diff --git a/libs/aws/Makefile b/libs/aws/Makefile index 98577244..d461eebd 100644 --- a/libs/aws/Makefile +++ b/libs/aws/Makefile @@ -10,6 +10,9 @@ integration_test integration_tests: TEST_FILE = tests/integration_tests/ test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + ###################### # LINTING AND FORMATTING ###################### diff --git a/libs/aws/langchain_aws/__init__.py b/libs/aws/langchain_aws/__init__.py index 81df8d7a..5e717c50 100644 --- a/libs/aws/langchain_aws/__init__.py +++ b/libs/aws/langchain_aws/__init__.py @@ -1,7 +1,7 @@ -from langchain_aws.chat_models import BedrockChat, ChatBedrock, ChatBedrockConverse +from langchain_aws.chat_models import ChatBedrock, ChatBedrockConverse from langchain_aws.embeddings import BedrockEmbeddings from langchain_aws.graphs import NeptuneAnalyticsGraph, NeptuneGraph -from langchain_aws.llms import Bedrock, BedrockLLM, SagemakerEndpoint +from langchain_aws.llms import BedrockLLM, SagemakerEndpoint from langchain_aws.retrievers import ( AmazonKendraRetriever, AmazonKnowledgeBasesRetriever, @@ -9,10 +9,8 @@ from langchain_aws.vectorstores.inmemorydb import InMemoryVectorStore __all__ = [ - "Bedrock", "BedrockEmbeddings", "BedrockLLM", - "BedrockChat", "ChatBedrock", "ChatBedrockConverse", "SagemakerEndpoint", diff --git a/libs/aws/langchain_aws/agents/base.py b/libs/aws/langchain_aws/agents/base.py index 826a98a4..f4942434 100644 --- a/libs/aws/langchain_aws/agents/base.py +++ b/libs/aws/langchain_aws/agents/base.py @@ -14,9 +14,9 @@ from langchain_core.callbacks import CallbackManager from langchain_core.load import dumpd from langchain_core.messages import AIMessage -from langchain_core.pydantic_v1 import root_validator from langchain_core.runnables import RunnableConfig, RunnableSerializable, ensure_config from langchain_core.tools import BaseTool +from pydantic import model_validator _DEFAULT_ACTION_GROUP_NAME = "DEFAULT_AG_" _TEST_AGENT_ALIAS_ID = "TSTALIASID" @@ -329,8 +329,9 @@ class BedrockAgentsRunnable(RunnableSerializable[Dict, OutputType]): endpoint_url: Optional[str] = None """Endpoint URL""" - @root_validator(skip_on_failure=True) - def validate_agent(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def validate_agent(cls, values: dict) -> Any: if values.get("client") is not None: return values diff --git a/libs/aws/langchain_aws/chat_models/__init__.py b/libs/aws/langchain_aws/chat_models/__init__.py index 5fcdd69f..12612c41 100644 --- a/libs/aws/langchain_aws/chat_models/__init__.py +++ b/libs/aws/langchain_aws/chat_models/__init__.py @@ -1,4 +1,4 @@ -from langchain_aws.chat_models.bedrock import BedrockChat, ChatBedrock +from langchain_aws.chat_models.bedrock import ChatBedrock from langchain_aws.chat_models.bedrock_converse import ChatBedrockConverse -__all__ = ["BedrockChat", "ChatBedrock", "ChatBedrockConverse"] +__all__ = ["ChatBedrock", "ChatBedrockConverse"] diff --git a/libs/aws/langchain_aws/chat_models/bedrock.py b/libs/aws/langchain_aws/chat_models/bedrock.py index 3a2e4760..58b2b033 100644 --- a/libs/aws/langchain_aws/chat_models/bedrock.py +++ b/libs/aws/langchain_aws/chat_models/bedrock.py @@ -15,7 +15,6 @@ cast, ) -from langchain_core._api.deprecation import deprecated from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models import ( BaseChatModel, @@ -34,10 +33,10 @@ from langchain_core.messages.ai import UsageMetadata from langchain_core.messages.tool import ToolCall, ToolMessage from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils.pydantic import TypeBaseModel, is_basemodel_subclass +from pydantic import BaseModel, ConfigDict from langchain_aws.chat_models.bedrock_converse import ChatBedrockConverse from langchain_aws.function_calling import ( @@ -214,7 +213,7 @@ def _merge_messages( """Merge runs of human/tool messages into single human messages with content blocks.""" # noqa: E501 merged: list = [] for curr in messages: - curr = curr.copy(deep=True) + curr = curr.model_copy(deep=True) if isinstance(curr, ToolMessage): if isinstance(curr.content, list) and all( isinstance(block, dict) and block.get("type") == "tool_result" @@ -417,10 +416,9 @@ def lc_attributes(self) -> Dict[str, Any]: return attributes - class Config: - """Configuration for this pydantic object.""" - - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def _get_ls_params( self, stop: Optional[List[str]] = None, **kwargs: Any @@ -708,7 +706,7 @@ def with_structured_output( .. code-block:: python from langchain_aws.chat_models.bedrock import ChatBedrock - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' @@ -732,7 +730,7 @@ class AnswerWithJustification(BaseModel): .. code-block:: python from langchain_aws.chat_models.bedrock import ChatBedrock - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' @@ -829,8 +827,3 @@ def _as_converse(self) -> ChatBedrockConverse: guardrail_config=(self.guardrails if self._guardrails_enabled else None), # type: ignore[call-arg] **kwargs, ) - - -@deprecated(since="0.1.0", removal="0.2.0", alternative="ChatBedrock") -class BedrockChat(ChatBedrock): - pass diff --git a/libs/aws/langchain_aws/chat_models/bedrock_converse.py b/libs/aws/langchain_aws/chat_models/bedrock_converse.py index ff8a3e26..c5e80bae 100644 --- a/libs/aws/langchain_aws/chat_models/bedrock_converse.py +++ b/libs/aws/langchain_aws/chat_models/bedrock_converse.py @@ -40,7 +40,6 @@ from langchain_core.output_parsers import JsonOutputKeyToolsParser, PydanticToolsParser from langchain_core.output_parsers.base import OutputParserLike from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils.function_calling import ( @@ -48,6 +47,8 @@ convert_to_openai_tool, ) from langchain_core.utils.pydantic import TypeBaseModel, is_basemodel_subclass +from pydantic import BaseModel, ConfigDict, Field, model_validator +from typing_extensions import Self from langchain_aws.function_calling import ToolsOutputParser @@ -152,7 +153,7 @@ class ChatBedrockConverse(BaseChatModel): Tool calling: .. code-block:: python - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' @@ -190,7 +191,7 @@ class GetPopulation(BaseModel): from typing import Optional - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class Joke(BaseModel): '''Joke to tell user.''' @@ -352,14 +353,14 @@ class Joke(BaseModel): model is used, ('auto', 'any') if a 'mistral-large' model is used, empty otherwise. """ - class Config: - """Configuration for this pydantic object.""" - - extra = "forbid" - allow_population_by_field_name = True + model_config = ConfigDict( + extra="forbid", + populate_by_name=True, + ) - @root_validator(pre=True) - def set_disable_streaming(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def set_disable_streaming(cls, values: Dict) -> Any: values["provider"] = ( values.get("provider") or (values.get("model_id", values["model"])).split(".")[0] @@ -372,15 +373,15 @@ def set_disable_streaming(cls, values: Dict) -> Dict: ) return values - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that AWS credentials to and python package exists in environment.""" - if values["client"] is not None: - return values + if self.client is not None: + return self try: - if values["credentials_profile_name"] is not None: - session = boto3.Session(profile_name=values["credentials_profile_name"]) + if self.credentials_profile_name is not None: + session = boto3.Session(profile_name=self.credentials_profile_name) else: session = boto3.Session() except ValueError as e: @@ -392,22 +393,20 @@ def validate_environment(cls, values: Dict) -> Dict: f"profile name are valid. Bedrock error: {e}" ) from e - values["region_name"] = ( - values.get("region_name") - or os.getenv("AWS_DEFAULT_REGION") - or session.region_name + self.region_name = ( + self.region_name or os.getenv("AWS_DEFAULT_REGION") or session.region_name ) client_params = {} - if values["region_name"]: - client_params["region_name"] = values["region_name"] - if values["endpoint_url"]: - client_params["endpoint_url"] = values["endpoint_url"] - if values["config"]: - client_params["config"] = values["config"] + if self.region_name: + client_params["region_name"] = self.region_name + if self.endpoint_url: + client_params["endpoint_url"] = self.endpoint_url + if self.config: + client_params["config"] = self.config try: - values["client"] = session.client("bedrock-runtime", **client_params) + self.client = session.client("bedrock-runtime", **client_params) except ValueError as e: raise ValueError(f"Error raised by bedrock service: {e}") except Exception as e: @@ -419,15 +418,15 @@ def validate_environment(cls, values: Dict) -> Dict: # As of 08/05/24 only claude-3 and mistral-large models support tool choice: # https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html - if values["supports_tool_choice_values"] is None: - if "claude-3" in values["model_id"]: - values["supports_tool_choice_values"] = ("auto", "any", "tool") - elif "mistral-large" in values["model_id"]: - values["supports_tool_choice_values"] = ("auto", "any") + if self.supports_tool_choice_values is None: + if "claude-3" in self.model_id: + self.supports_tool_choice_values = ("auto", "any", "tool") + elif "mistral-large" in self.model_id: + self.supports_tool_choice_values = ("auto", "any") else: - values["supports_tool_choice_values"] = () + self.supports_tool_choice_values = () - return values + return self def _generate( self, diff --git a/libs/aws/langchain_aws/embeddings/bedrock.py b/libs/aws/langchain_aws/embeddings/bedrock.py index fe5c9675..fe6fced1 100644 --- a/libs/aws/langchain_aws/embeddings/bedrock.py +++ b/libs/aws/langchain_aws/embeddings/bedrock.py @@ -5,8 +5,9 @@ import numpy as np from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.runnables.config import run_in_executor +from pydantic import BaseModel, ConfigDict, Field, model_validator +from typing_extensions import Self class BedrockEmbeddings(BaseModel, Embeddings): @@ -40,7 +41,7 @@ class BedrockEmbeddings(BaseModel, Embeddings): ) """ - client: Any #: :meta private: + client: Any = Field(default=None, exclude=True) #: :meta private: """Bedrock client.""" region_name: Optional[str] = None """The aws region e.g., `us-west-2`. Fallsback to AWS_DEFAULT_REGION env variable @@ -71,38 +72,38 @@ class BedrockEmbeddings(BaseModel, Embeddings): config: Any = None """An optional botocore.config.Config instance to pass to the client.""" - class Config: - """Configuration for this pydantic object.""" + model_config = ConfigDict( + extra="forbid", + protected_namespaces=(), + ) - extra = "forbid" - - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that AWS credentials to and python package exists in environment.""" - if values["client"] is not None: - return values + if self.client is not None: + return self try: import boto3 - if values["credentials_profile_name"] is not None: - session = boto3.Session(profile_name=values["credentials_profile_name"]) + if self.credentials_profile_name is not None: + session = boto3.Session(profile_name=self.credentials_profile_name) else: # use default credentials session = boto3.Session() client_params = {} - if values["region_name"]: - client_params["region_name"] = values["region_name"] + if self.region_name: + client_params["region_name"] = self.region_name - if values["endpoint_url"]: - client_params["endpoint_url"] = values["endpoint_url"] + if self.endpoint_url: + client_params["endpoint_url"] = self.endpoint_url - if values["config"]: - client_params["config"] = values["config"] + if self.config: + client_params["config"] = self.config - values["client"] = session.client("bedrock-runtime", **client_params) + self.client = session.client("bedrock-runtime", **client_params) except ImportError: raise ModuleNotFoundError( @@ -116,7 +117,7 @@ def validate_environment(cls, values: Dict) -> Dict: f"profile name are valid. Bedrock error: {e}" ) from e - return values + return self def _embedding_func(self, text: str) -> List[float]: """Call out to Bedrock embedding endpoint.""" diff --git a/libs/aws/langchain_aws/function_calling.py b/libs/aws/langchain_aws/function_calling.py index 338a3ce2..f6bde01b 100644 --- a/libs/aws/langchain_aws/function_calling.py +++ b/libs/aws/langchain_aws/function_calling.py @@ -3,6 +3,7 @@ import json from typing import ( + Annotated, Any, Callable, Dict, @@ -17,10 +18,10 @@ from langchain_core.output_parsers import BaseGenerationOutputParser from langchain_core.outputs import ChatGeneration, Generation from langchain_core.prompts.chat import AIMessage -from langchain_core.pydantic_v1 import BaseModel from langchain_core.tools import BaseTool from langchain_core.utils.function_calling import convert_to_openai_tool from langchain_core.utils.pydantic import TypeBaseModel +from pydantic import BaseModel, ConfigDict, SkipValidation from typing_extensions import TypedDict PYTHON_TO_JSON_TYPES = { @@ -160,10 +161,11 @@ class ToolDescription(TypedDict): class ToolsOutputParser(BaseGenerationOutputParser): first_tool_only: bool = False args_only: bool = False - pydantic_schemas: Optional[List[TypeBaseModel]] = None + pydantic_schemas: Optional[List[Annotated[TypeBaseModel, SkipValidation()]]] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any: """Parse a list of candidate model Generations into a specific format. diff --git a/libs/aws/langchain_aws/llms/__init__.py b/libs/aws/langchain_aws/llms/__init__.py index 5a4fa68b..83ccbf4f 100644 --- a/libs/aws/langchain_aws/llms/__init__.py +++ b/libs/aws/langchain_aws/llms/__init__.py @@ -1,6 +1,5 @@ from langchain_aws.llms.bedrock import ( ALTERNATION_ERROR, - Bedrock, BedrockBase, BedrockLLM, LLMInputOutputAdapter, @@ -9,7 +8,6 @@ __all__ = [ "ALTERNATION_ERROR", - "Bedrock", "BedrockBase", "BedrockLLM", "LLMInputOutputAdapter", diff --git a/libs/aws/langchain_aws/llms/bedrock.py b/libs/aws/langchain_aws/llms/bedrock.py index 172688ec..f8c1eccf 100644 --- a/libs/aws/langchain_aws/llms/bedrock.py +++ b/libs/aws/langchain_aws/llms/bedrock.py @@ -17,7 +17,6 @@ Union, ) -from langchain_core._api.deprecation import deprecated from langchain_core.callbacks import ( AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, @@ -26,7 +25,8 @@ from langchain_core.messages import AIMessageChunk, ToolCall from langchain_core.messages.tool import tool_call, tool_call_chunk from langchain_core.outputs import Generation, GenerationChunk, LLMResult -from langchain_core.pydantic_v1 import Field, root_validator +from pydantic import ConfigDict, Field, model_validator +from typing_extensions import Self from langchain_aws.function_calling import _tools_in_params from langchain_aws.utils import ( @@ -448,7 +448,7 @@ async def aprepare_output_stream( class BedrockBase(BaseLanguageModel, ABC): """Base class for Bedrock models.""" - client: Any = Field(exclude=True) #: :meta private: + client: Any = Field(default=None, exclude=True) #: :meta private: region_name: Optional[str] = None """The aws region e.g., `us-west-2`. Fallsback to AWS_DEFAULT_REGION env variable @@ -519,7 +519,7 @@ class BedrockBase(BaseLanguageModel, ABC): Optional[Mapping[str, str]]: A mapping with 'id' and 'version' keys. Example: - llm = Bedrock(model_id="", client=, + llm = BedrockLLM(model_id="", client=, model_kwargs={}, guardrails={ "id": "", @@ -529,7 +529,7 @@ class BedrockBase(BaseLanguageModel, ABC): 'run_manager' parameter of the 'generate', '_call' methods. Example: - llm = Bedrock(model_id="", client=, + llm = BedrockLLM(model_id="", client=, model_kwargs={}, guardrails={ "id": "", @@ -550,38 +550,38 @@ async def on_llm_error( ...Logic to handle guardrail intervention... """ # noqa: E501 - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that AWS credentials to and python package exists in environment.""" # Skip creating new client if passed in constructor - if values["client"] is not None: - return values + if self.client is not None: + return self try: import boto3 - if values["credentials_profile_name"] is not None: - session = boto3.Session(profile_name=values["credentials_profile_name"]) + if self.credentials_profile_name is not None: + session = boto3.Session(profile_name=self.credentials_profile_name) else: # use default credentials session = boto3.Session() - values["region_name"] = ( - values.get("region_name") + self.region_name = ( + self.region_name or os.getenv("AWS_DEFAULT_REGION") or session.region_name ) client_params = {} - if values["region_name"]: - client_params["region_name"] = values["region_name"] - if values["endpoint_url"]: - client_params["endpoint_url"] = values["endpoint_url"] - if values["config"]: - client_params["config"] = values["config"] + if self.region_name: + client_params["region_name"] = self.region_name + if self.endpoint_url: + client_params["endpoint_url"] = self.endpoint_url + if self.config: + client_params["config"] = self.config - values["client"] = session.client("bedrock-runtime", **client_params) + self.client = session.client("bedrock-runtime", **client_params) except ImportError: raise ModuleNotFoundError( @@ -597,7 +597,7 @@ def validate_environment(cls, values: Dict) -> Dict: f"profile name are valid. Bedrock error: {e}" ) from e - return values + return self @property def _identifying_params(self) -> Dict[str, Any]: @@ -958,16 +958,16 @@ class BedrockLLM(LLM, BedrockBase): """ - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: - model_id = values["model_id"] + @model_validator(mode="after") + def validate_environment(self) -> Self: + model_id = self.model_id if model_id.startswith("anthropic.claude-3"): raise ValueError( "Claude v3 models are not supported by this LLM." - "Please use `from langchain_community.chat_models import BedrockChat` " + "Please use `from langchain_aws import ChatBedrock` " "instead." ) - return super().validate_environment(values) + return self @property def _llm_type(self) -> str: @@ -1002,10 +1002,9 @@ def _get_ls_params( ls_params["ls_model_name"] = self.model_id return ls_params - class Config: - """Configuration for this pydantic object.""" - - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def _stream( self, @@ -1192,8 +1191,3 @@ def get_token_ids(self, text: str) -> List[int]: return get_token_ids_anthropic(text) else: return super().get_token_ids(text) - - -@deprecated(since="0.1.0", removal="0.2.0", alternative="BedrockLLM") -class