Merge branch 'main' into main

libs/community/langchain_google_community/bq_storage_vectorstores/bigquery.py

@@ -140,6 +140,9 @@ def initialize_bq_vector_index(cls, values: dict) -> dict:
             values["_logger"].debug("Not enough rows to create a vector index.")
             return values
 
+        if "_last_index_check" not in values:
+            values["_last_index_check"] = datetime.min
+
         if datetime.utcnow() - values["_last_index_check"] < INDEX_CHECK_INTERVAL:
             return values
 

libs/community/langchain_google_community/gmail/create_draft.py

@@ -24,11 +24,11 @@ class CreateDraftSchema(BaseModel):
         description="The subject of the message.",
     )
     cc: Optional[List[str]] = Field(
-        None,
+        default=None,
         description="The list of CC recipients.",
     )
     bcc: Optional[List[str]] = Field(
-        None,
+        default=None,
         description="The list of BCC recipients.",
     )
 

libs/community/langchain_google_community/gmail/send_message.py

@@ -27,11 +27,11 @@ class SendMessageSchema(BaseModel):
         description="The subject of the message.",
     )
     cc: Optional[Union[str, List[str]]] = Field(
-        None,
+        default=None,
         description="The list of CC recipients.",
     )
     bcc: Optional[Union[str, List[str]]] = Field(
-        None,
+        default=None,
         description="The list of BCC recipients.",
     )
 

libs/community/langchain_google_community/places_api.py

@@ -30,7 +30,7 @@ class GooglePlacesAPIWrapper(BaseModel):
     """
 
     gplaces_api_key: Optional[str] = None
-    google_map_client: Any  #: :meta private:
+    google_map_client: Any = None  #: :meta private:
     top_k_results: Optional[int] = None
 
     class Config:

libs/community/langchain_google_community/search.py

@@ -47,7 +47,7 @@ class GoogleSearchAPIWrapper(BaseModel):
 
     """
 
-    search_engine: Any  #: :meta private:
+    search_engine: Any = None  #: :meta private:
     google_api_key: Optional[str] = None
     google_cse_id: Optional[str] = None
     k: int = 10

libs/genai/langchain_google_genai/chat_models.py

@@ -786,9 +786,9 @@ class Joke(BaseModel):
 
     """  # noqa: E501
 
-    client: Any  #: :meta private:
-    async_client: Any  #: :meta private:
-    google_api_key: Optional[SecretStr] = Field(None, alias="api_key")
+    client: Any = None  #: :meta private:
+    async_client: Any = None  #: :meta private:
+    google_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
     """Google AI API key. 
         
     If not specified will be read from env var ``GOOGLE_API_KEY``."""
@@ -965,9 +965,18 @@ async def _agenerate(
         **kwargs: Any,
     ) -> ChatResult:
         if not self.async_client:
-            raise RuntimeError(
-                "Initialize ChatGoogleGenerativeAI with a running event loop "
-                "to use async methods."
+            updated_kwargs = {
+                **kwargs,
+                **{
+                    "tools": tools,
+                    "functions": functions,
+                    "safety_settings": safety_settings,
+                    "tool_config": tool_config,
+                    "generation_config": generation_config,
+                },
+            }
+            return await super()._agenerate(
+                messages, stop, run_manager, **updated_kwargs
             )
 
         request = self._prepare_request(
@@ -1036,27 +1045,43 @@ async def _astream(
         generation_config: Optional[Dict[str, Any]] = None,
         **kwargs: Any,
     ) -> AsyncIterator[ChatGenerationChunk]:
-        request = self._prepare_request(
-            messages,
-            stop=stop,
-            tools=tools,
-            functions=functions,
-            safety_settings=safety_settings,
-            tool_config=tool_config,
-            generation_config=generation_config,
-        )
-        async for chunk in await _achat_with_retry(
-            request=request,
-            generation_method=self.async_client.stream_generate_content,
-            **kwargs,
-            metadata=self.default_metadata,
-        ):
-            _chat_result = _response_to_result(chunk, stream=True)
-            gen = cast(ChatGenerationChunk, _chat_result.generations[0])
+        if not self.async_client:
+            updated_kwargs = {
+                **kwargs,
+                **{
+                    "tools": tools,
+                    "functions": functions,
+                    "safety_settings": safety_settings,
+                    "tool_config": tool_config,
+                    "generation_config": generation_config,
+                },
+            }
+            async for value in super()._astream(
+                messages, stop, run_manager, **updated_kwargs
+            ):
+                yield value
+        else:
+            request = self._prepare_request(
+                messages,
+                stop=stop,
+                tools=tools,
+                functions=functions,
+                safety_settings=safety_settings,
+                tool_config=tool_config,
+                generation_config=generation_config,
+            )
+            async for chunk in await _achat_with_retry(
+                request=request,
+                generation_method=self.async_client.stream_generate_content,
+                **kwargs,
+                metadata=self.default_metadata,
+            ):
+                _chat_result = _response_to_result(chunk, stream=True)
+                gen = cast(ChatGenerationChunk, _chat_result.generations[0])
 
