From 57de66a06f78028548ac3bf482d17e59c4e34a4c Mon Sep 17 00:00:00 2001 From: Bagatur Date: Mon, 26 Feb 2024 17:32:32 -0800 Subject: [PATCH 1/3] vertexai[minor]: with_structured_output --- .../langchain_google_vertexai/chat_models.py | 126 +++++++++++++++++- 1 file changed, 124 insertions(+), 2 deletions(-) diff --git a/libs/vertexai/langchain_google_vertexai/chat_models.py b/libs/vertexai/langchain_google_vertexai/chat_models.py index c7129a6d..6170f022 100644 --- a/libs/vertexai/langchain_google_vertexai/chat_models.py +++ b/libs/vertexai/langchain_google_vertexai/chat_models.py @@ -4,7 +4,8 @@ import json import logging from dataclasses import dataclass, field -from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union, cast +from operator import itemgetter +from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Type, Union, cast import proto # type: ignore[import-untyped] from google.cloud.aiplatform_v1beta1.types.content import Part as GapicPart @@ -13,6 +14,7 @@ AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, ) +from langchain_core.language_models import LanguageModelInput from langchain_core.language_models.chat_models import ( BaseChatModel, generate_from_stream, @@ -25,8 +27,14 @@ HumanMessage, SystemMessage, ) +from langchain_core.output_parsers.base import OutputParserLike +from langchain_core.output_parsers.openai_functions import ( + JsonOutputFunctionsParser, + PydanticOutputFunctionsParser, +) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import root_validator +from langchain_core.pydantic_v1 import BaseModel, root_validator +from langchain_core.runnables import Runnable, RunnablePassthrough from vertexai.generative_models import ( # type: ignore Candidate, Content, @@ -581,6 +589,120 @@ async def _astream( ), ) + def with_structured_output( + self, + schema: Union[Dict, Type[BaseModel]], + *, + include_raw: bool = False, + **kwargs: Any, + ) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]: + """Model wrapper that returns outputs formatted to match the given schema. + + Args: + schema: The output schema as a dict or a Pydantic class. If a Pydantic class + then the model output will be an object of that class. If a dict then + the model output will be a dict. With a Pydantic class the returned + attributes will be validated, whereas with a dict they will not be. If + `method` is "function_calling" and `schema` is a dict, then the dict + must match the OpenAI function-calling spec. + include_raw: If False then only the parsed structured output is returned. If + an error occurs during model output parsing it will be raised. If True + then both the raw model response (a BaseMessage) and the parsed model + response will be returned. If an error occurs during output parsing it + will be caught and returned as well. The final output is always a dict + with keys "raw", "parsed", and "parsing_error". + + Returns: + A Runnable that takes any ChatModel input. If include_raw is True then a + dict with keys — raw: BaseMessage, parsed: Optional[_DictOrPydantic], + parsing_error: Optional[BaseException]. If include_raw is False then just + _DictOrPydantic is returned, where _DictOrPydantic depends on the schema. + If schema is a Pydantic class then _DictOrPydantic is the Pydantic class. + If schema is a dict then _DictOrPydantic is a dict. + + Example: Pydantic schema, exclude raw: + .. code-block:: python + + from langchain_core.pydantic_v1 import BaseModel + from langchain_google_vertexai import ChatVertexAI + + class AnswerWithJustification(BaseModel): + '''An answer to the user question along with justification for the answer.''' + answer: str + justification: str + + llm = ChatVertexAI(model="gemini-pro", temperature=0) + structured_llm = llm.with_structured_output(AnswerWithJustification) + + structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers") + # -> AnswerWithJustification( + # answer='They weigh the same.', justification='A pound is a pound.' + # ) + + Example: Pydantic schema, include raw: + .. code-block:: python + + from langchain_core.pydantic_v1 import BaseModel + from langchain_google_vertexai import ChatVertexAI + + class