REST API implementation for VertexAI Gemini models.

PiperOrigin-RevId: 697802489

langfun/core/llms/__init__.py

@@ -120,6 +120,8 @@
 from langfun.core.llms.groq import GroqWhisper_Large_v3Turbo
 
 from langfun.core.llms.vertexai import VertexAI
+from langfun.core.llms.vertexai import VertexRestfulAI
+from langfun.core.llms.vertexai import VertexRestfulAIGemini1_5
 from langfun.core.llms.vertexai import VertexAIGemini1_5
 from langfun.core.llms.vertexai import VertexAIGeminiPro1_5
 from langfun.core.llms.vertexai import VertexAIGeminiPro1_5_Latest
@@ -134,6 +136,7 @@
 from langfun.core.llms.vertexai import VertexAIGeminiFlash1_5_0514
 from langfun.core.llms.vertexai import VertexAIGeminiPro1
 from langfun.core.llms.vertexai import VertexAIGeminiPro1Vision
+from langfun.core.llms.vertexai import VertexAIGeminiPro1Vision_001
 from langfun.core.llms.vertexai import VertexAIPalm2
 from langfun.core.llms.vertexai import VertexAIPalm2_32K
 from langfun.core.llms.vertexai import VertexAICustom

langfun/core/llms/rest.py

@@ -26,7 +26,7 @@ class REST(lf.LanguageModel):
   api_endpoint: Annotated[
       str,
       'The endpoint of the REST API.'
-  ]
+  ] = ''
 
   request: Annotated[
       Callable[[lf.Message, lf.LMSamplingOptions], dict[str, Any]],

langfun/core/llms/vertexai.py

@@ -19,11 +19,14 @@
 
 import langfun.core as lf
 from langfun.core import modalities as lf_modalities
+from langfun.core.llms import rest
 import pyglove as pg
 
 try:
   # pylint: disable=g-import-not-at-top
+  from google import auth as google_auth
   from google.auth import credentials as credentials_lib
+  from google.auth.transport import requests as auth_requests
   import vertexai
   from google.cloud.aiplatform import models as aiplatform_models
   from vertexai import generative_models
@@ -32,6 +35,8 @@
 
   Credentials = credentials_lib.Credentials
 except ImportError:
+  google_auth = None
+  auth_requests = None
   credentials_lib = None   # pylint: disable=invalid-name
   vertexai = None
   generative_models = None
@@ -127,6 +132,12 @@
         cost_per_1k_input_chars=0.000125,
         cost_per_1k_output_chars=0.000375,
     ),
+    'gemini-1.0-pro-vision-001': pg.Dict(
+        api='gemini',
+        rpm=100,
+        cost_per_1k_input_chars=0.000125,
+        cost_per_1k_output_chars=0.000375,
+    ),
     # PaLM APIs.
     'text-bison': pg.Dict(
         api='palm',
@@ -449,6 +460,239 @@ def _sample_endpoint_model(self, prompt: lf.Message) -> lf.LMSamplingResult:
     ])
 