-            if run_manager:
-                await run_manager.on_llm_new_token(gen.text)
-            yield gen
+                if run_manager:
+                    await run_manager.on_llm_new_token(gen.text)
+                yield gen
 
     def _prepare_request(
         self,

libs/genai/langchain_google_genai/embeddings.py

@@ -39,20 +39,20 @@ class GoogleGenerativeAIEmbeddings(BaseModel, Embeddings):
             embeddings.embed_query("What's our Q1 revenue?")
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
     model: str = Field(
         ...,
         description="The name of the embedding model to use. "
         "Example: models/embedding-001",
     )
     task_type: Optional[str] = Field(
-        None,
+        default=None,
         description="The task type. Valid options include: "
         "task_type_unspecified, retrieval_query, retrieval_document, "
         "semantic_similarity, classification, and clustering",
     )
     google_api_key: Optional[SecretStr] = Field(
-        None,
+        default=None,
         description="The Google API key to use. If not provided, "
         "the GOOGLE_API_KEY environment variable will be used.",
     )
@@ -64,18 +64,18 @@ class GoogleGenerativeAIEmbeddings(BaseModel, Embeddings):
         "provided, credentials will be ascertained from the GOOGLE_API_KEY envvar",
     )
     client_options: Optional[Dict] = Field(
-        None,
+        default=None,
         description=(
             "A dictionary of client options to pass to the Google API client, "
             "such as `api_endpoint`."
         ),
     )
     transport: Optional[str] = Field(
-        None,
+        default=None,
         description="A string, one of: [`rest`, `grpc`, `grpc_asyncio`].",
     )
     request_options: Optional[Dict] = Field(
-        None,
+        default=None,
         description="A dictionary of request options to pass to the Google API client."
         "Example: `{'timeout': 10}`",
     )

libs/genai/langchain_google_genai/llms.py

@@ -149,18 +149,18 @@ class _BaseGoogleGenerativeAI(BaseModel):
     """The maximum number of seconds to wait for a response."""
 
     client_options: Optional[Dict] = Field(
-        None,
+        default=None,
         description=(
             "A dictionary of client options to pass to the Google API client, "
             "such as `api_endpoint`."
         ),
     )
     transport: Optional[str] = Field(
-        None,
+        default=None,
         description="A string, one of: [`rest`, `grpc`, `grpc_asyncio`].",
     )
     additional_headers: Optional[Dict[str, str]] = Field(
-        None,
+        default=None,
         description=(
             "A key-value dictionary representing additional headers for the model call"
         ),
@@ -212,7 +212,7 @@ class GoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseLLM):
             llm = GoogleGenerativeAI(model="gemini-pro")
     """
 
-    client: Any  #: :meta private:
+    client: Any = None  #: :meta private:
 
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:

libs/genai/tests/integration_tests/test_chat_models.py

@@ -1,7 +1,7 @@
 """Test ChatGoogleGenerativeAI chat model."""
-
+import asyncio
 import json
-from typing import Generator, Optional, Type
+from typing import Generator, List, Optional, Type
 
 import pytest
 from langchain_core.language_models import BaseChatModel
@@ -465,3 +465,28 @@ class MyModel(BaseModel):
         }
     ]
     assert response == expected
+
+
+def test_ainvoke_without_eventloop() -> None:
+    model = ChatGoogleGenerativeAI(model="gemini-1.5-flash-001")
+
+    async def model_ainvoke(context: str) -> BaseMessage:
+        result = await model.ainvoke(context)
+        return result
+
+    result = asyncio.run(model_ainvoke("How can you help me?"))
+    assert isinstance(result, AIMessage)
+
+
+def test_astream_without_eventloop() -> None:
+    model = ChatGoogleGenerativeAI(model="gemini-1.5-flash-001")
+
+    async def model_astream(context: str) -> List[BaseMessageChunk]:
+        result = []
+        async for chunk in model.astream(context):
+            result.append(chunk)
+        return result
+
+    result = asyncio.run(model_astream("How can you help me?"))
+    assert len(result) > 0
+    assert isinstance(result[0], AIMessageChunk)

