From 3351d8eb044b8bbb9d88dab26ed11a3739e9eb92 Mon Sep 17 00:00:00 2001 From: Mason Daugherty Date: Thu, 18 Sep 2025 10:53:23 -0400 Subject: [PATCH 01/23] chore: lower bound to Python 3.10, keep formatting py39 --- .../workflows/_compile_integration_test.yml | 1 - .github/workflows/_lint.yml | 2 +- .github/workflows/_test.yml | 1 - libs/community/pyproject.toml | 3 +- libs/community/uv.lock | 596 +----- libs/genai/pyproject.toml | 3 +- libs/genai/uv.lock | 400 +--- libs/vertexai/pyproject.toml | 3 +- libs/vertexai/uv.lock | 1650 +++++++---------- 9 files changed, 747 insertions(+), 1912 deletions(-) diff --git a/.github/workflows/_compile_integration_test.yml b/.github/workflows/_compile_integration_test.yml index e002a2414..756a372fa 100644 --- a/.github/workflows/_compile_integration_test.yml +++ b/.github/workflows/_compile_integration_test.yml @@ -20,7 +20,6 @@ jobs: strategy: matrix: python-version: - - "3.9" - "3.10" - "3.11" - "3.12" diff --git a/.github/workflows/_lint.yml b/.github/workflows/_lint.yml index 07d6bc618..9b362ee68 100644 --- a/.github/workflows/_lint.yml +++ b/.github/workflows/_lint.yml @@ -35,7 +35,7 @@ jobs: # Starting new jobs is also relatively slow, # so linting on fewer versions makes CI faster. python-version: - - "3.9" + - "3.10" - "3.12" steps: - uses: actions/checkout@v4 diff --git a/.github/workflows/_test.yml b/.github/workflows/_test.yml index d3e78008f..c19a12ca6 100644 --- a/.github/workflows/_test.yml +++ b/.github/workflows/_test.yml @@ -24,7 +24,6 @@ jobs: strategy: matrix: python-version: - - "3.9" - "3.10" - "3.11" - "3.12" diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml index 0b9d57c31..b868a78eb 100644 --- a/libs/community/pyproject.toml +++ b/libs/community/pyproject.toml @@ -8,7 +8,7 @@ version = "2.0.10" description = "An integration package connecting miscellaneous Google's products and LangChain" authors = [] license = {text = "MIT"} -requires-python = ">=3.9" +requires-python = 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diff --git a/libs/vertexai/langchain_google_vertexai/_utils.py b/libs/vertexai/langchain_google_vertexai/_utils.py index 92953fd04..21af235c0 100644 --- a/libs/vertexai/langchain_google_vertexai/_utils.py +++ b/libs/vertexai/langchain_google_vertexai/_utils.py @@ -14,11 +14,11 @@ AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, ) -from vertexai.generative_models import ( # type: ignore[import-untyped] +from vertexai.generative_models import ( Candidate, Image, ) -from vertexai.language_models import ( # type: ignore[import-untyped] +from vertexai.language_models import ( TextGenerationResponse, ) @@ -167,33 +167,51 @@ def get_generation_info( logprobs: Union[bool, int] = False, ) -> Dict[str, Any]: # https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/gemini#response_body - info = { - "is_blocked": any(rating.blocked for rating in candidate.safety_ratings), - "safety_ratings": [ - { - "category": rating.category.name, - "probability_label": rating.probability.name, - "probability_score": rating.probability_score, - "blocked": rating.blocked, - "severity": rating.severity.name, - "severity_score": rating.severity_score, - } - # Image generation models sometime return ratings that are not - # included in the proto. - for rating in candidate.safety_ratings - if hasattr(rating.category, "name") - ], - "citation_metadata": ( - proto.Message.to_dict(candidate.citation_metadata) - if candidate.citation_metadata - else None - ), - "usage_metadata": usage_metadata, - "finish_reason": _get_finish_reason_string(candidate.finish_reason), - "finish_message": ( - candidate.finish_message if candidate.finish_message else None - ), - } + # Handle different response types - Candidate has different attributes than + # TextGenerationResponse. Check for attributes rather than isinstance to + # support mocks in tests. + if hasattr(candidate, "safety_ratings") and hasattr(candidate, "citation_metadata"): + info = { + "is_blocked": any(rating.blocked for rating in candidate.safety_ratings), + "safety_ratings": [ + { + "category": rating.category.name, + "probability_label": rating.probability.name, + "probability_score": rating.probability_score, + "blocked": rating.blocked, + "severity": rating.severity.name, + "severity_score": rating.severity_score, + } + # Image generation models sometime return ratings that are not + # included in the proto. + for rating in candidate.safety_ratings + if hasattr(rating.category, "name") + ], + "citation_metadata": ( + proto.Message.to_dict(candidate.citation_metadata) + if candidate.citation_metadata + else None + ), + "usage_metadata": usage_metadata, + "finish_reason": _get_finish_reason_string(candidate.finish_reason) + if hasattr(candidate, "finish_reason") + else None, + "finish_message": ( + candidate.finish_message + if hasattr(candidate, "finish_message") and candidate.finish_message + else None + ), + } + else: # TextGenerationResponse + # TextGenerationResponse doesn't have the same attributes as Candidate + info = { + "is_blocked": False, # TextGenerationResponse doesn't have safety_ratings + "safety_ratings": [], + "citation_metadata": None, + "usage_metadata": usage_metadata, + "finish_reason": None, # TextGenerationResponse doesn't have finish_reason + "finish_message": None, + } if hasattr(candidate, "avg_logprobs") and candidate.avg_logprobs is not None: if ( isinstance(candidate.avg_logprobs, float) diff --git a/libs/vertexai/langchain_google_vertexai/chat_models.py b/libs/vertexai/langchain_google_vertexai/chat_models.py index 1dac6390b..2cd0c08f8 100644 --- a/libs/vertexai/langchain_google_vertexai/chat_models.py +++ b/libs/vertexai/langchain_google_vertexai/chat_models.py @@ -16,6 +16,7 @@ Dict, List, Optional, + Sequence, Type, Union, cast, @@ -24,7 +25,7 @@ TypedDict, overload, ) -from collections.abc import AsyncIterator, Iterator, Sequence +from collections.abc import AsyncIterator, Iterator import proto # type: ignore[import-untyped] @@ -74,17 +75,18 @@ ) from langchain_core.utils.pydantic import is_basemodel_subclass from langchain_core.utils.utils import _build_model_kwargs -from vertexai.generative_models import ( # type: ignore +from vertexai.generative_models import ( Tool as VertexTool, + Candidate as VertexCandidate, ) -from vertexai.generative_models._generative_models import ( # type: ignore +from vertexai.generative_models._generative_models import ( ToolConfig, SafetySettingsType, GenerationConfigType, GenerationResponse, _convert_schema_dict_to_gapic, ) -from vertexai.language_models import ( # type: ignore +from vertexai.language_models import ( ChatMessage, InputOutputTextPair, ) @@ -227,10 +229,10 @@ def _parse_chat_history(history: List[BaseMessage]) -> _ChatHistory: if i == 0 and isinstance(message, SystemMessage): context = content elif isinstance(message, AIMessage): - vertex_message = ChatMessage(content=message.content, author="bot") + vertex_message = ChatMessage(content=content, author="bot") vertex_messages.append(vertex_message) elif isinstance(message, HumanMessage): - vertex_message = ChatMessage(content=message.content, author="user") + vertex_message = ChatMessage(content=content, author="user") vertex_messages.append(vertex_message) else: msg = f"Unexpected message with type {type(message)} at the position {i}." @@ -559,7 +561,7 @@ def _parse_examples(examples: List[BaseMessage]) -> List[InputOutputTextPair]: f"{type(example)} for the {i}th message." ) raise ValueError(msg) - input_text = example.content + input_text = cast("str", example.content) if i % 2 == 1: if not isinstance(example, AIMessage): msg = ( @@ -567,8 +569,10 @@ def _parse_examples(examples: List[BaseMessage]) -> List[InputOutputTextPair]: f"{type(example)} for the {i}th message." ) raise ValueError(msg) + # input_text is guaranteed to be set in the previous iteration + assert input_text is not None pair = InputOutputTextPair( - input_text=input_text, output_text=example.content + input_text=input_text, output_text=cast("str", example.content) ) example_pairs.append(pair) return example_pairs @@ -608,18 +612,19 @@ def _append_to_content( @overload def _parse_response_candidate( - response_candidate: Candidate, streaming: Literal[False] = False + response_candidate: Union[Candidate, VertexCandidate], + streaming: Literal[False] = False, ) -> AIMessage: ... @overload def _parse_response_candidate( - response_candidate: Candidate, streaming: Literal[True] + response_candidate: Union[Candidate, VertexCandidate], streaming: Literal[True] ) -> AIMessageChunk: ... def _parse_response_candidate( - response_candidate: Candidate, streaming: bool = False + response_candidate: Union[Candidate, VertexCandidate], streaming: bool = False ) -> AIMessage: content: Union[None, str, List[Union[str, dict[str, Any]]]] = None additional_kwargs = {} @@ -635,7 +640,7 @@ def _parse_response_candidate( except AttributeError: pass - if part.thought: + if hasattr(part, "thought") and part.thought: thinking_message = { "type": "thinking", "thinking": part.text, @@ -694,7 +699,9 @@ def _parse_response_candidate( if getattr(part, "thought_signature", None): # store dict of {tool_call_id: thought_signature} - if isinstance(part.thought_signature, bytes): + if hasattr(part, "thought_signature") and isinstance( + part.thought_signature, bytes + ): thought_signature = _bytes_to_base64(part.thought_signature) if ( _FUNCTION_CALL_THOUGHT_SIGNATURES_MAP_KEY @@ -1990,7 +1997,7 @@ def _safety_settings_gemini( return self._safety_settings_gemini(self.safety_settings) return None if isinstance(safety_settings, list): - return safety_settings + return cast("Sequence[SafetySetting]", safety_settings) if isinstance(safety_settings, dict): formatted_safety_settings = [] for category, threshold in safety_settings.items(): @@ -2006,8 +2013,8 @@ def _safety_settings_gemini( ) ) return formatted_safety_settings - msg = "safety_settings should be either" - raise ValueError(msg) + # This should be unreachable as all cases are handled above + raise ValueError("Unexpected safety_settings type") def _prepare_request_gemini( self, @@ -2041,7 +2048,7 @@ def _prepare_request_gemini( tool_config = _tool_choice_to_tool_config(tool_choice, all_names) else: pass - safety_settings = self._safety_settings_gemini(safety_settings) + formatted_safety_settings = self._safety_settings_gemini(safety_settings) logprobs = logprobs if logprobs is not None else self.logprobs logprobs = logprobs if isinstance(logprobs, (int, bool)) else False generation_config = self._generation_config_gemini( @@ -2100,18 +2107,22 @@ def _content_to_v1(contents: list[Content]) -> list[v1Content]: v1_tools = [v1Tool(**proto.Message.to_dict(t)) for t in formatted_tools] if tool_config: - v1_tool_config = v1ToolConfig( - function_calling_config=v1FunctionCallingConfig( - **proto.Message.to_dict(tool_config.function_calling_config) + v1_tool_config = ( + v1ToolConfig( + function_calling_config=v1FunctionCallingConfig( + **proto.Message.to_dict(tool_config.function_calling_config) + ) ) + if hasattr(tool_config, "function_calling_config") + else v1ToolConfig() ) - if safety_settings: + if formatted_safety_settings: v1_safety_settings = [ v1SafetySetting( category=s.category, method=s.method, threshold=s.threshold ) - for s in safety_settings + for s in formatted_safety_settings ] if (self.cached_content is not None) or (cached_content is not None): @@ -2267,7 +2278,7 @@ def _tool_config_gemini( self, tool_config: Optional[Union[_ToolConfigDict, ToolConfig]] = None ) -> Optional[GapicToolConfig]: if tool_config and not isinstance(tool_config, ToolConfig): - return _format_tool_config(cast("_ToolConfigDict", tool_config)) + return _format_tool_config(tool_config) return None async def _agenerate( From 888036ded2bde32105453540a8b6c77aeae0e5cb Mon Sep 17 00:00:00 2001 From: Mason Daugherty Date: Thu, 18 Sep 2025 13:46:13 -0400 Subject: [PATCH 03/23] chore: update min vers in `pyproject.toml` files --- libs/community/pyproject.toml | 11 +- libs/community/uv.lock | 1277 +++++++++++---------------------- libs/genai/pyproject.toml | 4 +- libs/genai/uv.lock | 8 +- libs/vertexai/pyproject.toml | 10 +- libs/vertexai/uv.lock | 339 +++++---- 6 files changed, 649 insertions(+), 1000 deletions(-) diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml index b868a78eb..9c007d1ff 100644 --- a/libs/community/pyproject.toml +++ b/libs/community/pyproject.toml @@ -8,13 +8,13 @@ version = "2.0.10" description = "An integration package connecting miscellaneous Google's products and LangChain" authors = [] license = {text = "MIT"} -requires-python = ">=3.10" +requires-python = ">=3.10,<3.13" readme = "README.md" repository = "https://github.com/langchain-ai/langchain-google" dependencies = [ - "langchain-core>=0.3,<1", - "langchain-community>=0.3,<1", + "langchain-core>=1.0.0a3", + "langchain-community>=0.3.29", "google-api-core>=2.25,<3", "google-api-python-client>=2.161,<3", "google-cloud-core>=2.4.3,<3", @@ -101,6 +101,11 @@ select = [ disallow_untyped_defs = "True" ignore_missing_imports = "True" +[tool.uv] +constraint-dependencies = [ + "llvmlite!=0.45.0; sys_platform == 'darwin' and platform_machine == 'arm64'" +] + [tool.coverage.run] omit = ["tests/*"] diff --git a/libs/community/uv.lock b/libs/community/uv.lock index 78deb4cff..3b2cf9d76 100644 --- a/libs/community/uv.lock +++ b/libs/community/uv.lock @@ -1,14 +1,18 @@ version = 1 revision = 3 -requires-python = ">=3.10" +requires-python = ">=3.10, <3.13" resolution-markers = [ - "python_full_version >= '3.13'", - "python_full_version >= '3.12.4' and python_full_version < '3.13'", - "python_full_version >= '3.12' and python_full_version < 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"2024-08-17T09:20:15.925Z" }, +] + [[package]] name = "yarl" version = "1.20.1" From 83158f48b76465d3da5cf26b0738a17fff99bb88 Mon Sep 17 00:00:00 2001 From: Mason Daugherty Date: Thu, 18 Sep 2025 13:59:46 -0400 Subject: [PATCH 04/23] chore: v1 migrations (#1195) --- .../langchain_google_genai/chat_models.py | 84 +++--- libs/genai/pyproject.toml | 2 +- .../tests/integration_tests/test_tools.py | 4 +- libs/genai/uv.lock | 8 +- .../langchain_google_vertexai/_base.py | 4 +- .../langchain_google_vertexai/_enums.py | 2 +- .../langchain_google_vertexai/_image_utils.py | 2 +- .../langchain_google_vertexai/chat_models.py | 2 +- .../langchain_google_vertexai/embeddings.py | 18 +- .../functions_utils.py | 8 +- .../langchain_google_vertexai/model_garden.py | 2 +- .../model_garden_maas/llama.py | 2 +- .../langchain_google_vertexai/utils.py | 11 +- .../vision_models.py | 2 +- .../integration_tests/test_chat_models.py | 40 +-- .../integration_tests/test_embeddings.py | 4 +- .../integration_tests/test_vectorstores.py | 2 +- .../tests/unit_tests/test_chat_models.py | 11 +- .../tests/unit_tests/test_function_utils.py | 12 +- .../tests/unit_tests/test_vision_models.py | 2 +- libs/vertexai/uv.lock | 271 +++++++++++------- 21 files changed, 293 insertions(+), 200 deletions(-) diff --git a/libs/genai/langchain_google_genai/chat_models.py b/libs/genai/langchain_google_genai/chat_models.py index f6755edf7..9eeb6c6fb 100644 --- a/libs/genai/langchain_google_genai/chat_models.py +++ b/libs/genai/langchain_google_genai/chat_models.py @@ -62,7 +62,11 @@ AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, ) -from langchain_core.language_models import LangSmithParams, LanguageModelInput +from langchain_core.language_models import ( + LangSmithParams, + LanguageModelInput, + is_openai_data_block, +) from langchain_core.language_models.chat_models import BaseChatModel from langchain_core.messages import ( AIMessage, @@ -287,56 +291,65 @@ async def _achat_with_retry(**kwargs: Any) -> Any: return await _achat_with_retry(**params) -def _is_lc_content_block(part: dict) -> bool: - return "type" in part - - -def _is_openai_image_block(block: dict) -> bool: - """Check if the block contains image data in OpenAI Chat Completions format.""" - if block.get("type") == "image_url": - if ( - (set(block.keys()) <= {"type", "image_url", "detail"}) - and (image_url := block.get("image_url")) - and isinstance(image_url, dict) - ): - url = image_url.get("url") - if isinstance(url, str): - return True - else: - return False - - return False - - def _convert_to_parts( raw_content: Union[str, Sequence[Union[str, dict]]], ) -> List[Part]: - """Converts a list of LangChain messages into a Google parts.""" - parts = [] + """Converts a list of LangChain messages into Google parts.""" content = [raw_content] if isinstance(raw_content, str) else raw_content image_loader = ImageBytesLoader() + + parts = [] + # Iterate over each item in the content list, constructing a list of Parts for part in content: if isinstance(part, str): parts.append(Part(text=part)) - elif isinstance(part, Mapping): - if _is_lc_content_block(part): + elif isinstance(part, Mapping): # e.g. a dict + if "type" in part: # e.g. a LangChain content block if part["type"] == "text": + # Either old text block or TextContentBlock parts.append(Part(text=part["text"])) elif is_data_content_block(part): - if part["source_type"] == "url": + # Handle both legacy LC blocks (with `source_type`) and standard v1 + if "source_type" in part: + # Catch legacy v0 formats (Base64CB, URLCB, IDCB) + # v1 content blocks won't have top-level a `source_type` key + if part["source_type"] == "url": + bytes_ = image_loader._bytes_from_url(part["url"]) + elif part["source_type"] == "base64": + bytes_ = base64.b64decode(part["data"]) + else: + # Unable to support IDContentBlock + msg = "source_type must be url or base64." + raise ValueError(msg) + elif "url" in part: + # Standard Data block format using URL bytes_ = image_loader._bytes_from_url(part["url"]) - elif part["source_type"] == "base64": + elif "base64" in part: + # Standard Data block format using base64 + bytes_ = base64.b64decode(part["base64"]) + elif "data" in part: + # Alternative base64 format unknown to us + # (This branch likely will never be hit) bytes_ = base64.b64decode(part["data"]) else: - msg = "source_type must be url or base64." + # `is_data_content_block` validates this, but just in case + msg = ( + "Data content block must contain 'url', 'base64', or " + "'data' field." + ) raise ValueError(msg) inline_data: dict = {"data": bytes_} if "mime_type" in part: inline_data["mime_type"] = part["mime_type"] else: - source = cast("str", part.get("url") or part.get("data")) + # Guess mime type based on data field if not provided + source = cast( + "str", + part.get("url") or part.get("base64") or part.get("data"), + ) mime_type, _ = mimetypes.guess_type(source) if not mime_type: + # Last resort - try to guess based on file bytes kind = filetype.guess(bytes_) if kind: mime_type = kind.mime @@ -344,12 +357,15 @@ def _convert_to_parts( inline_data["mime_type"] = mime_type parts.append(Part(inline_data=inline_data)) elif part["type"] == "image_url": + # Chat Completions image format img_url = part["image_url"] if isinstance(img_url, dict): if "url" not in img_url: msg = f"Unrecognized message image format: {img_url}" raise ValueError(msg) img_url = img_url["url"] + # Currently Google doesn't have a equivalent of OpenAI's + # `detail` field for images, so we ignore it if present. parts.append(image_loader.load_part(img_url)) # Handle media type like LangChain.js # https://github.com/langchain-ai/langchainjs/blob/e536593e2585f1dd7b0afc187de4d07cb40689ba/libs/langchain-google-common/src/utils/gemini.ts#L93-L106 @@ -415,7 +431,7 @@ def _convert_to_parts( ) raise ValueError(msg) else: - # Yolo + # Yolo. This doesn't have `type` and is thus likely not a content block logger.warning( "Unrecognized message part format. Assuming it's a text part." ) @@ -441,7 +457,7 @@ def _convert_tool_message_to_parts( other_blocks = [] for block in message.content: if isinstance(block, dict) and ( - is_data_content_block(block) or _is_openai_image_block(block) + is_data_content_block(block) or is_openai_data_block(block) ): media_blocks.append(block) else: @@ -1665,7 +1681,7 @@ def invoke( code_execution: Optional[bool] = None, stop: Optional[list[str]] = None, **kwargs: Any, - ) -> BaseMessage: + ) -> AIMessage: """Enable code execution. Supported on: gemini-1.5-pro, gemini-1.5-flash, gemini-2.0-flash, and gemini-2.0-pro. When enabled, the model can execute code to solve problems. @@ -2186,7 +2202,7 @@ def bind_tools( *, tool_choice: Optional[Union[_ToolChoiceType, bool]] = None, **kwargs: Any, - ) -> Runnable[LanguageModelInput, BaseMessage]: + ) -> Runnable[LanguageModelInput, AIMessage]: """Bind tool-like objects to this chat model. Assumes model is compatible with google-generativeAI tool-calling API. diff --git a/libs/genai/pyproject.toml b/libs/genai/pyproject.toml index ee17eb0a3..53cecb4e7 100644 --- a/libs/genai/pyproject.toml +++ b/libs/genai/pyproject.toml @@ -46,7 +46,7 @@ test = [ "pytest-socket>=0.7,<1", "numpy>=1.26.4; python_version<'3.13'", "numpy>=2.1.0; python_version>='3.13'", - "langchain-tests>=0.3,<1", + "langchain-tests>=1.0.0a1", ] test_integration = [ diff --git a/libs/genai/tests/integration_tests/test_tools.py b/libs/genai/tests/integration_tests/test_tools.py index 068e493eb..2b2643fbf 100644 --- a/libs/genai/tests/integration_tests/test_tools.py +++ b/libs/genai/tests/integration_tests/test_tools.py @@ -33,5 +33,5 @@ def test_multiple_tools() -> None: input_ = "What is the latest tennis score for Leonid?" result = model_with_tools.invoke(input_) - assert len(result.tool_calls) == 1 # type: ignore - assert result.tool_calls[0]["name"] == "check_tennis_score" # type: ignore + assert len(result.tool_calls) == 1 + assert result.tool_calls[0]["name"] == "check_tennis_score" diff --git a/libs/genai/uv.lock b/libs/genai/uv.lock index 75fa41742..c8df5ef18 100644 --- a/libs/genai/uv.lock +++ b/libs/genai/uv.lock @@ -516,7 +516,7 @@ dev = [] lint = [{ name = "ruff", specifier = ">=0.12.2,<0.13" }] test = [ { name = "freezegun", specifier = ">=1.5,<2" }, - 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) -> Runnable[LanguageModelInput, BaseMessage]: + ) -> Runnable[LanguageModelInput, AIMessage]: """Bind tool-like objects to this chat model. Assumes model is compatible with Vertex tool-calling API. diff --git a/libs/vertexai/langchain_google_vertexai/embeddings.py b/libs/vertexai/langchain_google_vertexai/embeddings.py index d97e5f101..942b34a68 100644 --- a/libs/vertexai/langchain_google_vertexai/embeddings.py +++ b/libs/vertexai/langchain_google_vertexai/embeddings.py @@ -6,7 +6,7 @@ from concurrent.futures import ThreadPoolExecutor, wait from enum import Enum, auto from functools import cached_property -from typing import Any, Dict, List, Literal, Optional, Tuple, Type +from typing import Any, Dict, List, Literal, Optional, Tuple, Type, Union from google.api_core.exceptions import ( Aborted, @@ -22,11 +22,11 @@ from langchain_core.language_models.llms import create_base_retry_decorator from pydantic import ConfigDict, model_validator from typing_extensions import Self -from vertexai.language_models import ( # type: ignore +from vertexai.language_models import ( TextEmbeddingInput, TextEmbeddingModel, ) -from vertexai.vision_models import ( # type: ignore +from vertexai.vision_models import ( Image, MultiModalEmbeddingModel, MultiModalEmbeddingResponse, @@ -142,6 +142,8 @@ def validate_environment(self) -> Self: "default_metadata": self.default_metadata, } self._init_vertexai(values) + if not self.model_name: + raise ValueError("model_name must be provided") _, user_agent = get_user_agent(f"{self.__class__.__name__}_{self.model_name}") with telemetry.tool_context_manager(user_agent): if ( @@ -308,7 +310,7 @@ def _get_text_embeddings_with_retry( ) -> List[List[float]]: """Makes a Vertex AI model request with retry logic.""" if embeddings_type and self.model_version.task_type_supported: - requests = [ + requests: Union[List[str], List[TextEmbeddingInput]] = [ TextEmbeddingInput(text=t, task_type=embeddings_type) for t in texts ] else: @@ -528,6 +530,8 @@ def embed_image( result: MultiModalEmbeddingResponse = self.instance[ "get_embeddings_with_retry" ](image=image, contextual_text=contextual_text, dimension=dimensions) + if result.image_embedding is None: + raise ValueError("Failed to generate image embedding") return result.image_embedding def embed_images( @@ -552,13 +556,17 @@ def embed_images( image_loader = self._image_bytes_loader_client - embeddings = [] + embeddings: List[List[float]] = [] for image_path in uris: bytes_image = image_loader.load_bytes(image_path) image = Image(bytes_image) result: MultiModalEmbeddingResponse = self.instance[ "get_embeddings_with_retry" ](image=image, contextual_text=contextual_text, dimension=dimensions) + if result.image_embedding is None: + raise ValueError( + f"Failed to generate embedding for image: {image_path}" + ) embeddings.append(result.image_embedding) return embeddings diff --git a/libs/vertexai/langchain_google_vertexai/functions_utils.py b/libs/vertexai/langchain_google_vertexai/functions_utils.py index a463c48b7..e2be45138 100644 --- a/libs/vertexai/langchain_google_vertexai/functions_utils.py +++ b/libs/vertexai/langchain_google_vertexai/functions_utils.py @@ -17,7 +17,7 @@ ) import google.cloud.aiplatform_v1beta1.types as gapic -import vertexai.generative_models as vertexai # type: ignore +import vertexai.generative_models as vertexai from google.cloud.aiplatform_v1beta1.types import ( ToolConfig as GapicToolConfig, ) @@ -332,7 +332,11 @@ def _format_to_gapic_tool(tools: _ToolsType) -> gapic.Tool: "code_execution", ] ): - fd = _format_to_gapic_function_declaration(tool) + # Cast needed: when dict lacks tool-specific fields, it's a function + # declaration + fd = _format_to_gapic_function_declaration( + cast("_FunctionDeclarationLike", tool) + ) gapic_tool.function_declarations.append(fd) continue # _ToolDictLike diff --git a/libs/vertexai/langchain_google_vertexai/model_garden.py b/libs/vertexai/langchain_google_vertexai/model_garden.py index b658af592..eb2385628 100644 --- a/libs/vertexai/langchain_google_vertexai/model_garden.py +++ b/libs/vertexai/langchain_google_vertexai/model_garden.py @@ -448,7 +448,7 @@ def bind_tools( Union[Dict[str, str], Literal["any", "auto"], str] ] = None, **kwargs: Any, - ) -> Runnable[LanguageModelInput, BaseMessage]: + ) -> Runnable[LanguageModelInput, AIMessage]: """Bind tool-like objects to this chat model.""" formatted_tools = [convert_to_anthropic_tool(tool) for tool in tools] if not tool_choice: diff --git a/libs/vertexai/langchain_google_vertexai/model_garden_maas/llama.py b/libs/vertexai/langchain_google_vertexai/model_garden_maas/llama.py index 62e8e6597..472869a26 100644 --- a/libs/vertexai/langchain_google_vertexai/model_garden_maas/llama.py +++ b/libs/vertexai/langchain_google_vertexai/model_garden_maas/llama.py @@ -518,7 +518,7 @@ def bind_tools( self, tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]], **kwargs: Any, - ) -> Runnable[LanguageModelInput, BaseMessage]: + ) -> Runnable[LanguageModelInput, AIMessage]: """Bind tool-like objects to this chat model.""" formatted_tools = [convert_to_openai_function(tool) for tool in tools] return super().bind(tools=formatted_tools, **kwargs) diff --git a/libs/vertexai/langchain_google_vertexai/utils.py b/libs/vertexai/langchain_google_vertexai/utils.py index 1f57d81d5..6af82f9eb 100644 --- a/libs/vertexai/langchain_google_vertexai/utils.py +++ b/libs/vertexai/langchain_google_vertexai/utils.py @@ -1,8 +1,8 @@ from datetime import datetime, timedelta -from typing import List, Optional +from typing import Any, List, Optional, cast from langchain_core.messages import BaseMessage -from vertexai.preview import caching # type: ignore +from vertexai.preview import caching from langchain_google_vertexai._image_utils import ImageBytesLoader from langchain_google_vertexai.chat_models import ( @@ -60,14 +60,17 @@ def create_context_cache( if tools is not None: tools = [_format_to_gapic_tool(tools)] + if model.full_model_name is None: + raise ValueError("Model must have a full_model_name to create cached content") + cached_content = caching.CachedContent.create( model_name=model.full_model_name, system_instruction=system_instruction, - contents=contents, + contents=cast("list[Any] | None", contents), ttl=time_to_live, expire_time=expire_time, tool_config=tool_config, - tools=tools, + tools=cast("list[Any] | None", tools), ) return cached_content.name diff --git a/libs/vertexai/langchain_google_vertexai/vision_models.py b/libs/vertexai/langchain_google_vertexai/vision_models.py index 34902c917..77bbb99df 100644 --- a/libs/vertexai/langchain_google_vertexai/vision_models.py +++ b/libs/vertexai/langchain_google_vertexai/vision_models.py @@ -11,7 +11,7 @@ from langchain_core.outputs.chat_generation import ChatGeneration from langchain_core.outputs.generation import Generation from pydantic import BaseModel, ConfigDict, Field -from vertexai.vision_models import ( # type: ignore[import-untyped] +from vertexai.vision_models import ( GeneratedImage, Image, ImageGenerationModel, diff --git a/libs/vertexai/tests/integration_tests/test_chat_models.py b/libs/vertexai/tests/integration_tests/test_chat_models.py index 39b939519..b3c92e2d5 100644 --- a/libs/vertexai/tests/integration_tests/test_chat_models.py +++ b/libs/vertexai/tests/integration_tests/test_chat_models.py @@ -5,7 +5,12 @@ import json import os import re -from typing import List, Literal, Optional, cast +from typing import Any, List, Literal, Optional, cast + +try: + from langgraph.graph.state import CompiledStateGraph +except ImportError: + CompiledStateGraph = Any # type: ignore[misc,assignment] import pytest import requests @@ -317,7 +322,7 @@ def get_climate_info(query: str) -> str: ) prompt_template = ChatPromptTemplate.from_messages( [ - ("human", "{input}"), + ("placeholder", "{messages}"), ("placeholder", "{agent_scratchpad}"), ] ) @@ -336,20 +341,17 @@ def get_climate_info(query: str) -> str: "mime_type": mime_type, } text_message = {"type": "text", "text": "Describe the attached media in 5 words."} - message = [media_message, text_message] tools = [get_climate_info] - agent = agents.create_tool_calling_agent( - llm=llm, + agent: CompiledStateGraph[Any, Any] = agents.create_agent( + model=llm, tools=tools, prompt=prompt_template, ) - agent_executor = agents.AgentExecutor( - agent=agent, - tools=tools, - verbose=False, + output = agent.invoke( + {"messages": [{"role": "user", "content": [text_message, media_message]}]} ) - output = agent_executor.invoke({"input": message}) - assert isinstance(output["output"], str) + assert "messages" in output + assert len(output["messages"]) > 0 @pytest.mark.flaky(retries=3) @@ -1247,18 +1249,20 @@ def get_secret_number() -> int: prompt = ChatPromptTemplate.from_messages( [ - ("human", "{input}"), + ("placeholder", "{messages}"), ("placeholder", "{agent_scratchpad}"), ] ) - agent = agents.create_tool_calling_agent( - llm=chat, + agent: CompiledStateGraph[Any, Any] = agents.create_agent( + model=chat, tools=tools, prompt=prompt, ) - agent_executor = agents.AgentExecutor(agent=agent, tools=tools, verbose=False) - response = agent_executor.invoke({"input": "what is the secret number?"}) - assert isinstance(response["output"], str) + response = agent.invoke( + {"messages": [{"role": "user", "content": "what is the secret number?"}]} + ) + assert "messages" in response + assert len(response["messages"]) > 0 @pytest.mark.release @@ -1324,7 +1328,7 @@ def test_langgraph_example() -> None: [ *messages, step1, - ToolMessage(content="9", tool_call_id=step1.tool_calls[0]["id"]), # type: ignore[attr-defined] + ToolMessage(content="9", tool_call_id=step1.tool_calls[0]["id"]), ], tools=[{"function_declarations": [add_declaration, multiply_declaration]}], ) diff --git a/libs/vertexai/tests/integration_tests/test_embeddings.py b/libs/vertexai/tests/integration_tests/test_embeddings.py index 6f104a9ad..9651d6aa5 100644 --- a/libs/vertexai/tests/integration_tests/test_embeddings.py +++ b/libs/vertexai/tests/integration_tests/test_embeddings.py @@ -5,8 +5,8 @@ """ import pytest -from vertexai.language_models import TextEmbeddingModel # type: ignore[import-untyped] -from vertexai.vision_models import ( # type: ignore[import-untyped] +from vertexai.language_models import TextEmbeddingModel +from vertexai.vision_models import ( MultiModalEmbeddingModel, ) diff --git a/libs/vertexai/tests/integration_tests/test_vectorstores.py b/libs/vertexai/tests/integration_tests/test_vectorstores.py index ce4022778..891df7bd8 100644 --- a/libs/vertexai/tests/integration_tests/test_vectorstores.py +++ b/libs/vertexai/tests/integration_tests/test_vectorstores.py @@ -15,7 +15,7 @@ from uuid import uuid4 import pytest -import vertexai # type: ignore[import-untyped] +import vertexai from google.cloud import storage # type: ignore[attr-defined, unused-ignore] from google.cloud.aiplatform.matching_engine import ( MatchingEngineIndex, diff --git a/libs/vertexai/tests/unit_tests/test_chat_models.py b/libs/vertexai/tests/unit_tests/test_chat_models.py index 68d8495e4..54abe658a 100644 --- a/libs/vertexai/tests/unit_tests/test_chat_models.py +++ b/libs/vertexai/tests/unit_tests/test_chat_models.py @@ -33,10 +33,10 @@ PydanticToolsParser, ) from pydantic import BaseModel -from vertexai.generative_models import ( # type: ignore +from vertexai.generative_models import ( SafetySetting as VertexSafetySetting, ) -from vertexai.language_models import ( # type: ignore +from vertexai.language_models import ( ChatMessage, InputOutputTextPair, ) @@ -1128,15 +1128,16 @@ def test_safety_settings_gemini() -> None: ) safety_settings = model._safety_settings_gemini([expected_safety_setting]) assert safety_settings == [expected_safety_setting] + # Ignores for tests that intentionally use invalid dict types safety_settings = model._safety_settings_gemini( - {"HARM_CATEGORY_DANGEROUS_CONTENT": "BLOCK_LOW_AND_ABOVE"} + {"HARM_CATEGORY_DANGEROUS_CONTENT": "BLOCK_LOW_AND_ABOVE"} # type: ignore[dict-item] ) assert safety_settings == [expected_safety_setting] - safety_settings = model._safety_settings_gemini({2: 1}) + safety_settings = model._safety_settings_gemini({2: 1}) # type: ignore[dict-item] assert safety_settings == [expected_safety_setting] threshold = SafetySetting.