fix null model meta references (#18109)

* fix null model meta references

* notebook

docs/docs/examples/llm/google_genai.ipynb

@@ -377,6 +377,9 @@
     "llm = GoogleGenAI(\n",
     "    model=\"gemini-2.0-flash\",\n",
     "    vertexai_config={\"project\": \"your-project-id\", \"location\": \"us-central1\"},\n",
+    "    # you should set the context window to the max input tokens for the model\n",
+    "    context_window=200000,\n",
+    "    max_tokens=512,\n",
     ")"
    ]
   },

llama-index-integrations/llms/llama-index-llms-google-genai/llama_index/llms/google_genai/base.py

@@ -33,7 +33,7 @@ from llama_index.core.base.llms.types import (
 )
 from llama_index.core.bridge.pydantic import BaseModel, Field, PrivateAttr
 from llama_index.core.callbacks import CallbackManager
-from llama_index.core.constants import DEFAULT_TEMPERATURE
+from llama_index.core.constants import DEFAULT_TEMPERATURE, DEFAULT_NUM_OUTPUTS
 from llama_index.core.llms.callbacks import llm_chat_callback, llm_completion_callback
 from llama_index.core.llms.function_calling import FunctionCallingLLM
 from llama_index.core.llms.llm import ToolSelection
@@ -88,8 +88,9 @@ class GoogleGenAI(FunctionCallingLLM):
         ge=0.0,
         le=2.0,
     )
-    generate_kwargs: dict = Field(
-        default_factory=dict, description="Kwargs for generation."
+    context_window: Optional[int] = Field(
+        default=None,
+        description="The context window of the model. If not provided, the default context window 200000 will be used.",
     )
     is_function_calling_model: bool = Field(
         default=True, description="Whether the model is a function calling model."
@@ -106,13 +107,14 @@ class GoogleGenAI(FunctionCallingLLM):
         api_key: Optional[str] = None,
         temperature: float = DEFAULT_TEMPERATURE,
         max_tokens: Optional[int] = None,
+        context_window: Optional[int] = None,
         vertexai_config: Optional[VertexAIConfig] = None,
         http_options: Optional[types.HttpOptions] = None,
         debug_config: Optional[google.genai.client.DebugConfig] = None,
         generation_config: Optional[types.GenerateContentConfig] = None,
         callback_manager: Optional[CallbackManager] = None,
         is_function_calling_model: bool = True,
-        **generate_kwargs: Any,
+        **kwargs: Any,
     ):
         # API keys are optional. The API can be authorised via OAuth (detected
         # environmentally) or by the GOOGLE_API_KEY environment variable.
@@ -149,18 +151,14 @@ class GoogleGenAI(FunctionCallingLLM):
 
         client = google.genai.Client(**config_params)
         model_meta = client.models.get(model=model)
-        if not max_tokens:
-            max_tokens = model_meta.output_token_limit
-        else:
-            max_tokens = min(max_tokens, model_meta.output_token_limit)
 
         super().__init__(
             model=model,
             temperature=temperature,
-            max_tokens=max_tokens,
-            generate_kwargs=generate_kwargs,
+            context_window=context_window,
             callback_manager=callback_manager,
             is_function_calling_model=is_function_calling_model,
+            **kwargs,
         )
 
         self.model = model
@@ -169,9 +167,16 @@ class GoogleGenAI(FunctionCallingLLM):
         # store this as a dict and not as a pydantic model so we can more easily
         # merge it later
         self._generation_config = (
-            generation_config.model_dump() if generation_config else {}
+            generation_config.model_dump()
+            if generation_config
+            else types.GenerateContentConfig(
+                temperature=temperature,
+                max_output_tokens=max_tokens,
+            ).model_dump()
+        )
+        self._max_tokens = (
+            max_tokens or model_meta.output_token_limit or DEFAULT_NUM_OUTPUTS
         )
-        self._max_tokens = max_tokens
 
     @classmethod
     def class_name(cls) -> str:
@@ -179,7 +184,12 @@ class GoogleGenAI(FunctionCallingLLM):
 
     @property
     def metadata(self) -> LLMMetadata:
-        total_tokens = (self._model_meta.input_token_limit or 0) + self._max_tokens
+        if self.context_window is None:
+            base = self._model_meta.input_token_limit or 200000
+            total_tokens = base + self._max_tokens
+        else:
+            total_tokens = self.context_window
+
         return LLMMetadata(
             context_window=total_tokens,
             num_output=self._max_tokens,

llama-index-integrations/llms/llama-index-llms-google-genai/pyproject.toml

@@ -27,7 +27,7 @@ exclude = ["**/BUILD"]
 license = "MIT"
 name = "llama-index-llms-google-genai"
 readme = "README.md"
-version = "0.1.2"
+version = "0.1.3"
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"