 
+@lf.use_init_args(['model'])
+class VertexRestfulAI(rest.REST):
+  """Language model served on VertexAI with REST API."""
+
+  model: pg.typing.Annotated[
+      pg.typing.Enum(
+          pg.MISSING_VALUE, list(SUPPORTED_MODELS_AND_SETTINGS.keys())
+      ),
+      (
+          'Vertex AI model name with REST API support. See '
+          'https://cloud.google.com/vertex-ai/generative-ai/docs/'
+          'model-reference/inference#supported-models'
+          ' for details.'
+      ),
+  ]
+
+  project: Annotated[
+      str | None,
+      (
+          'Vertex AI project ID. Or set from environment variable '
+          'VERTEXAI_PROJECT.'
+      ),
+  ] = None
+
+  location: Annotated[
+      str | None,
+      (
+          'Vertex AI service location. Or set from environment variable '
+          'VERTEXAI_LOCATION.'
+      ),
+  ] = None
+
+  credentials: Annotated[
+      Credentials | None,
+      (
+          'Credentials to use. If None, the default credentials to the '
+          'environment will be used.'
+      ),
+  ] = None
+
+  supported_modalities: Annotated[
+      list[str],
+      'A list of MIME types for supported modalities'
+  ] = []
+
+  def _on_bound(self):
+    super()._on_bound()
+    if google_auth is None:
+      raise ValueError(
+          'Please install "langfun[llm-google-vertex]" to use Vertex AI models.'
+      )
+    self._project = None
+    self._credentials = None
+
+  def _initialize(self):
+    project = self.project or os.environ.get('VERTEXAI_PROJECT', None)
+    if not project:
+      raise ValueError(
+          'Please specify `project` during `__init__` or set environment '
+          'variable `VERTEXAI_PROJECT` with your Vertex AI project ID.'
+      )
+
+    location = self.location or os.environ.get('VERTEXAI_LOCATION', None)
+    if not location:
+      raise ValueError(
+          'Please specify `location` during `__init__` or set environment '
+          'variable `VERTEXAI_LOCATION` with your Vertex AI service location.'
+      )
+
+    self._project = project
+    credentials = self.credentials
+    if credentials is None:
+      # Use default credentials.
+      credentials = google_auth.default(
+          scopes=['https://www.googleapis.com/auth/cloud-platform']
+      )
+    self._credentials = credentials
+
+  @property
+  def max_concurrency(self) -> int:
+    """Returns the maximum number of concurrent requests."""
+    return self.rate_to_max_concurrency(
+        requests_per_min=SUPPORTED_MODELS_AND_SETTINGS[self.model].rpm,
+        tokens_per_min=0,
+    )
+
+  def estimate_cost(
+      self,
+      num_input_tokens: int,
+      num_output_tokens: int
+  ) -> float | None:
+    """Estimate the cost based on usage."""
+    cost_per_1k_input_chars = SUPPORTED_MODELS_AND_SETTINGS[self.model].get(
+        'cost_per_1k_input_chars', None
+    )
+    cost_per_1k_output_chars = SUPPORTED_MODELS_AND_SETTINGS[self.model].get(
+        'cost_per_1k_output_chars', None
+    )
+    if cost_per_1k_output_chars is None or cost_per_1k_input_chars is None:
+      return None
+    return (
+        cost_per_1k_input_chars * num_input_tokens
+        + cost_per_1k_output_chars * num_output_tokens
+    ) * AVGERAGE_CHARS_PER_TOEKN / 1000
+
+  @functools.cached_property
+  def _session(self):
+    assert self._api_initialized
+    assert self._credentials is not None
+    assert auth_requests is not None
+    s = auth_requests.AuthorizedSession(self._credentials)
+    s.headers.update(self.headers or {})
+    return s
+
+  @property
+  def headers(self):
+    return {
+        'Content-Type': 'application/json; charset=utf-8',
+    }
+
+  @property
+  def api_endpoint(self) -> str:
+    return (
+        f'https://{self.location}-aiplatform.googleapis.com/v1/projects/'
+        f'{self.project}/locations/{self.location}/publishers/google/'
+        f'models/{self.model}:generateContent'
+    )
+
+  def request(
+      self, prompt: lf.Message, sampling_options: lf.LMSamplingOptions
+  ) -> dict[str, Any]:
+    request = dict(
+        generationConfig=self._generation_config(prompt, sampling_options)
+    )
+    request['contents'] = [self._content_from_message(prompt)]
+    return request
+
+  def _generation_config(
+      self, prompt: lf.Message, options: lf.LMSamplingOptions
+  ) -> dict[str, Any]:
+    """Returns a dict as generation config for prompt and LMSamplingOptions."""
+    config = dict(
+        temperature=options.temperature,
+        maxOutputTokens=options.max_tokens,
+        candidateCount=options.n,
+        topK=options.top_k,
+        topP=options.top_p,
+        stopSequences=options.stop,
+        seed=options.random_seed,
+        responseLogprobs=options.logprobs,
+        logprobs=options.top_logprobs,
+    )
+
+    if json_schema := prompt.metadata.get('json_schema'):
+      if not isinstance(json_schema, dict):
+        raise ValueError(
+            f'`json_schema` must be a dict, got {json_schema!r}.'
+        )
+      json_schema = pg.to_json(json_schema)
+      config['responseSchema'] = json_schema
+      config['responseMimeType'] = 'application/json'
+      prompt.metadata.formatted_text = (
+          prompt.text
+          + '\n\n [RESPONSE FORMAT (not part of prompt)]\n'
+          + pg.to_json_str(json_schema, json_indent=2)
+      )
+    return config
+
+  def _content_from_message(self, prompt: lf.Message) -> dict[str, Any]:
+    """Gets generation content from langfun message."""
+    parts = []
+    for lf_chunk in prompt.chunk():
+      if isinstance(lf_chunk, str):
+        parts.append({'text': lf_chunk})
+      elif isinstance(lf_chunk, lf_modalities.Mime):
+        try:
+          modalities = lf_chunk.make_compatible(
+              self.supported_modalities + ['text/plain']
+          )
+          if isinstance(modalities, lf_modalities.Mime):
+            modalities = [modalities]
+          for modality in modalities:
+            if modality.is_text:
+              parts.append({'text': modality.to_text()})
+            else:
+              parts.append({
+                  'bytes': modality.to_bytes(),
+                  'mimeType': modality.mime_type,
+              })
+        except lf.ModalityError as e:
+          raise lf.ModalityError(f'Unsupported modality: {lf_chunk!r}') from e
+      else:
+        raise lf.ModalityError(f'Unsupported modality: {lf_chunk!r}')
+    return dict(role='user', parts=parts)
+
+  def result(self, json: dict[str, Any]) -> lf.LMSamplingResult:
+    messages = [
+        self._message_from_content_parts(candidate['content']['parts'])
+        for candidate in json['candidates']
+    ]
+    usage = json['usageMetadata']
+    input_tokens = usage['promptTokenCount']
+    output_tokens = usage['candidatesTokenCount']
+    return lf.LMSamplingResult(
+        [lf.LMSample(message) for message in messages],
+        usage=lf.LMSamplingUsage(
+            prompt_tokens=input_tokens,
+            completion_tokens=output_tokens,
+            total_tokens=input_tokens + output_tokens,
+            estimated_cost=self.estimate_cost(
+                num_input_tokens=input_tokens,
+                num_output_tokens=output_tokens,
+            ),
+        ),
+    )
+
+  def _message_from_content_parts(
+      self, parts: list[dict[str, Any]]
+  ) -> lf.Message:
+    """Converts Vertex AI's content parts protocol to message."""
+    chunks = []
+    for part in parts:
+      if text_part := part.get('text'):
+        chunks.append(text_part)
+      elif inline_part := part.get('inlineData'):
+        chunks.append(
+            lf_modalities.Mime(inline_part['data'], inline_part['mimeType'])
+        )
+      else:
+        raise ValueError(f'Unsupported part: {part}')
+    return lf.AIMessage.from_chunks(chunks)
+
+
 class _ModelHub:
   """Vertex AI model hub."""
 