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE safety_settings = model._safety_settings_gemini( - {HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: threshold} + {HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: threshold} # type: ignore[dict-item] ) assert safety_settings == [expected_safety_setting] diff --git a/libs/vertexai/tests/unit_tests/test_function_utils.py b/libs/vertexai/tests/unit_tests/test_function_utils.py index 13ff245c2..67dfd0de7 100644 --- a/libs/vertexai/tests/unit_tests/test_function_utils.py +++ b/libs/vertexai/tests/unit_tests/test_function_utils.py @@ -9,7 +9,7 @@ import google.cloud.aiplatform_v1beta1.types as gapic import pytest -import vertexai.generative_models as vertexai # type: ignore +import vertexai.generative_models as vertexai from google.cloud.aiplatform_v1beta1.types import ( FunctionCallingConfig as GapicFunctionCallingConfig, ) @@ -42,6 +42,7 @@ _format_vertex_to_function_declaration, _FunctionDeclarationLike, _tool_choice_to_tool_config, + _ToolType, ) @@ -418,20 +419,20 @@ def test_format_to_gapic_function_declaration() -> None: def test_format_to_gapic_tool() -> None: - src = [src for src, _, _, _ in SRC_EXP_MOCKS_DESC] + src: List[_FunctionDeclarationLike] = [src for src, _, _, _ in SRC_EXP_MOCKS_DESC] fds = [fd for _, fd, _, _ in SRC_EXP_MOCKS_DESC] expected = gapic.Tool(function_declarations=fds) result = _format_to_gapic_tool(src) assert result == expected - src_2 = [ - *src, + additional_tools: List[_ToolType] = [ gapic.Tool(function_declarations=[search_model_exp]), vertexai.Tool.from_function_declarations( [vertexai.FunctionDeclaration.from_func(search)] ), {"function_declarations": [search_model_dict]}, ] + src_2 = src + additional_tools expected = gapic.Tool( function_declarations=[*fds, search_model_exp, search_vfd_exp, search_model_exp] ) @@ -466,7 +467,8 @@ def test_format_to_gapic_tool() -> None: assert result == src_5 with pytest.raises(ValueError) as exc_info1: - _ = _format_to_gapic_tool(["fake_tool"]) + # type ignore since we're testing invalid input + _ = _format_to_gapic_tool(["fake_tool"]) # type: ignore[list-item] assert str(exc_info1.value).startswith("Unsupported tool") with pytest.raises(Exception) as exc_info: diff --git a/libs/vertexai/tests/unit_tests/test_vision_models.py b/libs/vertexai/tests/unit_tests/test_vision_models.py index e3d457309..57715beb1 100644 --- a/libs/vertexai/tests/unit_tests/test_vision_models.py +++ b/libs/vertexai/tests/unit_tests/test_vision_models.py @@ -1,5 +1,5 @@ import pytest -from vertexai.vision_models import Image # type: ignore[import-untyped] +from vertexai.vision_models import Image 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00:00:00 2001 From: Mason Daugherty Date: Thu, 18 Sep 2025 16:24:09 -0400 Subject: [PATCH 06/23] feat: return `model_name` and `model_provider` in `response_metadata` --- .../langchain_google_genai/chat_models.py | 13 ++++- .../tests/unit_tests/test_chat_models.py | 47 +++++++++++++++++++ 2 files changed, 58 insertions(+), 2 deletions(-) diff --git a/libs/genai/langchain_google_genai/chat_models.py b/libs/genai/langchain_google_genai/chat_models.py index 9eeb6c6fb..7ce6bd7d6 100644 --- a/libs/genai/langchain_google_genai/chat_models.py +++ b/libs/genai/langchain_google_genai/chat_models.py @@ -605,10 +605,15 @@ def _append_to_content( def _parse_response_candidate( - response_candidate: Candidate, streaming: bool = False + response_candidate: Candidate, + streaming: bool = False, + model_name: Optional[str] = None, ) -> AIMessage: content: Union[None, str, List[Union[str, dict]]] = None additional_kwargs: Dict[str, Any] = {} + response_metadata: Dict[str, Any] = {"model_provider": "google_genai"} + if model_name: + response_metadata["model_name"] = model_name tool_calls = [] invalid_tool_calls = [] tool_call_chunks = [] @@ -743,12 +748,14 @@ def _parse_response_candidate( return AIMessageChunk( content=content, additional_kwargs=additional_kwargs, + response_metadata=response_metadata, tool_call_chunks=tool_call_chunks, ) return AIMessage( content=content, additional_kwargs=additional_kwargs, + response_metadata=response_metadata, tool_calls=tool_calls, invalid_tool_calls=invalid_tool_calls, ) @@ -823,7 +830,9 @@ def _response_to_result( ) except AttributeError: pass - message = _parse_response_candidate(candidate, streaming=stream) + message = _parse_response_candidate( + candidate, streaming=stream, model_name=response.model_version + ) message.usage_metadata = lc_usage if stream: generations.append( diff --git a/libs/genai/tests/unit_tests/test_chat_models.py b/libs/genai/tests/unit_tests/test_chat_models.py index 4f975cc96..10b6afe70 100644 --- a/libs/genai/tests/unit_tests/test_chat_models.py +++ b/libs/genai/tests/unit_tests/test_chat_models.py @@ -715,6 +715,53 @@ def test_parse_response_candidate(raw_candidate: dict, expected: AIMessage) -> N assert res_kw == exp_kw +def test_parse_response_candidate_includes_model_provider() -> None: + """Test `_parse_response_candidate` has `model_provider` in `response_metadata`.""" + raw_candidate = { + "content": {"parts": [{"text": "Hello, world!"}]}, + "finish_reason": 1, + "safety_ratings": [], + } + + response_candidate = glm.Candidate(raw_candidate) + result = _parse_response_candidate(response_candidate) + + assert hasattr(result, "response_metadata") + assert result.response_metadata["model_provider"] == "google_genai" + + # Streaming + result = _parse_response_candidate(response_candidate, streaming=True) + + assert hasattr(result, "response_metadata") + assert result.response_metadata["model_provider"] == "google_genai" + + +def test_parse_response_candidate_includes_model_name() -> None: + """Test that _parse_response_candidate includes model_name in response_metadata.""" + raw_candidate = { + "content": {"parts": [{"text": "Hello, world!"}]}, + "finish_reason": 1, + "safety_ratings": [], + } + + response_candidate = glm.Candidate(raw_candidate) + result = _parse_response_candidate( + response_candidate, model_name="gemini-2.5-flash" + ) + + assert hasattr(result, "response_metadata") + assert result.response_metadata["model_provider"] == "google_genai" + assert result.response_metadata["model_name"] == "gemini-2.5-flash" + + # No name + + result = _parse_response_candidate(response_candidate) + + assert hasattr(result, "response_metadata") + assert result.response_metadata["model_provider"] == "google_genai" + assert "model_name" not in result.response_metadata + + def test_serialize() -> None: llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", google_api_key="test-key") serialized = dumps(llm) From 8c43565d3d26713a51fac5226a891a4b0de778db Mon Sep 17 00:00:00 2001 From: Mason Daugherty Date: Thu, 18 Sep 2025 16:42:48 -0400 Subject: [PATCH 07/23] fix: resolve `VertexCandidate` type annotation stricter mypy enforcement during upgrade to 3.10 and enabling stricter type checking. `_parse_response_candidate` was already receiving (and handling) both types (via `hasattr`) but the signature only declared one --- libs/vertexai/langchain_google_vertexai/chat_models.py | 1 + 1 file changed, 1 insertion(+) diff --git a/libs/vertexai/langchain_google_vertexai/chat_models.py b/libs/vertexai/langchain_google_vertexai/chat_models.py index 30ee141cc..921041d9a 100644 --- a/libs/vertexai/langchain_google_vertexai/chat_models.py +++ b/libs/vertexai/langchain_google_vertexai/chat_models.py @@ -76,6 +76,7 @@ from langchain_core.utils.pydantic import is_basemodel_subclass from langchain_core.utils.utils import _build_model_kwargs from vertexai.generative_models import ( + Candidate as VertexCandidate, Tool as VertexTool, # TODO: migrate to google-genai since this is deprecated ) from vertexai.generative_models._generative_models import ( From e8b38982fad3b813c61bc5fafd59d74263a088a5 Mon Sep 17 00:00:00 2001 From: Mason Daugherty Date: Mon, 22 Sep 2025 22:51:17 -0400 Subject: [PATCH 08/23] chore: update projects/locks --- libs/community/pyproject.toml | 16 +- libs/community/uv.lock | 3145 +++++++++++++++++++++++++++++++++ libs/genai/uv.lock | 2033 +++++++++++++++++++++ libs/vertexai/pyproject.toml | 42 +- libs/vertexai/uv.lock | 3063 ++++++++++++++++++++++++++++++++ 5 files changed, 8270 insertions(+), 29 deletions(-) create mode 100644 libs/community/uv.lock create mode 100644 libs/genai/uv.lock create mode 100644 libs/vertexai/uv.lock diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml index e6509e323..68948c645 100644 --- a/libs/community/pyproject.toml +++ b/libs/community/pyproject.toml @@ -8,18 +8,18 @@ version = "2.0.10" description = "An integration 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changed, 8 insertions(+), 14 deletions(-) diff --git a/libs/vertexai/langchain_google_vertexai/chat_models.py b/libs/vertexai/langchain_google_vertexai/chat_models.py index adcf8b44f..fc69daede 100644 --- a/libs/vertexai/langchain_google_vertexai/chat_models.py +++ b/libs/vertexai/langchain_google_vertexai/chat_models.py @@ -2049,8 +2049,7 @@ def _prepare_request_gemini( tool_config = _tool_choice_to_tool_config(tool_choice, all_names) else: pass - # Type conversion handled internally - safety_settings = self._safety_settings_gemini(safety_settings) # type: ignore[assignment] + formatted_safety_settings = self._safety_settings_gemini(safety_settings) logprobs = logprobs if logprobs is not None else self.logprobs logprobs = logprobs if isinstance(logprobs, (int, bool)) else False generation_config = self._generation_config_gemini( @@ -2121,12 +2120,9 @@ def _content_to_v1(contents: list[Content]) -> list[v1Content]: if formatted_safety_settings: v1_safety_settings = [ v1SafetySetting( - # Ignores needed due to complex union type where mypy cannot - # infer which variant of SafetySetting is being used - # (Different SDK e.g. vertexai vs gapic has same name) - category=s.category, # type: ignore[union-attr] - method=s.method, # type: ignore[union-attr] - threshold=s.threshold, # type: ignore[union-attr] + category=s.category, + method=s.method, + threshold=s.threshold, ) for s in formatted_safety_settings ] @@ -2153,7 +2149,7 @@ def _content_to_v1(contents: list[Content]) -> list[v1Content]: return GenerateContentRequest( contents=contents, model=self.full_model_name, - safety_settings=safety_settings, + safety_settings=formatted_safety_settings, generation_config=generation_config, cached_content=full_cache_name, ) @@ -2175,7 +2171,7 @@ def _content_to_v1(contents: list[Content]) -> list[v1Content]: system_instruction=system_instruction, tools=formatted_tools, tool_config=tool_config, - safety_settings=safety_settings, + safety_settings=formatted_safety_settings, generation_config=generation_config, model=self.full_model_name, labels=self.labels, @@ -2671,8 +2667,7 @@ def _gemini_response_to_chat_result( info = get_generation_info( candidate, usage_metadata=usage, logprobs=logprobs ) - # Using default streaming=False, but mypy can't verify due to type mismatch - message = _parse_response_candidate(candidate) # type: ignore[call-overload] + message = _parse_response_candidate(candidate) message.response_metadata["model_name"] = self.model_name if isinstance(message, AIMessage): message.usage_metadata = lc_usage @@ -2720,8 +2715,7 @@ def _gemini_chunk_to_generation_chunk( generation_info = {} else: top_candidate = response_chunk.candidates[0] - # Type ignore since mypy infers literal as bool - message = _parse_response_candidate(top_candidate, streaming=True) # type: ignore[call-overload] + message = _parse_response_candidate(top_candidate, streaming=True) if lc_usage: message.usage_metadata = lc_usage generation_info = get_generation_info( From 840748e6dcc310fcfd1ffcbf25af1d71efc0a1e1 Mon Sep 17 00:00:00 2001 From: Mason Daugherty Date: Thu, 25 Sep 2025 23:26:06 -0400 Subject: [PATCH 10/23] !chore(vertex): remove `embed_image()` --- .../langchain_google_vertexai/embeddings.py | 42 ------------------- 1 file changed, 42 deletions(-) diff --git a/libs/vertexai/langchain_google_vertexai/embeddings.py b/libs/vertexai/langchain_google_vertexai/embeddings.py index 0489b3679..0e2b9874d 100644 --- a/libs/vertexai/langchain_google_vertexai/embeddings.py +++ b/libs/vertexai/langchain_google_vertexai/embeddings.py @@ -2,7 +2,6 @@ import re import string import threading -import warnings from concurrent.futures import ThreadPoolExecutor, wait from enum import Enum, auto from functools import cached_property @@ -17,7 +16,6 @@ ServiceUnavailable, ) from google.cloud.aiplatform import telemetry -from langchain_core._api.deprecation import deprecated from langchain_core.embeddings import Embeddings from langchain_core.language_models.llms import create_base_retry_decorator from pydantic import ConfigDict, model_validator @@ -500,46 +498,6 @@ def embed_query( """ return self.embed([text], 1, embeddings_task_type)[0] - @deprecated( - since="2.0.1", removal="3.0.0", alternative="VertexAIEmbeddings.embed_images()" - ) - def embed_image( - self, - image_path: str, - contextual_text: Optional[str] = None, - dimensions: Optional[int] = None, - ) -> List[float]: - """Embed an image. - - Args: - image_path: Path to image (Google Cloud Storage or web) to generate - embeddings for. - contextual_text: Text to generate embeddings for. - - Returns: - Embedding for the image. - """ - warnings.warn( - "The `embed_image()` API will be deprecated and replaced by \ - `embed_images()`. Change your usage to \ - `embed_images([image_path1, image_path2])` and note\ - that the result returned will be a list of image embeddings." - ) - if self.model_type != GoogleEmbeddingModelType.MULTIMODAL: - msg = "Only supported for multimodal models" - raise NotImplementedError(msg) - - image_loader = self._image_bytes_loader_client - bytes_image = image_loader.load_bytes(image_path) - image = Image(bytes_image) - result: MultiModalEmbeddingResponse = self.instance[ - "get_embeddings_with_retry" - ](image=image, contextual_text=contextual_text, dimension=dimensions) - if result.image_embedding is None: - msg = "Failed to generate image embedding" - raise ValueError(msg) - return result.image_embedding - def embed_images( self, uris: List[str], From 4e44c2451f251bb7c7b1416323901bcde0d2e905 Mon Sep 17 00:00:00 2001 From: Mason Daugherty Date: Thu, 25 Sep 2025 23:28:42 -0400 Subject: [PATCH 11/23] !chore(vertex): remove `embbedings` property --- .../langchain_google_vertexai/vectorstores/vectorstores.py | 7 ------- 1 file changed, 7 deletions(-) diff --git a/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py b/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py index 87d77db79..71be24601 100644 --- a/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py +++ b/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py @@ -8,7 +8,6 @@ NumericNamespace, ) from google.oauth2.service_account import Credentials -from langchain_core._api.deprecation import deprecated from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.vectorstores import VectorStore @@ -60,12 +59,6 @@ def __init__( ) self._embeddings = embeddings or embbedings or self._get_default_embeddings() - @property - @deprecated(since="0.1.0", removal="3.0.0", alternative="embeddings") - def embbedings(self) -> Embeddings: - """Returns the embeddings object.""" - return self._embeddings - @property def embeddings(self) -> Embeddings: return self._embeddings From ec6ccbade00ea11efe343d65c524f128fc6f084f Mon Sep 17 00:00:00 2001 From: Mason Daugherty Date: Thu, 25 Sep 2025 23:29:44 -0400 Subject: [PATCH 12/23] !chore(community): remove `BigQueryVectorSearch` --- .../langchain_google_community/__init__.py | 2 - .../bigquery_vector_search.py | 866 ------------------ .../test_bigquery_vector_search.py | 108 --- 3 files changed, 976 deletions(-) delete mode 100644 libs/community/langchain_google_community/bigquery_vector_search.py delete mode 100644 libs/community/tests/integration_tests/test_bigquery_vector_search.py diff --git a/libs/community/langchain_google_community/__init__.py b/libs/community/langchain_google_community/__init__.py index 2012fbb61..452ac72f4 100644 --- a/libs/community/langchain_google_community/__init__.py +++ b/libs/community/langchain_google_community/__init__.py @@ -1,5 +1,4 @@ from langchain_google_community.bigquery import BigQueryLoader -from langchain_google_community.bigquery_vector_search import BigQueryVectorSearch from langchain_google_community.bq_storage_vectorstores.bigquery import ( BigQueryVectorStore, ) @@ -64,7 +63,6 @@ __all__ = [ "BigQueryLoader", "BigQueryVectorStore", - "BigQueryVectorSearch", "CalendarCreateEvent", "CalendarDeleteEvent", "CalendarMoveEvent", diff --git a/libs/community/langchain_google_community/bigquery_vector_search.py b/libs/community/langchain_google_community/bigquery_vector_search.py deleted file mode 100644 index 8164df585..000000000 --- a/libs/community/langchain_google_community/bigquery_vector_search.py +++ /dev/null @@ -1,866 +0,0 @@ -"""Vector Store in Google Cloud BigQuery.""" - -from __future__ import annotations - -import asyncio -import json -import logging -import sys -import uuid -from datetime import datetime -from functools import partial -from threading import Lock, Thread -from typing import Any, Callable, Dict, List, Optional, Tuple, Type - -import numpy as np -from google.api_core.exceptions import ClientError -from langchain_community.vectorstores.utils import ( - DistanceStrategy, - maximal_marginal_relevance, -) -from langchain_core._api.deprecation import deprecated -from langchain_core.documents import Document -from langchain_core.embeddings import Embeddings -from langchain_core.vectorstores import VectorStore - -from langchain_google_community._utils import get_client_info - -DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.EUCLIDEAN_DISTANCE -DEFAULT_DOC_ID_COLUMN_NAME = "doc_id" # document id -DEFAULT_TEXT_EMBEDDING_COLUMN_NAME = "text_embedding" # embeddings vectors -DEFAULT_METADATA_COLUMN_NAME = "metadata" # document metadata -DEFAULT_CONTENT_COLUMN_NAME = "content" # text content, do not rename -DEFAULT_TOP_K = 4 # default number of documents returned from similarity search - -_MIN_INDEX_ROWS = 5000 # minimal number of rows for creating an index -_INDEX_CHECK_PERIOD_SECONDS = 60 # Do not check for index more often that this. -_vector_table_lock = Lock() # process-wide BigQueryVectorSearch table lock - - -@deprecated( - since="1.0.5", - removal="2.0.0", - alternative_import="langchain_google_community.BigQueryVectorStore", -) -class BigQueryVectorSearch(VectorStore): - """Google Cloud BigQuery vector store. - - To use, you need the following packages installed: - google-cloud-bigquery - """ - - def __init__( - self, - embedding: Embeddings, - project_id: str, - dataset_name: str, - table_name: str, - location: str = "US", - content_field: str = DEFAULT_CONTENT_COLUMN_NAME, - metadata_field: str = DEFAULT_METADATA_COLUMN_NAME, - text_embedding_field: str = DEFAULT_TEXT_EMBEDDING_COLUMN_NAME, - doc_id_field: str = DEFAULT_DOC_ID_COLUMN_NAME, - distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY, - credentials: Optional[Any] = None, - ): - """Constructor for BigQueryVectorSearch. - - Args: - embedding (Embeddings): Text Embedding model to use. - project_id (str): GCP project. - dataset_name (str): BigQuery dataset to store documents and embeddings. - table_name (str): BigQuery table name. - location (str, optional): BigQuery region. Defaults to - `US`(multi-region). - content_field (str): Specifies the column to store the content. - Defaults to `content`. - metadata_field (str): Specifies the column to store the metadata. - Defaults to `metadata`. - text_embedding_field (str): Specifies the column to store - the embeddings vector. - Defaults to `text_embedding`. - doc_id_field (str): Specifies the column to store the document id. - Defaults to `doc_id`. - distance_strategy (DistanceStrategy, optional): - Determines the strategy employed for calculating - the distance between vectors in the embedding space. - Defaults to EUCLIDEAN_DISTANCE. - Available options are: - - COSINE: Measures the similarity between two vectors of an inner - product space. - - EUCLIDEAN_DISTANCE: Computes the Euclidean distance between - two vectors. This metric considers the geometric distance in - the vector space, and might be more suitable for embeddings - that rely on spatial relationships. This is the default behavior - credentials (Credentials, optional): Custom Google Cloud credentials - to use. Defaults to None. - """ - try: - from google.cloud import bigquery # type: ignore[attr-defined] - - client_info = get_client_info(module="bigquery-vector-search") - self.bq_client = bigquery.Client( - project=project_id, - location=location, - credentials=credentials, - client_info=client_info, - ) - except ModuleNotFoundError: - raise ImportError( - "Could not import google-cloud-bigquery python package. " - "Please, install bigquery dependency group: " - "`pip install langchain-google-community[bigquery]`" - ) - self._logger = logging.getLogger(__name__) - self._creating_index = False - self._have_index = False - self.embedding_model = embedding - self.project_id = project_id - self.dataset_name = dataset_name - self.table_name = table_name - self.location = location - self.content_field = content_field - self.metadata_field = metadata_field - self.text_embedding_field = text_embedding_field - self.doc_id_field = doc_id_field - self.distance_strategy = distance_strategy - self._full_table_id = f"{self.project_id}.{self.dataset_name}.{self.table_name}" - self._logger.debug("Using table `%s`", self.full_table_id) - with _vector_table_lock: - self.vectors_table = self._initialize_table() - self._last_index_check = datetime.min - self._initialize_vector_index() - - def _initialize_table(self) -> Any: - """Validates or creates the BigQuery table.""" - from google.cloud import bigquery # type: ignore[attr-defined] - - table_ref = bigquery.TableReference.from_string(self._full_table_id) - table = self.bq_client.create_table(table_ref, exists_ok=True) - changed_schema = False - schema = table.schema.copy() - columns = {c.name: c for c in schema} - if self.doc_id_field not in columns: - changed_schema = True - schema.append( - bigquery.SchemaField(name=self.doc_id_field, field_type="STRING") - ) - elif ( - columns[self.doc_id_field].field_type != "STRING" - or columns[self.doc_id_field].mode == "REPEATED" - ): - raise ValueError(f"Column {self.doc_id_field} must be of STRING type") - if self.metadata_field not in columns: - changed_schema = True - schema.append( - bigquery.SchemaField(name=self.metadata_field, field_type="JSON") - ) - elif ( - columns[self.metadata_field].field_type not in ["JSON", "STRING"] - or columns[self.metadata_field].mode == "REPEATED" - ): - raise ValueError( - f"Column {self.metadata_field} must be of STRING or JSON type" - ) - if self.content_field not in columns: - changed_schema = True - schema.append( - bigquery.SchemaField(name=self.content_field, field_type="STRING") - ) - elif ( - columns[self.content_field].field_type != "STRING" - or columns[self.content_field].mode == "REPEATED" - ): - raise ValueError(f"Column {self.content_field} must be of STRING type") - if self.text_embedding_field not in columns: - changed_schema = True - schema.append( - bigquery.SchemaField( - name=self.text_embedding_field, - field_type="FLOAT64", - mode="REPEATED", - ) - ) - elif ( - columns[self.text_embedding_field].field_type not in ("FLOAT", "FLOAT64") - or columns[self.text_embedding_field].mode != "REPEATED" - ): - raise ValueError( - f"Column {self.text_embedding_field} must be of ARRAY type" - ) - if changed_schema: - self._logger.debug("Updated table `%s` schema.", self.full_table_id) - table.schema = schema - table = self.bq_client.update_table(table, fields=["schema"]) - return table - - def _initialize_vector_index(self) -> Any: - """ - A vector index in BigQuery table enables efficient - approximate vector search. - """ - from google.cloud import bigquery # type: ignore[attr-defined] - - if self._have_index or self._creating_index: - # Already have an index or in the process of creating one. - return - table = self.bq_client.get_table(self.vectors_table) - if (table.num_rows or 0) < _MIN_INDEX_ROWS: - # Not enough rows to create index. - self._logger.debug("Not enough rows to create a vector index.") - return - if ( - datetime.utcnow() - self._last_index_check - ).total_seconds() < _INDEX_CHECK_PERIOD_SECONDS: - return - with _vector_table_lock: - if self._creating_index or self._have_index: - return - self._last_index_check = datetime.utcnow() - # Check if index exists, create if necessary - check_query = ( - f"SELECT 1 FROM `{self.project_id}.{self.dataset_name}" - ".INFORMATION_SCHEMA.VECTOR_INDEXES` WHERE" - f" table_name = '{self.table_name}'" - ) - job = self.bq_client.query( - check_query, api_method=bigquery.enums.QueryApiMethod.QUERY - ) - if job.result().total_rows == 0: - # Need to create an index. Make it in a separate thread. - self._create_index_in_background() - else: - self._logger.debug("Vector index already exists.") - self._have_index = True - - def _create_index_in_background(self): # type: ignore[no-untyped-def] - if self._have_index or self._creating_index: - # Already have an index or in the process of creating one. - return - self._creating_index = True - self._logger.debug("Trying to create a vector index.") - thread = Thread(target=self._create_index, daemon=True) - thread.start() - - def _create_index(self): # type: ignore[no-untyped-def] - table = self.bq_client.get_table(self.vectors_table) - if (table.num_rows or 0) < _MIN_INDEX_ROWS: - # Not enough rows to create index. - return - if self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE: - distance_type = "EUCLIDEAN" - elif self.distance_strategy == DistanceStrategy.COSINE: - distance_type = "COSINE" - # Default to EUCLIDEAN_DISTANCE - else: - distance_type = "EUCLIDEAN" - index_name = f"{self.table_name}_langchain_index" - try: - sql = f""" - CREATE VECTOR INDEX IF NOT EXISTS - `{index_name}` - ON `{self.full_table_id}`({self.text_embedding_field}) - OPTIONS(distance_type="{distance_type}", index_type="IVF") - """ - self.bq_client.query(sql).result() - self._have_index = True - except ClientError as ex: - self._logger.debug("Vector index creation failed (%s).", ex.args[0]) - finally: - self._creating_index = False - - def _persist(self, data: Dict[str, Any]) -> None: - """Saves documents and embeddings to BigQuery.""" - from google.cloud import bigquery # type: ignore[attr-defined] - - data_len = len(data[list(data.keys())[0]]) - if data_len == 0: - return - - list_of_dicts = [dict(zip(data, t)) for t in zip(*data.values())] - - job_config = bigquery.LoadJobConfig() - job_config.schema = self.vectors_table.schema - job_config.schema_update_options = ( - bigquery.SchemaUpdateOption.ALLOW_FIELD_ADDITION - ) - job_config.write_disposition = bigquery.WriteDisposition.WRITE_APPEND - job = self.bq_client.load_table_from_json( - list_of_dicts, self.vectors_table, job_config=job_config - ) - job.result() - - @property - def embeddings(self) -> Optional[Embeddings]: - return self.embedding_model - - @property - def full_table_id(self) -> str: - return self._full_table_id - - def add_texts( # type: ignore[override] - self, - texts: List[str], - metadatas: Optional[List[dict]] = None, - **kwargs: Any, - ) -> List[str]: - """Run more texts through the embeddings and add to the vectorstore. - - Args: - texts: List of strings to add to the vectorstore. - metadatas: Optional list of metadata associated with the texts. - - Returns: - List of ids from adding the texts into the vectorstore. - """ - embs = self.embedding_model.embed_documents(texts) - return self.add_texts_with_embeddings(texts, embs, metadatas, **kwargs) - - def add_texts_with_embeddings( - self, - texts: List[str], - embs: List[List[float]], - metadatas: Optional[List[dict]] = None, - **kwargs: Any, - ) -> List[str]: - """Run more texts through the embeddings and add to the vectorstore. - - Args: - texts: List of strings to add to the vectorstore. - embs: List of lists of floats with text embeddings for texts. - metadatas: Optional list of metadata associated with the texts. - - Returns: - List of ids from adding the texts into the vectorstore. - """ - ids = [uuid.uuid4().hex for _ in texts] - values_dict: Dict[str, List[Any]] = { - self.content_field: texts, - self.doc_id_field: ids, - } - if not metadatas: - metadatas = [] - len_diff = len(ids) - len(metadatas) - add_meta = [None for _ in range(0, len_diff)] - metadatas = [m if m is not None else {} for m in metadatas + add_meta] - values_dict[self.metadata_field] = metadatas - values_dict[self.text_embedding_field] = embs - self._persist(values_dict) - return ids - - def get_documents( - self, ids: Optional[List[str]] = None, filter: Optional[Dict[str, Any]] = None - ) -> List[Document]: - """Search documents by their ids or metadata values. - - Args: - ids: List of ids of documents to retrieve from the vectorstore. - filter: Filter on metadata properties, e.g. - { - "str_property": "foo", - "int_property": 123 - } - Returns: - List of ids from adding the texts into the vectorstore. - """ - if ids and len(ids) > 0: - from google.cloud import bigquery # type: ignore[attr-defined] - - job_config = bigquery.QueryJobConfig( - query_parameters=[ - bigquery.ArrayQueryParameter("ids", "STRING", ids), - ] - ) - id_expr = f"{self.doc_id_field} IN UNNEST(@ids)" - else: - job_config = None - id_expr = "TRUE" - if filter: - filter_expressions = [] - for i in filter.items(): - if isinstance(i[1], float): - expr = ( - "ABS(CAST(JSON_VALUE(" - f"`{self.metadata_field}`,'$.{i[0]}') " - f"AS FLOAT64) - {i[1]}) " - f"<= {sys.float_info.epsilon}" - ) - else: - val = str(i[1]).replace('"', '\\"') - expr = f"JSON_VALUE(`{self.metadata_field}`,'$.{i[0]}') = \"{val}\"" - filter_expressions.append(expr) - filter_expression_str = " AND ".join(filter_expressions) - where_filter_expr = f" AND ({filter_expression_str})" - else: - where_filter_expr = "" - - job = self.bq_client.query( - f""" - SELECT * FROM `{self.full_table_id}` WHERE {id_expr} - {where_filter_expr} - """, - job_config=job_config, - ) - docs: List[Document] = [] - for row in job: - metadata = None - if self.metadata_field: - metadata = row[self.metadata_field] - if metadata: - if not isinstance(metadata, dict): - metadata = json.loads(metadata) - else: - metadata = {} - metadata["__id"] = row[self.doc_id_field] - doc = Document(page_content=row[self.content_field], metadata=metadata) - docs.append(doc) - return docs - - def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]: - """Delete by vector ID or other criteria. - - Args: - ids: List of ids to delete. - **kwargs: Other keyword arguments that subclasses might use. - - Returns: - Optional[bool]: True if deletion is successful, - False otherwise, None if not implemented. - """ - if not ids or len(ids) == 0: - return True - from google.cloud import bigquery # type: ignore[attr-defined] - - job_config = bigquery.QueryJobConfig( - query_parameters=[ - bigquery.ArrayQueryParameter("ids", "STRING", ids), - ] - ) - self.bq_client.query( - f""" - DELETE FROM `{self.full_table_id}` WHERE {self.doc_id_field} - IN UNNEST(@ids) - """, - job_config=job_config, - ).result() - return True - - async def adelete( - self, ids: Optional[List[str]] = None, **kwargs: Any - ) -> Optional[bool]: - """Delete by vector ID or other criteria. - - Args: - ids: List of ids to delete. - **kwargs: Other keyword arguments that subclasses might use. - - Returns: - Optional[bool]: True if deletion is successful, - False otherwise, None if not implemented. - """ - return await asyncio.get_running_loop().run_in_executor( - None, partial(self.delete, **kwargs), ids - ) - - def _search_with_score_and_embeddings_by_vector( - self, - embedding: List[float], - k: int = DEFAULT_TOP_K, - filter: Optional[Dict[str, Any]] = None, - brute_force: bool = False, - fraction_lists_to_search: Optional[float] = None, - ) -> List[Tuple[Document, List[float], float]]: - from google.cloud import bigquery # type: ignore[attr-defined] - - # Create an index if no index exists. - if not self._have_index and not self._creating_index: - self._initialize_vector_index() - # Prepare filter - filter_expr = "TRUE" - if filter: - filter_expressions = [] - for i in filter.items(): - if isinstance(i[1], float): - expr = ( - "ABS(CAST(JSON_VALUE(" - f"base.`{self.metadata_field}`,'$.{i[0]}') " - f"AS FLOAT64) - {i[1]}) " - f"<= {sys.float_info.epsilon}" - ) - else: - val = str(i[1]).replace('"', '\\"') - expr = ( - f"JSON_VALUE(base.`{self.metadata_field}`,'$.{i[0]}')" - f' = "{val}"' - ) - filter_expressions.append(expr) - filter_expression_str = " AND ".join(filter_expressions) - filter_expr += f" AND ({filter_expression_str})" - # Configure and run a query job. - job_config = bigquery.QueryJobConfig( - query_parameters=[ - bigquery.ArrayQueryParameter("v", "FLOAT64", embedding), - ], - use_query_cache=False, - priority=bigquery.QueryPriority.BATCH, - ) - if self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE: - distance_type = "EUCLIDEAN" - elif self.distance_strategy == DistanceStrategy.COSINE: - distance_type = "COSINE" - # Default to EUCLIDEAN_DISTANCE - else: - distance_type = "EUCLIDEAN" - if brute_force: - options_string = ",options => '{\"use_brute_force\":true}'" - elif fraction_lists_to_search: - if fraction_lists_to_search == 0 or fraction_lists_to_search >= 1.0: - raise ValueError( - "`fraction_lists_to_search` must be between 0.0 and 1.0" - ) - options_string = ( - ',options => \'{"fraction_lists_to_search":' - f"{fraction_lists_to_search}}}'" - ) - else: - options_string = "" - query = f""" - SELECT - base.