chore(deps): update all non-major dependencies

pyproject.toml

@@ -45,14 +45,14 @@ langgraph = [
 test = [
     "black[jupyter]==25.12.0",
     "isort==7.0.0",
-    "langgraph==1.0.4",
+    "langgraph==1.0.5",
     "mypy==1.19.1",
     "pytest-asyncio==0.26.0",
     "pytest==8.4.2",
     "pytest-cov==7.0.0",
     "pytest-depends==1.0.1",
     "Pillow==12.0.0",
-    "langchain-tests==1.1.0"
+    "langchain-tests==1.1.1"
 ]
 
 

requirements.txt

@@ -2,5 +2,5 @@ google-cloud-alloydb-connector[asyncpg]==1.11.0
 google-cloud-storage==3.7.0
 numpy==2.3.5; python_version >= "3.11"
 numpy==2.2.6; python_version == "3.10"
-langgraph==1.0.4
+langgraph==1.0.5
 langchain-postgres==0.0.16

samples/langchain_on_vertexai/prebuilt_langchain_agent_template.py

@@ -27,7 +27,7 @@
 )
 from langchain_core.documents import Document
 from langchain_google_vertexai import VertexAIEmbeddings
-from vertexai.preview import reasoning_engines  # type: ignore
+from vertexai import agent_engines
 
 from langchain_google_alloydb_pg import AlloyDBEngine, AlloyDBVectorStore
 
@@ -75,7 +75,7 @@ def similarity_search(query: str) -> list[Document]:
 
 # Uncomment to test locally
 
-# app = reasoning_engines.LangchainAgent(
+# app = agent_engines.LangchainAgent(
 #     model="gemini-2.0-flash-001",
 #     tools=[similarity_search],
 #     model_kwargs={
@@ -87,22 +87,24 @@ def similarity_search(query: str) -> list[Document]:
 
 # Initialize VertexAI
 vertexai.init(project=PROJECT_ID, location="us-central1", staging_bucket=STAGING_BUCKET)
+client = vertexai.Client(project=PROJECT_ID, location="us-central1")
 
 # Deploy to VertexAI
 DISPLAY_NAME = os.getenv("DISPLAY_NAME") or "PrebuiltAgent"
 
-remote_app = reasoning_engines.ReasoningEngine.create(
-    reasoning_engines.LangchainAgent(
+remote_app = client.agent_engines.create(
+    agent=agent_engines.LangchainAgent(
         model="gemini-2.0-flash-001",
         tools=[similarity_search],  # type: ignore[list-item]
         model_kwargs={
             "temperature": 0.1,
         },
     ),
-    requirements="requirements.txt",
-    display_name="PrebuiltAgent",
-    sys_version="3.11",
-    extra_packages=["config.py"],
+    config={
+        "requirements": "requirements.txt",
+        "extra_packages": ["config.py"],
+        "display_name": "PrebuiltAgent"
+    }
 )  # type: ignore[arg-type]
 
 print(remote_app.query(input="movies about engineers"))  # type: ignore[attr-defined]

samples/langchain_on_vertexai/retriever_agent_with_history_template.py

@@ -135,16 +135,19 @@ def set_up(self):
             history_messages_key="chat_history",
         )
 
-    def query(self, input: str, session_id: str, **kwargs: Any) -> str:
+    def query(self, **kwargs: Any) -> str:
         """Query the application.
 
         Args:
-            input: The user query.
-            session_id: The user's session id.
+            **kwargs: Keyword arguments. Expects "input" and "session_id".
 
         Returns:
             The LLM response dictionary.
         """
+        input = kwargs.get("input")
+        session_id = kwargs.get("session_id")
+        if not input or not session_id:
+            return "Please provide an input and a session_id."
         response = self.agent.invoke(
             {"input": input},
             config={"configurable": {"session_id": session_id}},

samples/langchain_on_vertexai/retriever_chain_template.py

@@ -109,17 +109,19 @@ def set_up(self):
         # an LLM to generate a response
         self.chain = create_retrieval_chain(retriever, combine_docs_chain)
 
-    def query(self, input: str, **kwargs: Any) -> str:
+    def query(self, **kwargs: Any) -> str:
         """Query the application.
 
         Args:
-            input: The user query.
-            **kwargs: Additional arguments for Protocol compliance.
+            **kwargs: Keyword arguments. Expects a key "input" with the user query.
 
         Returns:
             The LLM response dictionary.
         """
         # Define the runtime logic that serves user queries
+        input = kwargs.get("input")
+        if not input:
+            return "Please provide an input."
         response = self.chain.invoke({"input": input})
         return response["answer"]
 

samples/requirements.txt

@@ -1,4 +1,4 @@
-google-cloud-aiplatform[reasoningengine,langchain]==1.97.0
+google-cloud-aiplatform[reasoningengine,langchain]==1.132.0
 google-cloud-resource-manager==1.15.0
 langchain-community==0.3.31
 langchain-google-alloydb-pg==0.13.0