fixed gemma_hf

libs/vertexai/langchain_google_vertexai/__init__.py

@@ -3,6 +3,7 @@
 from langchain_google_vertexai.chat_models import ChatVertexAI
 from langchain_google_vertexai.functions_utils import PydanticFunctionsOutputParser
 from langchain_google_vertexai.gemma import (
+    GemmaChatLocalHF,
     GemmaChatLocalKaggle,
     GemmaChatVertexAIModelGarden,
     GemmaLocalHF,
@@ -12,6 +13,13 @@
 from langchain_google_vertexai.llms import VertexAI
 from langchain_google_vertexai.model_garden import VertexAIModelGarden
 from langchain_google_vertexai.vectorstores.vectorstores import VectorSearchVectorStore
+from langchain_google_vertexai.vision_models import (
+    VertexAIImageCaptioning,
+    VertexAIImageCaptioningChat,
+    VertexAIImageEditorChat,
+    VertexAIImageGeneratorChat,
+    VertexAIVisualQnAChat,
+)
 
 __all__ = [
     "ChatVertexAI",
@@ -29,4 +37,9 @@
     "PydanticFunctionsOutputParser",
     "create_structured_runnable",
     "VectorSearchVectorStore",
+    "VertexAIImageCaptioning",
+    "VertexAIImageCaptioningChat",
+    "VertexAIImageEditorChat",
+    "VertexAIImageGeneratorChat",
+    "VertexAIVisualQnAChat",
 ]

libs/vertexai/langchain_google_vertexai/gemma.py

@@ -197,7 +197,7 @@ def _generate(
         """Run the LLM on the given prompt and input."""
         params = {"max_length": self.max_tokens} if self.max_tokens else {}
         results = self.client.generate(prompts, **params)
-        results = results if isinstance(results, str) else [results]
+        results = [results] if isinstance(results, str) else results
         if stop:
             results = [enforce_stop_tokens(text, stop) for text in results]
         return LLMResult(generations=[[Generation(text=result)] for result in results])
@@ -268,7 +268,7 @@ def _default_params(self) -> Dict[str, Any]:
         params = {"max_length": self.max_tokens}
         return {k: v for k, v in params.items() if v is not None}
 
-    def _run(self, prompt: str, kwargs: Any) -> str:
+    def _run(self, prompt: str, **kwargs: Any) -> str:
         inputs = self.tokenizer(prompt, return_tensors="pt")
         generate_ids = self.client.generate(inputs.input_ids, **kwargs)
         return self.tokenizer.batch_decode(

libs/vertexai/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain-google-vertexai"
-version = "0.0.6"
+version = "0.0.7"
 description = "An integration package connecting GoogleVertexAI and LangChain"
 authors = []
 readme = "README.md"

libs/vertexai/tests/unit_tests/test_imports.py

@@ -16,6 +16,11 @@
     "PydanticFunctionsOutputParser",
     "create_structured_runnable",
     "VectorSearchVectorStore",
+    "VertexAIImageCaptioning",
+    "VertexAIImageCaptioningChat",
+    "VertexAIImageEditorChat",
+    "VertexAIImageGeneratorChat",
+    "VertexAIVisualQnAChat",
 ]