*, - distance AS _vector_search_distance - FROM VECTOR_SEARCH( - TABLE `{self.full_table_id}`, - "{self.text_embedding_field}", - (SELECT @v AS {self.text_embedding_field}), - distance_type => "{distance_type}", - top_k => {k} - {options_string} - ) - WHERE {filter_expr} - LIMIT {k} - """ - document_tuples: List[Tuple[Document, List[float], float]] = [] - # TODO(vladkol): Use jobCreationMode=JOB_CREATION_OPTIONAL when available. - job = self.bq_client.query( - query, job_config=job_config, api_method=bigquery.enums.QueryApiMethod.QUERY - ) - # Process job results. - for row in job: - metadata = row[self.metadata_field] - if metadata: - if not isinstance(metadata, dict): - metadata = json.loads(metadata) - else: - metadata = {} - metadata["__id"] = row[self.doc_id_field] - metadata["__job_id"] = job.job_id - doc = Document(page_content=row[self.content_field], metadata=metadata) - document_tuples.append( - (doc, row[self.text_embedding_field], row["_vector_search_distance"]) - ) - return document_tuples - - def similarity_search_with_score_by_vector( - self, - embedding: List[float], - k: int = DEFAULT_TOP_K, - filter: Optional[Dict[str, Any]] = None, - brute_force: bool = False, - fraction_lists_to_search: Optional[float] = None, - **kwargs: Any, - ) -> List[Tuple[Document, float]]: - """Return docs most similar to embedding vector. - - Args: - embedding: Embedding to look up documents similar to. - k: Number of Documents to return. Defaults to 4. - filter: Filter on metadata properties, e.g. - { - "str_property": "foo", - "int_property": 123 - } - brute_force: Whether to use brute force search. Defaults to False. - fraction_lists_to_search: Optional percentage of lists to search, - must be in range 0.0 and 1.0, exclusive. - If Node, uses service's default which is 0.05. - - Returns: - List of Documents most similar to the query vector with distance. - """ - del kwargs - document_tuples = self._search_with_score_and_embeddings_by_vector( - embedding, k, filter, brute_force, fraction_lists_to_search - ) - return [(doc, distance) for doc, _, distance in document_tuples] - - def similarity_search_by_vector( - self, - embedding: List[float], - k: int = DEFAULT_TOP_K, - filter: Optional[Dict[str, Any]] = None, - brute_force: bool = False, - fraction_lists_to_search: Optional[float] = None, - **kwargs: Any, - ) -> List[Document]: - """Return docs most similar to embedding vector. - - Args: - embedding: Embedding to look up documents similar to. - k: Number of Documents to return. Defaults to 4. - filter: Filter on metadata properties, e.g. - { - "str_property": "foo", - "int_property": 123 - } - brute_force: Whether to use brute force search. Defaults to False. - fraction_lists_to_search: Optional percentage of lists to search, - must be in range 0.0 and 1.0, exclusive. - If Node, uses service's default which is 0.05. - - Returns: - List of Documents most similar to the query vector. - """ - tuples = self.similarity_search_with_score_by_vector( - embedding, k, filter, brute_force, fraction_lists_to_search, **kwargs - ) - return [i[0] for i in tuples] - - def similarity_search_with_score( - self, - query: str, - k: int = DEFAULT_TOP_K, - filter: Optional[Dict[str, Any]] = None, - brute_force: bool = False, - fraction_lists_to_search: Optional[float] = None, - **kwargs: Any, - ) -> List[Tuple[Document, float]]: - """Run similarity search with score. - - Args: - query: search query text. - k: Number of Documents to return. Defaults to 4. - filter: Filter on metadata properties, e.g. - { - "str_property": "foo", - "int_property": 123 - } - brute_force: Whether to use brute force search. Defaults to False. - fraction_lists_to_search: Optional percentage of lists to search, - must be in range 0.0 and 1.0, exclusive. - If Node, uses service's default which is 0.05. - - Returns: - List of Documents most similar to the query vector, with similarity scores. - """ - emb = self.embedding_model.embed_query(query) # type: ignore - return self.similarity_search_with_score_by_vector( - emb, k, filter, brute_force, fraction_lists_to_search, **kwargs - ) - - def similarity_search( - self, - query: str, - k: int = DEFAULT_TOP_K, - filter: Optional[Dict[str, Any]] = None, - brute_force: bool = False, - fraction_lists_to_search: Optional[float] = None, - **kwargs: Any, - ) -> List[Document]: - """Run similarity search. - - Args: - query: search query text. - k: Number of Documents to return. Defaults to 4. - filter: Filter on metadata properties, e.g. - { - "str_property": "foo", - "int_property": 123 - } - brute_force: Whether to use brute force search. Defaults to False. - fraction_lists_to_search: Optional percentage of lists to search, - must be in range 0.0 and 1.0, exclusive. - If Node, uses service's default which is 0.05. - - Returns: - List of Documents most similar to the query vector. - """ - tuples = self.similarity_search_with_score( - query, k, filter, brute_force, fraction_lists_to_search, **kwargs - ) - return [i[0] for i in tuples] - - def _select_relevance_score_fn(self) -> Callable[[float], float]: - if self.distance_strategy == DistanceStrategy.COSINE: - return BigQueryVectorSearch._cosine_relevance_score_fn - else: - raise ValueError( - "Relevance score is not supported " - f"for `{self.distance_strategy}` distance." - ) - - def max_marginal_relevance_search( - self, - query: str, - k: int = DEFAULT_TOP_K, - fetch_k: int = DEFAULT_TOP_K * 5, - lambda_mult: float = 0.5, - filter: Optional[Dict[str, Any]] = None, - brute_force: bool = False, - fraction_lists_to_search: Optional[float] = None, - **kwargs: Any, - ) -> List[Document]: - """Return docs selected using the maximal marginal relevance. - - Maximal marginal relevance optimizes for similarity to query AND diversity - among selected documents. - - Args: - query: search query text. - k: Number of Documents to return. Defaults to 4. - fetch_k: Number of Documents to fetch to pass to MMR algorithm. - lambda_mult: Number between 0 and 1 that determines the degree - of diversity among the results with 0 corresponding - to maximum diversity and 1 to minimum diversity. - Defaults to 0.5. - filter: Filter on metadata properties, e.g. - { - "str_property": "foo", - "int_property": 123 - } - brute_force: Whether to use brute force search. Defaults to False. - fraction_lists_to_search: Optional percentage of lists to search, - must be in range 0.0 and 1.0, exclusive. - If Node, uses service's default which is 0.05. - Returns: - List of Documents selected by maximal marginal relevance. - """ - query_embedding = self.embedding_model.embed_query( # type: ignore - query - ) - doc_tuples = self._search_with_score_and_embeddings_by_vector( - query_embedding, fetch_k, filter, brute_force, fraction_lists_to_search - ) - doc_embeddings = [d[1] for d in doc_tuples] - mmr_doc_indexes = maximal_marginal_relevance( - np.array(query_embedding), doc_embeddings, lambda_mult=lambda_mult, k=k - ) - return [doc_tuples[i][0] for i in mmr_doc_indexes] - - def max_marginal_relevance_search_by_vector( - self, - embedding: List[float], - k: int = DEFAULT_TOP_K, - fetch_k: int = DEFAULT_TOP_K * 5, - lambda_mult: float = 0.5, - filter: Optional[Dict[str, Any]] = None, - brute_force: bool = False, - fraction_lists_to_search: Optional[float] = None, - **kwargs: Any, - ) -> List[Document]: - """Return docs selected using the maximal marginal relevance. - - Maximal marginal relevance optimizes for similarity to query AND diversity - among selected documents. - - Args: - embedding: Embedding to look up documents similar to. - k: Number of Documents to return. Defaults to 4. - fetch_k: Number of Documents to fetch to pass to MMR algorithm. - lambda_mult: Number between 0 and 1 that determines the degree - of diversity among the results with 0 corresponding - to maximum diversity and 1 to minimum diversity. - Defaults to 0.5. - filter: Filter on metadata properties, e.g. - { - "str_property": "foo", - "int_property": 123 - } - brute_force: Whether to use brute force search. Defaults to False. - fraction_lists_to_search: Optional percentage of lists to search, - must be in range 0.0 and 1.0, exclusive. - If Node, uses service's default which is 0.05. - Returns: - List of Documents selected by maximal marginal relevance. - """ - doc_tuples = self._search_with_score_and_embeddings_by_vector( - embedding, fetch_k, filter, brute_force, fraction_lists_to_search - ) - doc_embeddings = [d[1] for d in doc_tuples] - mmr_doc_indexes = maximal_marginal_relevance( - np.array(embedding), doc_embeddings, lambda_mult=lambda_mult, k=k - ) - return [doc_tuples[i][0] for i in mmr_doc_indexes] - - async def amax_marginal_relevance_search( - self, - query: str, - k: int = DEFAULT_TOP_K, - fetch_k: int = DEFAULT_TOP_K * 5, - lambda_mult: float = 0.5, - filter: Optional[Dict[str, Any]] = None, - brute_force: bool = False, - fraction_lists_to_search: Optional[float] = None, - **kwargs: Any, - ) -> List[Document]: - """Return docs selected using the maximal marginal relevance.""" - - func = partial( - self.max_marginal_relevance_search, - query, - k=k, - fetch_k=fetch_k, - lambda_mult=lambda_mult, - filter=filter, - brute_force=brute_force, - fraction_lists_to_search=fraction_lists_to_search, - **kwargs, - ) - return await asyncio.get_event_loop().run_in_executor(None, func) - - async def amax_marginal_relevance_search_by_vector( - self, - embedding: List[float], - k: int = DEFAULT_TOP_K, - fetch_k: int = DEFAULT_TOP_K * 5, - lambda_mult: float = 0.5, - filter: Optional[Dict[str, Any]] = None, - brute_force: bool = False, - fraction_lists_to_search: Optional[float] = None, - **kwargs: Any, - ) -> List[Document]: - """Return docs selected using the maximal marginal relevance.""" - return await asyncio.get_running_loop().run_in_executor( - None, - partial(self.max_marginal_relevance_search_by_vector, **kwargs), - embedding, - k, - fetch_k, - lambda_mult, - filter, - brute_force, - fraction_lists_to_search, - ) - - @classmethod - def from_texts( - cls: Type["BigQueryVectorSearch"], - texts: List[str], - embedding: Embeddings, - metadatas: Optional[List[dict]] = None, - **kwargs: Any, - ) -> "BigQueryVectorSearch": - """Return VectorStore initialized from texts and embeddings.""" - vs_obj = BigQueryVectorSearch(embedding=embedding, **kwargs) - vs_obj.add_texts(texts, metadatas) - return vs_obj - - def explore_job_stats(self, job_id: str) -> Dict: - """Return the statistics for a single job execution. - - Args: - job_id: The BigQuery Job id. - - Returns: - A dictionary of job statistics for a given job. - """ - return self.bq_client.get_job(job_id)._properties["statistics"] diff --git a/libs/community/tests/integration_tests/test_bigquery_vector_search.py b/libs/community/tests/integration_tests/test_bigquery_vector_search.py deleted file mode 100644 index 67b0e1ab0..000000000 --- a/libs/community/tests/integration_tests/test_bigquery_vector_search.py +++ /dev/null @@ -1,108 +0,0 @@ -"""Test BigQuery Vector Search. -In order to run this test, you need to install Google Cloud BigQuery SDK -pip install google-cloud-bigquery -Your end-user credentials would be used to make the calls (make sure you've run -`gcloud auth login` first). -""" - -import os -import uuid - -import pytest - -from langchain_google_community import BigQueryVectorSearch -from tests.integration_tests.fake import FakeEmbeddings - -TEST_TABLE_NAME = "langchain_test_table" - - -@pytest.fixture(scope="class") -def store(request: pytest.FixtureRequest) -> BigQueryVectorSearch: - """BigQueryVectorStore tests context. - - In order to run this test, you define PROJECT_ID environment variable - with GCP project id. - - Example: - export PROJECT_ID=... - """ - from google.cloud import bigquery # type: ignore[attr-defined] - - bigquery.Client(location="US").create_dataset( - TestBigQueryVectorStore.dataset_name, exists_ok=True - ) - TestBigQueryVectorStore.store = BigQueryVectorSearch( - project_id=os.environ.get("PROJECT_ID", None), # type: ignore[arg-type] - embedding=FakeEmbeddings(), # type: ignore[call-arg] - dataset_name=TestBigQueryVectorStore.dataset_name, - table_name=TEST_TABLE_NAME, - ) - TestBigQueryVectorStore.store.add_texts( - TestBigQueryVectorStore.texts, TestBigQueryVectorStore.metadatas - ) - - def teardown() -> None: - bigquery.Client(location="US").delete_dataset( - TestBigQueryVectorStore.dataset_name, - delete_contents=True, - not_found_ok=True, - ) - - request.addfinalizer(teardown) - return TestBigQueryVectorStore.store - - -class TestBigQueryVectorStore: - """BigQueryVectorStore tests class.""" - - dataset_name = uuid.uuid4().hex - store: BigQueryVectorSearch - texts = ["apple", "ice cream", "Saturn", "candy", "banana"] - metadatas = [ - { - "kind": "fruit", - }, - { - "kind": "treat", - }, - { - "kind": "planet", - }, - { - "kind": "treat", - }, - { - "kind": "fruit", - }, - ] - - @pytest.mark.skip(reason="investigating") - @pytest.mark.extended - def test_semantic_search(self, store: BigQueryVectorSearch) -> None: - """Test on semantic similarity.""" - docs = store.similarity_search("food", k=4) - print(docs) # noqa: T201 - kinds = [d.metadata["kind"] for d in docs] - assert "fruit" in kinds - assert "treat" in kinds - assert "planet" not in kinds - - @pytest.mark.skip(reason="investigating") - @pytest.mark.extended - def test_semantic_search_filter_fruits(self, store: BigQueryVectorSearch) -> None: - """Test on semantic similarity with metadata filter.""" - docs = store.similarity_search("food", filter={"kind": "fruit"}) - kinds = [d.metadata["kind"] for d in docs] - assert "fruit" in kinds - assert "treat" not in kinds - assert "planet" not in kinds - - @pytest.mark.skip(reason="investigating") - @pytest.mark.extended - def test_get_doc_by_filter(self, store: BigQueryVectorSearch) -> None: - """Test on document retrieval with metadata filter.""" - docs = store.get_documents(filter={"kind": "fruit"}) - kinds = [d.metadata["kind"] for d in docs] - assert "fruit" in kinds - assert "treat" not in kinds - assert "planet" not in kinds From 886ea5dd0cd637cd1779fc850d22bcc0d45692a0 Mon Sep 17 00:00:00 2001 From: Mason Daugherty Date: Thu, 25 Sep 2025 23:32:02 -0400 Subject: [PATCH 13/23] !chore(vertex): remove deprecated 'embbedings' parameter and warning --- .../vectorstores/vectorstores.py | 15 +-------------- 1 file changed, 1 insertion(+), 14 deletions(-) diff --git a/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py b/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py index 71be24601..d18885a07 100644 --- a/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py +++ b/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py @@ -31,7 +31,6 @@ def __init__( self, searcher: Searcher, document_storage: DocumentStorage, - embbedings: Optional[Embeddings] = None, # Deprecated parameter embeddings: Optional[Embeddings] = None, ) -> None: """Constructor. @@ -39,25 +38,13 @@ def __init__( Args: searcher: Object in charge of searching and storing the index. document_storage: Object in charge of storing and retrieving documents. - embbedings: Object in charge of transforming text to embbeddings. - Deprecated: Use 'embeddings' instead. embeddings: Object in charge of transforming text to embeddings. """ super().__init__() self._searcher = searcher self._document_storage = document_storage - # Add explicit warning when the misspelled parameter is used - if embbedings is not None: - warnings.warn( - message=( - "The parameter `embbedings` is deprecated due to a spelling error. " - "Please use `embeddings` instead. " - "Support for `embbedings` will be removed in a future version." - ), - category=DeprecationWarning, - ) - self._embeddings = embeddings or embbedings or self._get_default_embeddings() + self._embeddings = embeddings or self._get_default_embeddings() @property def embeddings(self) -> Embeddings: From b62dfdbb8d931a55e7af75ae7c786d53b75043df Mon Sep 17 00:00:00 2001 From: Mason Daugherty Date: Sun, 28 Sep 2025 22:44:01 -0400 Subject: [PATCH 14/23] updates to `_convert_to_parts` --- .../langchain_google_genai/chat_models.py | 55 +++++++++++-------- 1 file changed, 32 insertions(+), 23 deletions(-) diff --git a/libs/genai/langchain_google_genai/chat_models.py b/libs/genai/langchain_google_genai/chat_models.py index cc74e7d4f..3704af564 100644 --- a/libs/genai/langchain_google_genai/chat_models.py +++ b/libs/genai/langchain_google_genai/chat_models.py @@ -306,13 +306,14 @@ def _convert_to_parts( elif isinstance(part, Mapping): # e.g. a dict if "type" in part: # e.g. a LangChain content block if part["type"] == "text": - # Either old text block or TextContentBlock + # Either old dict-style CC text block or new TextContentBlock parts.append(Part(text=part["text"])) elif is_data_content_block(part): - # Handle both legacy LC blocks (with `source_type`) and standard v1 + # Handle both legacy LC blocks (with `source_type`) and blocks >= v1 + if "source_type" in part: - # Catch legacy v0 formats (Base64CB, URLCB, IDCB) - # v1 content blocks won't have top-level a `source_type` key + # Catch legacy v0 formats + # Safe since v1 content blocks don't have `source_type` key if part["source_type"] == "url": bytes_ = image_loader._bytes_from_url(part["url"]) elif part["source_type"] == "base64": @@ -322,17 +323,12 @@ def _convert_to_parts( msg = "source_type must be url or base64." raise ValueError(msg) elif "url" in part: - # Standard Data block format using URL + # v1 multimodal block w/ URL bytes_ = image_loader._bytes_from_url(part["url"]) elif "base64" in part: - # Standard Data block format using base64 + # v1 multimodal block w/ base64 bytes_ = base64.b64decode(part["base64"]) - elif "data" in part: - # Alternative base64 format unknown to us - # (This branch likely will never be hit) - bytes_ = base64.b64decode(part["data"]) else: - # `is_data_content_block` validates this, but just in case msg = ( "Data content block must contain 'url', 'base64', or " "'data' field." @@ -364,8 +360,6 @@ def _convert_to_parts( msg = f"Unrecognized message image format: {img_url}" raise ValueError(msg) img_url = img_url["url"] - # Currently Google doesn't have a equivalent of OpenAI's - # `detail` field for images, so we ignore it if present. parts.append(image_loader.load_part(img_url)) # Handle media type like LangChain.js # https://github.com/langchain-ai/langchainjs/blob/e536593e2585f1dd7b0afc187de4d07cb40689ba/libs/langchain-google-common/src/utils/gemini.ts#L93-L106 @@ -391,7 +385,28 @@ def _convert_to_parts( metadata = VideoMetadata(part["video_metadata"]) media_part.video_metadata = metadata parts.append(media_part) + elif part["type"] == "thinking": + # Pre-existing thinking block format + parts.append(Part(text=part["thinking"], thought=True)) + elif part["type"] == "reasoning": + # New ReasoningContentBlock + + extras = part.get("extras", {}) or {} + sig = extras.get("signature") + # Thought signature isn't stored in a standard key in RCB + + thought_sig = ( + sig.encode("utf-8") if isinstance(sig, str) else sig + ) # API requires bytes + parts.append( + Part( + text=part["reasoning"], + thought=True, + thought_signature=thought_sig, + ) + ) elif part["type"] == "executable_code": + # TODO: LangChain's native server execution tool block not handled if "executable_code" not in part or "language" not in part: msg = ( "Executable code part must have 'code' and 'language' " @@ -405,6 +420,7 @@ def _convert_to_parts( ) parts.append(executable_code_part) elif part["type"] == "code_execution_result": + # TODO: LangChain's native server execution result block not handled if "code_execution_result" not in part: msg = ( "Code execution result part must have " @@ -422,24 +438,17 @@ def _convert_to_parts( ) ) parts.append(code_execution_result_part) - elif part["type"] == "thinking": - parts.append(Part(text=part["thinking"], thought=True)) else: - msg = ( - f"Unrecognized message part type: {part['type']}. Only text, " - f"image_url, and media types are supported." - ) + msg = f"Unrecognized message part type: {part['type']}." raise ValueError(msg) else: - # Yolo. This doesn't have `type` and is thus likely not a content block + # Yolo. The input message content doesn't have a `type` key logger.warning( "Unrecognized message part format. Assuming it's a text part." ) parts.append(Part(text=str(part))) else: - # TODO: Maybe some of Google's native stuff - # would hit this branch. - msg = "Gemini only supports text and inline_data parts." + msg = "Unknown error occurred while converting LC message content to parts." raise ChatGoogleGenerativeAIError(msg) return parts From 8c86758bbefb88e46a925e7987776a1a1bf924f8 Mon Sep 17 00:00:00 2001 From: Leonid Kuligin Date: Mon, 29 Sep 2025 16:53:30 +0200 Subject: [PATCH 15/23] chore(vertex): remove Gemma integrations (#1220) Co-authored-by: Mason Daugherty --- .../langchain_google_vertexai/__init__.py | 14 - .../langchain_google_vertexai/gemma.py | 415 ------------------ .../tests/integration_tests/test_gemma.py | 91 ---- .../vertexai/tests/unit_tests/test_imports.py | 6 - 4 files changed, 526 deletions(-) delete mode 100644 libs/vertexai/langchain_google_vertexai/gemma.py delete mode 100644 libs/vertexai/tests/integration_tests/test_gemma.py diff --git a/libs/vertexai/langchain_google_vertexai/__init__.py b/libs/vertexai/langchain_google_vertexai/__init__.py index ff18bb288..405e6e25b 100644 --- a/libs/vertexai/langchain_google_vertexai/__init__.py +++ b/libs/vertexai/langchain_google_vertexai/__init__.py @@ -56,14 +56,6 @@ from langchain_google_vertexai.functions_utils import ( PydanticFunctionsOutputParser, ) -from langchain_google_vertexai.gemma import ( - GemmaChatLocalHF, - GemmaChatLocalKaggle, - GemmaChatVertexAIModelGarden, - GemmaLocalHF, - GemmaLocalKaggle, - GemmaVertexAIModelGarden, -) from langchain_google_vertexai.llms import VertexAI from langchain_google_vertexai.model_garden import VertexAIModelGarden from langchain_google_vertexai.model_garden_maas import get_vertex_maas_model @@ -89,12 +81,6 @@ "FunctionCallingConfig", "FunctionDeclaration", "GCSDocumentStorage", - "GemmaChatLocalHF", - "GemmaChatLocalKaggle", - "GemmaChatVertexAIModelGarden", - "GemmaLocalHF", - "GemmaLocalKaggle", - "GemmaVertexAIModelGarden", "HarmBlockThreshold", "HarmCategory", "Modality", diff --git a/libs/vertexai/langchain_google_vertexai/gemma.py b/libs/vertexai/langchain_google_vertexai/gemma.py deleted file mode 100644 index cec88e347..000000000 --- a/libs/vertexai/langchain_google_vertexai/gemma.py +++ /dev/null @@ -1,415 +0,0 @@ -import os -from typing import Any, Dict, List, Optional, cast - -from langchain_core.callbacks import ( - AsyncCallbackManagerForLLMRun, - CallbackManagerForLLMRun, -) -from langchain_core.language_models.chat_models import ( - BaseChatModel, -) -from langchain_core.language_models.llms import BaseLLM -from langchain_core.messages import ( - AIMessage, - BaseMessage, - HumanMessage, - SystemMessage, -) -from langchain_core.outputs import ( - ChatGeneration, - ChatResult, - Generation, - LLMResult, -) -from pydantic import BaseModel, ConfigDict, Field, model_validator -from typing_extensions import Self - -from langchain_google_vertexai._base import _BaseVertexAIModelGarden -from langchain_google_vertexai._utils import enforce_stop_tokens -from langchain_google_vertexai.model_garden import VertexAIModelGarden - -USER_CHAT_TEMPLATE = "user\n{prompt}\n" -MODEL_CHAT_TEMPLATE = "model\n{prompt}\n" - - -def gemma_messages_to_prompt(history: List[BaseMessage]) -> str: - """Converts a list of messages to a chat prompt for Gemma.""" - messages: List[str] = [] - if len(messages) == 1: - content = cast("str", history[0].content) - if isinstance(history[0], SystemMessage): - msg = "Gemma currently doesn't support system message!" - raise ValueError(msg) - return content - for message in history: - content = cast("str", message.content) - if isinstance(message, SystemMessage): - msg = "Gemma currently doesn't support system message!" - raise ValueError(msg) - if isinstance(message, AIMessage): - messages.append(MODEL_CHAT_TEMPLATE.format(prompt=content)) - elif isinstance(message, HumanMessage): - messages.append(USER_CHAT_TEMPLATE.format(prompt=content)) - else: - msg = f"Unexpected message with type {type(message)}" - raise ValueError(msg) - messages.append("model\n") - return "".join(messages) - - -def _parse_gemma_chat_response(response: str) -> str: - """Removes chat history from the response.""" - pattern = "model\n" - pos = response.rfind(pattern) - if pos == -1: - return response - text = response[(pos + len(pattern)) :] - pos = text.find("user\n") - if pos > 0: - return text[:pos] - return text - - -class _GemmaBase(BaseModel): - max_tokens: Optional[int] = None - """The maximum number of tokens to generate.""" - temperature: Optional[float] = None - """The temperature to use for sampling.""" - top_p: Optional[float] = None - """The top-p value to use for sampling.""" - top_k: Optional[int] = None - """The top-k value to use for sampling.""" - - model_config = ConfigDict(protected_namespaces=()) - - @property - def _default_params(self) -> Dict[str, Any]: - """Get the default parameters for calling gemma.""" - params = { - "max_tokens": self.max_tokens, - "temperature": self.temperature, - "top_p": self.top_p, - "top_k": self.top_k, - } - return dict(params.items()) - - def _get_params(self, **kwargs) -> Dict[str, Any]: - params = {k: kwargs.get(k, v) for k, v in self._default_params.items()} - return {k: v for k, v in params.items() if v is not None} - - -class GemmaVertexAIModelGarden(VertexAIModelGarden): - allowed_model_args: Optional[List[str]] = [ - "temperature", - "top_p", - "top_k", - "max_tokens", - ] - - @property - def _llm_type(self) -> str: - return "gemma_vertexai_model_garden" - - # Needed so that mypy doesn't flag missing aliased init args. - def __init__(self, **kwargs: Any) -> None: - super().__init__(**kwargs) - - -class GemmaChatVertexAIModelGarden(_GemmaBase, _BaseVertexAIModelGarden, BaseChatModel): - allowed_model_args: Optional[List[str]] = [ - "temperature", - "top_p", - "top_k", - "max_tokens", - "max_length", - ] - parse_response: bool = False - """Whether to post-process the chat response and clean repeations """ - """or multi-turn statements.""" - - def __init__(self, *, model_name: Optional[str] = None, **kwargs: Any) -> None: - """Needed for mypy typing to recognize model_name as a valid arg.""" - if model_name: - kwargs["model_name"] = model_name - super().__init__(**kwargs) - - model_config = ConfigDict( - populate_by_name=True, - protected_namespaces=(), - ) - - @property - def _llm_type(self) -> str: - return "gemma_vertexai_model_garden" - - @property - def _default_params(self) -> Dict[str, Any]: - """Get the default parameters for calling gemma.""" - # support both Gemma 1B and 2B - params = super()._default_params - params["max_length"] = self.max_tokens - return params - - def _generate( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> ChatResult: - request = self._get_params(**kwargs) - request["prompt"] = gemma_messages_to_prompt(messages) - output = self.client.predict(endpoint=self.endpoint_path, instances=[request]) - text = output.predictions[0] - if self.parse_response or kwargs.get("parse_response"): - text = _parse_gemma_chat_response(text) - if stop: - text = enforce_stop_tokens(text, stop) - generations = [ - ChatGeneration( - message=AIMessage(content=text), - ) - ] - return ChatResult(generations=generations) - - async def _agenerate( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> ChatResult: - """Top Level call.""" - request = self._get_params(**kwargs) - request["prompt"] = gemma_messages_to_prompt(messages) - output = await self.async_client.predict( - endpoint=self.endpoint_path, instances=[request] - ) - text = output.predictions[0] - if self.parse_response or kwargs.get("parse_response"): - text = _parse_gemma_chat_response(text) - if stop: - text = enforce_stop_tokens(text, stop) - generations = [ - ChatGeneration( - message=AIMessage(content=text), - ) - ] - return ChatResult(generations=generations) - - -class _GemmaLocalKaggleBase(_GemmaBase): - """Local gemma model loaded from Kaggle.""" - - client: Any = Field(default=None, exclude=True) #: :meta private: - keras_backend: str = "jax" - model_name: str = Field(default="gemma_2b_en", alias="model") - """Gemma model name.""" - - model_config = ConfigDict( - populate_by_name=True, - ) - - def __init__(self, *, model_name: Optional[str] = None, **kwargs: Any) -> None: - """Needed for mypy typing to recognize model_name as a valid arg.""" - if model_name: - kwargs["model_name"] = model_name - super().__init__(**kwargs) - - @model_validator(mode="after") - def validate_environment(self) -> Self: - """Validate that llama-cpp-python library is installed.""" - try: - os.environ["KERAS_BACKEND"] = self.keras_backend - from keras_nlp.models import GemmaCausalLM # type: ignore - except ImportError: - msg = ( - "Could not import GemmaCausalLM library. " - "Please install the GemmaCausalLM library to " - "use this model: pip install keras-nlp keras>=3 kaggle" - ) - raise ImportError(msg) - - self.client = GemmaCausalLM.from_preset(self.model_name) - return self - - @property - def _default_params(self) -> Dict[str, Any]: - """Get the default parameters for calling gemma.""" - params = {"max_length": self.max_tokens} - return {k: v for k, v in params.items() if v is not None} - - def _get_params(self, **kwargs) -> Dict[str, Any]: - mapping = {"max_tokens": "max_length"} - params = {mapping[k]: v for k, v in kwargs.items() if k in mapping} - return {**self._default_params, **params} - - -class GemmaLocalKaggle(_GemmaLocalKaggleBase, BaseLLM): - """Local gemma chat model loaded from Kaggle.""" - - def __init__(self, *, model_name: Optional[str] = None, **kwargs: Any) -> None: - """Only needed for typing.""" - if model_name: - kwargs["model_name"] = model_name - super().__init__(**kwargs) - - def _generate( - self, - prompts: List[str], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> LLMResult: - """Run the LLM on the given prompt and input.""" - params = self._get_params(**kwargs) - results = self.client.generate(prompts, **params) - 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]) - - @property - def _llm_type(self) -> str: - """Return type of llm.""" - return "gemma_local_kaggle" - - -class GemmaChatLocalKaggle(_GemmaLocalKaggleBase, BaseChatModel): - parse_response: bool = False - """Whether to post-process the chat response and clean repeations """ - """or multi-turn statements.""" - - def __init__(self, *, model_name: Optional[str] = None, **kwargs: Any) -> None: - """Needed for mypy typing to recognize model_name as a valid arg.""" - if model_name: - kwargs["model_name"] = model_name - super().__init__(**kwargs) - - def _generate( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> ChatResult: - params = self._get_params(**kwargs) - prompt = gemma_messages_to_prompt(messages) - text = self.client.generate(prompt, **params) - if self.parse_response or kwargs.get("parse_response"): - text = _parse_gemma_chat_response(text) - if stop: - text = enforce_stop_tokens(text, stop) - generation = ChatGeneration(message=AIMessage(content=text)) - return ChatResult(generations=[generation]) - - @property - def _llm_type(self) -> str: - """Return type of llm.""" - return "gemma_local_chat_kaggle" - - -class _GemmaLocalHFBase(_GemmaBase): - """Local gemma model loaded from HuggingFace.""" - - tokenizer: Any = None #: :meta private: - client: Any = Field(default=None, exclude=True) #: :meta private: - hf_access_token: str - cache_dir: Optional[str] = None - model_name: str = Field(default="google/gemma-2b", alias="model") - """Gemma model name.""" - - model_config = ConfigDict( - populate_by_name=True, - ) - - @model_validator(mode="after") - def validate_environment(self) -> Self: - """Validate that llama-cpp-python library is installed.""" - try: - from transformers import AutoTokenizer, GemmaForCausalLM # type: ignore - except ImportError: - msg = ( - "Could not import GemmaForCausalLM library. " - "Please install the GemmaForCausalLM library to " - "use this model: pip install transformers>=4.38.1" - ) - raise ImportError(msg) - - self.tokenizer = AutoTokenizer.from_pretrained( - self.model_name, token=self.hf_access_token - ) - self.client = GemmaForCausalLM.from_pretrained( - self.model_name, - token=self.hf_access_token, - cache_dir=self.cache_dir, - ) - return self - - @property - def _default_params(self) -> Dict[str, Any]: - """Get the default parameters for calling gemma.""" - params = {"max_length": self.max_tokens} - return {k: v for k, v in params.items() if v is not None} - - def _get_params(self, **kwargs) -> Dict[str, Any]: - mapping = {"max_tokens": "max_length"} - params = {mapping[k]: v for k, v in kwargs.items() if k in mapping} - return {**self._default_params, **params} - - def _run(self, prompt: str, **kwargs: Any) -> str: - inputs = self.tokenizer(prompt, return_tensors="pt") - params = self._get_params(**kwargs) - generate_ids = self.client.generate(inputs.input_ids, **params) - return self.tokenizer.batch_decode( - generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False - )[0] - - -class GemmaLocalHF(_GemmaLocalHFBase, BaseLLM): - """Local gemma model loaded from HuggingFace.""" - - def _generate( - self, - prompts: List[str], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> LLMResult: - """Run the LLM on the given prompt and input.""" - results = [self._run(prompt, **kwargs) for prompt in prompts] - if stop: - results = [enforce_stop_tokens(text, stop) for text in results] - return LLMResult(generations=[[Generation(text=text)] for text in results]) - - @property - def _llm_type(self) -> str: - """Return type of llm.""" - return "gemma_local_hf" - - -class GemmaChatLocalHF(_GemmaLocalHFBase, BaseChatModel): - parse_response: bool = False - """Whether to post-process the chat response and clean repeations """ - """or multi-turn statements.""" - - def _generate( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> ChatResult: - prompt = gemma_messages_to_prompt(messages) - text = self._run(prompt, **kwargs) - if self.parse_response or kwargs.get("parse_response"): - text = _parse_gemma_chat_response(text) - if stop: - text = enforce_stop_tokens(text, stop) - generation = ChatGeneration(message=AIMessage(content=text)) - return ChatResult(generations=[generation]) - - @property - def _llm_type(self) -> str: - """Return type of llm.""" - return "gemma_local_chat_hf" diff --git a/libs/vertexai/tests/integration_tests/test_gemma.py b/libs/vertexai/tests/integration_tests/test_gemma.py deleted file mode 100644 index c86d2822c..000000000 --- a/libs/vertexai/tests/integration_tests/test_gemma.py +++ /dev/null @@ -1,91 +0,0 @@ -import os - -import pytest -from langchain_core.messages import ( - AIMessage, - HumanMessage, -) - -from langchain_google_vertexai import ( - GemmaChatLocalKaggle, - GemmaChatVertexAIModelGarden, - GemmaLocalKaggle, - GemmaVertexAIModelGarden, -) - - -@pytest.mark.extended -def test_gemma_model_garden() -> None: - """In order to run this test, you should provide endpoint names. - - Example: - export GEMMA_ENDPOINT_ID=... - export PROJECT_ID=... - """ - endpoint_id = os.environ["GEMMA_ENDPOINT_ID"] - project = os.environ["PROJECT_ID"] - location = "us-central1" - llm = GemmaVertexAIModelGarden( - endpoint_id=endpoint_id, - project=project, - location=location, - ) - output = llm.invoke("What is the meaning of life?") - assert isinstance(output, str) - assert len(output) > 2 - assert llm._llm_type == "gemma_vertexai_model_garden" - - -@pytest.mark.extended -def test_gemma_chat_model_garden() -> None: - """In order to run this test, you should provide endpoint names. - - Example: - export GEMMA_ENDPOINT_ID=... - export PROJECT_ID=... - """ - endpoint_id = os.environ["GEMMA_ENDPOINT_ID"] - project = os.environ["PROJECT_ID"] - location = "us-central1" - llm = GemmaChatVertexAIModelGarden( - endpoint_id=endpoint_id, - project=project, - location=location, - ) - assert llm._llm_type == "gemma_vertexai_model_garden" - - text_question1, text_answer1 = "How much is 2+2?", "4" - text_question2 = "How much is 3+3?" - message1 = HumanMessage(content=text_question1) - message2 = AIMessage(content=text_answer1) - message3 = HumanMessage(content=text_question2) - output = llm.invoke([message1]) - assert isinstance(output, AIMessage) - assert len(output.content) > 2 - output = llm.invoke([message1, message2, message3]) - assert isinstance(output, AIMessage) - assert len(output.content) > 2 - - -@pytest.mark.gpu -def test_gemma_kaggle() -> None: - llm = GemmaLocalKaggle(model_name="gemma_2b_en") - output = llm.invoke("What is the meaning of life?") - assert isinstance(output, str) - assert len(output) > 2 - - -@pytest.mark.gpu -def test_gemma_chat_kaggle() -> None: - llm = GemmaChatLocalKaggle(model_name="gemma_2b_en") - text_question1, text_answer1 = "How much is 2+2?", "4" - text_question2 = "How much is 3+3?" - message1 = HumanMessage(content=text_question1) - message2 = AIMessage(content=text_answer1) - message3 = HumanMessage(content=text_question2) - output = llm.invoke([message1]) - assert isinstance(output, AIMessage) - assert len(output.content) > 2 - output = llm.invoke([message1, message2, message3]) - assert isinstance(output, AIMessage) - assert len(output.content) > 2 diff --git a/libs/vertexai/tests/unit_tests/test_imports.py b/libs/vertexai/tests/unit_tests/test_imports.py index feeec0ca2..9d7b0b4e9 100644 --- a/libs/vertexai/tests/unit_tests/test_imports.py +++ b/libs/vertexai/tests/unit_tests/test_imports.py @@ -7,12 +7,6 @@ "FunctionCallingConfig", "FunctionDeclaration", "GCSDocumentStorage", - "GemmaChatLocalHF", - "GemmaChatLocalKaggle", - "GemmaChatVertexAIModelGarden", - "GemmaLocalHF", - "GemmaLocalKaggle", - "GemmaVertexAIModelGarden", "HarmBlockThreshold", "HarmCategory", "Modality", From b094f06888d64197da5efb6aea772ac89fca440e Mon Sep 17 00:00:00 2001 From: Mason Daugherty Date: Mon, 29 Sep 2025 23:14:48 -0400 Subject: [PATCH 16/23] feat(genai): standard content (#1204) https://googleapis.dev/python/generativelanguage/latest/generativelanguage_v1beta/generative_service.html https://googleapis.dev/python/generativelanguage/latest/generativelanguage_v1beta/types_.html#google.ai.generativelanguage_v1beta.types.GenerateContentResponse TODO(genai) - [ ] Native use of new [server tools](https://github.com/langchain-ai/langchain/pull/33021) - [ ] Ensure sufficient streaming tests --- libs/genai/langchain_google_genai/_compat.py | 248 ++++++++++ .../langchain_google_genai/chat_models.py | 263 ++++++++-- libs/genai/pyproject.toml | 6 + .../integration_tests/test_chat_models.py | 340 ++++++++++++- .../tests/unit_tests/test_chat_models.py | 453 +++++++++++++++++- libs/genai/uv.lock | 243 +++++----- 6 files changed, 1375 insertions(+), 178 deletions(-) create mode 100644 libs/genai/langchain_google_genai/_compat.py diff --git a/libs/genai/langchain_google_genai/_compat.py b/libs/genai/langchain_google_genai/_compat.py new file mode 100644 index 000000000..84e661270 --- /dev/null +++ b/libs/genai/langchain_google_genai/_compat.py @@ -0,0 +1,248 @@ +"""Go from v1 content blocks to generativelanguage_v1beta format.""" + +import json +from typing import Any, Optional + +from langchain_core.messages import content as types + + +def