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

This commit is contained in:
Bowen Liang
2024-01-12 12:34:01 +08:00
committed by GitHub
parent cca9edc97a
commit cc9e74123c
413 changed files with 1635 additions and 1906 deletions

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@@ -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):

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@@ -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):

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@@ -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):

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@@ -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,

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@@ -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):