Merge branch 'release/v1.2'

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

@@ -1,7 +1,7 @@
-
 """
 Simple LLM service, performs text prompt completion using VertexAI on
 Google Cloud.   Input is prompt, output is response.
+Supports both Google's Gemini models and Anthropic's Claude models.
 """
 
 # 
@@ -17,7 +17,7 @@ Google Cloud.   Input is prompt, output is response.
 # This module's imports bring in a lot of libraries.
 
 from google.oauth2 import service_account
-import google
+import google.auth
 import vertexai
 import logging
 
@@ -27,6 +27,9 @@ from vertexai.generative_models import (
     HarmCategory, HarmBlockThreshold, Part, Tool, SafetySetting,
 )
 
+# Added for Anthropic model support
+from anthropic import AnthropicVertex, RateLimitError
+
 from .... exceptions import TooManyRequests
 from .... base import LlmService, LlmResult
 
@@ -35,7 +38,7 @@ logger = logging.getLogger(__name__)
 
 default_ident = "text-completion"
 
-default_model = 'gemini-2.0-flash-001'
+default_model = 'gemini-1.5-flash-001'
 default_region = 'us-central1'
 default_temperature = 0.0
 default_max_output = 8192
@@ -52,111 +55,148 @@ class Processor(LlmService):
         max_output = params.get("max_output", default_max_output)
 
         if private_key is None:
-            raise RuntimeError("Private key file not specified")
+            logger.warning("Private key file not specified, using Application Default Credentials")
 
         super(Processor, self).__init__(**params)
 
-        self.parameters = {
+        self.model = model
+        self.is_anthropic = 'claude' in self.model.lower()
+
+        # Shared parameters for both model types
+        self.api_params = {
             "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 = [
-            SafetySetting(
-                category = HarmCategory.HARM_CATEGORY_HARASSMENT,
-                threshold = block_level,
-            ),
-            SafetySetting(
-                category = HarmCategory.HARM_CATEGORY_HATE_SPEECH,
-                threshold = block_level,
-            ),
-            SafetySetting(
-                category = HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT,
-                threshold = block_level,
-            ),
-            SafetySetting(
-                category = HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
-                threshold = block_level,
-            ),
-        ]
-
         logger.info("Initializing VertexAI...")
 
+        # Unified credential and project ID loading
         if private_key:
             credentials = (
                 service_account.Credentials.from_service_account_file(
                     private_key
                 )
             )
+            project_id = credentials.project_id
         else:
-            credentials = None
+            credentials, project_id = google.auth.default()
 
-        if credentials:
-            vertexai.init(
-                location=region,
-                credentials=credentials,
-                project=credentials.project_id,
-            )
-        else:
-            vertexai.init(
-                location=region
+        if not project_id:
+            raise RuntimeError(
+                "Could not determine Google Cloud project ID. "
+                "Ensure it's set in your environment or service account."
             )
 
-        logger.info(f"Initializing model {model}")
-        self.llm = GenerativeModel(model)
-        self.model = model
+        # Initialize the appropriate client based on the model type
+        if self.is_anthropic:
+            logger.info(f"Initializing Anthropic model '{model}' via AnthropicVertex SDK")
+            # Initialize AnthropicVertex with credentials if provided, otherwise use ADC
+            anthropic_kwargs = {'region': region, 'project_id': project_id}
+            if credentials and private_key:  # Pass credentials only if from a file
+                anthropic_kwargs['credentials'] = credentials
+                logger.debug(f"Using service account credentials for Anthropic model")
+            else:
+                logger.debug(f"Using Application Default Credentials for Anthropic model")
+            
+            self.llm = AnthropicVertex(**anthropic_kwargs)
+        else:
+            # For Gemini models, initialize the Vertex AI SDK
+            logger.info(f"Initializing Google model '{model}' via Vertex AI SDK")
+            init_kwargs = {'location': region, 'project': project_id}
+            if credentials and private_key: # Pass credentials only if from a file
+                init_kwargs['credentials'] = credentials
+            
+            vertexai.init(**init_kwargs)
+
+            self.llm = GenerativeModel(model)
+
+            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 = [
+                SafetySetting(
+                    category = HarmCategory.HARM_CATEGORY_HARASSMENT,
+                    threshold = block_level,
+                ),
+                SafetySetting(
+                    category = HarmCategory.HARM_CATEGORY_HATE_SPEECH,
+                    threshold = block_level,
+                ),
+                SafetySetting(
+                    category = HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT,
+                    threshold = block_level,
+                ),
+                SafetySetting(
+                    category = HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
+                    threshold = block_level,
+                ),
+            ]
+
 
         logger.info("VertexAI initialization complete")
 
     async def generate_content(self, system, prompt):
 
         try:
+            if self.is_anthropic:
+                # Anthropic API uses a dedicated system prompt
+                logger.debug("Sending request to Anthropic model...")
+                response = self.llm.messages.create(
+                    model=self.model,
+                    system=system,
+                    messages=[{"role": "user", "content": prompt}],
+                    max_tokens=self.api_params['max_output_tokens'],
+                    temperature=self.api_params['temperature'],
+                    top_p=self.api_params['top_p'],
+                    top_k=self.api_params['top_k'],
+                )
 
-            prompt = system + "\n\n" + prompt
+                resp = LlmResult(
+                    text=response.content[0].text,
+                    in_token=response.usage.input_tokens,
+                    out_token=response.usage.output_tokens,
+                    model=self.model
+                )
+            else:
+                # Gemini API combines system and user prompts
+                logger.debug("Sending request to Gemini model...")
+                full_prompt = system + "\n\n" + prompt
 
-            response = self.llm.generate_content(
-                prompt, generation_config = self.generation_config,
-                safety_settings = self.safety_settings,
-            )
+                response = self.llm.generate_content(
+                    full_prompt, generation_config = self.generation_config,
+                    safety_settings = self.safety_settings,
+                )
 
-            resp = LlmResult(
-                text = response.text,
-                in_token = response.usage_metadata.prompt_token_count,
-                out_token = response.usage_metadata.candidates_token_count,
-                model = self.model
-            )
+                resp = LlmResult(
+                    text = response.text,
+                    in_token = response.usage_metadata.prompt_token_count,
+                    out_token = response.usage_metadata.candidates_token_count,
+                    model = self.model
+                )
 
             logger.info(f"Input Tokens: {resp.in_token}")
             logger.info(f"Output Tokens: {resp.out_token}")
-
             logger.debug("Send response...")
 
             return resp
 
-        except google.api_core.exceptions.ResourceExhausted as e:
-
+        except (google.api_core.exceptions.ResourceExhausted, RateLimitError) as e:
             logger.warning(f"Hit rate limit: {e}")
-
             # Leave rate limit retries to the base handler
             raise TooManyRequests()
 
         except Exception as e:
-
             # Apart from rate limits, treat all exceptions as unrecoverable
             logger.error(f"VertexAI LLM exception: {e}", exc_info=True)
             raise e
@@ -169,12 +209,12 @@ class Processor(LlmService):
         parser.add_argument(
             '-m', '--model',
             default=default_model,
-            help=f'LLM model (default: {default_model})'
+            help=f'LLM model (e.g., gemini-1.5-flash-001, claude-3-sonnet@20240229) (default: {default_model})'
         )
 
         parser.add_argument(
             '-k', '--private-key',
-            help=f'Google Cloud private JSON file'
+            help=f'Google Cloud private JSON file (optional, uses ADC if not provided)'
         )
 
         parser.add_argument(
@@ -199,4 +239,3 @@ class Processor(LlmService):
 
 def run():
     Processor.launch(default_ident, __doc__)
-