translate_citations_to_grounding_metadata( + citations: list[types.Citation], web_search_queries: Optional[list[str]] = None +) -> dict[str, Any]: + """Translate LangChain Citations to Google AI grounding metadata format. + + Args: + citations: List of Citation content blocks. + web_search_queries: Optional list of search queries that generated + the grounding data. + + Returns: + Google AI grounding metadata dictionary. + + Example: + >>> citations = [ + ... create_citation( + ... url="https://uefa.com/euro2024", + ... title="UEFA Euro 2024 Results", + ... start_index=0, + ... end_index=47, + ... cited_text="Spain won the UEFA Euro 2024 championship", + ... ) + ... ] + >>> metadata = translate_citations_to_grounding_metadata(citations) + >>> len(metadata["groundingChunks"]) + 1 + >>> metadata["groundingChunks"][0]["web"]["uri"] + 'https://uefa.com/euro2024' + """ + if not citations: + return {} + + # Group citations by text segment (start_index, end_index, cited_text) + segment_to_citations: dict[ + tuple[Optional[int], Optional[int], Optional[str]], list[types.Citation] + ] = {} + + for citation in citations: + key = ( + citation.get("start_index"), + citation.get("end_index"), + citation.get("cited_text"), + ) + if key not in segment_to_citations: + segment_to_citations[key] = [] + segment_to_citations[key].append(citation) + + # Build grounding chunks from unique URLs + url_to_chunk_index: dict[str, int] = {} + grounding_chunks: list[dict[str, Any]] = [] + + for citation in citations: + url = citation.get("url") + if url and url not in url_to_chunk_index: + url_to_chunk_index[url] = len(grounding_chunks) + grounding_chunks.append( + {"web": {"uri": url, "title": citation.get("title", "")}} + ) + + # Build grounding supports + grounding_supports: list[dict[str, Any]] = [] + + for ( + start_index, + end_index, + cited_text, + ), citations_group in segment_to_citations.items(): + if start_index is not None and end_index is not None and cited_text: + chunk_indices = [] + confidence_scores = [] + + for citation in citations_group: + url = citation.get("url") + if url and url in url_to_chunk_index: + chunk_indices.append(url_to_chunk_index[url]) + + # Extract confidence scores from extras if available + extras = citation.get("extras", {}) + google_metadata = extras.get("google_ai_metadata", {}) + scores = google_metadata.get("confidence_scores", []) + confidence_scores.extend(scores) + + support = { + "segment": { + "startIndex": start_index, + "endIndex": end_index, + "text": cited_text, + }, + "groundingChunkIndices": chunk_indices, + } + + if confidence_scores: + support["confidenceScores"] = confidence_scores + + grounding_supports.append(support) + + # Extract search queries from extras if not provided + if web_search_queries is None: + web_search_queries = [] + for citation in citations: + extras = citation.get("extras", {}) + google_metadata = extras.get("google_ai_metadata", {}) + queries = google_metadata.get("web_search_queries", []) + web_search_queries.extend(queries) + # Remove duplicates while preserving order + web_search_queries = list(dict.fromkeys(web_search_queries)) + + return { + "webSearchQueries": web_search_queries, + "groundingChunks": grounding_chunks, + "groundingSupports": grounding_supports, + } + + +def _convert_from_v1_to_generativelanguage_v1beta( + content: list[types.ContentBlock], model_provider: str | None +) -> list[dict[str, Any]]: + """Convert v1 content blocks to `google.ai.generativelanguage_v1beta.types.Content`. + + Args: + content: List of v1 `ContentBlock` objects. + model_provider: The model provider name that generated the v1 content. + + Returns: + List of dictionaries in `google.ai.generativelanguage_v1beta.types.Content` + format, ready to be sent to the API. + """ + new_content: list = [] + for block in content: + if not isinstance(block, dict) or "type" not in block: + continue + + block_dict = dict(block) # (For typing) + + # TextContentBlock + if block_dict["type"] == "text": + new_block = {"text": block_dict.get("text", "")} + new_content.append(new_block) + # Citations are only handled on output. Can't pass them back :/ + + # ReasoningContentBlock -> thinking + elif block_dict["type"] == "reasoning" and model_provider == "google_genai": + # Google requires passing back the thought_signature when available. + # Signatures are only provided when function calling is enabled. + # If no signature is available, we skip the reasoning block as it cannot + # be properly serialized back to the API. + if "extras" in block_dict and isinstance(block_dict["extras"], dict): + extras = block_dict["extras"] + if "signature" in extras: + new_block = { + "thought": True, + "text": block_dict.get("reasoning", ""), + "thought_signature": extras["signature"], + } + new_content.append(new_block) + # else: skip reasoning blocks without signatures + # TODO: log a warning? + # else: skip reasoning blocks without extras + # TODO: log a warning? + + # ImageContentBlock + elif block_dict["type"] == "image": + if base64 := block_dict.get("base64"): + new_block = { + "inline_data": { + "mime_type": block_dict.get("mime_type", "image/jpeg"), + "data": base64.encode("utf-8") + if isinstance(base64, str) + else base64, + } + } + new_content.append(new_block) + elif url := block_dict.get("url") and model_provider == "google_genai": + # Google file service + new_block = { + "file_data": { + "mime_type": block_dict.get("mime_type", "image/jpeg"), + "file_uri": block_dict[str(url)], + } + } + new_content.append(new_block) + + # TODO: AudioContentBlock -> audio once models support passing back in + + # FileContentBlock (documents) + elif block_dict["type"] == "file": + if base64 := block_dict.get("base64"): + new_block = { + "inline_data": { + "mime_type": block_dict.get( + "mime_type", "application/octet-stream" + ), + "data": base64.encode("utf-8") + if isinstance(base64, str) + else base64, + } + } + new_content.append(new_block) + elif url := block_dict.get("url") and model_provider == "google_genai": + # Google file service + new_block = { + "file_data": { + "mime_type": block_dict.get( + "mime_type", "application/octet-stream" + ), + "file_uri": block_dict[str(url)], + } + } + new_content.append(new_block) + + # ToolCall -> FunctionCall + elif block_dict["type"] == "tool_call": + function_call = { + "function_call": { + "name": block_dict.get("name", ""), + "args": block_dict.get("args", {}), + } + } + new_content.append(function_call) + + # ToolCallChunk -> FunctionCall + elif block_dict["type"] == "tool_call_chunk": + try: + args_str = block_dict.get("args") or "{}" + input_ = json.loads(args_str) if isinstance(args_str, str) else args_str + except json.JSONDecodeError: + input_ = {} + + function_call = { + "function_call": { + "name": block_dict.get("name", "no_tool_name_present"), + "args": input_, + } + } + new_content.append(function_call) + + # NonStandardContentBlock + # TODO: Handle new server tools + + return new_content diff --git a/libs/genai/langchain_google_genai/chat_models.py b/libs/genai/langchain_google_genai/chat_models.py index 70fa125f0..950f35e5a 100644 --- a/libs/genai/langchain_google_genai/chat_models.py +++ b/libs/genai/langchain_google_genai/chat_models.py @@ -58,6 +58,7 @@ ResourceExhausted, ServiceUnavailable, ) +from google.protobuf.json_format import MessageToDict from langchain_core.callbacks.manager import ( AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, @@ -78,6 +79,7 @@ ToolMessage, is_data_content_block, ) +from langchain_core.messages import content as types from langchain_core.messages.ai import UsageMetadata, add_usage, subtract_usage from langchain_core.messages.tool import invalid_tool_call, tool_call, tool_call_chunk from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser @@ -114,6 +116,9 @@ _BaseGoogleGenerativeAI, get_client_info, ) +from langchain_google_genai._compat import ( + _convert_from_v1_to_generativelanguage_v1beta, +) from langchain_google_genai._function_utils import ( _dict_to_gapic_schema, _tool_choice_to_tool_config, @@ -294,7 +299,12 @@ async def _achat_with_retry(**kwargs: Any) -> Any: def _convert_to_parts( raw_content: Union[str, Sequence[Union[str, dict]]], ) -> List[Part]: - """Converts a list of LangChain messages into Google parts.""" + """Converts LangChain message content into generativelanguage_v1beta parts. + + Used when preparing Human, System and AI messages for sending to the API. + + Handles both legacy (pre-v1) dict-based content blocks and v1 ContentBlock objects. + """ content = [raw_content] if isinstance(raw_content, str) else raw_content image_loader = ImageBytesLoader() @@ -303,11 +313,23 @@ def _convert_to_parts( for part in content: if isinstance(part, str): parts.append(Part(text=part)) - elif isinstance(part, Mapping): # e.g. a dict - if "type" in part: # e.g. a LangChain content block + elif isinstance(part, Mapping): + if "type" in part: if part["type"] == "text": # Either old dict-style CC text block or new TextContentBlock - parts.append(Part(text=part["text"])) + # Check if there's a signature attached to this text block + thought_sig = None + if "extras" in part and isinstance(part["extras"], dict): + sig = part["extras"].get("signature") + if sig and isinstance(sig, str): + # Decode base64-encoded signature back to bytes + thought_sig = base64.b64decode(sig) + if thought_sig: + parts.append( + Part(text=part["text"], thought_signature=thought_sig) + ) + else: + parts.append(Part(text=part["text"])) elif is_data_content_block(part): # Handle both legacy LC blocks (with `source_type`) and blocks >= v1 @@ -361,9 +383,9 @@ def _convert_to_parts( raise ValueError(msg) img_url = img_url["url"] parts.append(image_loader.load_part(img_url)) - # Handle media type like LangChain.js - # https://github.com/langchain-ai/langchainjs/blob/e536593e2585f1dd7b0afc187de4d07cb40689ba/libs/langchain-google-common/src/utils/gemini.ts#L93-L106 elif part["type"] == "media": + # Handle `media` following pattern established in LangChain.js + # https://github.com/langchain-ai/langchainjs/blob/e536593e2585f1dd7b0afc187de4d07cb40689ba/libs/langchain-google-common/src/utils/gemini.ts#L93-L106 if "mime_type" not in part: msg = f"Missing mime_type in media part: {part}" raise ValueError(msg) @@ -371,10 +393,12 @@ def _convert_to_parts( media_part = Part() if "data" in part: + # Embedded media media_part.inline_data = Blob( data=part["data"], mime_type=mime_type ) elif "file_uri" in part: + # Referenced files (e.g. stored in GCS) media_part.file_data = FileData( file_uri=part["file_uri"], mime_type=mime_type ) @@ -387,17 +411,32 @@ def _convert_to_parts( parts.append(media_part) elif part["type"] == "thinking": # Pre-existing thinking block format - parts.append(Part(text=part["thinking"], thought=True)) + thought_sig = None + if "signature" in part: + sig = part["signature"] + if sig and isinstance(sig, str): + # Decode base64-encoded signature back to bytes + thought_sig = base64.b64decode(sig) + parts.append( + Part( + text=part["thinking"], + thought=True, + thought_signature=thought_sig, + ) + ) + elif part["type"] == "function_call_signature": + # Signature for function_call Part - skip it here as it should be + # attached to the actual function_call Part + # This is handled separately in the history parsing logic + pass elif part["type"] == "reasoning": # New ReasoningContentBlock - extras = part.get("extras", {}) or {} sig = extras.get("signature") - # Thought signature isn't stored in a standard key in RCB - - thought_sig = ( - sig.encode("utf-8") if isinstance(sig, str) else sig - ) # API requires bytes + thought_sig = None + if sig and isinstance(sig, str): + # Decode base64-encoded signature back to bytes + thought_sig = base64.b64decode(sig) parts.append( Part( text=part["reasoning"], @@ -521,7 +560,20 @@ def _get_ai_message_tool_messages_parts( def _parse_chat_history( input_messages: Sequence[BaseMessage], convert_system_message_to_human: bool = False ) -> Tuple[Optional[Content], List[Content]]: - messages: List[Content] = [] + """Parses sequence of `BaseMessage` into system instruction and formatted messages. + + Args: + input_messages: Sequence of `BaseMessage` objects representing the chat history. + convert_system_message_to_human: Whether to convert the first system message + into a human message. Deprecated, use system instructions instead. + + Returns: + A tuple containing: + - An optional `google.ai.generativelanguage_v1beta.types.Content` representing + the system instruction (if any). + - A list of `google.ai.generativelanguage_v1beta.types.Content` representing the + formatted messages. + """ if convert_system_message_to_human: warnings.warn( @@ -530,6 +582,28 @@ def _parse_chat_history( DeprecationWarning, stacklevel=2, ) + input_messages = list(input_messages) # Make a mutable copy + + # Case where content was serialized to v1 format + for idx, message in enumerate(input_messages): + if ( + isinstance(message, AIMessage) + and message.response_metadata.get("output_version") == "v1" + ): + # Unpack known v1 content to v1beta format for the request + # + # Old content types and any previously serialized messages passed back in to + # history will skip this, but hit and processed in `_convert_to_parts` + input_messages[idx] = message.model_copy( + update={ + "content": _convert_from_v1_to_generativelanguage_v1beta( + cast(list[types.ContentBlock], message.content), + message.response_metadata.get("model_provider"), + ) + } + ) + + formatted_messages: List[Content] = [] system_instruction: Optional[Content] = None messages_without_tool_messages = [ @@ -552,19 +626,43 @@ def _parse_chat_history( role = "model" if message.tool_calls: ai_message_parts = [] - for tool_call in message.tool_calls: + # Extract any function_call_signature blocks from content + function_call_sigs: dict[int, bytes] = {} + if isinstance(message.content, list): + for idx, item in enumerate(message.content): + if ( + isinstance(item, dict) + and item.get("type") == "function_call_signature" + ): + sig_str = item.get("signature", "") + if sig_str and isinstance(sig_str, str): + # Decode base64-encoded signature back to bytes + sig_bytes = base64.b64decode(sig_str) + function_call_sigs[idx] = sig_bytes + + for tool_call_idx, tool_call in enumerate(message.tool_calls): function_call = FunctionCall( { "name": tool_call["name"], "args": tool_call["args"], } ) - ai_message_parts.append(Part(function_call=function_call)) + # Check if there's a signature for this function call + # (We use the index to match signature to function call) + sig = function_call_sigs.get(tool_call_idx) + if sig: + ai_message_parts.append( + Part(function_call=function_call, thought_signature=sig) + ) + else: + ai_message_parts.append(Part(function_call=function_call)) tool_messages_parts = _get_ai_message_tool_messages_parts( tool_messages=tool_messages, ai_message=message ) - messages.append(Content(role=role, parts=ai_message_parts)) - messages.append(Content(role="user", parts=tool_messages_parts)) + formatted_messages.append(Content(role=role, parts=ai_message_parts)) + formatted_messages.append( + Content(role="user", parts=tool_messages_parts) + ) continue if raw_function_call := message.additional_kwargs.get("function_call"): function_call = FunctionCall( @@ -575,7 +673,12 @@ def _parse_chat_history( ) parts = [Part(function_call=function_call)] else: - parts = _convert_to_parts(message.content) + if message.response_metadata.get("output_version") == "v1": + # Already converted to v1beta format above + parts = message.content # type: ignore[assignment] + else: + # Prepare request content parts from message.content field + parts = _convert_to_parts(message.content) elif isinstance(message, HumanMessage): role = "user" parts = _convert_to_parts(message.content) @@ -589,8 +692,11 @@ def _parse_chat_history( msg = f"Unexpected message with type {type(message)} at the position {i}." raise ValueError(msg) - messages.append(Content(role=role, parts=parts)) - return system_instruction, messages + # Final step; assemble the Content object to pass to the API + # If version = "v1", the parts are already in v1beta format and will be + # automatically converted using protobuf's auto-conversion + formatted_messages.append(Content(role=role, parts=parts)) + return system_instruction, formatted_messages # Helper function to append content consistently @@ -626,6 +732,8 @@ def _parse_response_candidate( tool_calls = [] invalid_tool_calls = [] tool_call_chunks = [] + # Track function call signatures separately to handle them conditionally + function_call_signatures: List[dict] = [] for part in response_candidate.content.parts: text: Optional[str] = None @@ -638,14 +746,40 @@ def _parse_response_candidate( except AttributeError: pass + # Extract thought signature if present (can be on any Part type) + # Signatures are binary data, encode to base64 string for JSON serialization + thought_sig: Optional[str] = None + if hasattr(part, "thought_signature") and part.thought_signature: + try: + # Encode binary signature to base64 string + thought_sig = base64.b64encode(part.thought_signature).decode("ascii") + if not thought_sig: # Empty string + thought_sig = None + except (AttributeError, TypeError): + thought_sig = None + if hasattr(part, "thought") and part.thought: thinking_message = { "type": "thinking", "thinking": part.text, } + # Include signature if present + if thought_sig: + thinking_message["signature"] = thought_sig content = _append_to_content(content, thinking_message) elif text is not None and text: - content = _append_to_content(content, text) + # Check if this text Part has a signature attached + if thought_sig: + # Text with signature needs structured block to preserve signature + # We use a v1 TextContentBlock + text_with_sig = { + "type": "text", + "text": text, + "extras": {"signature": thought_sig}, + } + content = _append_to_content(content, text_with_sig) + else: + content = _append_to_content(content, text) if hasattr(part, "executable_code") and part.executable_code is not None: if part.executable_code.code and part.executable_code.language: @@ -667,7 +801,11 @@ def _parse_response_candidate( } content = _append_to_content(content, execution_result) - if part.inline_data.mime_type.startswith("audio/"): + if ( + hasattr(part, "inline_data") + and part.inline_data + and part.inline_data.mime_type.startswith("audio/") + ): buffer = io.BytesIO() with wave.open(buffer, "wb") as wf: @@ -677,9 +815,17 @@ def _parse_response_candidate( wf.setframerate(24000) wf.writeframes(part.inline_data.data) - additional_kwargs["audio"] = buffer.getvalue() + audio_data = buffer.getvalue() + additional_kwargs["audio"] = audio_data + + # For backwards compatibility, audio stays in additional_kwargs by default + # and is accessible via .content_blocks property - if part.inline_data.mime_type.startswith("image/"): + if ( + hasattr(part, "inline_data") + and part.inline_data + and part.inline_data.mime_type.startswith("image/") + ): image_format = part.inline_data.mime_type[6:] image_message = { "type": "image_url", @@ -738,6 +884,23 @@ def _parse_response_candidate( id=tool_call_dict.get("id", str(uuid.uuid4())), ) ) + + # If this function_call Part has a signature, track it separately + # We'll add it to content only if there's other content present + if thought_sig: + sig_block = { + "type": "function_call_signature", + "signature": thought_sig, + } + function_call_signatures.append(sig_block) + + # Add function call signatures to content only if there's already other content + # This preserves backward compatibility where content is "" for + # function-only responses + if function_call_signatures and content is not None: + for sig_block in function_call_signatures: + content = _append_to_content(content, sig_block) + if content is None: content = "" if isinstance(content, list) and any( @@ -752,7 +915,6 @@ def _parse_response_candidate( Validate before using in production. """ ) - if streaming: return AIMessageChunk( content=content, @@ -773,8 +935,11 @@ def _parse_response_candidate( def _extract_grounding_metadata(candidate: Any) -> Dict[str, Any]: """Extract grounding metadata from candidate. - Uses `proto.Message.to_dict()` for complete unfiltered extraction first, - falls back to custom field extraction in cases of failure for robustness. + core's block translator converts this metadata into citation annotations. + + Uses `MessageToDict` for complete unfiltered extraction. + + Falls back to custom field extraction in cases of failure for robustness. """ if not hasattr(candidate, "grounding_metadata") or not candidate.grounding_metadata: return {} @@ -782,12 +947,26 @@ def _extract_grounding_metadata(candidate: Any) -> Dict[str, Any]: grounding_metadata = candidate.grounding_metadata try: - return proto.Message.to_dict(grounding_metadata) - except (AttributeError, TypeError): - # Fallback: field extraction + # proto-plus wraps protobuf messages - access ._pb to get the raw protobuf + # message that MessageToDict expects + pb_message = ( + grounding_metadata._pb + if hasattr(grounding_metadata, "_pb") + else grounding_metadata + ) + + return MessageToDict( # type: ignore[call-arg] + pb_message, + preserving_proto_field_name=True, + always_print_fields_with_no_presence=True, + # type stub issue - ensures that protobuf fields with default values + # (like start_index=0) are included in the output + ) + except (AttributeError, TypeError, ImportError): + # Attempt manual extraction of known fields result: Dict[str, Any] = {} - # Extract grounding chunks + # Grounding chunks if hasattr(grounding_metadata, "grounding_chunks"): grounding_chunks = [] for chunk in grounding_metadata.grounding_chunks: @@ -800,7 +979,7 @@ def _extract_grounding_metadata(candidate: Any) -> Dict[str, Any]: grounding_chunks.append(chunk_data) result["grounding_chunks"] = grounding_chunks - # Extract grounding supports + # Grounding supports if hasattr(grounding_metadata, "grounding_supports"): grounding_supports = [] for support in grounding_metadata.grounding_supports: @@ -823,7 +1002,7 @@ def _extract_grounding_metadata(candidate: Any) -> Dict[str, Any]: grounding_supports.append(support_data) result["grounding_supports"] = grounding_supports - # Extract web search queries + # Web search queries if hasattr(grounding_metadata, "web_search_queries"): result["web_search_queries"] = list(grounding_metadata.web_search_queries) @@ -895,6 +1074,7 @@ def _response_to_result( grounding_metadata = _extract_grounding_metadata(candidate) generation_info["grounding_metadata"] = grounding_metadata message = _parse_response_candidate(candidate, streaming=stream) + message.usage_metadata = lc_usage if not hasattr(message, "response_metadata"): @@ -1758,11 +1938,11 @@ def invoke( stop: Optional[list[str]] = None, **kwargs: Any, ) -> AIMessage: - """Enable code execution. Supported on: gemini-1.5-pro, gemini-1.5-flash, - gemini-2.0-flash, and gemini-2.0-pro. When enabled, the model can execute - code to solve problems. + """Override invoke to add code_execution parameter. + + Supported on: gemini-1.5-pro, gemini-1.5-flash, gemini-2.0-flash, and + gemini-2.0-pro. When enabled, the model can execute code to solve problems. """ - """Override invoke to add code_execution parameter.""" if code_execution is not None: if not self._supports_code_execution: @@ -2161,7 +2341,7 @@ def _prepare_request( ] request = GenerateContentRequest( model=self.model, - contents=history, + contents=history, # google.ai.generativelanguage_v1beta.types.Content tools=formatted_tools, tool_config=formatted_tool_config, safety_settings=formatted_safety_settings, @@ -2178,7 +2358,7 @@ def _prepare_request( return request def get_num_tokens(self, text: str) -> int: - """Get the number of tokens present in the text. + """Get the number of tokens present in the text. Uses the model's tokenizer. Useful for checking if an input will fit in a model's context window. @@ -2210,7 +2390,8 @@ def with_structured_output( parser: OutputParserLike - if method in ("json_mode", "json_schema"): # `json_schema` preferred + # `json_schema` preferred, but `json_mode` kept for backwards compatibility + if method in ("json_mode", "json_schema"): if isinstance(schema, type) and is_basemodel_subclass(schema): if issubclass(schema, BaseModelV1): schema_json = schema.schema() diff --git a/libs/genai/pyproject.toml b/libs/genai/pyproject.toml index 6b36583bd..4e22b1fba 100644 --- a/libs/genai/pyproject.toml +++ b/libs/genai/pyproject.toml @@ -55,6 +55,12 @@ test_integration = [ dev = [] +[tool.uv.sources] +langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core", branch = "mdrxy/genai" } +langchain-tests = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/standard-tests", branch = "mdrxy/genai" } +#langchain-core = { path = "../../../langchain/libs/core", editable = true } +#langchain-tests = { path = "../../../langchain/libs/standard-tests", editable = true } + [tool.ruff] target-version = "py39" fix = true diff --git a/libs/genai/tests/integration_tests/test_chat_models.py b/libs/genai/tests/integration_tests/test_chat_models.py index 8c350d57b..c22fc0901 100644 --- a/libs/genai/tests/integration_tests/test_chat_models.py +++ b/libs/genai/tests/integration_tests/test_chat_models.py @@ -141,8 +141,6 @@ async def test_chat_google_genai_invoke(is_async: bool) -> None: _check_usage_metadata(result) -# TODO: assert calling .content_blocks translates outcome to ImageContentBlock -# but do this in unit tests in langchain-core @pytest.mark.flaky(retries=3, delay=1) def test_chat_google_genai_invoke_with_image() -> None: """Test generating an image and then text from ChatGoogleGenerativeAI. @@ -179,9 +177,23 @@ def test_chat_google_genai_invoke_with_image() -> None: assert not result.content[0].startswith(" ") _check_usage_metadata(result) + # Test we can pass back in + next_message = {"role": "user", "content": "Thanks!"} + _ = llm.invoke([result, next_message]) + + # Test content_blocks property + content_blocks = result.content_blocks + assert isinstance(content_blocks, list) + assert len(content_blocks) == 2 + assert isinstance(content_blocks[0], dict) + assert content_blocks[0].get("type") == "text" + assert isinstance(content_blocks[1], dict) + assert content_blocks[1].get("type") == "image" + + # Test we can pass back in content_blocks + _ = llm.invoke(["What's this?", {"role": "assistant", "content": content_blocks}]) + -# TODO: assert calling .content_blocks translates outcome to AudioContentBlock -# but do this in unit tests in langchain-core def test_chat_google_genai_invoke_with_audio() -> None: """Test generating audio from ChatGoogleGenerativeAI.""" llm = ChatGoogleGenerativeAI( @@ -198,6 +210,16 @@ def test_chat_google_genai_invoke_with_audio() -> None: assert get_wav_type_from_bytes(audio_data) _check_usage_metadata(result) + # Test content_blocks property + content_blocks = result.content_blocks + assert isinstance(content_blocks, list) + assert len(content_blocks) == 1 + assert isinstance(content_blocks[0], dict) + assert content_blocks[0].get("type") == "audio" + + # Test we can pass back in + # TODO: no model currently supports audio input + def test_chat_google_genai_invoke_thinking_default() -> None: """Test invoke thinking model with default thinking config.""" @@ -241,11 +263,14 @@ def test_chat_google_genai_invoke_thinking() -> None: assert result.usage_metadata["output_token_details"]["reasoning"] > 0 -# TODO: parametrize this test to use output_version=v1 and then assert `content` is -# proper ReasoningContentBlock with google-specific thinking fields in `extras` -def test_chat_google_genai_invoke_thinking_include_thoughts() -> None: +@pytest.mark.parametrize("output_version", ["v0", "v1"]) +def test_chat_google_genai_invoke_thinking_include_thoughts( + output_version: str, +) -> None: """Test invoke thinking model with `include_thoughts` on the chat model.""" - llm = ChatGoogleGenerativeAI(model=_THINKING_MODEL, include_thoughts=True) + llm = ChatGoogleGenerativeAI( + model=_THINKING_MODEL, include_thoughts=True, output_version=output_version + ) input_message = { "role": "user", @@ -260,24 +285,163 @@ def test_chat_google_genai_invoke_thinking_include_thoughts() -> None: assert isinstance(result, AIMessage) content = result.content - assert isinstance(content[0], dict) - assert content[0].get("type") == "thinking" - assert isinstance(content[0].get("thinking"), str) + response_metadata = result.response_metadata + model_provider = response_metadata.get("model_provider", "google_genai") + assert model_provider == "google_genai" - assert isinstance(content[1], str) + if output_version == "v0": + assert isinstance(content[0], dict) + assert content[0].get("type") == "thinking" + assert isinstance(content[0].get("thinking"), str) - _check_usage_metadata(result) + assert isinstance(content[1], str) - assert result.usage_metadata is not None - if ( - "output_token_details" in result.usage_metadata - and "reasoning" in result.usage_metadata["output_token_details"] - ): - assert result.usage_metadata["output_token_details"]["reasoning"] > 0 + _check_usage_metadata(result) - # Test we can pass back in - next_message = {"role": "user", "content": "Thanks!"} - _ = llm.invoke([input_message, result, next_message]) + assert result.usage_metadata is not None + if ( + "output_token_details" in result.usage_metadata + and "reasoning" in result.usage_metadata["output_token_details"] + ): + assert result.usage_metadata["output_token_details"]["reasoning"] > 0 + + # We don't test passing back in here as it's covered in the next test + # (Google requires function declaration) + else: + # v1 + assert isinstance(content, list) + assert len(content) == 2 + assert isinstance(content[0], dict) + assert content[0].get("type") == "reasoning" + assert isinstance(content[0].get("reasoning"), str) + + assert isinstance(content[1], dict) + assert content[1].get("type") == "text" + assert isinstance(content[1].get("text"), str) + + _check_usage_metadata(result) + + # We don't test passing back in here as it's covered in the next test + # (Google requires function declaration) + + +@pytest.mark.flaky(retries=5, delay=1) +@pytest.mark.parametrize("output_version", ["v0", "v1"]) +def test_chat_google_genai_invoke_thinking_with_tools( + output_version: str, +) -> None: + """Test thinking with function calling to get thought signatures. + + Ensure we can pass the response back in. + """ + + @tool + def analyze_weather(location: str, date: str) -> dict: + """Analyze weather patterns for a location on a specific date. + + Args: + location: The city or region to analyze. + date: The date in YYYY-MM-DD format. + + Returns: + A dictionary with weather analysis. + """ + return { + "location": location, + "date": date, + "temperature": "72F", + "conditions": "sunny", + "analysis": "Pleasant weather expected", + } + + llm = ChatGoogleGenerativeAI( + model=_THINKING_MODEL, include_thoughts=True, output_version=output_version + ) + llm_with_tools = llm.bind_tools([analyze_weather]) + + input_message = { + "role": "user", + "content": ( + "I'm planning a trip to Paris. Should I check the weather for " + "tomorrow (2025-01-22) before deciding what to pack? " + "Please think through whether you need to use the weather tool, " + "and if so, use it to help me decide." + ), + } + + result = llm_with_tools.invoke([input_message]) + + assert isinstance(result, AIMessage) + content = result.content + + response_metadata = result.response_metadata + model_provider = response_metadata.get("model_provider", "google_genai") + assert model_provider == "google_genai" + + if output_version == "v0": + # v0 format: + # Signatures are attached to function_call Parts, not thinking Parts. + # They appear as separate function_call_signature blocks in content. + # [{"type": "thinking", "thinking": "..."}, + # {"type": "function_call_signature", "signature": "..."}] + + # Check for thinking blocks (should exist) + thinking_blocks = [ + item + for item in content + if isinstance(item, dict) and item.get("type") == "thinking" + ] + assert thinking_blocks, "Should have thinking blocks when include_thoughts=True" + + # Check for function_call_signature blocks (not in thinking blocks) + signature_blocks = [ + item + for item in content + if isinstance(item, dict) and item.get("type") == "function_call_signature" + ] + if signature_blocks: + # Signature should be present when using function calling + assert "signature" in signature_blocks[0] + assert isinstance(signature_blocks[0]["signature"], str) + assert len(signature_blocks[0]["signature"]) > 0 + + # Test we can pass the result back in (with signature) + next_message = {"role": "user", "content": "Thanks!"} + _ = llm_with_tools.invoke([input_message, result, next_message]) + else: + # v1 format: + # Signatures are attached to function_call Parts, not reasoning Parts. + # They appear as separate function_call_signature blocks in content. + # [{"type": "reasoning", "reasoning": "..."}, + # {"type": "function_call_signature", "signature": "..."}] + assert isinstance(content, list) + + # Check for reasoning blocks (should exist) + reasoning_blocks = [ + block + for block in content + if isinstance(block, dict) and block.get("type") == "reasoning" + ] + assert reasoning_blocks, ( + "Should have reasoning blocks when include_thoughts=True" + ) + + # Check for function_call_signature blocks (not in reasoning blocks) + signature_blocks = [ + block + for block in content + if isinstance(block, dict) + and block.get("type") == "function_call_signature" + ] + if signature_blocks: + # Signature should be present when using function calling + assert "signature" in signature_blocks[0] + assert isinstance(signature_blocks[0]["signature"], str) + assert len(signature_blocks[0]["signature"]) > 0 + + # Test we can pass the result back in (with signature) + next_message = {"role": "user", "content": "Thanks!"