mirror of
https://github.com/trustgraph-ai/trustgraph.git
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354 lines
11 KiB
Python
Executable file
354 lines
11 KiB
Python
Executable file
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"""
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Simple LLM service, performs text prompt completion using AWS Bedrock.
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Input is prompt, output is response. Mistral is default.
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"""
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import boto3
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import json
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import os
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import enum
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import logging
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from .... exceptions import TooManyRequests
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from .... base import LlmService, LlmResult
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# Module logger
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logger = logging.getLogger(__name__)
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default_ident = "text-completion"
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default_model = 'mistral.mistral-large-2407-v1:0'
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default_temperature = 0.0
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default_max_output = 2048
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default_top_p = 0.99
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default_top_k = 40
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# Actually, these could all just be None, no need to get environment
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# variables, as Boto3 would pick all these up if not passed in as args
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default_access_key_id = os.getenv("AWS_ACCESS_KEY_ID", None)
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default_secret_access_key = os.getenv("AWS_SECRET_ACCESS_KEY", None)
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default_session_token = os.getenv("AWS_SESSION_TOKEN", None)
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default_profile = os.getenv("AWS_PROFILE", None)
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default_region = os.getenv("AWS_DEFAULT_REGION", None)
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# Variant API handling depends on the model type
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class ModelHandler:
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def __init__(self):
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self.temperature = default_temperature
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self.max_output = default_max_output
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self.top_p = default_top_p
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self.top_k = default_top_k
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def set_temperature(self, temperature):
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self.temperature = temperature
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def set_max_output(self, max_output):
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self.max_output = max_output
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def set_top_p(self, top_p):
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self.top_p = top_p
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def set_top_k(self, top_k):
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self.top_k = top_k
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def encode_request(self, system, prompt):
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raise RuntimeError("format_request not implemented")
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def decode_response(self, response):
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raise RuntimeError("format_request not implemented")
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class Mistral(ModelHandler):
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def __init__(self):
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self.top_p = 0.99
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self.top_k = 40
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def encode_request(self, system, prompt):
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return json.dumps({
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"prompt": f"{system}\n\n{prompt}",
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"max_tokens": self.max_output,
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"temperature": self.temperature,
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"top_p": self.top_p,
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"top_k": self.top_k,
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})
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def decode_response(self, response):
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response_body = json.loads(response.get("body").read())
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return response_body['outputs'][0]['text']
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# Llama 3
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class Meta(ModelHandler):
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def __init__(self):
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self.top_p = 0.95
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def encode_request(self, system, prompt):
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return json.dumps({
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"prompt": f"{system}\n\n{prompt}",
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"max_gen_len": self.max_output,
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"temperature": self.temperature,
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"top_p": self.top_p,
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})
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def decode_response(self, response):
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model_response = json.loads(response["body"].read())
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return model_response["generation"]
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class Anthropic(ModelHandler):
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def __init__(self):
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self.top_p = 0.999
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def encode_request(self, system, prompt):
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return json.dumps({
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"anthropic_version": "bedrock-2023-05-31",
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"max_tokens": self.max_output,
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"temperature": self.temperature,
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"top_p": self.top_p,
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": f"{system}\n\n{prompt}",
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}
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]
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}
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]
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})
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def decode_response(self, response):
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model_response = json.loads(response["body"].read())
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return model_response['content'][0]['text']
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class Ai21(ModelHandler):
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def __init__(self):
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self.top_p = 0.9
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def encode_request(self, system, prompt):
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return json.dumps({
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"max_tokens": self.max_output,
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"temperature": self.temperature,
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"top_p": self.top_p,
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"messages": [
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{
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"role": "user",
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"content": f"{system}\n\n{prompt}"
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}
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]
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})
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def decode_response(self, response):
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content = response['body'].read()
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content_str = content.decode('utf-8')
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content_json = json.loads(content_str)
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return content_json['choices'][0]['message']['content']
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class Cohere(ModelHandler):
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def encode_request(self, system, prompt):
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return json.dumps({
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"max_tokens": self.max_output,
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"temperature": self.temperature,
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"message": f"{system}\n\n{prompt}",
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})
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def decode_response(self, response):
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content = response['body'].read()
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content_str = content.decode('utf-8')
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content_json = json.loads(content_str)
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return content_json['text']
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Default=Mistral
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class Processor(LlmService):
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def __init__(self, **params):
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logger.debug(f"Bedrock LLM initialized with params: {params}")
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model = params.get("model", default_model)
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temperature = params.get("temperature", default_temperature)
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max_output = params.get("max_output", default_max_output)
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aws_access_key_id = params.get(
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"aws_access_key_id", default_access_key_id
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)
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aws_secret_access_key = params.get(
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"aws_secret_access_key", default_secret_access_key
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)
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aws_session_token = params.get(