libs/vertexai/langchain_google_vertexai/_base.py

@@ -72,7 +72,7 @@ class _VertexAIBase(BaseModel):
     client_options: Optional["ClientOptions"] = Field(
         default=None, exclude=True
     )  #: :meta private:
-    api_endpoint: Optional[str] = Field(None, alias="base_url")
+    api_endpoint: Optional[str] = Field(default=None, alias="base_url")
     "Desired API endpoint, e.g., us-central1-aiplatform.googleapis.com"
     api_transport: Optional[str] = None
     """The desired API transport method, can be either 'grpc' or 'rest'. 

libs/vertexai/langchain_google_vertexai/model_garden.py

@@ -16,6 +16,7 @@
     Union,
 )
 
+from google.auth.credentials import Credentials
 from langchain_core.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
@@ -134,6 +135,7 @@ class ChatAnthropicVertex(_VertexAICommon, BaseChatModel):
     max_output_tokens: int = Field(default=1024, alias="max_tokens")
     access_token: Optional[str] = None
     stream_usage: bool = True  # Whether to include usage metadata in streaming output
+    credentials: Optional[Credentials] = None
 
     class Config:
         """Configuration for this pydantic object."""
@@ -156,12 +158,14 @@ def validate_environment(cls, values: Dict) -> Dict:
             region=values["location"],
             max_retries=values["max_retries"],
             access_token=values["access_token"],
+            credentials=values["credentials"],
         )
         values["async_client"] = AsyncAnthropicVertex(
             project_id=values["project"],
             region=values["location"],
             max_retries=values["max_retries"],
             access_token=values["access_token"],
+            credentials=values["credentials"],
         )
         return values
 

libs/vertexai/pyproject.toml

@@ -16,7 +16,7 @@ langchain-core = ">=0.2.9,<0.3"
 google-cloud-aiplatform = "^1.56.0"
 google-cloud-storage = "^2.17.0"
 # optional dependencies
-anthropic = { extras = ["vertexai"], version = ">=0.29.0,<1", optional = true }
+anthropic = { extras = ["vertexai"], version = ">=0.30.0,<1", optional = true }
 
 [tool.poetry.group.test]
 optional = true

libs/vertexai/tests/integration_tests/.env.example

@@ -2,4 +2,9 @@ PROJECT_ID=projecy_id
 FALCON_ENDPOINT_ID=falcon_endpoint_id
 GEMMA_ENDPOINT_ID=gemma_endpoint_id
 LLAMA_ENDPOINT_ID=llama_endpoint_id
-IMAGE_GCS_PATH=image_gcs_path
+IMAGE_GCS_PATH=image_gcs_path
+VECTOR_SEARCH_STAGING_BUCKET=VECTOR_SEARCH_STAGING_BUCKET
+VECTOR_SEARCH_STREAM_INDEX_ID=VECTOR_SEARCH_STREAM_INDEX_ID
+VECTOR_SEARCH_STREAM_ENDPOINT_ID=VECTOR_SEARCH_STREAM_ENDPOINT_ID
+VECTOR_SEARCH_BATCH_INDEX_ID=VECTOR_SEARCH_BATCH_INDEX_ID
+VECTOR_SEARCH_BATCH_ENDPOINT_ID=VECTOR_SEARCH_BATCH_ENDPOINT_ID

libs/vertexai/tests/integration_tests/terraform/main.tf

@@ -9,6 +9,11 @@ module "cloudbuild" {
     GEMMA_ENDPOINT_ID  = "",
     LLAMA_ENDPOINT_ID  = "",
     IMAGE_GCS_PATH     = "",
+    VECTOR_SEARCH_STAGING_BUCKET="",
+    VECTOR_SEARCH_STREAM_INDEX_ID="",
+    VECTOR_SEARCH_STREAM_ENDPOINT_ID="",
+    VECTOR_SEARCH_BATCH_INDEX_ID="",
+    VECTOR_SEARCH_BATCH_ENDPOINT_ID="",
   }
   cloudbuild_secret_vars = {
     GOOGLE_API_KEY = ""