} + _ = llm_with_tools.invoke([input_message, result, next_message]) def test_chat_google_genai_invoke_thinking_disabled() -> None: @@ -522,7 +686,6 @@ def search( assert "brown" in content_str.lower() -# TODO: check .content_blocks result def test_chat_vertexai_gemini_function_calling() -> None: """Test function calling with Gemini models. @@ -585,6 +748,13 @@ def my_tool(name: str, age: int, likes: list[str]) -> None: arguments = json.loads(str(arguments_str)) _check_tool_call_args(arguments) + # Test .content_blocks property + content_blocks = response.content_blocks + assert isinstance(content_blocks, list) + assert len(content_blocks) == 1 + assert isinstance(content_blocks[0], dict) + assert content_blocks[0].get("type") == "tool_call" + @pytest.mark.parametrize( ("model_name", "method"), @@ -726,9 +896,6 @@ async def model_ainvoke(context: str) -> BaseMessage: assert isinstance(invoke_result, AIMessage) -# TODO: in unit tests, translate to new content blocks in langchain-core - - @pytest.mark.parametrize("use_streaming", [False, True]) def test_search_builtin(use_streaming: bool) -> None: llm = ChatGoogleGenerativeAI(model="models/gemini-2.0-flash-001").bind_tools( @@ -767,6 +934,127 @@ def test_search_builtin(use_streaming: bool) -> None: _ = llm.invoke([input_message, response, next_message]) +@pytest.mark.parametrize("use_streaming", [False, True]) +def test_search_builtin_with_citations(use_streaming: bool) -> None: + """Test that citations are properly extracted from grounding metadata.""" + llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash").bind_tools( + [{"google_search": {}}] + ) + input_message = { + "role": "user", + "content": "Who won the 2024 UEFA Euro championship? Use search/citations.", + } + + if use_streaming: + full: Optional[BaseMessageChunk] = None + for chunk in llm.stream([input_message]): + assert isinstance(chunk, AIMessageChunk) + full = chunk if full is None else full + chunk + assert isinstance(full, AIMessageChunk) + + assert "grounding_metadata" in full.response_metadata + grounding = full.response_metadata["grounding_metadata"] + + assert "grounding_chunks" in grounding or "grounding_chunks" in grounding + assert "grounding_supports" in grounding or "grounding_supports" in grounding + + content_blocks = full.content_blocks + text_blocks_with_citations = [ + block + for block in content_blocks + if block.get("type") == "text" and block.get("annotations") + ] + + assert len(text_blocks_with_citations) > 0, ( + "Expected citations in text block if grounding metadata present" + ) + + for block in text_blocks_with_citations: + annotations = block.get("annotations", []) + citations = [ + ann + for ann in annotations # type: ignore[attr-defined] + if annotations and ann.get("type") == "citation" + ] + + for citation in citations: + # Required fields + assert citation.get("type") == "citation" + assert "id" in citation + + # Optional but expected fields from Google AI + if "url" in citation: + assert isinstance(citation["url"], str) + assert citation["url"].startswith("http") + if "title" in citation: + assert isinstance(citation["title"], str) + if "start_index" in citation: + assert isinstance(citation["start_index"], int) + assert citation["start_index"] >= 0 + if "end_index" in citation: + assert isinstance(citation["end_index"], int) + assert citation["end_index"] > citation.get("start_index", 0) + if "cited_text" in citation: + assert isinstance(citation["cited_text"], str) + if "extras" in citation: + google_metadata = citation["extras"].get("google_ai_metadata", {}) + if google_metadata: + assert isinstance(google_metadata, dict) + else: + # Test invoke + response = llm.invoke([input_message]) + assert isinstance(response, AIMessage) + + assert "grounding_metadata" in response.response_metadata + grounding = response.response_metadata["grounding_metadata"] + + assert "grounding_chunks" in grounding or "grounding_chunks" in grounding + assert "grounding_supports" in grounding or "grounding_supports" in grounding + + content_blocks = response.content_blocks + text_blocks_with_citations = [ + block + for block in content_blocks + if block.get("type") == "text" and block.get("annotations") + ] + + assert len(text_blocks_with_citations) > 0, ( + "Expected citations in text blocks if grounding metadata present" + ) + + for block in text_blocks_with_citations: + block_annotations = block.get("annotations", []) + citations = [ + ann + for ann in block_annotations # type: ignore[attr-defined] + if ann.get("type") == "citation" + ] + + for citation in citations: + # Required fields + assert citation.get("type") == "citation" + assert "id" in citation + + # Optional but expected fields from Google AI + if "url" in citation: + assert isinstance(citation["url"], str) + assert citation["url"].startswith("http") + if "title" in citation: + assert isinstance(citation["title"], str) + if "start_index" in citation: + assert isinstance(citation["start_index"], int) + assert citation["start_index"] >= 0 + if "end_index" in citation: + assert isinstance(citation["end_index"], int) + assert citation["end_index"] > citation.get("start_index", 0) + if "cited_text" in citation: + assert isinstance(citation["cited_text"], str) + if "extras" in citation: + google_metadata = citation["extras"].get("google_ai_metadata", {}) + if google_metadata: + assert isinstance(google_metadata, dict) + + @pytest.mark.parametrize("use_streaming", [False, True]) def test_code_execution_builtin(use_streaming: bool) -> None: llm = ChatGoogleGenerativeAI(model="models/gemini-2.0-flash-001").bind_tools( diff --git a/libs/genai/tests/unit_tests/test_chat_models.py b/libs/genai/tests/unit_tests/test_chat_models.py index e3055c61b..8dbd15c1a 100644 --- a/libs/genai/tests/unit_tests/test_chat_models.py +++ b/libs/genai/tests/unit_tests/test_chat_models.py @@ -27,12 +27,15 @@ SystemMessage, ToolMessage, ) +from langchain_core.messages.block_translators.google_genai import ( + _convert_to_v1_from_genai, +) from langchain_core.messages.tool import tool_call as create_tool_call from langchain_core.outputs import ChatGeneration, ChatResult from pydantic import SecretStr from pydantic_core._pydantic_core import ValidationError -from langchain_google_genai import HarmBlockThreshold, HarmCategory +from langchain_google_genai import HarmBlockThreshold, HarmCategory, Modality from langchain_google_genai.chat_models import ( ChatGoogleGenerativeAI, _chat_with_retry, @@ -943,6 +946,7 @@ def test_retry_decorator_with_custom_parameters() -> None: "start_index": 0, "end_index": 13, "text": "Test response", + "part_index": 0, }, "grounding_chunk_indices": [0], "confidence_scores": [0.95], @@ -1015,6 +1019,271 @@ def test_response_to_result_grounding_metadata( assert grounding_metadata == expected_grounding_metadata +def test_grounding_metadata_to_citations_conversion() -> None: + """Test grounding metadata is properly converted to citations in content blocks.""" + + raw_response = { + "candidates": [ + { + "content": { + "parts": [ + { + "text": ( + "Spain won the UEFA Euro 2024 championship by " + "defeating England 2-1 in the final." + ) + } + ] + }, + "grounding_metadata": { + "grounding_chunks": [ + { + "web": { + "uri": "https://uefa.com/euro2024", + "title": "UEFA Euro 2024 Results", + } + }, + { + "web": { + "uri": "https://bbc.com/sport/football", + "title": "BBC Sport Football", + } + }, + ], + "grounding_supports": [ + { + "segment": { + "start_index": 0, + "end_index": 40, + "text": "Spain won the UEFA Euro 2024 championship", + "part_index": 0, + }, + "grounding_chunk_indices": [0], + "confidence_scores": [0.95], + }, + { + "segment": { + "start_index": 41, + "end_index": 78, + "text": "by defeating England 2-1 in the final", + "part_index": 0, + }, + "grounding_chunk_indices": [0, 1], + "confidence_scores": [0.92, 0.88], + }, + ], + "web_search_queries": [ + "UEFA Euro 2024 winner", + "Euro 2024 final score", + ], + }, + } + ], + "prompt_feedback": {"block_reason": 0, "safety_ratings": []}, + "usage_metadata": { + "prompt_token_count": 10, + "candidates_token_count": 20, + "total_token_count": 30, + }, + } + + response = GenerateContentResponse(raw_response) + result = _response_to_result(response, stream=False) + + assert len(result.generations) == 1 + message = result.generations[0].message + + assert "grounding_metadata" in message.response_metadata + + # Verify grounding metadata structure uses snake_case + gm = message.response_metadata["grounding_metadata"] + assert "grounding_chunks" in gm + assert "grounding_supports" in gm + assert "web_search_queries" in gm + + # Verify all required fields are present with correct casing + assert len(gm["grounding_chunks"]) == 2 + assert len(gm["grounding_supports"]) == 2 + + # Verify first support has all fields including start_index + first_support = gm["grounding_supports"][0] + assert "segment" in first_support + assert "start_index" in first_support["segment"] + assert first_support["segment"]["start_index"] == 0 + assert "end_index" in first_support["segment"] + assert "grounding_chunk_indices" in first_support + assert "confidence_scores" in first_support + + content_blocks = message.content_blocks + text_blocks_with_citations = [ + block + for block in content_blocks + if block.get("type") == "text" and block.get("annotations") + ] + assert len(text_blocks_with_citations) > 0, "Expected citations in text blocks" + + for block in text_blocks_with_citations: + annotations = block.get("annotations", []) + citations = [ann for ann in annotations if ann.get("type") == "citation"] # type: ignore[attr-defined] + assert len(citations) > 0, "Expected at least one citation" + + for citation in citations: + assert citation.get("type") == "citation" + assert "id" in citation + if "url" in citation: + assert isinstance(citation["url"], str) + + +def test_empty_grounding_metadata_no_citations() -> None: + """Test that empty grounding metadata doesn't create citations.""" + raw_response = { + "candidates": [ + { + "content": { + "parts": [{"text": "This is a response without grounding."}] + }, + "grounding_metadata": {}, + } + ], + "prompt_feedback": {"block_reason": 0, "safety_ratings": []}, + "usage_metadata": { + "prompt_token_count": 5, + "candidates_token_count": 8, + "total_token_count": 13, + }, + } + + response = GenerateContentResponse(raw_response) + result = _response_to_result(response, stream=False) + + message = result.generations[0].message + content_blocks = message.content_blocks + + text_blocks_with_citations = [ + block + for block in content_blocks + if block.get("type") == "text" and block.get("annotations") + ] + + assert len(text_blocks_with_citations) == 0 + + +def test_grounding_metadata_missing_optional_fields() -> None: + """Test handling of grounding metadata with missing optional fields.""" + raw_response = { + "candidates": [ + { + "content": {"parts": [{"text": "Sample text"}]}, + "grounding_metadata": { + "grounding_chunks": [ + { + "web": { + "uri": "https://example.com", + # Missing 'title' + } + } + ], + "grounding_supports": [ + { + "segment": { + # Missing 'text' + "start_index": 0, + "end_index": 11, + "part_index": 0, + }, + "grounding_chunk_indices": [0], + } + ], + # Missing 'web_search_queries' (optional field) + }, + } + ], + "prompt_feedback": {"block_reason": 0, "safety_ratings": []}, + "usage_metadata": { + "prompt_token_count": 5, + "candidates_token_count": 3, + "total_token_count": 8, + }, + } + + response = GenerateContentResponse(raw_response) + result = _response_to_result(response, stream=False) + + message = result.generations[0].message + + # Verify grounding metadata is present even with missing optional fields + assert "grounding_metadata" in message.response_metadata + gm = message.response_metadata["grounding_metadata"] + + # Verify structure is correct (snake_case from MessageToDict) + assert "grounding_chunks" in gm + assert "grounding_supports" in gm + + # Verify optional fields can be missing + chunk = gm["grounding_chunks"][0] + assert "web" in chunk + assert "uri" in chunk["web"] + # Title is missing, which is OK + + support = gm["grounding_supports"][0] + assert "segment" in support + # Text is missing from segment, which is OK + assert "start_index" in support["segment"] + assert "end_index" in support["segment"] + + # web_search_queries is optional and missing in this test + + +def test_grounding_metadata_multiple_parts() -> None: + """Test grounding metadata with multiple content parts.""" + raw_response = { + "candidates": [ + { + "content": { + "parts": [ + {"text": "First part. "}, + {"text": "Second part with citation."}, + ] + }, + "grounding_metadata": { + "grounding_chunks": [ + {"web": {"uri": "https://example.com", "title": "Example"}} + ], + "grounding_supports": [ + { + "segment": { + "start_index": 12, # Points to second part + "end_index": 38, + "text": "Second part with citation", + "part_index": 1, # Indicates which part + }, + "grounding_chunk_indices": [0], + } + ], + }, + } + ], + "prompt_feedback": {"block_reason": 0, "safety_ratings": []}, + "usage_metadata": { + "prompt_token_count": 10, + "candidates_token_count": 10, + "total_token_count": 20, + }, + } + + response = GenerateContentResponse(raw_response) + result = _response_to_result(response, stream=False) + + message = result.generations[0].message + + # Verify grounding metadata is present + assert "grounding_metadata" in message.response_metadata + grounding = message.response_metadata["grounding_metadata"] + # grounding metadata from proto.Message.to_dict() uses snake_case + assert len(grounding["grounding_supports"]) == 1 + assert grounding["grounding_supports"][0]["segment"]["part_index"] == 1 + + @pytest.mark.parametrize( "is_async,mock_target,method_name", [ @@ -1408,3 +1677,185 @@ def test_modalities_override_in_generation_config() -> None: # Verify that the _generate method was called mock_generate.assert_called_once() + + +def test_chat_google_genai_image_content_blocks() -> None: + """Test generating an image with mocked response and content_blocks translation.""" + mock_response = GenerateContentResponse( + { + "candidates": [ + { + "content": { + "parts": [ + {"text": "Meow!"}, + { + "inline_data": { + "mime_type": "image/png", + "data": base64.b64decode( + "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAf" + "FcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg==" + ), + } + }, + ] + }, + "finish_reason": "STOP", + } + ], + "usage_metadata": { + "prompt_token_count": 10, + "candidates_token_count": 5, + "total_token_count": 15, + }, + } + ) + + llm = ChatGoogleGenerativeAI( + model="models/gemini-2.5-flash", + google_api_key=SecretStr("test-key"), + ) + + with patch.object(llm.client, "generate_content", return_value=mock_response): + result = llm.invoke( + "Say 'meow!' and then Generate an image of a cat.", + generation_config={ + "top_k": 2, + "top_p": 1, + "temperature": 0.7, + "response_modalities": ["TEXT", "IMAGE"], + }, + ) + + assert isinstance(result, AIMessage) + assert isinstance(result.content, list) + assert isinstance(result.content[0], str) + assert isinstance(result.content[1], dict) + assert result.content[1].get("type") == "image_url" + assert not result.content[0].startswith(" ") + + content_blocks = result.content_blocks + assert len(content_blocks) == 2 + + text_block = content_blocks[0] + assert text_block["type"] == "text" + assert isinstance(text_block["text"], str) + + image_block = content_blocks[1] + assert isinstance(image_block, dict) + assert image_block["type"] == "image" + assert "base64" in image_block + assert "mime_type" in image_block + assert image_block["mime_type"] == "image/png" + assert image_block["base64"] == ( + "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDw" + "AChwGA60e6kgAAAABJRU5ErkJggg==" + ) + + # Pre-v1 we returned images using chat completions format. This translation logic + # is already covered by unit tests in langchain core, but we add a simple test here + # to ensure our translator function is wired up correctly + image_blocks_v1 = _convert_to_v1_from_genai(result) + assert len(image_blocks_v1) == 2 + assert image_blocks_v1[0] == text_block + assert image_blocks_v1[1] == image_block + + +def test_content_blocks_translation_with_mixed_image_content() -> None: + """Test converting with mixed image and text content.""" + mixed_content = [ + "Here is the image you requested:", + { + "type": "image_url", + "image_url": { + "url": base64.b64encode( + base64.b64decode( + "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk" + "YPhfDwAChwGA60e6kgAAAABJRU5ErkJggg==" + ) + ).decode(), + }, + }, + ] + msg = AIMessage(content=mixed_content) # type: ignore[arg-type] + msg.response_metadata = {"model_provider": "google_genai"} + + content_blocks = msg.content_blocks + + assert len(content_blocks) == 2 + + # First block should be text + text_block = content_blocks[0] + assert text_block["type"] == "text" + assert text_block["text"] == "Here is the image you requested:" + + # Second block should be ImageContentBlock + image_block = content_blocks[1] + assert image_block["type"] == "image" + assert "base64" in image_block + + +def test_chat_google_genai_invoke_with_audio_mocked() -> None: + """Test generating audio with mocked response and content_blocks translation.""" + mock_response = GenerateContentResponse( + { + "candidates": [ + { + # Empty content when audio is in additional_kwargs + "content": {"parts": []}, + "finish_reason": "STOP", + } + ], + "usage_metadata": { + "prompt_token_count": 10, + "candidates_token_count": 5, + "total_token_count": 15, + }, + } + ) + + wav_bytes = ( # (minimal WAV header) + b"RIFF$\x00\x00\x00WAVEfmt \x10\x00\x00\x00\x01\x00\x01\x00" + b"\x44\xac\x00\x00\x88X\x01\x00\x02\x00\x10\x00data\x00\x00\x00\x00" + ) + + llm = ChatGoogleGenerativeAI( + model="models/gemini-2.5-flash-preview-tts", + google_api_key=SecretStr("test-key"), + response_modalities=[Modality.AUDIO], + ) + + with patch.object(llm.client, "generate_content", return_value=mock_response): + with patch( + "langchain_google_genai.chat_models._parse_response_candidate" + ) as mock_parse: + mock_parse.return_value = AIMessage( + content="", + additional_kwargs={"audio": wav_bytes}, + usage_metadata={ + "input_tokens": 10, + "output_tokens": 5, + "total_tokens": 15, + }, + response_metadata={"model_provider": "google_genai"}, + ) + + result = llm.invoke( + "Please say The quick brown fox jumps over the lazy dog", + ) + + assert isinstance(result, AIMessage) + assert result.content == "" + audio_data = result.additional_kwargs.get("audio") + assert isinstance(audio_data, bytes) + assert len(audio_data) >= 12 + assert audio_data[0:4] == b"RIFF" + assert audio_data[8:12] == b"WAVE" + + # Test content_blocks translation to AudioContentBlock + blocks = 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_convert_to_parts( metadata = VideoMetadata(part["video_metadata"]) media_part.video_metadata = metadata parts.append(media_part) + elif part["type"] == "function_call_signature": + # Signature for function_call Part - skip it here as it should be + # attached to the actual function_call Part + # This is handled separately in the history parsing logic + pass elif part["type"] == "thinking": - # Pre-existing thinking block format + # Pre-existing thinking block format that we continue to store as thought_sig = None if "signature" in part: sig = part["signature"] @@ -424,13 +429,8 @@ def _convert_to_parts( thought_signature=thought_sig, ) ) - elif part["type"] == "function_call_signature": - # Signature for function_call Part - skip it here as it should be - # attached to the actual function_call Part - # This is handled separately in the history parsing logic - pass elif part["type"] == "reasoning": - # New ReasoningContentBlock + # ReasoningContentBlock (when output_version = "v1") extras = part.get("extras", {}) or {} sig = extras.get("signature") thought_sig = None @@ -444,8 +444,24 @@ def _convert_to_parts( thought_signature=thought_sig, ) ) + elif part["type"] == "server_tool_call": + if part.get("name") == "code_interpreter": + args = part.get("args", {}) + code = args.get("code", "") + language = args.get("language", "python") + executable_code_part = Part( + executable_code=ExecutableCode(language=language, code=code) + ) + parts.append(executable_code_part) + else: + warnings.warn( + f"Server tool call with name '{part.get('name')}' is not " + "currently supported by Google GenAI. Only " + "'code_interpreter' is supported.", + stacklevel=2, + ) elif part["type"] == "executable_code": - # TODO: LangChain's native server execution tool block not handled + # Legacy executable_code format (backward compat) if "executable_code" not in part or "language" not in part: msg = ( "Executable code part must have 'code' and 'language' " @@ -458,8 +474,22 @@ def _convert_to_parts( ) ) parts.append(executable_code_part) + elif part["type"] == "server_tool_result": + output = part.get("output", "") + status = part.get("status", "success") + # Map status to outcome: success → 1 (OUTCOME_OK), error → 2 + outcome = 1 if status == "success" else 2 + # Check extras for original outcome if available + if "extras" in part and "outcome" in part["extras"]: + outcome = part["extras"]["outcome"] + code_execution_result_part = Part( + code_execution_result=CodeExecutionResult( + output=str(output), outcome=outcome + ) + ) + parts.append(code_execution_result_part) elif part["type"] == "code_execution_result": - # TODO: LangChain's native server execution result block not handled + # Legacy code_execution_result format (backward compat) if "code_execution_result" not in part: msg = ( "Code execution result part must have " @@ -783,10 +813,12 @@ def _parse_response_candidate( if hasattr(part, "executable_code") and part.executable_code is not None: if part.executable_code.code and part.executable_code.language: + code_id = str(uuid.uuid4()) # Generate ID if not present, needed later code_message = { "type": "executable_code", "executable_code": part.executable_code.code, "language": part.executable_code.language, + "id": code_id, } content = _append_to_content(content, code_message) @@ -794,10 +826,13 @@ def _parse_response_candidate( hasattr(part, "code_execution_result") and part.code_execution_result is not None ) and part.code_execution_result.output: + # outcome: 1 = OUTCOME_OK (success), else = error + outcome = part.code_execution_result.outcome execution_result = { "type": "code_execution_result", "code_execution_result": part.code_execution_result.output, - "outcome": part.code_execution_result.outcome, + "outcome": outcome, + "tool_call_id": "", # Linked via block translator } content = _append_to_content(content, execution_result) diff --git a/libs/genai/tests/integration_tests/test_chat_models.py b/libs/genai/tests/integration_tests/test_chat_models.py index c22fc0901..2942334d7 100644 --- a/libs/genai/tests/integration_tests/test_chat_models.py +++ b/libs/genai/tests/integration_tests/test_chat_models.py @@ -1055,6 +1055,7 @@ def test_search_builtin_with_citations(use_streaming: bool) -> None: assert isinstance(google_metadata, dict) +@pytest.mark.filterwarnings("ignore::UserWarning") @pytest.mark.parametrize("use_streaming", [False, True]) def test_code_execution_builtin(use_streaming: bool) -> None: llm = ChatGoogleGenerativeAI(model="models/gemini-2.0-flash-001").bind_tools( @@ -1068,38 +1069,56 @@ def test_code_execution_builtin(use_streaming: bool) -> None: if use_streaming: # Test streaming mode full: Optional[BaseMessageChunk] = None - with pytest.warns(match="executable_code"): - for chunk in llm.stream([input_message]): - assert isinstance(chunk, AIMessageChunk) - full = chunk if full is None else full + chunk + for chunk in llm.stream([input_message]): + assert isinstance(chunk, AIMessageChunk) + full = chunk if full is None else full + chunk assert isinstance(full, AIMessageChunk) - # Should get both code and result blocks + # Check raw content still has legacy format (backward compatibility) blocks = [block for block in full.content if isinstance(block, dict)] expected_block_types = {"executable_code", "code_execution_result"} assert {block.get("type") for block in blocks} == expected_block_types - # Test we can process chat history without raising errors + content_blocks = full.content_blocks + standard_blocks = [block for block in content_blocks if isinstance(block, dict)] + standard_types = {block.get("type") for block in standard_blocks} + assert ( + "server_tool_call" in standard_types or "executable_code" in standard_types + ) + assert ( + "server_tool_result" in standard_types + or "code_execution_result" in standard_types + ) + + # Test passing back in chat history without raising errors next_message = { "role": "user", "content": "Can you show me the calculation again with comments?", } - with pytest.warns(match="executable_code"): - _ = llm.invoke([input_message, full, next_message]) + _ = llm.invoke([input_message, full, next_message]) else: - # Test invoke mode - with pytest.warns(match="executable_code"): - response = llm.invoke([input_message]) + # Invoke + response = llm.invoke([input_message]) blocks = [block for block in response.content if isinstance(block, dict)] - # Should get both code and result blocks + # Check raw content still has legacy format (backward compatibility) expected_block_types = {"executable_code", 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Sep 2025 00:56:59 -0400 Subject: [PATCH 20/23] release(genai): 3.0.0a1 --- libs/genai/pyproject.toml | 2 +- libs/genai/uv.lock | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/libs/genai/pyproject.toml b/libs/genai/pyproject.toml index c1618b567..f369f50bf 100644 --- a/libs/genai/pyproject.toml +++ b/libs/genai/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "pdm.backend" [project] name = "langchain-google-genai" -version = "2.1.12" +version = "3.0.0a1" description = "An integration package connecting Google's genai package and LangChain" authors = [] license = {text = "MIT"} diff --git a/libs/genai/uv.lock b/libs/genai/uv.lock index d301c7a6d..1f314d5e7 100644 --- a/libs/genai/uv.lock +++ b/libs/genai/uv.lock @@ -471,7 +471,7 @@ dependencies = [ [[package]] name = "langchain-google-genai" -version = "2.1.12" +version = "3.0.0a1" source = { editable = "." } dependencies = [ { name = "filetype" }, From b44da037d19cd2d574cccd5a28f574b5aca96005 Mon Sep 17 00:00:00 2001 From: Mason Daugherty Date: Wed, 1 Oct 2025 23:03:14 -0400 Subject: [PATCH 21/23] chore(genai): bump version --- libs/genai/pyproject.toml | 4 +--- libs/genai/uv.lock | 19 +++++++++++++------ 2 files changed, 14 insertions(+), 9 deletions(-) diff --git a/libs/genai/pyproject.toml b/libs/genai/pyproject.toml index f369f50bf..d6e62616d 100644 --- a/libs/genai/pyproject.toml +++ b/libs/genai/pyproject.toml @@ -13,7 +13,7 @@ readme = "README.md" repository = "https://github.com/langchain-ai/langchain-google" dependencies = [ - "langchain-core>=1.0.0a4,<2.0.0", + "langchain-core>=1.0.0a6,<2.0.0", "google-ai-generativelanguage>=0.7.0,<1.0.0", "pydantic>=2.0.0,<3.0.0", "filetype>=1.2.0,<2.0.0", @@ -56,8 +56,6 @@ test_integration = [ dev = [] [tool.uv.sources] -langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core", branch = "mdrxy/genai" } -langchain-tests = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/standard-tests", branch = "mdrxy/genai" } #langchain-core = { path = "../../../langchain/libs/core", editable = true } #langchain-tests = { path = "../../../langchain/libs/standard-tests", editable = true } diff --git a/libs/genai/uv.lock b/libs/genai/uv.lock index 1f314d5e7..bff567794 100644 --- a/libs/genai/uv.lock +++ b/libs/genai/uv.lock @@ -456,10 +456,9 @@ wheels = [ [[package]] name = "langchain-core" -version = "1.0.0a4" -source = { git = "https://github.com/langchain-ai/langchain.git?subdirectory=libs%2Fcore&branch=mdrxy%2Fgenai#940d85bb1594b641431729a8376b78c7b82a26ab" } +version = "1.0.0a6" +source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "filetype" }, { name = "jsonpatch" }, { name = "langsmith" }, { name = "packaging" }, @@ -468,6 +467,10 @@ dependencies = [ { name = "tenacity" }, { name = "typing-extensions" }, ] +sdist = { url = "https://files.pythonhosted.org/packages/c5/14/8ba54f4d2c40e3528e511a106aa95e6625083fb42607f1dc6797ce32181e/langchain_core-1.0.0a6.tar.gz", hash = "sha256:8c6d60cae6d7e6c3e3555ad05a4e65d0ae8167a7ee7b40233663209b019ffeae", size = 617086, upload-time = "2025-10-02T03:01:27.997Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/65/83/b53010d854f76c4373f66bc05a3ea2b85644ddf928e5af7e41db5cc2f832/langchain_core-1.0.0a6-py3-none-any.whl", hash = "sha256:46bb8f5dfe3637cf5fa594a8e40ddd2d4fd1cb48559afaeeece0ea83bd715a22", size = 484625, upload-time = "2025-10-02T03:01:26.46Z" }, +] [[package]] name = "langchain-google-genai" @@ -514,7 +517,7 @@ typing = [ requires-dist = [ { name = "filetype", specifier = ">=1.2.0,<2.0.0" }, { name = "google-ai-generativelanguage", specifier = ">=0.7.0,<1.0.0" }, - { name = "langchain-core", git = "https://github.com/langchain-ai/langchain.git?subdirectory=libs%2Fcore&branch=mdrxy%2Fgenai" }, + { name = "langchain-core", specifier = ">=1.0.0a6,<2.0.0" }, { name = "pydantic", specifier = ">=2.0.0,<3.0.0" }, ] @@ -523,7 +526,7 @@ dev = [] lint = [{ name = "ruff", specifier = ">=0.13.1,<0.14.0" }] test = [ { name = "freezegun", specifier = ">=1.5.0,<2.0.0" }, - { name = "langchain-tests", git = "https://github.com/langchain-ai/langchain.git?subdirectory=libs%2Fstandard-tests&branch=mdrxy%2Fgenai" }, + { name = "langchain-tests", specifier = ">=1.0.0a1,<2.0.0" }, { name = "numpy", marker = "python_full_version < '3.13'", specifier = ">=1.26.4" }, { name = "numpy", marker = "python_full_version >= '3.13'", specifier = ">=2.1.0" }, { name = "pytest", specifier = ">=8.4.0,<9.0.0" }, @@ -546,7 +549,7 @@ typing = [ [[package]] name = "langchain-tests" version = "1.0.0a1" -source = { git = "https://github.com/langchain-ai/langchain.git?subdirectory=libs%2Fstandard-tests&branch=mdrxy%2Fgenai#940d85bb1594b641431729a8376b78c7b82a26ab" } +source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "httpx" }, { name = "langchain-core" }, @@ -561,6 +564,10 @@ dependencies = [ { name = "syrupy" }, { name = "vcrpy" }, ] +sdist = { url = "https://files.pythonhosted.org/packages/dc/57/98a5f4f3a31e917b764eee823408fe346d5aeb2997b93b38ed2739a79da0/langchain_tests-1.0.0a1.tar.gz", hash = "sha256:0f6bff9f9e2d02120b67f8e308726dcd43ae98c2ea7a9594ceada1c769000c04", size = 46485, upload-time = "2025-09-16T17:44:46.208Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/aa/23/7bfee56f9997a7235e03f739388e682a26381faca7f3fd3ded437a441b35/langchain_tests-1.0.0a1-py3-none-any.whl", hash = "sha256:860270f641ea2a88061963ae4cbb50579d7d5f0cf3c4170faac43ea920d14cd8", size = 49113, upload-time = "2025-09-16T17:44:45.331Z" }, +] [[package]] name = "langsmith" From e1c1801b5d5ebde7ec4e1bf85ebda25d3e048cfa Mon Sep 17 00:00:00 2001 From: Mason Daugherty Date: Fri, 10 Oct 2025 08:05:29 -0400 Subject: [PATCH 22/23] feat(vertex): standard content (#1224) --- .../langchain_google_vertexai/_base.py | 34 +- .../langchain_google_vertexai/chat_models.py | 65 +- libs/vertexai/pyproject.toml | 6 + .../integration_tests/test_chat_models.py | 197 ++-- .../tests/unit_tests/test_chat_models.py | 538 +++++------ libs/vertexai/uv.lock | 849 ++++++++++-------- 6 files changed, 913 insertions(+), 776 deletions(-) diff --git a/libs/vertexai/langchain_google_vertexai/_base.py b/libs/vertexai/langchain_google_vertexai/_base.py index 73695b226..9bcdc9f9d 100644 --- a/libs/vertexai/langchain_google_vertexai/_base.py +++ b/libs/vertexai/langchain_google_vertexai/_base.py @@ -192,23 +192,23 @@ def _library_version(self) -> str: class _VertexAICommon(_VertexAIBase): client_preview: Any = Field(default=None, exclude=True) #: :meta private: model_name: Optional[str] = Field(default=None, alias="model") - "Underlying model name." + """Underlying model name.""" temperature: Optional[float] = None - "Sampling temperature, it controls the degree of randomness in token selection." + """Sampling temperature, it controls the degree of randomness in token selection.""" frequency_penalty: Optional[float] = None - "Positive values penalize tokens that repeatedly appear in the generated text, " - "decreasing the probability of repeating content." + """Positive values penalize tokens that repeatedly appear in the generated text, + decreasing the probability of repeating content.""" presence_penalty: Optional[float] = None - "Positive values penalize tokens that already appear in the generated text, " - "increasing the probability of generating more diverse content." + """Positive values penalize tokens that already appear in the generated text, + increasing the probability of generating more diverse content.""" max_output_tokens: Optional[int] = Field(default=None, alias="max_tokens") - "Token limit determines the maximum amount of text output from one prompt." + """Token limit determines the maximum amount of text output from one prompt.""" top_p: Optional[float] = None - "Tokens are selected from most probable to least until the sum of their " - "probabilities equals the top-p value. Top-p is ignored for Codey models." + """Tokens are selected from most probable to least until the sum of their + probabilities equals the top-p value. Top-p is ignored for Codey models.""" top_k: Optional[int] = None - "How the model selects tokens for output, the next token is selected from " - "among the top-k most probable tokens. Top-k is ignored for Codey models." + """How the model selects tokens for output, the next token is selected from + among the top-k most probable tokens. Top-k is ignored for Codey models.""" n: int = 1 """How many completions to generate for each prompt.""" seed: Optional[int] = None @@ -229,7 +229,7 @@ class _VertexAICommon(_VertexAIBase): HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE, } - """ # noqa: E501 + """ # noqa: E501 tuned_model_name: Optional[str] = None """The name of a tuned model.""" @@ -316,15 +316,15 @@ class _BaseVertexAIModelGarden(_VertexAIBase): async_client: Any = Field(default=None, exclude=True) #: :meta private: endpoint_id: str - "A name of an endpoint where the model has been deployed." + """A name of an endpoint where the model has been deployed.""" allowed_model_args: Optional[List[str]] = None - "Allowed optional args to be passed to the model." + """Allowed optional args to be passed to the model.""" prompt_arg: str = "prompt" result_arg: Optional[str] = "generated_text" - "Set result_arg to None if output of the model is expected to be a string." - "Otherwise, if it's a dict, provided an argument that contains the result." + """Set result_arg to None if output of the model is expected to be a string. + Otherwise, if it's a dict, provided an argument that contains the result.""" single_example_per_request: bool = True - "LLM endpoint currently serves only the first example in the request" + """LLM endpoint currently serves only the first example in the request""" @model_validator(mode="after") def validate_environment(self) -> Self: diff --git a/libs/vertexai/langchain_google_vertexai/chat_models.py b/libs/vertexai/langchain_google_vertexai/chat_models.py index fc69daede..f4ad8821d 100644 --- a/libs/vertexai/langchain_google_vertexai/chat_models.py +++ b/libs/vertexai/langchain_google_vertexai/chat_models.py @@ -1,4 +1,7 @@ -"""Wrapper around Google VertexAI chat-based models.""" +"""Wrapper around Google VertexAI chat-based models. + +Vertex supports both v1 and v1beta1 endpoints (`endpoint_version` parameter). +""" from __future__ import annotations # noqa import ast @@ -7,7 +10,6 @@ import json import logging import re -from dataclasses import dataclass, field from operator import itemgetter import uuid from typing import ( @@ -87,7 +89,6 @@ _convert_schema_dict_to_gapic, ) from vertexai.language_models import ( - ChatMessage, # TODO: migrate to google-genai since this is deprecated InputOutputTextPair, ) from google.cloud.aiplatform_v1.types import ( @@ -195,14 +196,6 @@ def _base64_to_bytes(input_str: str) -> bytes: return base64.b64decode(input_str.encode("utf-8")) -@dataclass -class _ChatHistory: - """Represents a context and a history of messages.""" - - history: List[ChatMessage] = field(default_factory=list) - context: Optional[str] = None - - class _GeminiGenerateContentKwargs(TypedDict): generation_config: Optional[GenerationConfigType] safety_settings: Optional[SafetySettingsType] @@ -210,41 +203,27 @@ class _GeminiGenerateContentKwargs(TypedDict): tool_config: Optional[ToolConfig] -def _parse_chat_history(history: List[BaseMessage]) -> _ChatHistory: - """Parse a sequence of messages into history. +def _parse_chat_history_gemini( + history: List[BaseMessage], + imageBytesLoader: ImageBytesLoader, + perform_literal_eval_on_string_raw_content: Optional[bool] = False, +) -> tuple[Content | None, list[Content]]: + """Parse LangChain message history into Gemini format. + + .. warning:: + perform_literal_eval_on_string_raw_content should only be set to True if you + fully trust the input, as it may execute arbitrary code. Args: - history: The list of messages to re-create the history of the chat. + history: List of LangChain messages. + imageBytesLoader: An ImageBytesLoader instance to handle image loading. + perform_literal_eval_on_string_raw_content: Whether to attempt to parse string + content as Python literals (e.g., lists or dicts). Defaults to False. Returns: - A parsed chat history. - - Raises: - ValueError: If a sequence of message has a SystemMessage