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chore: avoid implicit optional in type annotations of method (#8727)

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This commit is contained in:
Bowen Liang 2024-10-09 14:36:43 +08:00 • committed by GitHub
commit 240b66d737
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37 changed files with 91 additions and 71 deletions
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2
api/core/agent/cot_agent_runner.py
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@ -369,7 +369,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
return message
def _organize_historic_prompt_messages(
self, current_session_messages: list[PromptMessage] = None
self, current_session_messages: Optional[list[PromptMessage]] = None
) -> list[PromptMessage]:
"""
organize historic prompt messages

2
api/core/agent/cot_chat_agent_runner.py
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@ -27,7 +27,7 @@ class CotChatAgentRunner(CotAgentRunner):
return SystemPromptMessage(content=system_prompt)
def _organize_user_query(self, query, prompt_messages: list[PromptMessage] = None) -> list[PromptMessage]:
def _organize_user_query(self, query, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
"""
Organize user query
"""

3
api/core/agent/cot_completion_agent_runner.py
View file

@ -1,4 +1,5 @@
import json
from typing import Optional
from core.agent.cot_agent_runner import CotAgentRunner
from core.model_runtime.entities.message_entities import AssistantPromptMessage, PromptMessage, UserPromptMessage
@ -21,7 +22,7 @@ class CotCompletionAgentRunner(CotAgentRunner):
return system_prompt
def _organize_historic_prompt(self, current_session_messages: list[PromptMessage] = None) -> str:
def _organize_historic_prompt(self, current_session_messages: Optional[list[PromptMessage]] = None) -> str:
"""
Organize historic prompt
"""

6
api/core/agent/fc_agent_runner.py
View file

@ -2,7 +2,7 @@ import json
import logging
from collections.abc import Generator
from copy import deepcopy
from typing import Any, Union
from typing import Any, Optional, Union
from core.agent.base_agent_runner import BaseAgentRunner
from core.app.apps.base_app_queue_manager import PublishFrom
@ -370,7 +370,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
return tool_calls
def _init_system_message(
self, prompt_template: str, prompt_messages: list[PromptMessage] = None
self, prompt_template: str, prompt_messages: Optional[list[PromptMessage]] = None
) -> list[PromptMessage]:
"""
Initialize system message
@ -385,7 +385,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
return prompt_messages
def _organize_user_query(self, query, prompt_messages: list[PromptMessage] = None) -> list[PromptMessage]:
def _organize_user_query(self, query, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
"""
Organize user query
"""

4
api/core/indexing_runner.py
View file

@ -211,9 +211,9 @@ class IndexingRunner:
tenant_id: str,
extract_settings: list[ExtractSetting],
tmp_processing_rule: dict,
doc_form: str = None,
doc_form: Optional[str] = None,
doc_language: str = "English",
dataset_id: str = None,
dataset_id: Optional[str] = None,
indexing_technique: str = "economy",
) -> dict:
"""

2
api/core/model_runtime/model_providers/anthropic/llm/llm.py
View file

@ -169,7 +169,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
stop: Optional[list[str]] = None,
stream: bool = True,
user: Optional[str] = None,
callbacks: list[Callback] = None,
callbacks: Optional[list[Callback]] = None,
) -> Union[LLMResult, Generator]:
"""
Code block mode wrapper for invoking large language model

2
api/core/model_runtime/model_providers/bedrock/llm/llm.py
View file

@ -92,7 +92,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
stop: Optional[list[str]] = None,
stream: bool = True,
user: Optional[str] = None,
callbacks: list[Callback] = None,
callbacks: Optional[list[Callback]] = None,
) -> Union[LLMResult, Generator]:
"""
Code block mode wrapper for invoking large language model

2
api/core/model_runtime/model_providers/fireworks/llm/llm.py
View file

@ -511,7 +511,7 @@ class FireworksLargeLanguageModel(_CommonFireworks, LargeLanguageModel):
model: str,
messages: list[PromptMessage],
tools: Optional[list[PromptMessageTool]] = None,
credentials: dict = None,
credentials: Optional[dict] = None,
) -> int:
"""
Approximate num tokens with GPT2 tokenizer.

2
api/core/model_runtime/model_providers/openai/llm/llm.py
View file

@ -111,7 +111,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
stop: Optional[list[str]] = None,
stream: bool = True,
user: Optional[str] = None,
callbacks: list[Callback] = None,
callbacks: Optional[list[Callback]] = None,
) -> Union[LLMResult, Generator]:
"""
Code block mode wrapper for invoking large language model

2
api/core/model_runtime/model_providers/openai_api_compatible/llm/llm.py
View file

@ -688,7 +688,7 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
model: str,
messages: list[PromptMessage],
tools: Optional[list[PromptMessageTool]] = None,
credentials: dict = None,
credentials: Optional[dict] = None,
) -> int:
"""
Approximate num tokens with GPT2 tokenizer.