Bedrock(BedrockLLM): - pass diff --git a/libs/aws/langchain_aws/llms/sagemaker_endpoint.py b/libs/aws/langchain_aws/llms/sagemaker_endpoint.py index c8706669..5f47d64e 100644 --- a/libs/aws/langchain_aws/llms/sagemaker_endpoint.py +++ b/libs/aws/langchain_aws/llms/sagemaker_endpoint.py @@ -8,7 +8,8 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk -from langchain_core.pydantic_v1 import root_validator +from pydantic import ConfigDict, model_validator +from typing_extensions import Self INPUT_TYPE = TypeVar("INPUT_TYPE", bound=Union[str, List[str]]) OUTPUT_TYPE = TypeVar("OUTPUT_TYPE", bound=Union[str, List[List[float]], Iterator]) @@ -249,32 +250,29 @@ def transform_output(self, output: bytes) -> str: .. _boto3: """ - class Config: - """Configuration for this pydantic object.""" + model_config = ConfigDict( + extra="forbid", + ) - extra = "forbid" - - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Dont do anything if client provided externally""" - if values.get("client") is not None: - return values + if self.client is not None: + return self """Validate that AWS credentials to and python package exists in environment.""" try: import boto3 try: - if values["credentials_profile_name"] is not None: - session = boto3.Session( - profile_name=values["credentials_profile_name"] - ) + if self.credentials_profile_name is not None: + session = boto3.Session(profile_name=self.credentials_profile_name) else: # use default credentials session = boto3.Session() - values["client"] = session.client( - "sagemaker-runtime", region_name=values["region_name"] + self.client = session.client( + "sagemaker-runtime", region_name=self.region_name ) except Exception as e: @@ -289,7 +287,7 @@ def validate_environment(cls, values: Dict) -> Dict: "Could not import boto3 python package. " "Please install it with `pip install boto3`." ) - return values + return self @property def _identifying_params(self) -> Mapping[str, Any]: diff --git a/libs/aws/langchain_aws/retrievers/bedrock.py b/libs/aws/langchain_aws/retrievers/bedrock.py index d899eadc..d0960002 100644 --- a/libs/aws/langchain_aws/retrievers/bedrock.py +++ b/libs/aws/langchain_aws/retrievers/bedrock.py @@ -5,8 +5,8 @@ from botocore.exceptions import UnknownServiceError from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.retrievers import BaseRetriever +from pydantic import BaseModel, ConfigDict, Field, model_validator from typing_extensions import Annotated FilterValue = Union[Dict[str, Any], List[Any], int, float, str, bool, None] @@ -30,8 +30,9 @@ class SearchFilter(BaseModel): startsWith: Optional[Filter] = None stringContains: Optional[Filter] = None - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) class VectorSearchConfig(BaseModel, extra="allow"): # type: ignore[call-arg] @@ -87,10 +88,13 @@ class AmazonKnowledgeBasesRetriever(BaseRetriever): endpoint_url: Optional[str] = None client: Any retrieval_config: RetrievalConfig - min_score_confidence: Annotated[Optional[float], Field(ge=0.0, le=1.0)] + min_score_confidence: Annotated[ + Optional[float], Field(ge=0.0, le=1.0, default=None) + ] - @root_validator(pre=True) - def create_client(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def create_client(cls, values: Dict[str, Any]) -> Any: if values.get("client") is not None: return values @@ -158,7 +162,7 @@ def _get_relevant_documents( response = self.client.retrieve( retrievalQuery={"text": query.strip()}, knowledgeBaseId=self.knowledge_base_id, - retrievalConfiguration=self.retrieval_config.dict(exclude_none=True), + retrievalConfiguration=self.retrieval_config.model_dump(exclude_none=True), ) results = response["retrievalResults"] documents = [] diff --git a/libs/aws/langchain_aws/retrievers/kendra.py b/libs/aws/langchain_aws/retrievers/kendra.py index 9a4e3af6..b0e5b44b 100644 --- a/libs/aws/langchain_aws/retrievers/kendra.py +++ b/libs/aws/langchain_aws/retrievers/kendra.py @@ -13,13 +13,13 @@ from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import ( +from langchain_core.retrievers import BaseRetriever +from pydantic import ( BaseModel, Field, - root_validator, - validator, + field_validator, + model_validator, ) -from langchain_core.retrievers import BaseRetriever from typing_extensions import Annotated @@ -371,14 +371,15 @@ class AmazonKendraRetriever(BaseRetriever): user_context: Optional[Dict] = None min_score_confidence: Annotated[Optional[float], Field(ge=0.0, le=1.0)] - @validator("top_k") + @field_validator("top_k") def validate_top_k(cls, value: int) -> int: if