not at the - first place. + Tuple of (system_instruction, aiplatform_v1beta1 content). """ - vertex_messages, context = [], None - for i, message in enumerate(history): - content = cast("str", message.content) - if i == 0 and isinstance(message, SystemMessage): - context = content - elif isinstance(message, AIMessage): - vertex_message = ChatMessage(content=content, author="bot") - vertex_messages.append(vertex_message) - elif isinstance(message, HumanMessage): - vertex_message = ChatMessage(content=content, author="user") - vertex_messages.append(vertex_message) - else: - msg = f"Unexpected message with type {type(message)} at the position {i}." - raise ValueError(msg) - return _ChatHistory(context=context, history=vertex_messages) - -def _parse_chat_history_gemini( - history: List[BaseMessage], - imageBytesLoader: ImageBytesLoader, - perform_literal_eval_on_string_raw_content: Optional[bool] = False, -) -> tuple[Content | None, list[Content]]: def _convert_to_prompt(part: Union[str, Dict]) -> Optional[Part]: if isinstance(part, str): return Part(text=part) @@ -335,6 +314,12 @@ def _convert_to_prompt(part: Union[str, Dict]) -> Optional[Part]: raise ValueError(msg) def _convert_to_parts(message: BaseMessage) -> List[Part]: + """Parse LangChain message content into Google parts. + + Used when preparing Human, System and AI messages for sending to the API. + Handles both legacy (pre-v1) dict-based content blocks and v1 ContentBlock + objects. + """ raw_content = message.content # If a user sends a multimodal request with agents, then the full input diff --git a/libs/vertexai/pyproject.toml b/libs/vertexai/pyproject.toml index b65f0710b..e6178a9aa 100644 --- a/libs/vertexai/pyproject.toml +++ b/libs/vertexai/pyproject.toml @@ -75,6 +75,12 @@ test_integration = [ dev = [] +[tool.uv.sources] +langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core", branch = "mdrxy/genai" } +langchain-tests = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/standard-tests", branch = "mdrxy/genai" } +#langchain-core = { path = "../../../langchain/libs/core", editable = true } +#langchain-tests = { path = "../../../langchain/libs/standard-tests", editable = true } + [tool.ruff] fix = true diff --git a/libs/vertexai/tests/integration_tests/test_chat_models.py b/libs/vertexai/tests/integration_tests/test_chat_models.py index b3c92e2d5..04dbb6420 100644 --- a/libs/vertexai/tests/integration_tests/test_chat_models.py +++ b/libs/vertexai/tests/integration_tests/test_chat_models.py @@ -58,7 +58,7 @@ model_names_to_test = [_DEFAULT_MODEL_NAME] endpoint_versions = ["v1", "v1beta1"] -rate_limiter = InMemoryRateLimiter(requests_per_second=1.0) +RATE_LIMITER = InMemoryRateLimiter(requests_per_second=1.0) def _check_usage_metadata(message: AIMessage) -> None: @@ -71,26 +71,63 @@ def _check_usage_metadata(message: AIMessage) -> None: ) == message.usage_metadata["total_tokens"] +def _check_tool_calls(response: BaseMessage, expected_name: str) -> None: + """Check tool calls are as expected.""" + assert isinstance(response, AIMessage) + assert isinstance(response.content, str) + assert response.content == "" + function_call = response.additional_kwargs.get("function_call") + assert function_call + assert function_call["name"] == expected_name + arguments_str = function_call.get("arguments") + assert arguments_str + arguments = json.loads(arguments_str) + assert arguments == { + "name": "Erick", + "age": 27.0, + } + tool_calls = response.tool_calls + assert len(tool_calls) == 1 + tool_call = tool_calls[0] + assert tool_call["name"] == expected_name + assert tool_call["args"] == {"age": 27.0, "name": "Erick"} + + @pytest.mark.release @pytest.mark.parametrize("model_name", model_names_to_test) @pytest.mark.parametrize("endpoint_version", endpoint_versions) def test_initialization(model_name: Optional[str], endpoint_version: str) -> None: - """Test chat model initialization.""" + """Test `ChatVertexAI` initialization. + + TODO: why is this a integration test? + """ model = ChatVertexAI( model_name=model_name, - rate_limiter=rate_limiter, + rate_limiter=RATE_LIMITER, endpoint_version=endpoint_version, ) assert model._llm_type == "vertexai" +@pytest.mark.xfail(reason="can't create service account key on gcp") +@pytest.mark.release +def test_init_from_credentials_obj() -> None: + credentials_dict = json.loads(os.environ["GOOGLE_VERTEX_AI_WEB_CREDENTIALS"]) + credentials = service_account.Credentials.from_service_account_info( + credentials_dict + ) + llm = ChatVertexAI(model="gemini-2.0-flash-001", credentials=credentials) + llm.invoke("how are you") + + @pytest.mark.release @pytest.mark.parametrize("model_name", model_names_to_test) @pytest.mark.parametrize("endpoint_version", endpoint_versions) def test_vertexai_single_call(model_name: Optional[str], endpoint_version: str) -> None: + """Test making a single invoke call.""" model = ChatVertexAI( model_name=model_name, - rate_limiter=rate_limiter, + rate_limiter=RATE_LIMITER, endpoint_version=endpoint_version, ) message = HumanMessage(content="Hello") @@ -103,8 +140,12 @@ def test_vertexai_single_call(model_name: Optional[str], endpoint_version: str) @pytest.mark.release @pytest.mark.xfail(reason="vertex api doesn't respect n/candidate_count") def test_candidates() -> None: + """Test making a single invoke call with `n>1`. + + # TODO: what is chat-bison@001? is it marked for deprecation? + """ model = ChatVertexAI( - model_name="chat-bison@001", temperature=0.3, n=2, rate_limiter=rate_limiter + model_name="chat-bison@001", temperature=0.3, n=2, rate_limiter=RATE_LIMITER ) message = HumanMessage(content="Hello") response = model.generate(messages=[[message]]) @@ -114,9 +155,10 @@ def test_candidates() -> None: @pytest.mark.release -async def test_vertexai_agenerate() -> None: +async def test_vertexai_generate() -> None: + # TODO: parameterize with sync/async generate method model = ChatVertexAI( - temperature=0, model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter + temperature=0, model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER ) message = HumanMessage(content="Hello") response = await model.agenerate([[message]]) @@ -139,8 +181,9 @@ async def test_vertexai_agenerate() -> None: @pytest.mark.release def test_vertexai_stream() -> None: + # TODO: parameterize with astream equivalent model = ChatVertexAI( - temperature=0, model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter + temperature=0, model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER ) message = HumanMessage(content="Hello") @@ -166,8 +209,9 @@ def test_vertexai_stream() -> None: @pytest.mark.release async def test_vertexai_astream() -> None: + # TODO: parameterize with stream equivalent model = ChatVertexAI( - temperature=0, model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter + temperature=0, model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER ) message = HumanMessage(content="Hello") @@ -192,7 +236,8 @@ async def test_vertexai_astream() -> None: @pytest.mark.release def test_multimodal() -> None: - llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) + """Test multimodal input with a gcs image URL in chat completions format.""" + llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) gcs_url = ( "gs://cloud-samples-data/generative-ai/image/320px-Felis_catus-cat_on_snow.jpg" ) @@ -210,18 +255,18 @@ def test_multimodal() -> None: assert isinstance(output, AIMessage) _check_usage_metadata(output) - llm = ChatVertexAI(model_name="gemini-2.5-pro", rate_limiter=rate_limiter) + llm = ChatVertexAI(model_name="gemini-2.5-pro", rate_limiter=RATE_LIMITER) for chunk in llm.stream([message]): assert isinstance(chunk, AIMessageChunk) -video_param = pytest.param( +VIDEO_PARAM = pytest.param( "gs://cloud-samples-data/generative-ai/video/pixel8.mp4", "video/mp4", id="video", ) -multimodal_inputs = [ - video_param, +MULTIMODAL_INPUTS = [ + VIDEO_PARAM, pytest.param( "gs://cloud-samples-data/generative-ai/audio/pixel.mp3", "audio/mp3", id="audio" ), @@ -234,9 +279,10 @@ def test_multimodal() -> None: @pytest.mark.release -@pytest.mark.parametrize(("file_uri", "mime_type"), multimodal_inputs) +@pytest.mark.parametrize(("file_uri", "mime_type"), MULTIMODAL_INPUTS) def test_multimodal_media_file_uri(file_uri, mime_type) -> None: - llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) + """Test multimodal input with gcs file URIs (video, audio, image).""" + llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) media_message = { "type": "media", "file_uri": file_uri, @@ -252,10 +298,11 @@ def test_multimodal_media_file_uri(file_uri, mime_type) -> None: @pytest.mark.release -@pytest.mark.parametrize(("file_uri", "mime_type"), multimodal_inputs) +@pytest.mark.parametrize(("file_uri", "mime_type"), MULTIMODAL_INPUTS) @pytest.mark.first def test_multimodal_media_inline_base64(file_uri, mime_type) -> None: - llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) + """Test multimodal input with base64 encoded media content (video, audio, image).""" + llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) storage_client = storage.Client() blob = storage.Blob.from_string(file_uri, client=storage_client) media_base64 = base64.b64encode(blob.download_as_bytes()).decode() @@ -276,6 +323,7 @@ def test_multimodal_media_inline_base64(file_uri, mime_type) -> None: @pytest.mark.release @pytest.mark.first def test_multimodal_media_inline_base64_template() -> None: + """Test multimodal input with base64 encoded media content using prompt template.""" llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME) prompt_template = ChatPromptTemplate( [ @@ -309,6 +357,7 @@ def test_multimodal_media_inline_base64_template() -> None: @pytest.mark.extended def test_multimodal_media_inline_base64_agent() -> None: + """Test multimodal input with base64 encoded media content using a ReAct agent.""" from langchain import agents @tool @@ -357,7 +406,7 @@ def get_climate_info(query: str) -> str: @pytest.mark.flaky(retries=3) def test_audio_timestamp() -> None: storage_client = storage.Client() - llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) + llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) file_uri = ( "gs://cloud-samples-data/generative-ai/audio/audio_summary_clean_energy.mp3" @@ -417,9 +466,9 @@ def test_parse_history_gemini_multimodal_FC() -> None: @pytest.mark.xfail(reason="investigating") @pytest.mark.release -@pytest.mark.parametrize(("file_uri", "mime_type"), [video_param]) +@pytest.mark.parametrize(("file_uri", "mime_type"), [VIDEO_PARAM]) def test_multimodal_video_metadata(file_uri, mime_type) -> None: - llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) + llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) media_message = { "type": "media", "file_uri": file_uri, @@ -442,7 +491,7 @@ def test_multimodal_video_metadata(file_uri, mime_type) -> None: @pytest.mark.release @pytest.mark.parametrize("model_name", model_names_to_test) def test_vertexai_single_call_with_history(model_name: Optional[str]) -> None: - model = ChatVertexAI(model_name=model_name, rate_limiter=rate_limiter) + model = ChatVertexAI(model_name=model_name, rate_limiter=RATE_LIMITER) text_question1, text_answer1 = "How much is 2+2?", "4" text_question2 = "How much is 3+3?" message1 = HumanMessage(content=text_question1) @@ -455,7 +504,7 @@ def test_vertexai_single_call_with_history(model_name: Optional[str]) -> None: @pytest.mark.release def test_vertexai_system_message() -> None: - model = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) + model = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) system_instruction = """CymbalBank is a bank located in London""" text_question1 = "Where is Cymbal located? Provide only the name of the city." sys_message = SystemMessage(content=system_instruction) @@ -469,7 +518,7 @@ def test_vertexai_system_message() -> None: @pytest.mark.release def test_vertexai_single_call_with_no_system_messages() -> None: - model = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) + model = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) text_question1, text_answer1 = "How much is 2+2?", "4" text_question2 = "How much is 3+3?" message1 = HumanMessage(content=text_question1) @@ -482,10 +531,11 @@ def test_vertexai_single_call_with_no_system_messages() -> None: @pytest.mark.release def test_vertexai_single_call_previous_blocked_response() -> None: - """If a previous call was blocked, the AIMessage will have empty content which - should be ignored. + """If a previous call was blocked, the AIMessage will have empty content. + + Empty content should be ignored. """ - model = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) + model = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) text_question2 = "How much is 3+3?" # Previous blocked response included in history. This can happen with a LangGraph # ReAct agent. @@ -516,7 +566,7 @@ def test_vertexai_single_call_previous_blocked_response() -> None: @pytest.mark.parametrize("model_name", model_names_to_test) def test_get_num_tokens_from_messages(model_name: str) -> None: model = ChatVertexAI( - model_name=model_name, temperature=0.0, rate_limiter=rate_limiter + model_name=model_name, temperature=0.0, rate_limiter=RATE_LIMITER ) message = HumanMessage(content="Hello") token = model.get_num_tokens_from_messages(messages=[message]) @@ -524,28 +574,6 @@ def test_get_num_tokens_from_messages(model_name: str) -> None: assert token == 3 -def _check_tool_calls(response: BaseMessage, expected_name: str) -> None: - """Check tool calls are as expected.""" - assert isinstance(response, AIMessage) - assert isinstance(response.content, str) - assert response.content == "" - function_call = response.additional_kwargs.get("function_call") - assert function_call - assert function_call["name"] == expected_name - arguments_str = function_call.get("arguments") - assert arguments_str - arguments = json.loads(arguments_str) - assert arguments == { - "name": "Erick", - "age": 27.0, - } - tool_calls = response.tool_calls - assert len(tool_calls) == 1 - tool_call = tool_calls[0] - assert tool_call["name"] == expected_name - assert tool_call["args"] == {"age": 27.0, "name": "Erick"} - - @pytest.mark.extended @pytest.mark.parametrize("endpoint_version", endpoint_versions) def test_chat_vertexai_gemini_function_calling(endpoint_version: str) -> None: @@ -561,7 +589,7 @@ class MyModel(BaseModel): model = ChatVertexAI( model_name=_DEFAULT_MODEL_NAME, safety_settings=safety, - rate_limiter=rate_limiter, + rate_limiter=RATE_LIMITER, endpoint_version=endpoint_version, ).bind_tools([MyModel]) response = model.invoke([message]) @@ -574,7 +602,7 @@ def my_model(name: str, age: int) -> None: model = ChatVertexAI( model_name=_DEFAULT_MODEL_NAME, safety_settings=safety, - rate_limiter=rate_limiter, + rate_limiter=RATE_LIMITER, ).bind_tools([my_model]) response = model.invoke([message]) _check_tool_calls(response, "my_model") @@ -619,7 +647,7 @@ class MyModel(BaseModel): model = ChatVertexAI( model_name=_DEFAULT_MODEL_NAME, safety_settings=safety, - rate_limiter=rate_limiter, + rate_limiter=RATE_LIMITER, ).bind( functions=[MyModel], tool_config={ @@ -700,7 +728,7 @@ class MyModel(BaseModel): llm = ChatVertexAI( model_name=_DEFAULT_MODEL_NAME, safety_settings=safety, - rate_limiter=rate_limiter, + rate_limiter=RATE_LIMITER, ) model = llm.with_structured_output(MyModel, method=method) message = HumanMessage(content="My name is Erick and I am 27 years old") @@ -787,7 +815,7 @@ def search( model_name=_DEFAULT_MODEL_NAME, safety_settings=safety, temperature=0, - rate_limiter=rate_limiter, + rate_limiter=RATE_LIMITER, ) llm_with_search = llm.bind( functions=tools, @@ -1023,13 +1051,13 @@ def test_chat_vertexai_gemini_thinking_configurable() -> None: @pytest.mark.extended @pytest.mark.first def test_prediction_client_transport() -> None: - model = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) + model = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) assert model.prediction_client.transport.kind == "grpc" assert model.async_prediction_client.transport.kind == "grpc_asyncio" model = ChatVertexAI( - model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter, api_transport="rest" + model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER, api_transport="rest" ) assert model.prediction_client.transport.kind == "rest" @@ -1041,7 +1069,7 @@ def test_prediction_client_transport() -> None: @pytest.mark.extended def test_structured_output_schema_json() -> None: model = ChatVertexAI( - rate_limiter=rate_limiter, + rate_limiter=RATE_LIMITER, model_name=_DEFAULT_MODEL_NAME, response_mime_type="application/json", response_schema={ @@ -1081,7 +1109,7 @@ def test_structured_output_schema_json() -> None: "required": ["recipe_name"], }, }, - rate_limiter=rate_limiter, + rate_limiter=RATE_LIMITER, ) with pytest.raises(ValueError, match="response_mime_type"): response = model.invoke("List a few popular cookie recipes") @@ -1095,7 +1123,7 @@ class MyModel(TypedDict): llm = ChatVertexAI( model_name="gemini-2.0-flash-exp", - rate_limiter=rate_limiter, + rate_limiter=RATE_LIMITER, ) model = llm.with_structured_output(MyModel, method="json_mode") message = HumanMessage(content="My name is Erick and I am 28 years old") @@ -1117,7 +1145,7 @@ def test_structured_output_schema_enum() -> None: model_name=_DEFAULT_MODEL_NAME, response_schema={"type": "STRING", "enum": ["drama", "comedy", "documentary"]}, response_mime_type="text/x.enum", - rate_limiter=rate_limiter, + rate_limiter=RATE_LIMITER, ) response = model.invoke( @@ -1152,7 +1180,7 @@ def test_context_catching() -> None: cached_content = create_context_cache( ChatVertexAI( model_name=_DEFAULT_MODEL_NAME, - rate_limiter=rate_limiter, + rate_limiter=RATE_LIMITER, ), messages=[ SystemMessage(content=system_instruction), @@ -1179,7 +1207,7 @@ def test_context_catching() -> None: chat = ChatVertexAI( model_name=_DEFAULT_MODEL_NAME, cached_content=cached_content, - rate_limiter=rate_limiter, + rate_limiter=RATE_LIMITER, ) response = chat.invoke("What is the secret number?") @@ -1188,7 +1216,7 @@ def test_context_catching() -> None: assert isinstance(response.content, str) # Using cached content in request - chat = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) + chat = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) response = chat.invoke("What is the secret number?", cached_content=cached_content) assert isinstance(response, AIMessage) @@ -1392,17 +1420,6 @@ async def test_astream_events_langgraph_example() -> None: assert output.additional_kwargs["function_call"]["name"] == "multiply" -@pytest.mark.xfail(reason="can't create service account key on gcp") -@pytest.mark.release -def test_init_from_credentials_obj() -> None: - credentials_dict = json.loads(os.environ["GOOGLE_VERTEX_AI_WEB_CREDENTIALS"]) - credentials = service_account.Credentials.from_service_account_info( - credentials_dict - ) - llm = ChatVertexAI(model="gemini-2.0-flash-001", credentials=credentials) - llm.invoke("how are you") - - @pytest.mark.xfail(reason="can't add labels to the gemini content") @pytest.mark.release def test_label_metadata() -> None: @@ -1429,15 +1446,6 @@ def test_label_metadata_invoke_method() -> None: ) -@pytest.mark.release -def test_response_metadata_avg_logprobs() -> None: - llm = ChatVertexAI(model="gemini-2.0-flash-001") - response = llm.invoke("Hello!") - probs = response.response_metadata.get("avg_logprobs") - if probs is not None: - assert isinstance(probs, float) - - @pytest.fixture def multimodal_pdf_chain() -> RunnableSerializable: prompt = ChatPromptTemplate.from_messages( @@ -1463,6 +1471,7 @@ def multimodal_pdf_chain() -> RunnableSerializable: @pytest.mark.release def test_multimodal_pdf_input_gcs(multimodal_pdf_chain: RunnableSerializable) -> None: + # TODO: parallelize with url and b64 tests gcs_uri = "gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf" # GCS URI response = multimodal_pdf_chain.invoke({"image": gcs_uri}) @@ -1471,6 +1480,7 @@ def test_multimodal_pdf_input_gcs(multimodal_pdf_chain: RunnableSerializable) -> @pytest.mark.release def test_multimodal_pdf_input_url(multimodal_pdf_chain: RunnableSerializable) -> None: + # TODO: parallelize with gcs and b64 tests url = "https://abc.xyz/assets/95/eb/9cef90184e09bac553796896c633/2023q4-alphabet-earnings-release.pdf" # URL response = multimodal_pdf_chain.invoke({"image": url}) @@ -1479,6 +1489,7 @@ def test_multimodal_pdf_input_url(multimodal_pdf_chain: RunnableSerializable) -> @pytest.mark.release def test_multimodal_pdf_input_b64(multimodal_pdf_chain: RunnableSerializable) -> None: + # TODO: parallelize with gcs and url tests url = "https://abc.xyz/assets/95/eb/9cef90184e09bac553796896c633/2023q4-alphabet-earnings-release.pdf" request_response = requests.get(url, allow_redirects=True) # B64 @@ -1490,6 +1501,15 @@ def test_multimodal_pdf_input_b64(multimodal_pdf_chain: RunnableSerializable) -> assert isinstance(response, AIMessage) +@pytest.mark.release +def test_response_metadata_avg_logprobs() -> None: + llm = ChatVertexAI(model="gemini-2.0-flash-001") + response = llm.invoke("Hello!") + probs = response.response_metadata.get("avg_logprobs") + if probs is not None: + assert isinstance(probs, float) + + @pytest.mark.xfail(reason="logprobs are subject to daily quotas") @pytest.mark.release def test_logprobs() -> None: @@ -1520,6 +1540,7 @@ def test_logprobs() -> None: def test_location_init() -> None: + """Test how location is set in ChatVertexAI depending on vertexai.init settings.""" # If I don't initialize vertexai before, defaults to us-central-1 llm = ChatVertexAI(model="gemini-2.0-flash-001", logprobs=2) assert llm.location == "us-central1" @@ -1551,7 +1572,7 @@ def test_vertexai_global_location_single_call( model = ChatVertexAI( model_name=model_name, location="global", - rate_limiter=rate_limiter, + rate_limiter=RATE_LIMITER, endpoint_version=endpoint_version, ) assert model.location == "global" @@ -1581,13 +1602,14 @@ class People(BaseModel): def test_search_builtin() -> None: + """Test the built-in search tool.""" llm = ChatVertexAI(model=_DEFAULT_MODEL_NAME).bind_tools([{"google_search": {}}]) input_message = { "role": "user", "content": "What is today's news?", } response = llm.invoke([input_message]) - # Grounding metadata is optional and may not be present in all responses + if "grounding_metadata" in response.response_metadata: assert response.response_metadata["grounding_metadata"] is not None @@ -1597,7 +1619,7 @@ def test_search_builtin() -> None: assert isinstance(chunk, AIMessageChunk) full = chunk if full is None else full + chunk assert isinstance(full, AIMessageChunk) - # Grounding metadata is optional and may not be present in all responses + if "grounding_metadata" in full.response_metadata: assert full.response_metadata["grounding_metadata"] is not None @@ -1610,6 +1632,7 @@ def test_search_builtin() -> None: def test_code_execution_builtin() -> None: + """Test the built-in code execution tool.""" llm = ChatVertexAI(model=_DEFAULT_MODEL_NAME).bind_tools([{"code_execution": {}}]) input_message = { "role": "user", diff --git a/libs/vertexai/tests/unit_tests/test_chat_models.py b/libs/vertexai/tests/unit_tests/test_chat_models.py index 88e96cdb8..785f41e2a 100644 --- a/libs/vertexai/tests/unit_tests/test_chat_models.py +++ b/libs/vertexai/tests/unit_tests/test_chat_models.py @@ -1,4 +1,4 @@ -"""Test chat model integration.""" +"""Chat model unit tests.""" import base64 import json @@ -37,7 +37,6 @@ SafetySetting as VertexSafetySetting, # TODO: migrate to google-genai ) from vertexai.language_models import ( - ChatMessage, # TODO: migrate to google-genai since this is deprecated InputOutputTextPair, ) @@ -46,7 +45,6 @@ from langchain_google_vertexai.chat_models import ( ChatVertexAI, _bytes_to_base64, - _parse_chat_history, _parse_chat_history_gemini, _parse_examples, _parse_response_candidate, @@ -62,50 +60,54 @@ def clear_prediction_client_cache() -> None: def test_init() -> None: + """Test initialization of `ChatVertexAI` with different parameter names. + + Done since we have aliasing of some parameters for consistency with other LLMs. + """ for llm in [ ChatVertexAI( - model_name="gemini-pro", + model_name="gemini-2-5-flash", project="test-project", max_output_tokens=10, stop=["bar"], location="moon-dark1", ), ChatVertexAI( - model="gemini-pro", + model="gemini-2-5-flash", + project="test-proj", max_tokens=10, stop_sequences=["bar"], location="moon-dark1", - project="test-proj", ), ]: - assert llm.model_name == "gemini-pro" + assert llm.model_name == "gemini-2-5-flash" assert llm.max_output_tokens == 10 assert llm.stop == ["bar"] ls_params = llm._get_ls_params() assert ls_params == { "ls_provider": "google_vertexai", - "ls_model_name": "gemini-pro", + "ls_model_name": "gemini-2-5-flash", "ls_model_type": "chat", "ls_temperature": None, "ls_max_tokens": 10, "ls_stop": ["bar"], } - # test initialization with an invalid argument to check warning + # Test initialization with an invalid argument to check warning with patch("langchain_google_vertexai.chat_models.logger.warning") as mock_warning: # Suppress UserWarning during test execution - we're testing the warning # mechanism via logger mock assertions, not via pytest's warning system with warnings.catch_warnings(): warnings.simplefilter("ignore", UserWarning) llm = ChatVertexAI( - model_name="gemini-pro", + model_name="gemini-2-5-flash", project="test-project", safety_setting={ "HARM_CATEGORY_DANGEROUS_CONTENT": "BLOCK_LOW_AND_ABOVE" }, # Invalid arg ) - assert llm.model_name == "gemini-pro" + assert llm.model_name == "gemini-2-5-flash" assert llm.project == "test-project" mock_warning.assert_called_once() call_args = mock_warning.call_args[0][0] @@ -117,13 +119,18 @@ def test_init() -> None: ("model", "location"), [ ( - "gemini-1.0-pro-001", + "gemini-2-5-flash", "moon-dark1", ), - ("publishers/google/models/gemini-1.0-pro-001", "moon-dark2"), + ("publishers/google/models/gemini-2-5-flash", "moon-dark2"), ], ) def test_init_client(model: str, location: str) -> None: + """Test initialization of `ChatVertexAI` with different models and locations. + + Ensure the user agent is set correctly and the full model name is constructed using + the provided project and location. + """ config = {"model": model, "location": location} llm = ChatVertexAI( **{k: v for k, v in config.items() if v is not None}, project="test-proj" @@ -144,15 +151,76 @@ def test_init_client(model: str, location: str) -> None: assert "langchain-google-vertexai" in client_info.client_library_version assert "ChatVertexAI" in client_info.client_library_version assert llm.full_model_name == ( - f"projects/test-proj/locations/{location}/publishers/google/models/gemini-1.0-pro-001" + f"projects/test-proj/locations/{location}/publishers/google/models/gemini-2-5-flash" ) +def test_init_client_with_custom_api_endpoint() -> None: + """Test that custom API endpoint and transport are set correctly.""" + config = { + "model": "gemini-2.5-pro", + "api_endpoint": "https://example.com", + "api_transport": "rest", + } + llm = ChatVertexAI( + **{k: v for k, v in config.items() if v is not None}, project="test-proj" + ) + with patch( + "langchain_google_vertexai._client_utils.v1beta1PredictionServiceClient" + ) as mock_prediction_service: + response = GenerateContentResponse(candidates=[]) + mock_prediction_service.return_value.generate_content.return_value = response + + llm._generate_gemini(messages=[]) + mock_prediction_service.assert_called_once() + client_options = mock_prediction_service.call_args.kwargs["client_options"] + transport = mock_prediction_service.call_args.kwargs["transport"] + assert client_options.api_endpoint == "https://example.com" + assert transport == "rest" + + +def test_init_client_with_custom_base_url(clear_prediction_client_cache: Any) -> None: + """Test that custom base URL and transport are set correctly.""" + config = { + "model": "gemini-2.5-pro", + "base_url": "https://example.com", + "api_transport": "rest", + } + llm = ChatVertexAI( + **{k: v for k, v in config.items() if v is not None}, project="test-proj" + ) + with patch( + "langchain_google_vertexai._client_utils.v1beta1PredictionServiceClient" + ) as mock_prediction_service: + response = GenerateContentResponse(candidates=[]) + mock_prediction_service.return_value.generate_content.return_value = response + + llm._generate_gemini(messages=[]) + mock_prediction_service.assert_called_once() + client_options = mock_prediction_service.call_args.kwargs["client_options"] + transport = mock_prediction_service.call_args.kwargs["transport"] + assert client_options.api_endpoint == "https://example.com" + assert transport == "rest" + + +def test_init_client_with_custom_model_kwargs() -> None: + """Test that custom model kwargs are set correctly.""" + llm = ChatAnthropicVertex( + project="test-project", + location="test-location", + model_kwargs={"thinking": {"type": "enabled", "budget_tokens": 1024}}, + ) + assert llm.model_kwargs == {"thinking": {"type": "enabled", "budget_tokens": 1024}} + + default_params = llm._default_params + assert default_params["thinking"] == {"type": "enabled", "budget_tokens": 1024} + + @pytest.mark.parametrize( ("model", "location"), [ ( - "gemini-1.0-pro-001", + "gemini-2-5-flash", "moon-dark1", ), ], @@ -160,6 +228,7 @@ def test_init_client(model: str, location: str) -> None: def test_model_name_presence_in_chat_results( model: str, location: str, clear_prediction_client_cache: Any ) -> None: + """Ensure the model name is present in the response metadata of messages.""" config = {"model": model, "location": location} llm = ChatVertexAI( **{k: v for k, v in config.items() if v is not None}, project="test-proj" @@ -176,25 +245,143 @@ def test_model_name_presence_in_chat_results( assert isinstance(llm_response.generations[0].message, AIMessage) assert ( llm_response.generations[0].message.response_metadata["model_name"] - == "gemini-1.0-pro-001" + == "gemini-2-5-flash" ) def test_tuned_model_name() -> None: + """Test initialization of `ChatVertexAI` with a tuned model name. + + Tuned models must be specified using the full resource name. + """ llm = ChatVertexAI( - model_name="gemini-pro", + model_name="gemini-2-5-flash", project="test-project", tuned_model_name="projects/123/locations/europe-west4/endpoints/456", max_tokens=500, ) - assert llm.model_name == "gemini-pro" + assert llm.model_name == "gemini-2-5-flash" assert llm.tuned_model_name == "projects/123/locations/europe-west4/endpoints/456" assert llm.full_model_name == "projects/123/locations/europe-west4/endpoints/456" assert llm.max_output_tokens == 500 - assert llm.max_tokens == 500 + assert llm.max_tokens == 500 # Alias + + +def test_default_params_gemini() -> None: + """Test that default parameters are set correctly in the request.""" + user_prompt = "Hello" + + with patch( + "langchain_google_vertexai._client_utils.v1beta1PredictionServiceClient" + ) as mc: + response = GenerateContentResponse( + candidates=[Candidate(content=Content(parts=[Part(text="Hi")]))] + ) + mock_generate_content = MagicMock(return_value=response) + mc.return_value.generate_content = mock_generate_content + + model = ChatVertexAI(model_name="gemini-2-5-flash", project="test-project") + message = HumanMessage(content=user_prompt) + _ = model.invoke([message]) + mock_generate_content.assert_called_once() + assert ( + mock_generate_content.call_args.kwargs["request"].contents[0].role == "user" + ) + assert ( + mock_generate_content.call_args.kwargs["request"].contents[0].parts[0].text + == "Hello" + ) + expected = GenerationConfig( + candidate_count=1, + ) + assert ( + mock_generate_content.call_args.kwargs["request"].generation_config + == expected + ) + assert mock_generate_content.call_args.kwargs["request"].tools == [] + assert not mock_generate_content.call_args.kwargs["request"].tool_config + assert not mock_generate_content.call_args.kwargs["request"].safety_settings + + +def test_generation_config_gemini() -> None: + """Test that generation config is set correctly in the request when overridden.""" + model = ChatVertexAI( + model_name="gemini-2-5-flash", + project="test-project", + temperature=0.2, + top_k=3, + frequency_penalty=0.2, + presence_penalty=0.6, + ) + generation_config = model._generation_config_gemini( + temperature=0.3, + stop=["stop"], + candidate_count=2, + frequency_penalty=0.9, + presence_penalty=0.8, + ) + # Note merged settings, with method args taking precedence (e.g. temperature) + expected = GenerationConfig( + stop_sequences=["stop"], + temperature=0.3, + top_k=3, + candidate_count=2, + frequency_penalty=0.9, + presence_penalty=0.8, + ) + assert generation_config == expected + + +def test_safety_settings_gemini_init() -> None: + """Test that safety settings are set correctly when provided at init.""" + expected_safety_setting = [ + VertexSafetySetting( + category=HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, + threshold=SafetySetting.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, + method=SafetySetting.HarmBlockMethod.SEVERITY, + ) + ] + model = ChatVertexAI( + model_name="gemini-2-5-flash", + temperature=0.2, + top_k=3, + project="test-project", + safety_settings=expected_safety_setting, + ) + safety_settings = model._safety_settings_gemini(None) + assert safety_settings == expected_safety_setting + + +def test_safety_settings_gemini() -> None: + """Test that safety settings are set correctly in the request.""" + model = ChatVertexAI( + model_name="gemini-2-5-flash", temperature=0.2, top_k=3, project="test-project" + ) + expected_safety_setting = SafetySetting( + category=HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, + threshold=SafetySetting.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, + ) + safety_settings = model._safety_settings_gemini([expected_safety_setting]) + assert safety_settings == [expected_safety_setting] + # Ignores for tests that intentionally use invalid dict types + safety_settings = model._safety_settings_gemini( + # Ignore since testing string conversion + {"HARM_CATEGORY_DANGEROUS_CONTENT": "BLOCK_LOW_AND_ABOVE"} # type: ignore[dict-item] + ) + assert safety_settings == [expected_safety_setting] + # Ignore since testing int conversion + safety_settings = model._safety_settings_gemini({2: 1}) # type: ignore[dict-item] + assert safety_settings == [expected_safety_setting] + threshold = SafetySetting.