improve: introduce isort for linting Python imports (#1983)

api/core/index/base.py

@@ -1,9 +1,9 @@
 from __future__ import annotations
-from abc import abstractmethod, ABC
-from typing import List, Any
 
-from langchain.schema import Document, BaseRetriever
+from abc import ABC, abstractmethod
+from typing import Any, List
 
+from langchain.schema import BaseRetriever, Document
 from models.dataset import Dataset
 
 

api/core/index/index.py

@@ -1,11 +1,10 @@
-from flask import current_app
-from langchain.embeddings import OpenAIEmbeddings
-
 from core.embedding.cached_embedding import CacheEmbedding
-from core.index.keyword_table_index.keyword_table_index import KeywordTableIndex, KeywordTableConfig
+from core.index.keyword_table_index.keyword_table_index import KeywordTableConfig, KeywordTableIndex
 from core.index.vector_index.vector_index import VectorIndex
 from core.model_manager import ModelManager
 from core.model_runtime.entities.model_entities import ModelType
+from flask import current_app
+from langchain.embeddings import OpenAIEmbeddings
 from models.dataset import Dataset
 
 

api/core/index/keyword_table_index/jieba_keyword_table_handler.py

@@ -2,9 +2,8 @@ import re
 from typing import Set
 
 import jieba
-from jieba.analyse import default_tfidf
-
 from core.index.keyword_table_index.stopwords import STOPWORDS
+from jieba.analyse import default_tfidf
 
 
 class JiebaKeywordTableHandler:

api/core/index/keyword_table_index/keyword_table_index.py

@@ -1,14 +1,13 @@
 import json
 from collections import defaultdict
-from typing import Any, List, Optional, Dict
-
-from langchain.schema import Document, BaseRetriever
-from pydantic import BaseModel, Field, Extra
+from typing import Any, Dict, List, Optional
 
 from core.index.base import BaseIndex
 from core.index.keyword_table_index.jieba_keyword_table_handler import JiebaKeywordTableHandler
 from extensions.ext_database import db
-from models.dataset import Dataset, DocumentSegment, DatasetKeywordTable
+from langchain.schema import BaseRetriever, Document
+from models.dataset import Dataset, DatasetKeywordTable, DocumentSegment
+from pydantic import BaseModel, Extra, Field
 
 
 class KeywordTableConfig(BaseModel):

api/core/index/vector_index/base.py

@@ -1,16 +1,16 @@
 import json
 import logging
 from abc import abstractmethod
-from typing import List, Any, cast
-
-from langchain.embeddings.base import Embeddings
-from langchain.schema import Document, BaseRetriever
-from langchain.vectorstores import VectorStore
+from typing import Any, List, cast
 
 from core.index.base import BaseIndex
 from extensions.ext_database import db
-from models.dataset import Dataset, DocumentSegment, DatasetCollectionBinding
+from langchain.embeddings.base import Embeddings
+from langchain.schema import BaseRetriever, Document
+from langchain.vectorstores import VectorStore
+from models.dataset import Dataset, DatasetCollectionBinding
 from models.dataset import Document as DatasetDocument
+from models.dataset import DocumentSegment
 
 
 class BaseVectorIndex(BaseIndex):

api/core/index/vector_index/milvus_vector_index.py

@@ -1,14 +1,13 @@
-from typing import cast, Any, List
-
-from langchain.embeddings.base import Embeddings
-from langchain.schema import Document
-from langchain.vectorstores import VectorStore
-from pydantic import BaseModel, root_validator
+from typing import Any, List, cast
 
 from core.index.base import BaseIndex
 from core.index.vector_index.base import BaseVectorIndex
 from core.vector_store.milvus_vector_store import MilvusVectorStore
+from langchain.embeddings.base import Embeddings
+from langchain.schema import Document
+from langchain.vectorstores import VectorStore
 from models.dataset import Dataset
+from pydantic import BaseModel, root_validator
 
 
 class MilvusConfig(BaseModel):

api/core/index/vector_index/qdrant_vector_index.py

@@ -1,18 +1,17 @@
 import os
-from typing import Optional, Any, List, cast
+from typing import Any, List, Optional, cast
 
 import qdrant_client
-from langchain.embeddings.base import Embeddings
-from langchain.schema import Document, BaseRetriever
-from langchain.vectorstores import VectorStore
-from pydantic import BaseModel
-from qdrant_client.http.models import HnswConfigDiff
-
 from core.index.base import BaseIndex
 from core.index.vector_index.base import BaseVectorIndex
 from core.vector_store.qdrant_vector_store import QdrantVectorStore
 from extensions.ext_database import db
+from langchain.embeddings.base import Embeddings
+from langchain.schema import BaseRetriever, Document
+from langchain.vectorstores import VectorStore
 from models.dataset import Dataset, DatasetCollectionBinding
+from pydantic import BaseModel
+from qdrant_client.http.models import HnswConfigDiff
 
 
 class QdrantConfig(BaseModel):

api/core/index/vector_index/vector_index.py

@@ -1,10 +1,9 @@
 import json
 
-from flask import current_app
-from langchain.embeddings.base import Embeddings
-
 from core.index.vector_index.base import BaseVectorIndex
 from extensions.ext_database import db
+from flask import current_app
+from langchain.embeddings.base import Embeddings
 from models.dataset import Dataset, Document
 
 
@@ -29,7 +28,7 @@ class VectorIndex:
             raise ValueError(f"Vector store must be specified.")
 
         if vector_type == "weaviate":
-            from core.index.vector_index.weaviate_vector_index import WeaviateVectorIndex, WeaviateConfig
+            from core.index.vector_index.weaviate_vector_index import WeaviateConfig, WeaviateVectorIndex
 
             return WeaviateVectorIndex(
                 dataset=dataset,
@@ -42,7 +41,7 @@ class VectorIndex:
                 attributes=attributes
             )
         elif vector_type == "qdrant":
-            from core.index.vector_index.qdrant_vector_index import QdrantVectorIndex, QdrantConfig
+            from core.index.vector_index.qdrant_vector_index import QdrantConfig, QdrantVectorIndex
 
             return QdrantVectorIndex(
                 dataset=dataset,
@@ -55,7 +54,7 @@ class VectorIndex:
                 embeddings=embeddings
             )
         elif vector_type == "milvus":
-            from core.index.vector_index.milvus_vector_index import MilvusVectorIndex, MilvusConfig
+            from core.index.vector_index.milvus_vector_index import MilvusConfig, MilvusVectorIndex
 
             return MilvusVectorIndex(
                 dataset=dataset,

api/core/index/vector_index/weaviate_vector_index.py

@@ -1,16 +1,15 @@
-from typing import Optional, cast, Any, List
+from typing import Any, List, Optional, cast
 
 import requests
 import weaviate
-from langchain.embeddings.base import Embeddings
-from langchain.schema import Document, BaseRetriever
-from langchain.vectorstores import VectorStore
-from pydantic import BaseModel, root_validator
-
 from core.index.base import BaseIndex
 from core.index.vector_index.base import BaseVectorIndex
 from core.vector_store.weaviate_vector_store import WeaviateVectorStore
+from langchain.embeddings.base import Embeddings
+from langchain.schema import BaseRetriever, Document
+from langchain.vectorstores import VectorStore
 from models.dataset import Dataset
+from pydantic import BaseModel, root_validator
 
 
 class WeaviateConfig(BaseModel):