value < 0: raise ValueError(f"top_k ({value}) cannot be negative.") return value - @root_validator(pre=True) - def create_client(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def create_client(cls, values: Dict[str, Any]) -> Any: if values.get("client") is not None: return values @@ -424,13 +425,13 @@ def _kendra_query(self, query: str) -> Sequence[ResultItem]: kendra_kwargs["UserContext"] = self.user_context response = self.client.retrieve(**kendra_kwargs) - r_result = RetrieveResult.parse_obj(response) + r_result = RetrieveResult.model_validate(response) if r_result.ResultItems: return r_result.ResultItems # Retrieve API returned 0 results, fall back to Query API response = self.client.query(**kendra_kwargs) - q_result = QueryResult.parse_obj(response) + q_result = QueryResult.model_validate(response) return q_result.ResultItems def _get_top_k_docs(self, result_items: Sequence[ResultItem]) -> List[Document]: diff --git a/libs/aws/langchain_aws/vectorstores/inmemorydb/base.py b/libs/aws/langchain_aws/vectorstores/inmemorydb/base.py index d9a16270..63587f0c 100644 --- a/libs/aws/langchain_aws/vectorstores/inmemorydb/base.py +++ b/libs/aws/langchain_aws/vectorstores/inmemorydb/base.py @@ -30,6 +30,7 @@ from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.vectorstores import VectorStore, VectorStoreRetriever +from pydantic import ConfigDict from langchain_aws.utilities.redis import ( _array_to_buffer, @@ -1406,10 +1407,9 @@ class InMemoryVectorStoreRetriever(VectorStoreRetriever): ] """Allowed search types.""" - class Config: - """Configuration for this pydantic object.""" - - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def _get_relevant_documents( self, query: str, *, run_manager: CallbackManagerForRetrieverRun diff --git a/libs/aws/langchain_aws/vectorstores/inmemorydb/schema.py b/libs/aws/langchain_aws/vectorstores/inmemorydb/schema.py index ebd41633..d4a0f86f 100644 --- a/libs/aws/langchain_aws/vectorstores/inmemorydb/schema.py +++ b/libs/aws/langchain_aws/vectorstores/inmemorydb/schema.py @@ -7,7 +7,7 @@ import numpy as np import yaml # type: ignore[import-untyped] -from langchain_core.pydantic_v1 import BaseModel, Field, validator +from pydantic import BaseModel, Field, field_validator from typing_extensions import TYPE_CHECKING, Literal from langchain_aws.vectorstores.inmemorydb.constants import INMEMORYDB_VECTOR_DTYPE_MAP @@ -99,11 +99,13 @@ class InMemoryDBVectorField(InMemoryDBField): distance_metric: InMemoryDBDistanceMetric = Field(default="COSINE") initial_cap: Optional[int] = None - @validator("algorithm", "datatype", "distance_metric", pre=True, each_item=True) + @field_validator("algorithm", "datatype", "distance_metric", mode="before") + @classmethod def uppercase_strings(cls, v: str) -> 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"https://github.com/langchain-ai/langchain-aws/tree/main/libs/aws" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" -langchain-core = ">=0.2.33,<0.3" +python = ">=3.9,<4.0" +langchain-core = ">=0.3.0,<0.4" boto3 = ">=1.34.131,<1.35.0" numpy = [ {version = "^1", python = "<3.12"}, {version = "^1.26.0", python = ">=3.12"} ] + pydantic = ">=2,<3" [tool.poetry.group.test] optional = true @@ -27,9 +28,10 @@ pytest = "^7.4.3" pytest-cov = "^4.1.0" syrupy = "^4.0.2" pytest-asyncio = "^0.23.2" -langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core" } +pytest-watcher = "^0.3.4" +langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core"} langchain-standard-tests = {git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/standard-tests"} -langchain = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/langchain" } +langchain = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/langchain"} [tool.poetry.group.codespell] @@ -52,13 +54,13 @@ ruff = "^0.1.8" [tool.poetry.group.typing.dependencies] mypy = "^1.7" types-requests = "^2.28.11.5" -langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core" } +langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core"} [tool.poetry.group.dev] optional = true [tool.poetry.group.dev.dependencies] -langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core" } +langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core"} [tool.ruff.lint] select = [ diff --git a/libs/aws/scripts/check_pydantic.sh b/libs/aws/scripts/check_pydantic.sh deleted file mode 100755 index 06b5bb81..00000000 --- a/libs/aws/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/aws/tests/callbacks.py b/libs/aws/tests/callbacks.py index 542c6ae6..821d066a 100644 --- a/libs/aws/tests/callbacks.py +++ b/libs/aws/tests/callbacks.py @@ -7,7 +7,7 @@ from langchain_core.callbacks import AsyncCallbackHandler, BaseCallbackHandler from langchain_core.messages import BaseMessage from langchain_core.outputs import LLMResult -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel class BaseFakeCallbackHandler(BaseModel): @@ -255,7 +255,8 @@ def on_retriever_error( ) -> Any: self.on_retriever_error_common() - def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": + # Overriding since BaseModel has __deepcopy__ method as well + def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": # type: ignore return self @@ -412,5 +413,6 @@ async def on_text( ) -> None: self.on_text_common() - def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler": + # Overriding since BaseModel has __deepcopy__ method as well + def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler": # type: ignore return self diff --git a/libs/aws/tests/integration_tests/chat_models/test_bedrock.py b/libs/aws/tests/integration_tests/chat_models/test_bedrock.py index bac193f9..c8d92b3c 100644 --- a/libs/aws/tests/integration_tests/chat_models/test_bedrock.py +++ b/libs/aws/tests/integration_tests/chat_models/test_bedrock.py @@ -12,8 +12,8 @@ SystemMessage, ) from langchain_core.outputs import ChatGeneration, LLMResult -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.runnables import RunnableConfig +from pydantic import BaseModel, Field from langchain_aws.chat_models.bedrock import ChatBedrock from tests.callbacks import FakeCallbackHandler, FakeCallbackHandlerWithTokenCounts @@ -29,7 +29,7 @@ def chat() -> ChatBedrock: @pytest.mark.scheduled def test_chat_bedrock(chat: ChatBedrock) -> None: - """Test BedrockChat wrapper.""" + """Test ChatBedrock wrapper.""" system = SystemMessage(content="You are a helpful assistant.") human = HumanMessage(content="Hello") response = chat.invoke([system, human]) @@ -39,7 +39,7 @@ def test_chat_bedrock(chat: ChatBedrock) -> None: @pytest.mark.scheduled def test_chat_bedrock_generate(chat: ChatBedrock) -> None: - """Test BedrockChat wrapper with generate.""" + """Test ChatBedrock wrapper with generate.""" message = HumanMessage(content="Hello") response = chat.generate([[message], [message]]) assert isinstance(response, LLMResult) @@ -53,7 +53,7 @@ def test_chat_bedrock_generate(chat: ChatBedrock) -> None: @pytest.mark.scheduled def test_chat_bedrock_generate_with_token_usage(chat: ChatBedrock) -> None: - """Test BedrockChat wrapper with generate.""" + """Test ChatBedrock wrapper with generate.""" message = HumanMessage(content="Hello") response = chat.generate([[message], [message]]) assert isinstance(response, LLMResult) @@ -200,7 +200,7 @@ async def test_bedrock_astream(model_id: str) -> None: @pytest.mark.scheduled async def test_bedrock_abatch(chat: ChatBedrock) -> None: - """Test streaming tokens from BedrockChat.""" + """Test streaming tokens from ChatBedrock.""" result = await chat.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"]) for token in result: assert isinstance(token.content, str) @@ -208,7 +208,7 @@ async def test_bedrock_abatch(chat: ChatBedrock) -> None: @pytest.mark.scheduled async def test_bedrock_abatch_tags(chat: ChatBedrock) -> None: - """Test batch tokens from BedrockChat.""" + """Test batch tokens from ChatBedrock.""" result = await chat.abatch( ["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]} ) @@ -218,7 +218,7 @@ async def test_bedrock_abatch_tags(chat: ChatBedrock) -> None: @pytest.mark.scheduled def test_bedrock_batch(chat: ChatBedrock) -> None: - """Test batch tokens from BedrockChat.""" + """Test batch tokens from ChatBedrock.""" result = chat.batch(["I'm Pickle Rick", "I'm not Pickle Rick"]) for token in result: assert isinstance(token.content, str) @@ -226,14 +226,14 @@ def test_bedrock_batch(chat: ChatBedrock) -> None: @pytest.mark.scheduled async def test_bedrock_ainvoke(chat: ChatBedrock) -> None: - """Test invoke tokens from BedrockChat.""" + """Test invoke tokens from ChatBedrock.""" result = await chat.