diff --git a/api/core/indexing_runner.py b/api/core/indexing_runner.py index 9aaf85dc0f..8d0a8b99b4 100644 --- a/api/core/indexing_runner.py +++ b/api/core/indexing_runner.py @@ -737,7 +737,7 @@ class IndexingRunner: def _update_document_index_status( document_id: str, after_indexing_status: IndexingStatus, - extra_update_params: dict[Any, Any] | None = None, + extra_update_params: Mapping[Any, Any] | None = None, ): """ Update the document indexing status. @@ -764,7 +764,7 @@ class IndexingRunner: db.session.commit() @staticmethod - def _update_segments_by_document(dataset_document_id: str, update_params: dict[Any, Any]): + def _update_segments_by_document(dataset_document_id: str, update_params: Mapping[Any, Any]): """ Update the document segment by document id. """ diff --git a/api/core/rag/embedding/cached_embedding.py b/api/core/rag/embedding/cached_embedding.py index f5f5f541da..9f1c73ec88 100644 --- a/api/core/rag/embedding/cached_embedding.py +++ b/api/core/rag/embedding/cached_embedding.py @@ -106,7 +106,7 @@ class CacheEmbedding(Embeddings): return text_embeddings - def embed_multimodal_documents(self, multimodel_documents: list[dict]) -> list[list[float]]: + def embed_multimodal_documents(self, multimodel_documents: list[dict[str, Any]]) -> list[list[float]]: """Embed file documents.""" # use doc embedding cache or store if not exists multimodel_embeddings: list[Any] = [None for _ in range(len(multimodel_documents))] diff --git a/api/core/rag/embedding/embedding_base.py b/api/core/rag/embedding/embedding_base.py index ab190d2c42..7ae5c09ab7 100644 --- a/api/core/rag/embedding/embedding_base.py +++ b/api/core/rag/embedding/embedding_base.py @@ -11,7 +11,7 @@ class Embeddings(ABC): raise NotImplementedError @abstractmethod - def embed_multimodal_documents(self, multimodel_documents: list[dict]) -> list[list[float]]: + def embed_multimodal_documents(self, multimodel_documents: list[dict[str, Any]]) -> list[list[float]]: """Embed file documents.""" raise NotImplementedError diff --git a/api/core/rag/retrieval/output_parser/react_output.py b/api/core/rag/retrieval/output_parser/react_output.py index 9a14d41716..29abae4280 100644 --- a/api/core/rag/retrieval/output_parser/react_output.py +++ b/api/core/rag/retrieval/output_parser/react_output.py @@ -1,7 +1,7 @@ from __future__ import annotations from dataclasses import dataclass -from typing import NamedTuple, Union +from typing import Any, NamedTuple, Union @dataclass @@ -10,7 +10,7 @@ class ReactAction: tool: str """The name of the Tool to execute.""" - tool_input: Union[str, dict] + tool_input: Union[str, dict[str, Any]] """The input to pass in to the Tool.""" log: str """Additional information to log about the action.""" @@ -19,7 +19,7 @@ class ReactAction: class ReactFinish(NamedTuple): """The final return value of an ReactFinish.""" - return_values: dict + return_values: dict[str, Any] """Dictionary of return values.""" log: str """Additional information to log about the return value""" diff --git a/api/core/rag/retrieval/router/multi_dataset_react_route.py b/api/core/rag/retrieval/router/multi_dataset_react_route.py index dd280cdf6a..9b223075d8 100644 --- a/api/core/rag/retrieval/router/multi_dataset_react_route.py +++ b/api/core/rag/retrieval/router/multi_dataset_react_route.py @@ -1,5 +1,5 @@ from collections.abc import Generator, Sequence -from typing import Union +from typing import Any, Union from graphon.model_runtime.entities.llm_entities import LLMResult, LLMUsage from graphon.model_runtime.entities.message_entities import PromptMessage, PromptMessageRole, PromptMessageTool @@ -139,7 +139,7 @@ class ReactMultiDatasetRouter: def _invoke_llm( self, - completion_param: dict, + completion_param: dict[str, Any], model_instance: ModelInstance, prompt_messages: list[PromptMessage], stop: list[str], diff --git a/api/core/rag/splitter/text_splitter.py b/api/core/rag/splitter/text_splitter.py index 8977611f93..7f2117e2dd 100644 --- a/api/core/rag/splitter/text_splitter.py +++ b/api/core/rag/splitter/text_splitter.py @@ -63,7 +63,7 @@ class TextSplitter(BaseDocumentTransformer, ABC): def split_text(self, text: str) -> list[str]: """Split text into multiple components.""" - def create_documents(self, texts: list[str], metadatas: list[dict] | None = None) -> list[Document]: + def create_documents(self, texts: list[str], metadatas: list[dict[str, Any]] | None = None) -> list[Document]: """Create documents from a list of texts.""" _metadatas = metadatas or [{}] * len(texts) documents = []