AnswerWithJustification(BaseModel): + '''An answer to the user question along with justification for the answer.''' + answer: str + justification: str + + llm = ChatVertexAI(model="gemini-pro", temperature=0) + structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True) + + structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers") + # -> { + # 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}), + # 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'), + # 'parsing_error': None + # } + + Example: Dict schema, exclude raw: + .. code-block:: python + + from langchain_core.pydantic_v1 import BaseModel + from langchain_core.utils.function_calling import convert_to_openai_tool + from langchain_google_vertexai import ChatVertexAI + + class AnswerWithJustification(BaseModel): + '''An answer to the user question along with justification for the answer.''' + answer: str + justification: str + + dict_schema = convert_to_openai_tool(AnswerWithJustification) + llm = ChatVertexAI(model="gemini-pro", temperature=0) + structured_llm = llm.with_structured_output(dict_schema) + + structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers") + # -> { + # 'answer': 'They weigh the same', + # 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.' + # } + + """ # noqa: E501 + if kwargs: + raise ValueError(f"Received unsupported arguments {kwargs}") + if isinstance(schema, type) and issubclass(schema, BaseModel): + parser: OutputParserLike = PydanticOutputFunctionsParser( + pydantic_schema=schema + ) + else: + parser = JsonOutputFunctionsParser() + llm = self.bind(functions=[schema]) + if include_raw: + parser_with_fallback = RunnablePassthrough.assign( + parsed=itemgetter("raw") | parser, parsing_error=lambda _: None + ).with_fallbacks( + [RunnablePassthrough.assign(parsed=lambda _: None)], + exception_key="parsing_error", + ) + return {"raw": llm} | parser_with_fallback + else: + return llm | parser + def _start_chat( self, history: _ChatHistory, **kwargs: Any ) -> Union[ChatSession, CodeChatSession]: From 4bda0c0dca48d36467af12ca239441d72b6474df Mon Sep 17 00:00:00 2001 From: Bagatur Date: Mon, 26 Feb 2024 17:34:06 -0800 Subject: [PATCH 2/3] core dep --- libs/vertexai/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/vertexai/pyproject.toml b/libs/vertexai/pyproject.toml index ff0f6976..0db02c7c 100644 --- a/libs/vertexai/pyproject.toml +++ b/libs/vertexai/pyproject.toml @@ -12,7 +12,7 @@ license = "MIT" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -langchain-core = ">=0.1.23,<0.2" +langchain-core = ">=0.1.27,<0.2" google-cloud-aiplatform = "^1.42.1" google-cloud-storage = "^2.14.0" types-requests = "^2.31.0" From 56f28cd9a4e7b5e8e8847d2a94d3e11de860a367 Mon Sep 17 00:00:00 2001 From: Bagatur Date: Mon, 26 Feb 2024 17:35:53 -0800 Subject: [PATCH 3/3] poetry --- libs/vertexai/poetry.lock | 436 ++++++++++++++++++++++---------------- 1 file changed, 248 insertions(+), 188 deletions(-) diff --git a/libs/vertexai/poetry.lock b/libs/vertexai/poetry.lock index a82cf1ab..712d7ae2 100644 --- a/libs/vertexai/poetry.lock +++ b/libs/vertexai/poetry.lock @@ -178,13 +178,13 @@ tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "p [[package]] name = "cachetools" -version = "5.3.2" +version = "5.3.3" description = "Extensible memoizing collections and decorators" optional = false python-versions = ">=3.7" files = [ - {file = "cachetools-5.3.2-py3-none-any.whl", hash = "sha256:861f35a13a451f94e301ce2bec7cac63e881232ccce7ed67fab9b5df4d3beaa1"}, - {file = "cachetools-5.3.2.tar.gz", hash = "sha256:086ee420196f7b2ab9ca2db2520aca326318b68fe5ba8bc4d49cca91add450f2"}, + {file = "cachetools-5.3.3-py3-none-any.whl", hash = "sha256:0abad1021d3f8325b2fc1d2e9c8b9c9d57b04c3932657a72465447332c24d945"}, + {file = "cachetools-5.3.3.tar.gz", hash = "sha256:ba29e2dfa0b8b556606f097407ed1aa62080ee108ab0dc5ec9d6a723a007d105"}, ] [[package]] @@ -486,13 +486,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"] [[package]] name = "google-api-python-client" -version = "2.118.0" +version = "2.119.0" description = "Google API Client Library for Python" optional = false 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