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]}) assert isinstance(result.content, str) @pytest.mark.scheduled def test_bedrock_invoke(chat: ChatBedrock) -> None: - """Test invoke tokens from BedrockChat.""" + """Test invoke tokens from ChatBedrock.""" result = chat.invoke("I'm Pickle Rick", config=dict(tags=["foo"])) assert isinstance(result.content, str) assert "usage" in result.additional_kwargs diff --git a/libs/aws/tests/integration_tests/chat_models/test_bedrock_converse.py b/libs/aws/tests/integration_tests/chat_models/test_bedrock_converse.py index cbcc478a..d50ec031 100644 --- a/libs/aws/tests/integration_tests/chat_models/test_bedrock_converse.py +++ b/libs/aws/tests/integration_tests/chat_models/test_bedrock_converse.py @@ -5,8 +5,8 @@ import pytest from langchain_core.language_models import BaseChatModel from langchain_core.messages import HumanMessage -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_standard_tests.integration_tests import ChatModelIntegrationTests +from pydantic import BaseModel, Field from typing_extensions import Annotated, TypedDict from langchain_aws import ChatBedrockConverse diff --git a/libs/aws/tests/integration_tests/retrievers/test_amazon_kendra_retriever.py b/libs/aws/tests/integration_tests/retrievers/test_amazon_kendra_retriever.py index 24ecb9dc..fad63c22 100644 --- a/libs/aws/tests/integration_tests/retrievers/test_amazon_kendra_retriever.py +++ b/libs/aws/tests/integration_tests/retrievers/test_amazon_kendra_retriever.py @@ -56,7 +56,7 @@ def test_get_relevant_documents(retriever, mock_client) -> None: # type: ignore query = "test query" - docs = retriever.get_relevant_documents(query) + docs = retriever.invoke(query) # Only documents with confidence score of HIGH are returned assert len(docs) == 2 diff --git a/libs/aws/tests/unit_tests/__snapshots__/test_standard.ambr b/libs/aws/tests/unit_tests/__snapshots__/test_standard.ambr new file mode 100644 index 00000000..6f98f04b --- /dev/null +++ b/libs/aws/tests/unit_tests/__snapshots__/test_standard.ambr @@ -0,0 +1,80 @@ +# serializer version: 1 +# name: TestBedrockAsConverseStandard.test_serdes[serialized] + dict({ + 'id': list([ + 'langchain', + 'chat_models', + 'bedrock', + 'ChatBedrock', + ]), + 'kwargs': dict({ + 'beta_use_converse_api': True, + 'guardrails': dict({ + 'guardrailIdentifier': None, + 'guardrailVersion': None, + 'trace': None, + }), + 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0', + 'model_kwargs': dict({ + 'max_tokens': 100, + 'stop': list([ + ]), + 'temperature': 0, + }), + 'provider_stop_reason_key_map': dict({ + 'ai21': 'finishReason', + 'amazon': 'completionReason', + 'anthropic': 'stop_reason', + 'cohere': 'finish_reason', + 'mistral': 'stop_reason', + }), + 'provider_stop_sequence_key_name_map': dict({ + 'ai21': 'stop_sequences', + 'amazon': 'stopSequences', + 'anthropic': 'stop_sequences', + 'cohere': 'stop_sequences', + 'mistral': 'stop_sequences', + }), + 'region_name': 'us-east-1', + }), + 'lc': 1, + 'name': 'ChatBedrock', + 'type': 'constructor', + }) +# --- +# name: TestBedrockStandard.test_serdes[serialized] + dict({ + 'id': list([ + 'langchain', + 'chat_models', + 'bedrock', + 'ChatBedrock', + ]), + 'kwargs': dict({ + 'guardrails': dict({ + 'guardrailIdentifier': None, + 'guardrailVersion': None, + 'trace': None, + }), + 'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0', + 'provider_stop_reason_key_map': dict({ + 'ai21': 'finishReason', + 'amazon': 'completionReason', + 'anthropic': 'stop_reason', + 'cohere': 'finish_reason', + 'mistral': 'stop_reason', + }), + 'provider_stop_sequence_key_name_map': dict({ + 'ai21': 'stop_sequences', + 'amazon': 'stopSequences', + 'anthropic': 'stop_sequences', + 'cohere': 'stop_sequences', + 'mistral': 'stop_sequences', + }), + 'region_name': 'us-east-1', + }), + 'lc': 1, + 'name': 'ChatBedrock', + 'type': 'constructor', + }) +# --- diff --git a/libs/aws/tests/unit_tests/chat_models/test_bedrock.py b/libs/aws/tests/unit_tests/chat_models/test_bedrock.py index 8e3f8082..d2fbaf4e 100644 --- a/libs/aws/tests/unit_tests/chat_models/test_bedrock.py +++ b/libs/aws/tests/unit_tests/chat_models/test_bedrock.py @@ -7,9 +7,9 @@ import pytest from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.runnables import RunnableBinding from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_aws import ChatBedrock from langchain_aws.chat_models.bedrock import ( diff --git a/libs/aws/tests/unit_tests/chat_models/test_bedrock_converse.py b/libs/aws/tests/unit_tests/chat_models/test_bedrock_converse.py index 4bc8f17b..0a257c09 100644 --- a/libs/aws/tests/unit_tests/chat_models/test_bedrock_converse.py +++ b/libs/aws/tests/unit_tests/chat_models/test_bedrock_converse.py @@ -12,9 +12,9 @@ ToolCall, ToolMessage, ) -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.runnables import RunnableBinding from langchain_standard_tests.unit_tests import ChatModelUnitTests +from pydantic import BaseModel, Field from langchain_aws import ChatBedrockConverse from langchain_aws.chat_models.bedrock_converse import (