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"aws_session_token", default_session_token
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)
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aws_region = params.get(
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"aws_region", default_region
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)
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aws_profile = params.get(
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"aws_profile", default_profile
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)
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super(Processor, self).__init__(
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**params | {
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"model": model,
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"temperature": temperature,
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"max_output": max_output,
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}
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)
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# Store default configuration
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self.default_model = model
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self.temperature = temperature
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self.max_output = max_output
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# Cache for model variants to avoid re-initialization
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self.model_variants = {}
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self.session = boto3.Session(
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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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profile_name=aws_profile,
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region_name=aws_region,
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)
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self.bedrock = self.session.client(service_name='bedrock-runtime')
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logger.info("Bedrock LLM service initialized")
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def determine_variant(self, model):
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# FIXME: Missing, Amazon models, Deepseek
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# This set of conditions deals with normal bedrock on-demand usage
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if model.startswith("mistral"):
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return Mistral
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elif model.startswith("meta"):
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return Meta
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elif model.startswith("anthropic"):
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return Anthropic
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elif model.startswith("ai21"):
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return Ai21
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elif model.startswith("cohere"):
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return Cohere
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# The inference profiles
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if model.startswith("us.meta"):
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return Meta
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elif model.startswith("us.anthropic"):
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return Anthropic
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elif model.startswith("eu.meta"):
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return Meta
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elif model.startswith("eu.anthropic"):
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return Anthropic
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return Default
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def _get_or_create_variant(self, model_name):
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"""Get cached model variant or create new one"""
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if model_name not in self.model_variants:
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logger.info(f"Creating model variant for '{model_name}'")
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variant_class = self.determine_variant(model_name)
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variant = variant_class()
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variant.set_temperature(self.temperature)
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variant.set_max_output(self.max_output)
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self.model_variants[model_name] = variant
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return self.model_variants[model_name]
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async def generate_content(self, system, prompt, model=None):
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# Use provided model or fall back to default
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model_name = model or self.default_model
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logger.debug(f"Using model: {model_name}")
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try:
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# Get the appropriate variant for this model
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variant = self._get_or_create_variant(model_name)
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promptbody = variant.encode_request(system, prompt)
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accept = 'application/json'
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contentType = 'application/json'
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response = self.bedrock.invoke_model(
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body=promptbody,
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modelId=model_name,
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accept=accept,
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contentType=contentType
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)
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# Response structure decode
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outputtext = variant.decode_response(response)
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metadata = response['ResponseMetadata']['HTTPHeaders']
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inputtokens = int(metadata['x-amzn-bedrock-input-token-count'])
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outputtokens = int(metadata['x-amzn-bedrock-output-token-count'])
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logger.debug(f"LLM output: {outputtext}")
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logger.info(f"Input Tokens: {inputtokens}")
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logger.info(f"Output Tokens: {outputtokens}")
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resp = LlmResult(
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text = outputtext,
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in_token = inputtokens,
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out_token = outputtokens,
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model = model_name
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)
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return resp
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except self.bedrock.exceptions.ThrottlingException as e:
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logger.warning(f"Hit rate limit: {e}")
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# Leave rate limit retries to the base handler
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raise TooManyRequests()
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except Exception as e:
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# Apart from rate limits, treat all exceptions as unrecoverable
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logger.error(f"Bedrock LLM exception ({type(e).__name__}): {e}", exc_info=True)
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raise e
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@staticmethod
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def add_args(parser):
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LlmService.add_args(parser)
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parser.add_argument(
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'-m', '--model',
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default="mistral.mistral-large-2407-v1:0",
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help=f'Bedrock model (default: Mistral-Large-2407)'
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)
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parser.add_argument(
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'-z', '--aws-access-key-id',
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default=default_access_key_id,
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help=f'AWS access key ID'
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)
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parser.add_argument(
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'-k', '--aws-secret-access-key',
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default=default_secret_access_key,
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help=f'AWS secret access key'
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)
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parser.add_argument(
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'-r', '--aws-region',
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default=default_region,
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help=f'AWS region'
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)
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parser.add_argument(
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'--aws-profile', '--profile',
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default=default_profile,
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help=f'AWS profile name'
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)
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parser.add_argument(
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'-t', '--temperature',
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type=float,
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default=default_temperature,
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help=f'LLM temperature parameter (default: {default_temperature})'
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)
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parser.add_argument(
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'-x', '--max-output',
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type=int,
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default=default_max_output,
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help=f'LLM max output tokens (default: {default_max_output})'
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)
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def run():
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Processor.launch(default_ident, __doc__)
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