)
|
|
|
|
print("Send response...", flush=True)
|
|
r = TextCompletionResponse(
|
|
error=None,
|
|
response=outputtext,
|
|
in_token=inputtokens,
|
|
out_token=outputtokens,
|
|
model=str(self.model),
|
|
)
|
|
|
|
self.send(r, properties={"id": id})
|
|
|
|
print("Done.", flush=True)
|
|
|
|
|
|
# FIXME: Wrong exception, don't know what Bedrock throws
|
|
# for a rate limit
|
|
except TooManyRequests:
|
|
|
|
print("Send rate limit response...", flush=True)
|
|
|
|
r = TextCompletionResponse(
|
|
error=Error(
|
|
type = "rate-limit",
|
|
message = str(e),
|
|
),
|
|
response=None,
|
|
in_token=None,
|
|
out_token=None,
|
|
model=None,
|
|
)
|
|
|
|
self.producer.send(r, properties={"id": id})
|
|
|
|
self.consumer.acknowledge(msg)
|
|
|
|
except Exception as e:
|
|
|
|
print(f"Exception: {e}")
|
|
|
|
print("Send error response...", flush=True)
|
|
|
|
r = TextCompletionResponse(
|
|
error=Error(
|
|
type = "llm-error",
|
|
message = str(e),
|
|
),
|
|
response=None,
|
|
in_token=None,
|
|
out_token=None,
|
|
model=None,
|
|
)
|
|
|
|
self.consumer.acknowledge(msg)
|
|
|
|
@staticmethod
|
|
def add_args(parser):
|
|
|
|
ConsumerProducer.add_args(
|
|
parser, default_input_queue, default_subscriber,
|
|
default_output_queue,
|
|
)
|
|
|
|
parser.add_argument(
|
|
'-m', '--model',
|
|
default="mistral.mistral-large-2407-v1:0",
|
|
help=f'Bedrock model (default: Mistral-Large-2407)'
|
|
)
|
|
|
|
parser.add_argument(
|
|
'-z', '--aws-id-key',
|
|
default=default_aws_id_key,
|
|
help=f'AWS ID Key'
|
|
)
|
|
|
|
parser.add_argument(
|
|
'-k', '--aws-secret',
|
|
default=default_aws_secret,
|
|
help=f'AWS Secret Key'
|
|
)
|
|
|
|
parser.add_argument(
|
|
'-r', '--aws-region',
|
|
default=default_aws_region,
|
|
help=f'AWS Region'
|
|
)
|
|
|
|
parser.add_argument(
|
|
'-t', '--temperature',
|
|
type=float,
|
|
default=default_temperature,
|
|
help=f'LLM temperature parameter (default: {default_temperature})'
|
|
)
|
|
|
|
parser.add_argument(
|
|
'-x', '--max-output',
|
|
type=int,
|
|
default=default_max_output,
|
|
help=f'LLM max output tokens (default: {default_max_output})'
|
|
)
|
|
|
|
def run():
|
|
|
|
Processor.start(module, __doc__)
|
|
|