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244da4aec1
* Added Anthropic support for VertexAI * Update tests to match code * Fixed private.json usage with Anthropic (I think). * Fixed test --------- Co-authored-by: Cyber MacGeddon <cybermaggedon@gmail.com>
241 lines
No EOL
8.6 KiB
Python
Executable file
241 lines
No EOL
8.6 KiB
Python
Executable file
"""
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Simple LLM service, performs text prompt completion using VertexAI on
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Google Cloud. Input is prompt, output is response.
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Supports both Google's Gemini models and Anthropic's Claude models.
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"""
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#
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# Somewhat perplexed by the Google Cloud SDK choices. We're going off this
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# one, which uses the google-cloud-aiplatform library:
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# https://cloud.google.com/python/docs/reference/vertexai/1.94.0
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# It seems it is possible to invoke VertexAI from the google-genai
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# SDK too:
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# https://googleapis.github.io/python-genai/genai.html#module-genai.client
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# That would make this code look very much like the GoogleAIStudio
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# code. And maybe not reliant on the google-cloud-aiplatform library?
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#
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# This module's imports bring in a lot of libraries.
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from google.oauth2 import service_account
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import google.auth
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import vertexai
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import logging
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# Why is preview here?
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from vertexai.generative_models import (
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Content, FunctionDeclaration, GenerativeModel, GenerationConfig,
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HarmCategory, HarmBlockThreshold, Part, Tool, SafetySetting,
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)
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# Added for Anthropic model support
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from anthropic import AnthropicVertex, RateLimitError
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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 = 'gemini-1.5-flash-001'
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default_region = 'us-central1'
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default_temperature = 0.0
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default_max_output = 8192
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default_private_key = "private.json"
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class Processor(LlmService):
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def __init__(self, **params):
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region = params.get("region", default_region)
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model = params.get("model", default_model)
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private_key = params.get("private_key", default_private_key)
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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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if private_key is None:
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logger.warning("Private key file not specified, using Application Default Credentials")
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super(Processor, self).__init__(**params)
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self.model = model
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self.is_anthropic = 'claude' in self.model.lower()
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# Shared parameters for both model types
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self.api_params = {
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"temperature": temperature,
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"top_p": 1.0,
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"top_k": 32,
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"max_output_tokens": max_output,
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}
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logger.info("Initializing VertexAI...")
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# Unified credential and project ID loading
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if private_key:
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credentials = (
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service_account.Credentials.from_service_account_file(
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private_key
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)
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)
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project_id = credentials.project_id
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else:
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credentials, project_id = google.auth.default()
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if not project_id:
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raise RuntimeError(
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"Could not determine Google Cloud project ID. "
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"Ensure it's set in your environment or service account."
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)
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# Initialize the appropriate client based on the model type
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if self.is_anthropic:
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logger.info(f"Initializing Anthropic model '{model}' via AnthropicVertex SDK")
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# Initialize AnthropicVertex with credentials if provided, otherwise use ADC
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anthropic_kwargs = {'region': region, 'project_id': project_id}
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if credentials and private_key: # Pass credentials only if from a file
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anthropic_kwargs['credentials'] = credentials
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logger.debug(f"Using service account credentials for Anthropic model")
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else:
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logger.debug(f"Using Application Default Credentials for Anthropic model")
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self.llm = AnthropicVertex(**anthropic_kwargs)
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else:
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# For Gemini models, initialize the Vertex AI SDK
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logger.info(f"Initializing Google model '{model}' via Vertex AI SDK")
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init_kwargs = {'location': region, 'project': project_id}
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if credentials and private_key: # Pass credentials only if from a file
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init_kwargs['credentials'] = credentials
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vertexai.init(**init_kwargs)
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self.llm = GenerativeModel(model)
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self.generation_config = GenerationConfig(
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temperature=temperature,
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top_p=1.0,
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top_k=10,
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candidate_count=1,
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max_output_tokens=max_output,
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)
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# Block none doesn't seem to work
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block_level = HarmBlockThreshold.BLOCK_ONLY_HIGH
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# block_level = HarmBlockThreshold.BLOCK_NONE
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self.safety_settings = [
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SafetySetting(
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category = HarmCategory.HARM_CATEGORY_HARASSMENT,
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threshold = block_level,
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),
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SafetySetting(
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category = HarmCategory.HARM_CATEGORY_HATE_SPEECH,
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threshold = block_level,
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),
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SafetySetting(
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category = HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT,
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threshold = block_level,
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),
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SafetySetting(
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category = HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
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threshold = block_level,
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),
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]
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logger.info("VertexAI initialization complete")
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async def generate_content(self, system, prompt):
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try:
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if self.is_anthropic:
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# Anthropic API uses a dedicated system prompt
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logger.debug("Sending request to Anthropic model...")
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response = self.llm.messages.create(
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model=self.model,
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system=system,
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messages=[{"role": "user", "content": prompt}],
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max_tokens=self.api_params['max_output_tokens'],
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temperature=self.api_params['temperature'],
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top_p=self.api_params['top_p'],
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top_k=self.api_params['top_k'],
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)
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resp = LlmResult(
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text=response.content[0].text,
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in_token=response.usage.input_tokens,
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out_token=response.usage.output_tokens,
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model=self.model
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)
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else:
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# Gemini API combines system and user prompts
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logger.debug("Sending request to Gemini model...")
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full_prompt = system + "\n\n" + prompt
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response = self.llm.generate_content(
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full_prompt, generation_config = self.generation_config,
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safety_settings = self.safety_settings,
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)
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resp = LlmResult(
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text = response.text,
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in_token = response.usage_metadata.prompt_token_count,
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out_token = response.usage_metadata.candidates_token_count,
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model = self.model
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)
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logger.info(f"Input Tokens: {resp.in_token}")
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logger.info(f"Output Tokens: {resp.out_token}")
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logger.debug("Send response...")
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return resp
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except (google.api_core.exceptions.ResourceExhausted, RateLimitError) 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"VertexAI LLM exception: {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=default_model,
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help=f'LLM model (e.g., gemini-1.5-flash-001, claude-3-sonnet@20240229) (default: {default_model})'
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)
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parser.add_argument(
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'-k', '--private-key',
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help=f'Google Cloud private JSON file (optional, uses ADC if not provided)'
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)
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parser.add_argument(
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'-r', '--region',
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default=default_region,
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help=f'Google Cloud region (default: {default_region})',
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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__) |