2
api/core/model_runtime/model_providers/sagemaker/tts/tts.py
View file

@ -77,7 +77,7 @@ class SageMakerText2SpeechModel(TTSModel):
"""
pass
def _detect_lang_code(self, content: str, map_dict: dict = None):
def _detect_lang_code(self, content: str, map_dict: Optional[dict] = None):
map_dict = {"zh": "<|zh|>", "en": "<|en|>", "ja": "<|jp|>", "zh-TW": "<|yue|>", "ko": "<|ko|>"}
response = self.comprehend_client.detect_dominant_language(Text=content)

2
api/core/model_runtime/model_providers/wenxin/llm/llm.py
View file

@ -64,7 +64,7 @@ class ErnieBotLargeLanguageModel(LargeLanguageModel):
stop: Optional[list[str]] = None,
stream: bool = True,
user: Optional[str] = None,
callbacks: list[Callback] = None,
callbacks: Optional[list[Callback]] = None,
) -> Union[LLMResult, Generator]:
"""
Code block mode wrapper for invoking large language model

14
api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/assistant/assistant.py
View file

@ -41,8 +41,8 @@ class Assistant(BaseAPI):
conversation_id: Optional[str] = None,
attachments: Optional[list[assistant_create_params.AssistantAttachments]] = None,
metadata: dict | None = None,
request_id: str = None,
user_id: str = None,
request_id: Optional[str] = None,
user_id: Optional[str] = None,
extra_headers: Headers | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
@ -72,9 +72,9 @@ class Assistant(BaseAPI):
def query_support(
self,
*,
assistant_id_list: list[str] = None,
request_id: str = None,
user_id: str = None,
assistant_id_list: Optional[list[str]] = None,
request_id: Optional[str] = None,
user_id: Optional[str] = None,
extra_headers: Headers | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
@ -99,8 +99,8 @@ class Assistant(BaseAPI):
page: int = 1,
page_size: int = 10,
*,
request_id: str = None,
user_id: str = None,
request_id: Optional[str] = None,
user_id: Optional[str] = None,
extra_headers: Headers | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,

10
api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/files.py
View file

@ -1,7 +1,7 @@
from __future__ import annotations
from collections.abc import Mapping
from typing import TYPE_CHECKING, Literal, cast
from typing import TYPE_CHECKING, Literal, Optional, cast
import httpx
@ -34,11 +34,11 @@ class Files(BaseAPI):
def create(
self,
*,
file: FileTypes = None,
upload_detail: list[UploadDetail] = None,
file: Optional[FileTypes] = None,
upload_detail: Optional[list[UploadDetail]] = None,
purpose: Literal["fine-tune", "retrieval", "batch"],
knowledge_id: str = None,
sentence_size: int = None,
knowledge_id: Optional[str] = None,
sentence_size: Optional[int] = None,
extra_headers: Headers | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,

8
api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/knowledge/document/document.py
View file

@ -34,12 +34,12 @@ class Document(BaseAPI):
def create(
self,
*,
file: FileTypes = None,
file: Optional[FileTypes] = None,
custom_separator: Optional[list[str]] = None,
upload_detail: list[UploadDetail] = None,
upload_detail: Optional[list[UploadDetail]] = None,
purpose: Literal["retrieval"],
knowledge_id: str = None,
sentence_size: int = None,
knowledge_id: Optional[str] = None,
sentence_size: Optional[int] = None,
extra_headers: Headers | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,

8
api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/videos/videos.py
View file

@ -31,11 +31,11 @@ class Videos(BaseAPI):
self,
model: str,
*,
prompt: str = None,
image_url: str = None,
prompt: Optional[str] = None,
image_url: Optional[str] = None,
sensitive_word_check: Optional[SensitiveWordCheckRequest] | NotGiven = NOT_GIVEN,
request_id: str = None,
user_id: str = None,
request_id: Optional[str] = None,
user_id: Optional[str] = None,
extra_headers: Headers | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,

2
api/core/rag/datasource/vdb/relyt/relyt_vector.py
View file

@ -162,7 +162,7 @@ class RelytVector(BaseVector):
else:
return None
def delete_by_uuids(self, ids: list[str] = None):
def delete_by_uuids(self, ids: Optional[list[str]] = None):
"""Delete by vector IDs.
Args:

6
api/core/rag/datasource/vdb/vector_factory.py
View file

@ -1,5 +1,5 @@
from abc import ABC, abstractmethod
from typing import Any
from typing import Any, Optional
from configs import dify_config
from core.embedding.cached_embedding import CacheEmbedding
@ -25,7 +25,7 @@ class AbstractVectorFactory(ABC):
class Vector:
def __init__(self, dataset: Dataset, attributes: list = None):
def __init__(self, dataset: Dataset, attributes: Optional[list] = None):
if attributes is None:
attributes = ["doc_id", "dataset_id", "document_id", "doc_hash"]
self._dataset = dataset
@ -106,7 +106,7 @@ class Vector:
case _:
raise ValueError(f"Vector store {vector_type} is not supported.")
def create(self, texts: list = None, **kwargs):
def create(self, texts: Optional[list] = None, **kwargs):
if texts:
embeddings = self._embeddings.embed_documents([document.page_content for document in texts])
self._vector_processor.create(texts=texts, embeddings=embeddings, **kwargs)

4
api/core/rag/extractor/extract_processor.py
View file

@ -1,7 +1,7 @@
import re
import tempfile
from pathlib import Path
from typing import Union
from typing import Optional, Union
from urllib.parse import unquote
from configs import dify_config
@ -84,7 +84,7 @@ class ExtractProcessor:
@classmethod
def extract(
cls, extract_setting: ExtractSetting, is_automatic: bool = False, file_path: str = None
cls, extract_setting: ExtractSetting, is_automatic: bool = False, file_path: Optional[str] = None
) -> list[Document]:
if extract_setting.datasource_type == DatasourceType.FILE.value:
with tempfile.TemporaryDirectory() as temp_dir:

3
api/core/rag/extractor/unstructured/unstructured_epub_extractor.py
View file

@ -1,4 +1,5 @@
import logging
from typing import Optional
from core.rag.extractor.extractor_base import BaseExtractor
from core.rag.models.document import Document
@ -17,7 +18,7 @@ class UnstructuredEpubExtractor(BaseExtractor):
def __init__(
self,
file_path: str,
api_url: str = None,
api_url: Optional[str] = None,
):
"""Initialize with file path."""
self._file_path = file_path

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