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE + safety_settings = model._safety_settings_gemini( + # Ignore since testing enum conversion + {HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: threshold} # type: ignore[dict-item] + ) + assert safety_settings == [expected_safety_setting] def test_parse_examples_correct() -> None: + """Test parsing of message examples into `InputOutputTextPair` objects.""" text_question = ( "Hello, could you recommend a good movie for me to watch this evening, please?" ) @@ -213,6 +400,11 @@ def test_parse_examples_correct() -> None: def test_parse_examples_failes_wrong_sequence() -> None: + """Out of order messages should raise an error. + + Ensure that the _parsed_examples function raises an error if the messages are not + in the correct Human-AI alternating sequence. + """ with pytest.raises(ValueError) as exc_info: _ = _parse_examples([AIMessage(content="a")]) assert ( @@ -221,33 +413,8 @@ def test_parse_examples_failes_wrong_sequence() -> None: ) -def test_parse_chat_history_correct() -> None: - text_context = ( - "My name is Peter. You are my personal assistant. My " - "favorite movies are Lord of the Rings and Hobbit." - ) - context = SystemMessage(content=text_context) - text_question = ( - "Hello, could you recommend a good movie for me to watch this evening, please?" - ) - question = HumanMessage(content=text_question) - text_answer = ( - "Sure, You might enjoy The Lord of the Rings: The Fellowship of the Ring " - "(2001): This is the first movie in the Lord of the Rings trilogy." - ) - answer = AIMessage(content=text_answer) - history = _parse_chat_history([context, question, answer, question, answer]) - assert history.context == context.content - assert len(history.history) == 4 - assert history.history == [ - ChatMessage(content=text_question, author="user"), - ChatMessage(content=text_answer, author="bot"), - ChatMessage(content=text_question, author="user"), - ChatMessage(content=text_answer, author="bot"), - ] - - def test_parse_history_gemini() -> None: + """Test parsing of chat history into Gemini format.""" system_input = "You're supposed to answer math questions." text_question1, text_answer1 = "How much is 2+2?", "4" text_question2 = "How much is 3+3?" @@ -270,6 +437,10 @@ def test_parse_history_gemini() -> None: def test_parse_history_gemini_number() -> None: + """Ensure that numeric strings are parsed correctly as text. + + TODO: remove this? Seems like an edge case of limited value. + """ system_input = "You're supposed to answer math questions." text_question1 = "54321.1" system_message = SystemMessage(content=system_input) @@ -287,6 +458,12 @@ def test_parse_history_gemini_number() -> None: def test_parse_history_gemini_function_empty_list() -> None: + """Test parsing of chat history with an empty tool call response. + + If the model calls a function/tool but the response is an empty list, it should + still be parsed correctly (meaning the function call is recorded, and the function + response is recorded as an empty content). + """ system_input = "You're supposed to answer math questions." text_question1 = "Solve the following equation. x^2+16=0" fn_name_1 = "root" @@ -338,6 +515,7 @@ def test_parse_history_gemini_function_empty_list() -> None: def test_parse_history_gemini_function() -> None: + """Test parsing of chat history with multiple tool calls and responses.""" system_input = "You're supposed to answer math questions." text_question1 = "Which is bigger 2+2, 3*3 or 4-4?" fn_name_1 = "add" @@ -435,7 +613,7 @@ def test_parse_history_gemini_function() -> None: @pytest.mark.parametrize( - ("source_history", "expected_sm", "expected_history"), + ("source_history", "expected_sys_message", "expected_history"), [ ( [ @@ -601,8 +779,13 @@ def test_parse_history_gemini_function() -> None: ], ) def test_parse_history_gemini_multi( - source_history, expected_sm, expected_history + source_history, expected_sys_message, expected_history ) -> None: + """Test parsing of chat history with multiple messages of same type. + + Needed to ensure that multiple system messages are combined into one, and that + multiple AI messages are combined into one. + """ image_bytes_loader = ImageBytesLoader() sm, result_history = _parse_chat_history_gemini( history=source_history, imageBytesLoader=image_bytes_loader @@ -610,42 +793,53 @@ def test_parse_history_gemini_multi( for result, expected in zip(result_history, expected_history): assert result == expected - assert sm == expected_sm + assert sm == expected_sys_message -def test_default_params_gemini() -> None: - user_prompt = "Hello" +def test_parse_chat_history_gemini_with_literal_eval() -> None: + """Test that string content representing a list is parsed correctly.""" + instruction = "Describe the attached media in 5 words." + text_message = {"type": "text", "text": instruction} + message = str([text_message]) + history: list[BaseMessage] = [HumanMessage(content=message)] + image_bytes_loader = ImageBytesLoader() + _, response = _parse_chat_history_gemini( + history=history, + imageBytesLoader=image_bytes_loader, + perform_literal_eval_on_string_raw_content=True, + ) + parts = [ + Part(text=instruction), + ] + expected = [Content(role="user", parts=parts)] + assert expected == response - with patch( - "langchain_google_vertexai._client_utils.v1beta1PredictionServiceClient" - ) as mc: - response = GenerateContentResponse( - candidates=[Candidate(content=Content(parts=[Part(text="Hi")]))] - ) - mock_generate_content = MagicMock(return_value=response) - mc.return_value.generate_content = mock_generate_content - model = ChatVertexAI(model_name="gemini-pro", project="test-project") - message = HumanMessage(content=user_prompt) - _ = model.invoke([message]) - mock_generate_content.assert_called_once() - assert ( - mock_generate_content.call_args.kwargs["request"].contents[0].role == "user" - ) - assert ( - mock_generate_content.call_args.kwargs["request"].contents[0].parts[0].text - == "Hello" - ) - expected = GenerationConfig( - candidate_count=1, - ) - assert ( - mock_generate_content.call_args.kwargs["request"].generation_config - == expected - ) - assert mock_generate_content.call_args.kwargs["request"].tools == [] - assert not mock_generate_content.call_args.kwargs["request"].tool_config - assert not mock_generate_content.call_args.kwargs["request"].safety_settings +def test_parse_chat_history_gemini_without_literal_eval() -> None: + """Test that string content is not parsed when literal eval is disabled.""" + instruction = "Describe the attached media in 5 words." + text_message = {"type": "text", "text": instruction} + message = str([text_message]) + history: list[BaseMessage] = [HumanMessage(content=message)] + image_bytes_loader = ImageBytesLoader() + _, response = _parse_chat_history_gemini( + history=history, + imageBytesLoader=image_bytes_loader, + perform_literal_eval_on_string_raw_content=False, + ) + parts = [ + Part(text=message), + ] + expected = [Content(role="user", parts=parts)] + assert expected == response + + +def test_python_literal_inputs() -> None: + """In relation to literal eval, ensure that inputs are not misinterpreted.""" + llm = ChatVertexAI(model="gemini-2.5-flash", project="test-project") + + for input_string in ["None", "(1, 2)", "[1, 2, 3]", "{1, 2, 3}"]: + _ = llm._prepare_request_gemini([HumanMessage(input_string)]) @pytest.mark.parametrize( @@ -1041,6 +1235,13 @@ def test_parse_response_candidate(raw_candidate, expected) -> None: def test_parser_multiple_tools() -> None: + """Test parsing of multiple function calls into Pydantic models. + + Uses two simple Pydantic models as tools: Add and Multiply. The model is expected + to call both functions in a single response, and the parser should correctly parse + the response into instances of the respective Pydantic models. + """ + class Add(BaseModel): arg1: int arg2: int @@ -1084,6 +1285,8 @@ class Multiply(BaseModel): llm = model | parser result = llm.invoke([message]) mock_generate_content.assert_called_once() + + # Verify that the result is a list of Pydantic model instances assert isinstance(result, list) assert isinstance(result[0], Add) assert result[0] == Add(arg1=1, arg2=2) @@ -1091,80 +1294,8 @@ class Multiply(BaseModel): assert result[1] == Multiply(arg1=3, arg2=3) -def test_generation_config_gemini() -> None: - model = ChatVertexAI( - model_name="gemini-pro", - project="test-project", - temperature=0.2, - top_k=3, - frequency_penalty=0.2, - presence_penalty=0.6, - ) - generation_config = model._generation_config_gemini( - temperature=0.3, - stop=["stop"], - candidate_count=2, - frequency_penalty=0.9, - presence_penalty=0.8, - ) - expected = GenerationConfig( - stop_sequences=["stop"], - temperature=0.3, - top_k=3, - candidate_count=2, - frequency_penalty=0.9, - presence_penalty=0.8, - ) - assert generation_config == expected - - -def test_safety_settings_gemini() -> None: - model = ChatVertexAI( - model_name="gemini-pro", temperature=0.2, top_k=3, project="test-project" - ) - expected_safety_setting = SafetySetting( - category=HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, - threshold=SafetySetting.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, - ) - safety_settings = model._safety_settings_gemini([expected_safety_setting]) - assert safety_settings == [expected_safety_setting] - # Ignores for tests that intentionally use invalid dict types - safety_settings = model._safety_settings_gemini( - # Ignore since testing string conversion - {"HARM_CATEGORY_DANGEROUS_CONTENT": "BLOCK_LOW_AND_ABOVE"} # type: ignore[dict-item] - ) - assert safety_settings == [expected_safety_setting] - # Ignore since testing int conversion - safety_settings = model._safety_settings_gemini({2: 1}) # type: ignore[dict-item] - assert safety_settings == [expected_safety_setting] - threshold = SafetySetting.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE - safety_settings = model._safety_settings_gemini( - # Ignore since testing enum conversion - {HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: threshold} # type: ignore[dict-item] - ) - assert safety_settings == [expected_safety_setting] - - -def test_safety_settings_gemini_init() -> None: - expected_safety_setting = [ - VertexSafetySetting( - category=HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, - threshold=SafetySetting.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, - method=SafetySetting.HarmBlockMethod.SEVERITY, - ) - ] - model = ChatVertexAI( - model_name="gemini-pro", - temperature=0.2, - top_k=3, - project="test-project", - safety_settings=expected_safety_setting, - ) - safety_settings = model._safety_settings_gemini(None) - assert safety_settings == expected_safety_setting - - def test_multiple_fc() -> None: + """Test parsing of multiple function calls in a single response.""" prompt = ( "I'm trying to decide whether to go to London or Zurich this weekend. How " "hot are those cities? How about Singapore? Or maybe Tokyo. I want to go " @@ -1250,100 +1381,6 @@ def test_multiple_fc() -> None: assert history == expected -def test_parse_chat_history_gemini_with_literal_eval() -> None: - instruction = "Describe the attached media in 5 words." - text_message = {"type": "text", "text": instruction} - message = str([text_message]) - history: list[BaseMessage] = [HumanMessage(content=message)] - image_bytes_loader = ImageBytesLoader() - _, response = _parse_chat_history_gemini( - history=history, - imageBytesLoader=image_bytes_loader, - perform_literal_eval_on_string_raw_content=True, - ) - parts = [ - Part(text=instruction), - ] - expected = [Content(role="user", parts=parts)] - assert expected == response - - -def test_parse_chat_history_gemini_without_literal_eval() -> None: - instruction = "Describe the attached media in 5 words." - text_message = {"type": "text", "text": instruction} - message = str([text_message]) - history: list[BaseMessage] = [HumanMessage(content=message)] - image_bytes_loader = ImageBytesLoader() - _, response = _parse_chat_history_gemini( - history=history, - imageBytesLoader=image_bytes_loader, - perform_literal_eval_on_string_raw_content=False, - ) - parts = [ - Part(text=message), - ] - expected = [Content(role="user", parts=parts)] - assert expected == response - - -def test_init_client_with_custom_api_endpoint() -> None: - config = { - "model": "gemini-2.5-pro", - "api_endpoint": "https://example.com", - "api_transport": "rest", - } - llm = ChatVertexAI( - **{k: v for k, v in config.items() if v is not None}, project="test-proj" - ) - with patch( - "langchain_google_vertexai._client_utils.v1beta1PredictionServiceClient" - ) as mock_prediction_service: - response = GenerateContentResponse(candidates=[]) - mock_prediction_service.return_value.generate_content.return_value = response - - llm._generate_gemini(messages=[]) - mock_prediction_service.assert_called_once() - client_options = mock_prediction_service.call_args.kwargs["client_options"] - transport = mock_prediction_service.call_args.kwargs["transport"] - assert client_options.api_endpoint == "https://example.com" - assert transport == "rest" - - -def test_init_client_with_custom_base_url(clear_prediction_client_cache: Any) -> None: - config = { - "model": "gemini-2.5-pro", - "base_url": "https://example.com", - "api_transport": "rest", - } - llm = ChatVertexAI( - **{k: v for k, v in config.items() if v is not None}, project="test-proj" - ) - with patch( - "langchain_google_vertexai._client_utils.v1beta1PredictionServiceClient" - ) as mock_prediction_service: - response = GenerateContentResponse(candidates=[]) - mock_prediction_service.return_value.generate_content.return_value = response - - llm._generate_gemini(messages=[]) - mock_prediction_service.assert_called_once() - client_options = mock_prediction_service.call_args.kwargs["client_options"] - transport = mock_prediction_service.call_args.kwargs["transport"] - assert client_options.api_endpoint == "https://example.com" - assert transport == "rest" - - -def test_init_client_with_custom_model_kwargs() -> None: - llm = ChatAnthropicVertex( - project="test-project", - location="test-location", - model_kwargs={"thinking": {"type": "enabled", "budget_tokens": 1024}}, - ) - assert llm.model_kwargs == {"thinking": {"type": "enabled", "budget_tokens": 1024}} - - default_params = llm._default_params - assert default_params["thinking"] == {"type": "enabled", "budget_tokens": 1024} - - def test_anthropic_format_output() -> None: """Test format output handles different content structures correctly.""" @@ -1478,6 +1515,7 @@ def model_dump(self): def test_thinking_configuration() -> None: + """Test that thinking configuration is set correctly.""" input_message = HumanMessage("Query requiring reasoning.") # Test init params @@ -1503,6 +1541,7 @@ def test_thinking_configuration() -> None: def test_thought_signature() -> None: + """Test that thought signatures are correctly parsed and included in requests.""" llm = ChatVertexAI( model="gemini-2.5-flash", project="test-project", @@ -1560,13 +1599,6 @@ def test_thought_signature() -> None: ] -def test_python_literal_inputs() -> None: - llm = ChatVertexAI(model="gemini-2.5-flash", project="test-project") - - for input_string in ["None", "(1, 2)", "[1, 2, 3]", "{1, 2, 3}"]: - _ = llm._prepare_request_gemini([HumanMessage(input_string)]) - - def test_v1_function_parts() -> None: llm = ChatVertexAI( model="gemini-2.5-flash", project="test-project", endpoint_version="v1" diff --git a/libs/vertexai/uv.lock b/libs/vertexai/uv.lock index 0eef743f0..ed49d7beb 100644 --- a/libs/vertexai/uv.lock +++ b/libs/vertexai/uv.lock @@ -21,7 +21,7 @@ wheels = [ [[package]] name = "anthropic" -version = "0.68.0" +version = "0.69.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, @@ -33,14 +33,14 @@ dependencies = [ { name = "sniffio" }, { name = "typing-extensions" }, ] -sdist = { url = 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Fri, 10 Oct 2025 11:38:30 -0400 Subject: [PATCH 23/23] Revert "feat(vertex): standard content" (#1240) Reverts langchain-ai/langchain-google#1224 --- .../langchain_google_vertexai/_base.py | 34 +- .../langchain_google_vertexai/chat_models.py | 65 +- libs/vertexai/pyproject.toml | 6 - .../integration_tests/test_chat_models.py | 197 ++-- .../tests/unit_tests/test_chat_models.py | 538 ++++++----- libs/vertexai/uv.lock | 849 ++++++++---------- 6 files changed, 776 insertions(+), 913 deletions(-) diff --git a/libs/vertexai/langchain_google_vertexai/_base.py b/libs/vertexai/langchain_google_vertexai/_base.py index 9bcdc9f9d..73695b226 100644 --- a/libs/vertexai/langchain_google_vertexai/_base.py +++ b/libs/vertexai/langchain_google_vertexai/_base.py @@ -192,23 +192,23 @@ def _library_version(self) -> str: class _VertexAICommon(_VertexAIBase): client_preview: Any = Field(default=None, exclude=True) #: :meta private: model_name: Optional[str] = Field(default=None, alias="model") - """Underlying model name.""" + "Underlying model name." temperature: Optional[float] = None - """Sampling temperature, it controls the degree of randomness in token selection.""" + "Sampling temperature, it controls the degree of randomness in token selection." frequency_penalty: Optional[float] = None - """Positive values penalize tokens that repeatedly appear in the generated text, - decreasing the probability of repeating content.""" + "Positive values penalize tokens that repeatedly appear in the generated text, " + "decreasing the probability of repeating content." presence_penalty: Optional[float] = None - """Positive values penalize tokens that already appear in the generated text, - increasing the probability of generating more diverse content.""" + "Positive values penalize tokens that already appear in the generated text, " + "increasing the probability of generating more diverse content." max_output_tokens: Optional[int] = Field(default=None, alias="max_tokens") - """Token limit determines the maximum amount of text output from one prompt.""" + "Token limit determines the maximum amount of text output from one prompt." top_p: Optional[float] = None - """Tokens are selected from most probable to least until the sum of their - probabilities equals the top-p value. Top-p is ignored for Codey models.""" + "Tokens are selected from most probable to least until the sum of their " + "probabilities equals the top-p value. Top-p is ignored for Codey models." top_k: Optional[int] = None - """How the model selects tokens for output, the next token is selected from - among the top-k most probable tokens. Top-k is ignored for Codey models.""" + "How the model selects tokens for output, the next token is selected from " + "among the top-k most probable tokens. Top-k is ignored for Codey models." n: int = 1 """How many completions to generate for each prompt.""" seed: Optional[int] = None @@ -229,7 +229,7 @@ class _VertexAICommon(_VertexAIBase): HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE, } - """ # noqa: E501 + """ # noqa: E501 tuned_model_name: Optional[str] = None """The name of a tuned model.""" @@ -316,15 +316,15 @@ class _BaseVertexAIModelGarden(_VertexAIBase): async_client: Any = Field(default=None, exclude=True) #: :meta private: endpoint_id: str - """A name of an endpoint where the model has been deployed.""" + "A name of an endpoint where the model has been deployed." allowed_model_args: Optional[List[str]] = None - """Allowed optional args to be passed to the model.""" + "Allowed optional args to be passed to the model." prompt_arg: str = "prompt" result_arg: Optional[str] = "generated_text" - """Set result_arg to None if output of the model is expected to be a string. - Otherwise, if it's a dict, provided an argument that contains the result.""" + "Set result_arg to None if output of the model is expected to be a string." + "Otherwise, if it's a dict, provided an argument that contains the result." single_example_per_request: bool = True - """LLM endpoint currently serves only the first example in the request""" + "LLM endpoint currently serves only the first example in the request" @model_validator(mode="after") def validate_environment(self) -> Self: diff --git a/libs/vertexai/langchain_google_vertexai/chat_models.py b/libs/vertexai/langchain_google_vertexai/chat_models.py index f4ad8821d..fc69daede 100644 --- a/libs/vertexai/langchain_google_vertexai/chat_models.py +++ b/libs/vertexai/langchain_google_vertexai/chat_models.py @@ -1,7 +1,4 @@ -"""Wrapper around Google VertexAI chat-based models. - -Vertex supports both v1 and v1beta1 endpoints (`endpoint_version` parameter). -""" +"""Wrapper around Google VertexAI chat-based models.""" from __future__ import annotations # noqa import ast @@ -10,6 +7,7 @@ import json import logging import re +from dataclasses import dataclass, field from operator import itemgetter import uuid from typing import ( @@ -89,6 +87,7 @@ _convert_schema_dict_to_gapic, ) from vertexai.language_models import ( + ChatMessage, # TODO: migrate to google-genai since this is deprecated InputOutputTextPair, ) from google.cloud.aiplatform_v1.types import ( @@ -196,6 +195,14 @@ def _base64_to_bytes(input_str: str) -> bytes: return base64.b64decode(input_str.encode("utf-8")) +@dataclass +class _ChatHistory: + """Represents a context and a history of messages.""" + + history: List[ChatMessage] = field(default_factory=list) + context: Optional[str] = None + + class _GeminiGenerateContentKwargs(TypedDict): generation_config: Optional[GenerationConfigType] safety_settings: Optional[SafetySettingsType] @@ -203,27 +210,41 @@ class _GeminiGenerateContentKwargs(TypedDict): tool_config: Optional[ToolConfig] -def _parse_chat_history_gemini( - history: List[BaseMessage], - imageBytesLoader: ImageBytesLoader, - perform_literal_eval_on_string_raw_content: Optional[bool] = False, -) -> tuple[Content | None, list[Content]]: - """Parse LangChain message history into Gemini format. - - .. warning:: - perform_literal_eval_on_string_raw_content should only be set to True if you - fully trust the input, as it may execute arbitrary code. +def _parse_chat_history(history: List[BaseMessage]) -> _ChatHistory: + """Parse a sequence of messages into history. Args: - history: List of LangChain messages. - imageBytesLoader: An ImageBytesLoader instance to handle image loading. - perform_literal_eval_on_string_raw_content: Whether to attempt to parse string - content as Python literals (e.g., lists or dicts). Defaults to False. + history: The list of messages to re-create the history of the chat. Returns: - Tuple of (system_instruction, aiplatform_v1beta1 content). + A parsed chat history. + + Raises: + ValueError: If a sequence of message has a SystemMessage not at the + first place. """ + vertex_messages, context = [], None + for i, message in enumerate(history): + content = cast("str", message.content) + if i == 0 and isinstance(message, SystemMessage): + context = content + elif isinstance(message, AIMessage): + vertex_message = ChatMessage(content=content, author="bot") + vertex_messages.append(vertex_message) + elif isinstance(message, HumanMessage): + vertex_message = ChatMessage(content=content, author="user") + vertex_messages.append(vertex_message) + else: + msg = f"Unexpected message with type {type(message)} at the position {i}." + raise ValueError(msg) + return _ChatHistory(context=context, history=vertex_messages) + +def _parse_chat_history_gemini( + history: List[BaseMessage], + imageBytesLoader: ImageBytesLoader, + perform_literal_eval_on_string_raw_content: Optional[bool] = False, +) -> tuple[Content | None, list[Content]]: def _convert_to_prompt(part: Union[str, Dict]) -> Optional[Part]: if isinstance(part, str): return Part(text=part) @@ -314,12 +335,6 @@ def _convert_to_prompt(part: Union[str, Dict]) -> Optional[Part]: raise ValueError(msg) def _convert_to_parts(message: BaseMessage) -> List[Part]: - """Parse LangChain message content into Google parts. - - Used when preparing Human, System and AI messages for sending to the API. - Handles both legacy (pre-v1) dict-based content blocks and v1 ContentBlock - objects. - """ raw_content = message.content # If a user sends a multimodal request with agents, then the full input diff --git a/libs/vertexai/pyproject.toml b/libs/vertexai/pyproject.toml index e6178a9aa..b65f0710b 100644 --- a/libs/vertexai/pyproject.toml +++ b/libs/vertexai/pyproject.toml @@ -75,12 +75,6 @@ test_integration = [ dev = [] -[tool.uv.sources] -langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core", branch = "mdrxy/genai" } -langchain-tests = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/standard-tests", branch = "mdrxy/genai" } -#langchain-core = { path = "../../../langchain/libs/core", editable = true } -#langchain-tests = { path = "../../../langchain/libs/standard-tests", editable = true } - [tool.ruff] fix = true diff --git a/libs/vertexai/tests/integration_tests/test_chat_models.py b/libs/vertexai/tests/integration_tests/test_chat_models.py index 04dbb6420..b3c92e2d5 100644 --- a/libs/vertexai/tests/integration_tests/test_chat_models.py +++ b/libs/vertexai/tests/integration_tests/test_chat_models.py @@ -58,7 +58,7 @@ model_names_to_test = [_DEFAULT_MODEL_NAME] endpoint_versions = ["v1", "v1beta1"] -RATE_LIMITER = InMemoryRateLimiter(requests_per_second=1.0) +rate_limiter = InMemoryRateLimiter(requests_per_second=1.0) def _check_usage_metadata(message: AIMessage) -> None: @@ -71,63 +71,26 @@ def _check_usage_metadata(message: AIMessage) -> None: ) == message.usage_metadata["total_tokens"] -def _check_tool_calls(response: BaseMessage, expected_name: str) -> None: - """Check tool calls are as expected.""" - assert isinstance(response, AIMessage) - assert isinstance(response.content, str) - assert response.content == "" - function_call = response.additional_kwargs.get("function_call") - assert function_call - assert function_call["name"] == expected_name - arguments_str = function_call.get("arguments") - assert arguments_str - arguments = json.loads(arguments_str) - assert arguments == { - "name": "Erick", - "age": 27.0, - } - tool_calls = response.tool_calls - assert len(tool_calls) == 1 - tool_call = tool_calls[0] - assert tool_call["name"] == expected_name - assert tool_call["args"] == {"age": 27.0, "name": "Erick"} - - @pytest.mark.release @pytest.mark.parametrize("model_name", model_names_to_test) @pytest.mark.parametrize("endpoint_version", endpoint_versions) def test_initialization(model_name: Optional[str], endpoint_version: str) -> None: - """Test `ChatVertexAI` initialization. - - TODO: why is this a integration test? - """ + """Test chat model initialization.""" model = ChatVertexAI( model_name=model_name, - rate_limiter=RATE_LIMITER, + rate_limiter=rate_limiter, endpoint_version=endpoint_version, ) assert model._llm_type == "vertexai" -@pytest.mark.xfail(reason="can't create service account key on gcp") -@pytest.mark.release -def test_init_from_credentials_obj() -> None: - credentials_dict = json.loads(os.environ["GOOGLE_VERTEX_AI_WEB_CREDENTIALS"]) - credentials = service_account.Credentials.from_service_account_info( - credentials_dict - ) - llm = ChatVertexAI(model="gemini-2.0-flash-001", credentials=credentials) - llm.invoke("how are you") - - @pytest.mark.release @pytest.mark.parametrize("model_name", model_names_to_test) @pytest.mark.parametrize("endpoint_version", endpoint_versions) def test_vertexai_single_call(model_name: Optional[str], endpoint_version: str) -> None: - """Test making a single invoke call.""" model = ChatVertexAI( model_name=model_name, - rate_limiter=RATE_LIMITER, + rate_limiter=rate_limiter, endpoint_version=endpoint_version, ) message = HumanMessage(content="Hello") @@ -140,12 +103,8 @@ def test_vertexai_single_call(model_name: Optional[str], endpoint_version: str) @pytest.mark.release @pytest.mark.xfail(reason="vertex api doesn't respect n/candidate_count") def test_candidates() -> None: - """Test making a single invoke call with `n>1`. - - # TODO: what is chat-bison@001? is it marked for deprecation? - """ model = ChatVertexAI( - model_name="chat-bison@001", temperature=0.3, n=2, rate_limiter=RATE_LIMITER + model_name="chat-bison@001", temperature=0.3, n=2, rate_limiter=rate_limiter ) message = HumanMessage(content="Hello") response = model.generate(messages=[[message]]) @@ -155,10 +114,9 @@ def test_candidates() -> None: @pytest.mark.release -async def test_vertexai_generate() -> None: - # TODO: parameterize with sync/async generate method +async def test_vertexai_agenerate() -> None: model = ChatVertexAI( - temperature=0, model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER + temperature=0, model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter ) message = HumanMessage(content="Hello") response = await model.agenerate([[message]]) @@ -181,9 +139,8 @@ async def test_vertexai_generate() -> None: @pytest.mark.release def test_vertexai_stream() -> None: - # TODO: parameterize with astream equivalent model = ChatVertexAI( - temperature=0, model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER + temperature=0, model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter ) message = HumanMessage(content="Hello") @@ -209,9 +166,8 @@ def test_vertexai_stream() -> None: @pytest.mark.release async def test_vertexai_astream() -> None: - # TODO: parameterize with stream equivalent model = ChatVertexAI( - temperature=0, model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER + temperature=0, model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter ) message = HumanMessage(content="Hello") @@ -236,8 +192,7 @@ async def test_vertexai_astream() -> None: @pytest.mark.release def test_multimodal() -> None: - """Test multimodal input with a gcs image URL in chat completions format.""" - llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) + llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) gcs_url = ( "gs://cloud-samples-data/generative-ai/image/320px-Felis_catus-cat_on_snow.jpg" ) @@ -255,18 +210,18 @@ def test_multimodal() -> None: assert isinstance(output, AIMessage) _check_usage_metadata(output) - llm = ChatVertexAI(model_name="gemini-2.5-pro", rate_limiter=RATE_LIMITER) + llm = ChatVertexAI(model_name="gemini-2.5-pro", rate_limiter=rate_limiter) for chunk in llm.stream([message]): assert isinstance(chunk, AIMessageChunk) -VIDEO_PARAM = pytest.param( +video_param = pytest.param( "gs://cloud-samples-data/generative-ai/video/pixel8.mp4", "video/mp4", id="video", ) -MULTIMODAL_INPUTS = [ - VIDEO_PARAM, +multimodal_inputs = [ + video_param, pytest.param( "gs://cloud-samples-data/generative-ai/audio/pixel.mp3", "audio/mp3", id="audio" ), @@ -279,10 +234,9 @@ def test_multimodal() -> None: @pytest.mark.release -@pytest.mark.parametrize(("file_uri", "mime_type"), MULTIMODAL_INPUTS) +@pytest.mark.parametrize(("file_uri", "mime_type"), multimodal_inputs) def test_multimodal_media_file_uri(file_uri, mime_type) -> None: - """Test multimodal input with gcs file URIs (video, audio, image).""" - llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) + llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) media_message = { "type": "media", "file_uri": file_uri, @@ -298,11 +252,10 @@ def test_multimodal_media_file_uri(file_uri, mime_type) -> None: @pytest.mark.release -@pytest.mark.parametrize(("file_uri", "mime_type"), MULTIMODAL_INPUTS) +@pytest.mark.parametrize(("file_uri", "mime_type"), multimodal_inputs) @pytest.mark.first def test_multimodal_media_inline_base64(file_uri, mime_type) -> None: - """Test multimodal input with base64 encoded media content (video, audio, image).""" - llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) + llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) storage_client = storage.Client() blob = storage.Blob.from_string(file_uri, client=storage_client) media_base64 = base64.b64encode(blob.download_as_bytes()).decode() @@ -323,7 +276,6 @@ def test_multimodal_media_inline_base64(file_uri, mime_type) -> None: @pytest.mark.release @pytest.mark.first def test_multimodal_media_inline_base64_template() -> None: - """Test multimodal input with base64 encoded media content using prompt template.""" llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME) prompt_template = ChatPromptTemplate( [ @@ -357,7 +309,6 @@ def test_multimodal_media_inline_base64_template() -> None: @pytest.mark.extended def test_multimodal_media_inline_base64_agent() -> None: - """Test multimodal input with base64 encoded media content using a ReAct agent.""" from langchain import agents @tool @@ -406,7 +357,7 @@ def get_climate_info(query: str) -> str: @pytest.mark.flaky(retries=3) def test_audio_timestamp() -> None: storage_client = storage.Client() - llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) + llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) file_uri = ( "gs://cloud-samples-data/generative-ai/audio/audio_summary_clean_energy.mp3" @@ -466,9 +417,9 @@ def test_parse_history_gemini_multimodal_FC() -> None: @pytest.mark.xfail(reason="investigating") @pytest.mark.release -@pytest.mark.parametrize(("file_uri", "mime_type"), [VIDEO_PARAM]) +@pytest.mark.parametrize(("file_uri", "mime_type"), [video_param]) def test_multimodal_video_metadata(file_uri, mime_type) -> None: - llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) + llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) media_message = { "type": "media", "file_uri": file_uri, @@ -491,7 +442,7 @@ def test_multimodal_video_metadata(file_uri, mime_type) -> None: @pytest.mark.release @pytest.mark.parametrize("model_name", model_names_to_test) def test_vertexai_single_call_with_history(model_name: Optional[str]) -> None: - model = ChatVertexAI(model_name=model_name, rate_limiter=RATE_LIMITER) + model = ChatVertexAI(model_name=model_name, rate_limiter=rate_limiter) text_question1, text_answer1 = "How much is 2+2?", "4" text_question2 = "How much is 3+3?" message1 = HumanMessage(content=text_question1) @@ -504,7 +455,7 @@ def test_vertexai_single_call_with_history(model_name: Optional[str]) -> None: @pytest.mark.release def test_vertexai_system_message() -> None: - model = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) + model = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) system_instruction = """CymbalBank is a bank located in London""" text_question1 = "Where is Cymbal located? Provide only the name of the city." sys_message = SystemMessage(content=system_instruction) @@ -518,7 +469,7 @@ def test_vertexai_system_message() -> None: @pytest.mark.release def test_vertexai_single_call_with_no_system_messages() -> None: - model = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) + model = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) text_question1, text_answer1 = "How much is 2+2?", "4" text_question2 = "How much is 3+3?" message1 = HumanMessage(content=text_question1) @@ -531,11 +482,10 @@ def test_vertexai_single_call_with_no_system_messages() -> None: @pytest.mark.release def test_vertexai_single_call_previous_blocked_response() -> None: - """If a previous call was blocked, the AIMessage will have empty content. - - Empty content should be ignored. + """If a previous call was blocked, the AIMessage will have empty content which + should be ignored. """ - model = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) + model = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) text_question2 = "How much is 3+3?" # Previous blocked response included in history. This can happen with a LangGraph # ReAct agent. @@ -566,7 +516,7 @@ def test_vertexai_single_call_previous_blocked_response() -> None: @pytest.mark.parametrize("model_name", model_names_to_test) def test_get_num_tokens_from_messages(model_name: str) -> None: model = ChatVertexAI( - model_name=model_name, temperature=0.0, rate_limiter=RATE_LIMITER + model_name=model_name, temperature=0.0, rate_limiter=rate_limiter ) message = HumanMessage(content="Hello") token = model.get_num_tokens_from_messages(messages=[message]) @@ -574,6 +524,28 @@ def test_get_num_tokens_from_messages(model_name: str) -> None: assert token == 3 +def _check_tool_calls(response: BaseMessage, expected_name: str) -> None: + """Check tool calls are as expected.""" + assert isinstance(response, AIMessage) + assert isinstance(response.content, str) + assert response.content == "" + function_call = response.additional_kwargs.get("function_call") + assert function_call + assert function_call["name"] == expected_name + arguments_str = function_call.get("arguments") + assert arguments_str + arguments = json.loads(arguments_str) + assert arguments == { + "name": "Erick", + "age": 27.0, + } + tool_calls = response.tool_calls + assert len(tool_calls) == 1 + tool_call = tool_calls[0] + assert tool_call["name"] == expected_name + assert tool_call["args"] == {"age": 27.0, "name": "Erick"} + + @pytest.mark.extended @pytest.mark.parametrize("endpoint_version", endpoint_versions) def test_chat_vertexai_gemini_function_calling(endpoint_version: str) -> None: @@ -589,7 +561,7 @@ class MyModel(BaseModel): model = ChatVertexAI( model_name=_DEFAULT_MODEL_NAME, safety_settings=safety, - rate_limiter=RATE_LIMITER, + rate_limiter=rate_limiter, endpoint_version=endpoint_version, ).bind_tools([MyModel]) response = model.invoke([message]) @@ -602,7 +574,7 @@ def my_model(name: str, age: int) -> None: model = ChatVertexAI( model_name=_DEFAULT_MODEL_NAME, safety_settings=safety, - rate_limiter=RATE_LIMITER, + rate_limiter=rate_limiter, ).bind_tools([my_model]) response = model.invoke([message]) _check_tool_calls(response, "my_model") @@ -647,7 +619,7 @@ class MyModel(BaseModel): model = ChatVertexAI( model_name=_DEFAULT_MODEL_NAME, safety_settings=safety, - rate_limiter=RATE_LIMITER, + rate_limiter=rate_limiter, ).bind( functions=[MyModel], tool_config={ @@ -728,7 +700,7 @@ class MyModel(BaseModel): llm = ChatVertexAI( model_name=_DEFAULT_MODEL_NAME, safety_settings=safety, - rate_limiter=RATE_LIMITER, + rate_limiter=rate_limiter, ) model = llm.with_structured_output(MyModel, method=method) message = HumanMessage(content="My name is Erick and I am 27 years old") @@ -815,7 +787,7 @@ def search( model_name=_DEFAULT_MODEL_NAME, safety_settings=safety, temperature=0, - rate_limiter=RATE_LIMITER, + rate_limiter=rate_limiter, ) llm_with_search = llm.bind( functions=tools, @@ -1051,13 +1023,13 @@ def test_chat_vertexai_gemini_thinking_configurable() -> None: @pytest.mark.extended @pytest.mark.first def test_prediction_client_transport() -> None: - model = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) + model = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) assert model.prediction_client.transport.kind == "grpc" assert model.async_prediction_client.transport.kind == "grpc_asyncio" model = ChatVertexAI( - model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER, api_transport="rest" + model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter, api_transport="rest" ) assert model.prediction_client.transport.kind == "rest" @@ -1069,7 +1041,7 @@ def test_prediction_client_transport() -> None: @pytest.mark.extended def test_structured_output_schema_json() -> None: model = ChatVertexAI( - rate_limiter=RATE_LIMITER, + rate_limiter=rate_limiter, model_name=_DEFAULT_MODEL_NAME, response_mime_type="application/json", response_schema={ @@ -1109,7 +1081,7 @@ def test_structured_output_schema_json() -> None: "required": ["recipe_name"], }, }, - rate_limiter=RATE_LIMITER, + rate_limiter=rate_limiter, ) with pytest.raises(ValueError, match="response_mime_type"): response = model.invoke("List a few popular cookie recipes") @@ -1123,7 +1095,7 @@ class MyModel(TypedDict): llm = ChatVertexAI( model_name="gemini-2.0-flash-exp", - rate_limiter=RATE_LIMITER, + rate_limiter=rate_limiter, ) model = llm.with_structured_output(MyModel, method="json_mode") message = HumanMessage(content="My name is Erick and I am 28 years old") @@ -1145,7 +1117,7 @@ def test_structured_output_schema_enum() -> None: model_name=_DEFAULT_MODEL_NAME, response_schema={"type": "STRING", "enum": ["drama", "comedy", "documentary"]}, response_mime_type="text/x.enum", - rate_limiter=RATE_LIMITER, + rate_limiter=rate_limiter, ) response = model.invoke( @@ -1180,7 +1152,7 @@ def test_context_catching() -> None: cached_content = create_context_cache( ChatVertexAI( model_name=_DEFAULT_MODEL_NAME, - rate_limiter=RATE_LIMITER, + rate_limiter=rate_limiter, ), messages=[ SystemMessage(content=system_instruction), @@ -1207,7 +1179,7 @@ def test_context_catching() -> None: chat = ChatVertexAI( model_name=_DEFAULT_MODEL_NAME, cached_content=cached_content, - rate_limiter=RATE_LIMITER, + rate_limiter=rate_limiter, ) response = chat.invoke("What is the secret number?") @@ -1216,7 +1188,7 @@ def test_context_catching() -> None: assert isinstance(response.content, str) # Using cached content in request - chat = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=RATE_LIMITER) + chat = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, rate_limiter=rate_limiter) response = chat.invoke("What is the secret number?", cached_content=cached_content) assert isinstance(response, AIMessage) @@ -1420,6 +1392,17 @@ async def test_astream_events_langgraph_example() -> None: assert output.additional_kwargs["function_call"]["name"] == "multiply" +@pytest.mark.xfail(reason="can't create service account key on gcp") +@pytest.mark.release +def test_init_from_credentials_obj() -> None: + credentials_dict = json.loads(os.environ["GOOGLE_VERTEX_AI_WEB_CREDENTIALS"]) + credentials = service_account.Credentials.from_service_account_info( + credentials_dict + ) + llm = ChatVertexAI(model="gemini-2.0-flash-001", credentials=credentials) + llm.invoke("how are you") + + @pytest.mark.xfail(reason="can't add labels to the gemini content") @pytest.mark.release def test_label_metadata() -> None: @@ -1446,6 +1429,15 @@ def test_label_metadata_invoke_method() -> None: ) +@pytest.mark.release +def test_response_metadata_avg_logprobs() -> None: + llm = ChatVertexAI(model="gemini-2.0-flash-001") + response = llm.invoke("Hello!") + probs = response.response_metadata.get("avg_logprobs") + if probs is not None: + assert isinstance(probs, float) + + @pytest.fixture def multimodal_pdf_chain() -> RunnableSerializable: prompt = ChatPromptTemplate.from_messages( @@ -1471,7 +1463,6 @@ def multimodal_pdf_chain() -> RunnableSerializable: @pytest.mark.release def test_multimodal_pdf_input_gcs(multimodal_pdf_chain: RunnableSerializable) -> None: - # TODO: parallelize with url and b64 tests gcs_uri = "gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf" # GCS URI response = multimodal_pdf_chain.invoke({"image": gcs_uri}) @@ -1480,7 +1471,6 @@ def test_multimodal_pdf_input_gcs(multimodal_pdf_chain: RunnableSerializable) -> @pytest.mark.release def test_multimodal_pdf_input_url(multimodal_pdf_chain: RunnableSerializable) -> None: - # TODO: parallelize with gcs and b64 tests url = "https://abc.xyz/assets/95/eb/9cef90184e09bac553796896c633/2023q4-alphabet-earnings-release.pdf" # URL response = multimodal_pdf_chain.invoke({"image": url}) @@ -1489,7 +1479,6 @@ def test_multimodal_pdf_input_url(multimodal_pdf_chain: RunnableSerializable) -> @pytest.mark.release def test_multimodal_pdf_input_b64(multimodal_pdf_chain: RunnableSerializable) -> None: - # TODO: parallelize with gcs and url tests url = "https://abc.xyz/assets/95/eb/9cef90184e09bac553796896c633/2023q4-alphabet-earnings-release.pdf" request_response = requests.get(url, allow_redirects=True) # B64 @@ -1501,15 +1490,6 @@ def test_multimodal_pdf_input_b64(multimodal_pdf_chain: RunnableSerializable) -> assert isinstance(response, AIMessage) -@pytest.mark.release -def test_response_metadata_avg_logprobs() -> None: - llm = ChatVertexAI(model="gemini-2.0-flash-001") - response = llm.invoke("Hello!") - probs = response.response_metadata.get("avg_logprobs") - if probs is not None: - assert isinstance(probs, float) - - @pytest.mark.xfail(reason="logprobs are subject to daily quotas") @pytest.mark.release def test_logprobs() -> None: @@ -1540,7 +1520,6 @@ def test_logprobs() -> None: def test_location_init() -> None: - """Test how location is set in ChatVertexAI depending on vertexai.init settings.""" # If I don't initialize vertexai before, defaults to us-central-1 llm = ChatVertexAI(model="gemini-2.0-flash-001", logprobs=2) assert llm.location == "us-central1" @@ -1572,7 +1551,7 @@ def test_vertexai_global_location_single_call( model = ChatVertexAI( model_name=model_name, location="global", - rate_limiter=RATE_LIMITER, + rate_limiter=rate_limiter, endpoint_version=endpoint_version, ) assert model.location == "global" @@ -1602,14 +1581,13 @@ class People(BaseModel): def test_search_builtin() -> None: - """Test the built-in search tool.""" llm = ChatVertexAI(model=_DEFAULT_MODEL_NAME).bind_tools([{"google_search": {}}]) input_message = { "role": "user", "content": "What is today's news?", } response = llm.invoke([input_message]) - + # Grounding metadata is optional and may not be present in all responses if "grounding_metadata" in response.response_metadata: assert response.response_metadata["grounding_metadata"] is not None @@ -1619,7 +1597,7 @@ def test_search_builtin() -> None: assert isinstance(chunk, AIMessageChunk) full = chunk if full is None else full + chunk assert isinstance(full, AIMessageChunk) - + # Grounding metadata is optional and may not be present in all responses if "grounding_metadata" in full.response_metadata: assert full.response_metadata["grounding_metadata"] is not None @@ -1632,7 +1610,6 @@ def test_search_builtin() -> None: def test_code_execution_builtin() -> None: - """Test the built-in code execution tool.""" llm = ChatVertexAI(model=_DEFAULT_MODEL_NAME).bind_tools([{"code_execution": {}}]) input_message = { "role": "user", diff --git a/libs/vertexai/tests/unit_tests/test_chat_models.py b/libs/vertexai/tests/unit_tests/test_chat_models.py index 785f41e2a..88e96cdb8 100644 --- a/libs/vertexai/tests/unit_tests/test_chat_models.py +++ b/libs/vertexai/tests/unit_tests/test_chat_models.py @@ -1,4 +1,4 @@ -"""Chat model unit tests.""" +"""Test chat model integration.""" import base64 import json @@ -37,6 +37,7 @@ SafetySetting as VertexSafetySetting, # TODO: migrate to google-genai ) from vertexai.language_models import ( + ChatMessage, # TODO: migrate to google-genai since this is deprecated InputOutputTextPair, ) @@ -45,6 +46,7 @@ from langchain_google_vertexai.chat_models import ( ChatVertexAI, _bytes_to_base64, + _parse_chat_history, _parse_chat_history_gemini, _parse_examples, _parse_response_candidate, @@ -60,54 +62,50 @@ def clear_prediction_client_cache() -> None: def test_init() -> None: - """Test initialization of `ChatVertexAI` with different parameter names. - - Done since we have aliasing of some parameters for consistency with other LLMs. - """ for llm in [ ChatVertexAI( - model_name="gemini-2-5-flash", + model_name="gemini-pro", project="test-project", max_output_tokens=10, stop=["bar"], location="moon-dark1", ), ChatVertexAI( - model="gemini-2-5-flash", - project="test-proj", + model="gemini-pro", max_tokens=10, stop_sequences=["bar"], location="moon-dark1", + project="test-proj", ), ]: - assert llm.model_name == "gemini-2-5-flash" + assert llm.model_name == "gemini-pro" assert llm.max_output_tokens == 10 assert llm.stop == ["bar"] ls_params = llm._get_ls_params() assert ls_params == { "ls_provider": "google_vertexai", - "ls_model_name": "gemini-2-5-flash", + "ls_model_name": "gemini-pro", "ls_model_type": "chat", "ls_temperature": None, "ls_max_tokens": 10, "ls_stop": ["bar"], } - # Test initialization with an invalid argument to check warning + # test initialization with an invalid argument to check warning with patch("langchain_google_vertexai.chat_models.logger.warning") as mock_warning: # Suppress UserWarning during test execution - we're testing the warning # mechanism via logger mock assertions, not via pytest's warning system with warnings.catch_warnings(): warnings.simplefilter("ignore", UserWarning) llm = ChatVertexAI( - model_name="gemini-2-5-flash", + model_name="gemini-pro", project="test-project", safety_setting={ "HARM_CATEGORY_DANGEROUS_CONTENT": "BLOCK_LOW_AND_ABOVE" }, # Invalid arg ) - assert llm.model_name == "gemini-2-5-flash" + assert llm.model_name == "gemini-pro" assert llm.project == "test-project" mock_warning.assert_called_once() call_args = mock_warning.call_args[0][0] @@ -119,18 +117,13 @@ def test_init() -> None: ("model", "location"), [ ( - "gemini-2-5-flash", + "gemini-1.0-pro-001", "moon-dark1", ), - ("publishers/google/models/gemini-2-5-flash", "moon-dark2"), + ("publishers/google/models/gemini-1.0-pro-001", "moon-dark2"), ], ) def test_init_client(model: str, location: str) -> None: - """Test initialization of `ChatVertexAI` with different models and locations. - - Ensure the user agent is set correctly and the full model name is constructed using - the provided project and location. - """ config = {"model": model, "location": location} llm = ChatVertexAI( **{k: v for k, v in config.items() if v is not None}, project="test-proj" @@ -151,76 +144,15 @@ def test_init_client(model: str, location: str) -> None: assert "langchain-google-vertexai" in client_info.client_library_version assert "ChatVertexAI" in client_info.client_library_version assert llm.full_model_name == ( - f"projects/test-proj/locations/{location}/publishers/google/models/gemini-2-5-flash" + f"projects/test-proj/locations/{location}/publishers/google/models/gemini-1.0-pro-001" ) -def test_init_client_with_custom_api_endpoint() -> None: - """Test that custom API endpoint and transport are set correctly.""" - config = { - "model": "gemini-2.5-pro", - "api_endpoint": "https://example.com", - "api_transport": "rest", - } - llm = ChatVertexAI( - **{k: v for k, v in config.items() if v is not None}, project="test-proj" - ) - with patch( - "langchain_google_vertexai._client_utils.v1beta1PredictionServiceClient" - ) as mock_prediction_service: - response = GenerateContentResponse(candidates=[]) - mock_prediction_service.return_value.generate_content.return_value = response - - llm._generate_gemini(messages=[]) - mock_prediction_service.assert_called_once() - client_options = mock_prediction_service.call_args.kwargs["client_options"] - transport = mock_prediction_service.call_args.kwargs["transport"] - assert client_options.api_endpoint == "https://example.com" - assert transport == "rest" - - -def test_init_client_with_custom_base_url(clear_prediction_client_cache: Any) -> None: - """Test that custom base URL and transport are set correctly.""" - config = { - "model": "gemini-2.5-pro", - "base_url": "https://example.com", - "api_transport": "rest", - } - llm = ChatVertexAI( - **{k: v for k, v in config.items() if v is not None}, project="test-proj" - ) - with patch( - "langchain_google_vertexai._client_utils.v1beta1PredictionServiceClient" - ) as mock_prediction_service: - response = GenerateContentResponse(candidates=[]) - mock_prediction_service.return_value.generate_content.return_value = response - - llm._generate_gemini(messages=[]) - mock_prediction_service.assert_called_once() - client_options = mock_prediction_service.call_args.kwargs["client_options"] - transport = mock_prediction_service.call_args.kwargs["transport"] - assert client_options.api_endpoint == "https://example.com" - assert transport == "rest" - - -def test_init_client_with_custom_model_kwargs() -> None: - """Test that custom model kwargs are set correctly.""" - llm = ChatAnthropicVertex( - project="test-project", - location="test-location", - model_kwargs={"thinking": {"type": "enabled", "budget_tokens": 1024}}, - ) - assert llm.model_kwargs == {"thinking": {"type": "enabled", "budget_tokens": 1024}} - - default_params = llm._default_params - assert default_params["thinking"] == {"type": "enabled", "budget_tokens": 1024} - - @pytest.mark.parametrize( ("model", "location"), [ ( - "gemini-2-5-flash", + "gemini-1.0-pro-001", "moon-dark1", ), ], @@ -228,7 +160,6 @@ def test_init_client_with_custom_model_kwargs() -> None: def test_model_name_presence_in_chat_results( model: str, location: str, clear_prediction_client_cache: Any ) -> None: - """Ensure the model name is present in the response metadata of messages.""" config = {"model": model, "location": location} llm = ChatVertexAI( **{k: v for k, v in config.items() if v is not None}, project="test-proj" @@ -245,143 +176,25 @@ def test_model_name_presence_in_chat_results( assert isinstance(llm_response.generations[0].message, AIMessage) assert ( llm_response.generations[0].message.response_metadata["model_name"] - == "gemini-2-5-flash" + == "gemini-1.0-pro-001" ) def test_tuned_model_name() -> None: - """Test initialization of `ChatVertexAI` with a tuned model name. - - Tuned models must be specified using the full resource name. - """ llm = ChatVertexAI( - model_name="gemini-2-5-flash", + model_name="gemini-pro", project="test-project", tuned_model_name="projects/123/locations/europe-west4/endpoints/456", max_tokens=500, ) - assert llm.model_name == "gemini-2-5-flash" + assert llm.model_name == "gemini-pro" assert llm.tuned_model_name == "projects/123/locations/europe-west4/endpoints/456" assert llm.full_model_name == "projects/123/locations/europe-west4/endpoints/456" assert llm.max_output_tokens == 500 - assert llm.max_tokens == 500 # Alias - - -def test_default_params_gemini() -> None: - """Test that default parameters are set correctly in the request.""" - user_prompt = "Hello" - - with patch( - "langchain_google_vertexai._client_utils.v1beta1PredictionServiceClient" - ) as mc: - response = GenerateContentResponse( - candidates=[Candidate(content=Content(parts=[Part(text="Hi")]))] - ) - mock_generate_content = MagicMock(return_value=response) - mc.return_value.generate_content = mock_generate_content - - model = ChatVertexAI(model_name="gemini-2-5-flash", project="test-project") - message = HumanMessage(content=user_prompt) - _ = model.invoke([message]) - mock_generate_content.assert_called_once() - assert ( - mock_generate_content.call_args.kwargs["request"].contents[0].role == "user" - ) - assert ( - mock_generate_content.call_args.kwargs["request"].contents[0].parts[0].text - == "Hello" - ) - expected = GenerationConfig( - candidate_count=1, - ) - assert ( - mock_generate_content.call_args.kwargs["request"].generation_config - == expected - ) - assert mock_generate_content.call_args.kwargs["request"].tools == [] - assert not mock_generate_content.call_args.kwargs["request"].tool_config - assert not mock_generate_content.call_args.kwargs["request"].safety_settings - - -def test_generation_config_gemini() -> None: - """Test that generation config is set correctly in the request when overridden.""" - model = ChatVertexAI( - model_name="gemini-2-5-flash", - project="test-project", - temperature=0.2, - top_k=3, - frequency_penalty=0.2, - presence_penalty=0.6, - ) - generation_config = model._generation_config_gemini( - temperature=0.3, - stop=["stop"], - candidate_count=2, - frequency_penalty=0.9, - presence_penalty=0.8, - ) - # Note merged settings, with method args taking precedence (e.g. temperature) - expected = GenerationConfig( - stop_sequences=["stop"], - temperature=0.3, - top_k=3, - candidate_count=2, - frequency_penalty=0.9, - presence_penalty=0.8, - ) - assert generation_config == expected - - -def test_safety_settings_gemini_init() -> None: - """Test that safety settings are set correctly when provided at init.""" - expected_safety_setting = [ - VertexSafetySetting( - category=HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, - threshold=SafetySetting.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, - method=SafetySetting.HarmBlockMethod.SEVERITY, - ) - ] - model = ChatVertexAI( - model_name="gemini-2-5-flash", - temperature=0.2, - top_k=3, - project="test-project", - safety_settings=expected_safety_setting, - ) - safety_settings = model._safety_settings_gemini(None) - assert safety_settings == expected_safety_setting - - -def test_safety_settings_gemini() -> None: - """Test that safety settings are set correctly in the request.""" - model = ChatVertexAI( - model_name="gemini-2-5-flash", temperature=0.2, top_k=3, project="test-project" - ) - expected_safety_setting = SafetySetting( - category=HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, - threshold=SafetySetting.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, - ) - safety_settings = model._safety_settings_gemini([expected_safety_setting]) - assert safety_settings == [expected_safety_setting] - # Ignores for tests that intentionally use invalid dict types - safety_settings = model._safety_settings_gemini( - # Ignore since testing string conversion - {"HARM_CATEGORY_DANGEROUS_CONTENT": "BLOCK_LOW_AND_ABOVE"} # type: ignore[dict-item] - ) - assert safety_settings == [expected_safety_setting] - # Ignore since testing int conversion - safety_settings = model._safety_settings_gemini({2: 1}) # type: ignore[dict-item] - assert safety_settings == [expected_safety_setting] - threshold = SafetySetting.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE - safety_settings = model._safety_settings_gemini( - # Ignore since testing enum conversion - {HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: threshold} # type: ignore[dict-item] - ) - assert safety_settings == [expected_safety_setting] + assert llm.max_tokens == 500 def test_parse_examples_correct() -> None: - """Test parsing of message examples into `InputOutputTextPair` objects.""" text_question = ( "Hello, could you recommend a good movie for me to watch this evening, please?" ) @@ -400,11 +213,6 @@ def test_parse_examples_correct() -> None: def test_parse_examples_failes_wrong_sequence() -> None: - """Out of order messages should raise an error. - - Ensure that the _parsed_examples function raises an error if the messages are not - in the correct Human-AI alternating sequence. - """ with pytest.raises(ValueError) as exc_info: _ = _parse_examples([AIMessage(content="a")]) assert ( @@ -413,8 +221,33 @@ def test_parse_examples_failes_wrong_sequence() -> None: ) +def test_parse_chat_history_correct() -> None: + text_context = ( + "My name is Peter. You are my personal assistant. My " + "favorite movies are Lord of the Rings and Hobbit." + ) + context = SystemMessage(content=text_context) + text_question = ( + "Hello, could you recommend a good movie for me to watch this evening, please?" + ) + question = HumanMessage(content=text_question) + text_answer = ( + "Sure, You might enjoy The Lord of the Rings: The Fellowship of the Ring " + "(2001): This is the first movie in the Lord of the Rings trilogy." + ) + answer = AIMessage(content=text_answer) + history = _parse_chat_history([context, question, answer, question, answer]) + assert history.context == context.content + assert len(history.history) == 4 + assert history.history == [ + ChatMessage(content=text_question, author="user"), + ChatMessage(content=text_answer, author="bot"), + ChatMessage(content=text_question, author="user"), + ChatMessage(content=text_answer, author="bot"), + ] + + def test_parse_history_gemini() -> None: - """Test parsing of chat history into Gemini format.""" system_input = "You're supposed to answer math questions." text_question1, text_answer1 = "How much is 2+2?", "4" text_question2 = "How much is 3+3?" @@ -437,10 +270,6 @@ def test_parse_history_gemini() -> None: def test_parse_history_gemini_number() -> None: - """Ensure that numeric strings are parsed correctly as text. - - TODO: remove this? Seems like an edge case of limited value. - """ system_input = "You're supposed to answer math questions." text_question1 = "54321.1" system_message = SystemMessage(content=system_input) @@ -458,12 +287,6 @@ def test_parse_history_gemini_number() -> None: def test_parse_history_gemini_function_empty_list() -> None: - """Test parsing of chat history with an empty tool call response. - - If the model calls a function/tool but the response is an empty list, it should - still be parsed correctly (meaning the function call is recorded, and the function - response is recorded as an empty content). - """ system_input = "You're supposed to answer math questions." text_question1 = "Solve the following equation. x^2+16=0" fn_name_1 = "root" @@ -515,7 +338,6 @@ def test_parse_history_gemini_function_empty_list() -> None: def test_parse_history_gemini_function() -> None: - """Test parsing of chat history with multiple tool calls and responses.""" system_input = "You're supposed to answer math questions." text_question1 = "Which is bigger 2+2, 3*3 or 4-4?" fn_name_1 = "add" @@ -613,7 +435,7 @@ def test_parse_history_gemini_function() -> None: @pytest.mark.parametrize( - ("source_history", "expected_sys_message", "expected_history"), + ("source_history", "expected_sm", "expected_history"), [ ( [ @@ -779,13 +601,8 @@ def test_parse_history_gemini_function() -> None: ], ) def test_parse_history_gemini_multi( - source_history, expected_sys_message, expected_history + source_history, expected_sm, expected_history ) -> None: - """Test parsing of chat history with multiple messages of same type. - - Needed to ensure that multiple system messages are combined into one, and that - multiple AI messages are combined into one. - """ image_bytes_loader = ImageBytesLoader() sm, result_history = _parse_chat_history_gemini( history=source_history, imageBytesLoader=image_bytes_loader @@ -793,53 +610,42 @@ def test_parse_history_gemini_multi( for result, expected in zip(result_history, expected_history): assert result == expected - assert sm == expected_sys_message + assert sm == expected_sm -def test_parse_chat_history_gemini_with_literal_eval() -> None: - """Test that string content representing a list is parsed correctly.""" - instruction = "Describe the attached media in 5 words." - text_message = {"type": "text", "text": instruction} - message = str([text_message]) - history: list[BaseMessage] = [HumanMessage(content=message)] - image_bytes_loader = ImageBytesLoader() - _, response = _parse_chat_history_gemini( - history=history, - imageBytesLoader=image_bytes_loader, - perform_literal_eval_on_string_raw_content=True, - ) - parts = [ - Part(text=instruction), - ] - expected = [Content(role="user", parts=parts)] - assert expected == response - - -def test_parse_chat_history_gemini_without_literal_eval() -> None: - """Test that string content is not parsed when literal eval is disabled.""" - instruction = "Describe the attached media in 5 words." - text_message = {"type": "text", "text": instruction} - message = str([text_message]) - history: list[BaseMessage] = [HumanMessage(content=message)] - image_bytes_loader = ImageBytesLoader() - _, response = _parse_chat_history_gemini( - history=history, - imageBytesLoader=image_bytes_loader, - perform_literal_eval_on_string_raw_content=False, - ) - parts = [ - Part(text=message), - ] - expected = [Content(role="user", parts=parts)] - assert expected == response - +def test_default_params_gemini() -> None: + user_prompt = "Hello" -def test_python_literal_inputs() -> None: - """In relation to literal eval, ensure that inputs are not misinterpreted.""" - llm = ChatVertexAI(model="gemini-2.5-flash", project="test-project") + with patch( + "langchain_google_vertexai._client_utils.v1beta1PredictionServiceClient" + ) as mc: + response = GenerateContentResponse( + candidates=[Candidate(content=Content(parts=[Part(text="Hi")]))] + ) + mock_generate_content = MagicMock(return_value=response) + mc.return_value.generate_content = mock_generate_content - for input_string in ["None", "(1, 2)", "[1, 2, 3]", "{1, 2, 3}"]: - _ = llm._prepare_request_gemini([HumanMessage(input_string)]) + model = ChatVertexAI(model_name="gemini-pro", project="test-project") + message = HumanMessage(content=user_prompt) + _ = model.invoke([message]) + mock_generate_content.assert_called_once() + assert ( + mock_generate_content.call_args.kwargs["request"].contents[0].role == "user" + ) + assert ( + mock_generate_content.call_args.kwargs["request"].contents[0].parts[0].text + == "Hello" + ) + expected = GenerationConfig( + candidate_count=1, + ) + assert ( + mock_generate_content.call_args.kwargs["request"].generation_config + == expected + ) + assert mock_generate_content.call_args.kwargs["request"].tools == [] + assert not mock_generate_content.call_args.kwargs["request"].tool_config + assert not mock_generate_content.call_args.kwargs["request"].safety_settings @pytest.mark.parametrize( @@ -1235,13 +1041,6 @@ def test_parse_response_candidate(raw_candidate, expected) -> None: def test_parser_multiple_tools() -> None: - """Test parsing of multiple function calls into Pydantic models. - - Uses two simple Pydantic models as tools: Add and Multiply. The model is expected - to call both functions in a single response, and the parser should correctly parse - the response into instances of the respective Pydantic models. - """ - class Add(BaseModel): arg1: int arg2: int @@ -1285,8 +1084,6 @@ class Multiply(BaseModel): llm = model | parser result = llm.invoke([message]) mock_generate_content.assert_called_once() - - # Verify that the result is a list of Pydantic model instances assert isinstance(result, list) assert isinstance(result[0], Add) assert result[0] == Add(arg1=1, arg2=2) @@ -1294,8 +1091,80 @@ class Multiply(BaseModel): assert result[1] == Multiply(arg1=3, arg2=3) +def test_generation_config_gemini() -> None: + model = ChatVertexAI( + model_name="gemini-pro", + project="test-project", + temperature=0.2, + top_k=3, + frequency_penalty=0.2, + presence_penalty=0.6, + ) + generation_config = model._generation_config_gemini( + temperature=0.3, + stop=["stop"], + candidate_count=2, + frequency_penalty=0.9, + presence_penalty=0.8, + ) + expected = GenerationConfig( + stop_sequences=["stop"], + temperature=0.3, + top_k=3, + candidate_count=2, + frequency_penalty=0.9, + presence_penalty=0.8, + ) + assert generation_config == expected + + +def test_safety_settings_gemini() -> None: + model = ChatVertexAI( + model_name="gemini-pro", temperature=0.2, top_k=3, project="test-project" + ) + expected_safety_setting = SafetySetting( + category=HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, + threshold=SafetySetting.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, + ) + safety_settings = model._safety_settings_gemini([expected_safety_setting]) + assert safety_settings == [expected_safety_setting] + # Ignores for tests that intentionally use invalid dict types + safety_settings = model._safety_settings_gemini( + # Ignore since testing string conversion + {"HARM_CATEGORY_DANGEROUS_CONTENT": "BLOCK_LOW_AND_ABOVE"} # type: ignore[dict-item] + ) + assert safety_settings == [expected_safety_setting] + # Ignore since testing int conversion + safety_settings = model._safety_settings_gemini({2: 1}) # type: ignore[dict-item] + assert safety_settings == [expected_safety_setting] + threshold = SafetySetting.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE + safety_settings = model._safety_settings_gemini( + # Ignore since testing enum conversion + {HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: threshold} # type: ignore[dict-item] + ) + assert safety_settings == [expected_safety_setting] + + +def test_safety_settings_gemini_init() -> None: + expected_safety_setting = [ + VertexSafetySetting( + category=HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, + threshold=SafetySetting.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, + method=SafetySetting.HarmBlockMethod.SEVERITY, + ) + ] + model = ChatVertexAI( + model_name="gemini-pro", + temperature=0.2, + top_k=3, + project="test-project", + safety_settings=expected_safety_setting, + ) + safety_settings = model._safety_settings_gemini(None) + assert safety_settings == expected_safety_setting + + def test_multiple_fc() -> None: - """Test parsing of multiple function calls in a single response.""" prompt = ( "I'm trying to decide whether to go to London or Zurich this weekend. How " "hot are those cities? How about Singapore? Or maybe Tokyo. I want to go " @@ -1381,6 +1250,100 @@ def test_multiple_fc() -> None: assert history == expected +def test_parse_chat_history_gemini_with_literal_eval() -> None: + instruction = "Describe the attached media in 5 words." + text_message = {"type": "text", "text": instruction} + message = str([text_message]) + history: list[BaseMessage] = [HumanMessage(content=message)] + image_bytes_loader = ImageBytesLoader() + _, response = _parse_chat_history_gemini( + history=history, + imageBytesLoader=image_bytes_loader, + perform_literal_eval_on_string_raw_content=True, + ) + parts = [ + Part(text=instruction), + ] + expected = [Content(role="user", parts=parts)] + assert expected == response + + +def test_parse_chat_history_gemini_without_literal_eval() -> None: + instruction = "Describe the attached media in 5 words." + text_message = {"type": "text", "text": instruction} + message = str([text_message]) + history: list[BaseMessage] = [HumanMessage(content=message)] + image_bytes_loader = ImageBytesLoader() + _, response = _parse_chat_history_gemini( + history=history, + imageBytesLoader=image_bytes_loader, + perform_literal_eval_on_string_raw_content=False, + ) + parts = [ + Part(text=message), + ] + expected = [Content(role="user", parts=parts)] + assert expected == response + + +def test_init_client_with_custom_api_endpoint() -> None: + config = { + "model": "gemini-2.5-pro", + "api_endpoint": "https://example.com", + "api_transport": "rest", + } + llm = ChatVertexAI( + **{k: v for k, v in config.items() if v is not None}, project="test-proj" + ) + with patch( + "langchain_google_vertexai._client_utils.v1beta1PredictionServiceClient" + ) as mock_prediction_service: + response = GenerateContentResponse(candidates=[]) + mock_prediction_service.return_value.generate_content.return_value = response + + llm._generate_gemini(messages=[]) + mock_prediction_service.assert_called_once() + client_options = mock_prediction_service.call_args.kwargs["client_options"] + transport = mock_prediction_service.call_args.kwargs["transport"] + assert client_options.api_endpoint == "https://example.com" + assert transport == "rest" + + +def test_init_client_with_custom_base_url(clear_prediction_client_cache: Any) -> None: + config = { + "model": "gemini-2.5-pro", + "base_url": "https://example.com", + "api_transport": "rest", + } + llm = ChatVertexAI( + **{k: v for k, v in config.items() if v is not None}, project="test-proj" + ) + with patch( + "langchain_google_vertexai._client_utils.v1beta1PredictionServiceClient" + ) as mock_prediction_service: + response = GenerateContentResponse(candidates=[]) + mock_prediction_service.return_value.generate_content.return_value = response + + llm._generate_gemini(messages=[]) + mock_prediction_service.assert_called_once() + client_options = mock_prediction_service.call_args.kwargs["client_options"] + transport = mock_prediction_service.call_args.kwargs["transport"] + assert client_options.api_endpoint == "https://example.com" + assert transport == "rest" + + +def test_init_client_with_custom_model_kwargs() -> None: + llm = ChatAnthropicVertex( + project="test-project", + location="test-location", + model_kwargs={"thinking": {"type": "enabled", "budget_tokens": 1024}}, + ) + assert llm.model_kwargs == {"thinking": {"type": "enabled", "budget_tokens": 1024}} + + default_params = llm._default_params + assert default_params["thinking"] == {"type": "enabled", "budget_tokens": 1024} + + def test_anthropic_format_output() -> None: """Test format output handles different content structures correctly.""" @@ -1515,7 +1478,6 @@ def model_dump(self): def test_thinking_configuration() -> None: - """Test that thinking configuration is set correctly.""" input_message = HumanMessage("Query requiring reasoning.") # Test init params @@ -1541,7 +1503,6 @@ def test_thinking_configuration() -> None: def test_thought_signature() -> None: - """Test that thought signatures are correctly parsed and included in requests.""" llm = ChatVertexAI( model="gemini-2.5-flash", project="test-project", @@ -1599,6 +1560,13 @@ def test_thought_signature() -> None: ] +def test_python_literal_inputs() -> None: + llm = ChatVertexAI(model="gemini-2.5-flash", project="test-project") + + for input_string in ["None", "(1, 2)", "[1, 2, 3]", "{1, 2, 3}"]: + _ = llm._prepare_request_gemini([HumanMessage(input_string)]) + + def test_v1_function_parts() -> None: llm = ChatVertexAI( model="gemini-2.5-flash", project="test-project", endpoint_version="v1" diff --git a/libs/vertexai/uv.lock b/libs/vertexai/uv.lock index ed49d7beb..0eef743f0 100644 --- a/libs/vertexai/uv.lock +++ b/libs/vertexai/uv.lock @@ -21,7 +21,7 @@ wheels = [ [[package]] name = "anthropic" -version = "0.69.0" +version = "0.68.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, @@ -33,14 +33,14 @@ dependencies = [ { name = "sniffio" }, { name = "typing-extensions" }, ] -sdist = { url = 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