@@ -547,13 +791,21 @@ class VertexAIGeminiPro1_5(VertexAIGemini1_5):  # pylint: disable=invalid-name
   model = 'gemini-1.5-pro'
 
 
-class VertexAIGeminiPro1_5_002(VertexAIGemini1_5):  # pylint: disable=invalid-name
+class VertexRestfulAIGemini1_5(VertexRestfulAI):  # pylint: disable=invalid-name
+  """Vertex AI Gemini 1.5 model with REST API."""
+
+  supported_modalities: pg.typing.List(str).freeze(   # pytype: disable=invalid-annotation
+      _DOCUMENT_TYPES + _IMAGE_TYPES + _AUDIO_TYPES + _VIDEO_TYPES
+  )
+
+
+class VertexAIGeminiPro1_5_002(VertexRestfulAIGemini1_5):  # pylint: disable=invalid-name
   """Vertex AI Gemini 1.5 Pro model."""
 
   model = 'gemini-1.5-pro-002'
 
 
-class VertexAIGeminiPro1_5_001(VertexAIGemini1_5):  # pylint: disable=invalid-name
+class VertexAIGeminiPro1_5_001(VertexRestfulAIGemini1_5):  # pylint: disable=invalid-name
   """Vertex AI Gemini 1.5 Pro model."""
 
   model = 'gemini-1.5-pro-001'
@@ -583,13 +835,13 @@ class VertexAIGeminiFlash1_5(VertexAIGemini1_5):  # pylint: disable=invalid-name
   model = 'gemini-1.5-flash'
 
 
-class VertexAIGeminiFlash1_5_002(VertexAIGemini1_5):  # pylint: disable=invalid-name
+class VertexAIGeminiFlash1_5_002(VertexRestfulAIGemini1_5):  # pylint: disable=invalid-name
   """Vertex AI Gemini 1.5 Flash model."""
 
   model = 'gemini-1.5-flash-002'
 
 
-class VertexAIGeminiFlash1_5_001(VertexAIGemini1_5):  # pylint: disable=invalid-name
+class VertexAIGeminiFlash1_5_001(VertexRestfulAIGemini1_5):  # pylint: disable=invalid-name
   """Vertex AI Gemini 1.5 Flash model."""
 
   model = 'gemini-1.5-flash-001'
@@ -601,21 +853,30 @@ class VertexAIGeminiFlash1_5_0514(VertexAIGemini1_5):  # pylint: disable=invalid
   model = 'gemini-1.5-flash-preview-0514'
 
 
-class VertexAIGeminiPro1(VertexAI):  # pylint: disable=invalid-name
+class VertexAIGeminiPro1(VertexRestfulAI):  # pylint: disable=invalid-name
   """Vertex AI Gemini 1.0 Pro model."""
 
   model = 'gemini-1.0-pro'
 
 
 class VertexAIGeminiPro1Vision(VertexAI):  # pylint: disable=invalid-name
-  """Vertex AI Gemini 1.0 Pro model."""
+  """Vertex AI Gemini 1.0 Pro Vision model."""
 
   model = 'gemini-1.0-pro-vision'
   supported_modalities: pg.typing.List(str).freeze(   # pytype: disable=invalid-annotation
       _IMAGE_TYPES + _VIDEO_TYPES
   )
 
 
+class VertexAIGeminiPro1Vision_001(VertexRestfulAI):  # pylint: disable=invalid-name
+  """Vertex AI Gemini 1.0 Pro Vision model with REST API."""
+
+  model = 'gemini-1.0-pro-vision-001'
+  supported_modalities: pg.typing.List(str).freeze(  # pytype: disable=invalid-annotation
+      _IMAGE_TYPES + _VIDEO_TYPES
+  )
+
+
 class VertexAIPalm2(VertexAI):  # pylint: disable=invalid-name
   """Vertex AI PaLM2 text generation model."""