Fix VertexAI package. Add Python packaging to Makefile. (#85)

Bump version & generate templates.

trustgraph-vertexai/trustgraph/model/text_completion/vertexai/llm.py

@@ -0,0 +1,257 @@
+
+"""
+Simple LLM service, performs text prompt completion using VertexAI on
+Google Cloud.   Input is prompt, output is response.
+"""
+
+import vertexai
+import time
+from prometheus_client import Histogram
+
+from google.oauth2 import service_account
+import google
+
+from vertexai.preview.generative_models import (
+    Content,
+    FunctionDeclaration,
+    GenerativeModel,
+    GenerationConfig,
+    HarmCategory,
+    HarmBlockThreshold,
+    Part,
+    Tool,
+)
+
+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 = 'gemini-1.0-pro-001'
+default_region = 'us-central1'
+default_temperature = 0.0
+default_max_output = 8192
+
+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)
+        region = params.get("region", default_region)
+        model = params.get("model", default_model)
+        private_key = params.get("private_key")
+        temperature = params.get("temperature", default_temperature)
+        max_output = params.get("max_output", default_max_output)
+
+        super(Processor, self).__init__(
+            **params | {
+                "input_queue": input_queue,
+                "output_queue": output_queue,
+                "subscriber": subscriber,
+                "input_schema": TextCompletionRequest,
+                "output_schema": TextCompletionResponse,
+            }
+        )
+
+        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.parameters = {
+            "temperature": temperature,
+            "top_p": 1.0,
+            "top_k": 32,
+            "candidate_count": 1,
+            "max_output_tokens": max_output,
+        }
+
+        self.generation_config = GenerationConfig(
+            temperature=temperature,
+            top_p=1.0,
+            top_k=10,
+            candidate_count=1,
+            max_output_tokens=max_output,
+        )
+
+        # Block none doesn't seem to work
+        block_level = HarmBlockThreshold.BLOCK_ONLY_HIGH
+        #     block_level = HarmBlockThreshold.BLOCK_NONE
+
+        self.safety_settings = {
+            HarmCategory.HARM_CATEGORY_HARASSMENT: block_level,
+            HarmCategory.HARM_CATEGORY_HATE_SPEECH: block_level,
+            HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: block_level,
+            HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: block_level,
+        }
+
+        print("Initialise VertexAI...", flush=True)
+
+        if private_key:
+            credentials = service_account.Credentials.from_service_account_file(private_key)
+        else:
+            credentials = None
+
+        if credentials:
+            vertexai.init(
+                location=region,
+                credentials=credentials,
+                project=credentials.project_id,
+            )
+        else:
+            vertexai.init(
+                location=region
+            )
+
+        print(f"Initialise model {model}", flush=True)
+        self.llm = GenerativeModel(model)
+        self.model = model
+
+        print("Initialisation complete", flush=True)
+
+    def handle(self, msg):
+
+        try:
+
+            v = msg.value()
+
+            # Sender-produced ID
+
+            id = msg.properties()["id"]
+
+            print(f"Handling prompt {id}...", flush=True)
+
+            prompt = v.prompt
+
+            with __class__.text_completion_metric.time():
+
+                response = self.llm.generate_content(
+                    prompt, generation_config=self.generation_config,
+                    safety_settings=self.safety_settings
+                )
+
+            resp = response.text
+            inputtokens = int(response.usage_metadata.prompt_token_count)
+            outputtokens = int(response.usage_metadata.candidates_token_count)
+            print(resp, 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=resp,
+                in_token=inputtokens,
+                out_token=outputtokens,
+                model=self.model
+            )
+
+            self.producer.send(r, properties={"id": id})
+
+            print("Done.", flush=True)
+
+            # Acknowledge successful processing of the message
+            self.consumer.acknowledge(msg)
+
+        except google.api_core.exceptions.ResourceExhausted as e:
+
+            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.producer.send(r, properties={"id": id})
+
+            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=default_model,
+            help=f'LLM model (default: {default_model})'
+        )
+        # Also: text-bison-32k
+
+        parser.add_argument(
+            '-k', '--private-key',
+            help=f'Google Cloud private JSON file'
+        )
+
+        parser.add_argument(
+            '-r', '--region',
+            default=default_region,
+            help=f'Google Cloud region (default: {default_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__)
+