move embeddings to schema (#10696)

pull/10666/head
Harrison Chase 1 year ago committed by GitHub
parent ce61840e3b
commit 12ff780089
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23

@ -6,9 +6,9 @@ from typing import Any, Dict, List, Optional, Union
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.llm import LLMChain
from langchain.chains.sql_database.prompt import PROMPT, SQL_PROMPTS
from langchain.embeddings.base import Embeddings
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import BaseOutputParser, BasePromptTemplate
from langchain.schema.base import Embeddings
from langchain.schema.language_model import BaseLanguageModel
from langchain.tools.sql_database.prompt import QUERY_CHECKER
from langchain.utilities.sql_database import SQLDatabase

@ -51,12 +51,12 @@ except ImportError:
from sqlalchemy.ext.declarative import declarative_base
from langchain.embeddings.base import Embeddings
from langchain.llms.base import LLM, get_prompts
from langchain.load.dump import dumps
from langchain.load.load import loads
from langchain.schema import ChatGeneration, Generation
from langchain.schema.cache import RETURN_VAL_TYPE, BaseCache
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_env
from langchain.vectorstores.redis import Redis as RedisVectorstore

@ -12,8 +12,8 @@ from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.hyde.prompts import PROMPT_MAP
from langchain.chains.llm import LLMChain
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import Extra
from langchain.schema.embeddings import Embeddings
from langchain.schema.language_model import BaseLanguageModel

@ -5,8 +5,8 @@ from typing import Any, Dict, List, Optional, Sequence, Tuple, Type
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.router.base import RouterChain
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import Extra
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore

@ -3,9 +3,9 @@ from typing import Any, Callable, List, Sequence
import numpy as np
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Field
from langchain.schema import BaseDocumentTransformer, Document
from langchain.schema.embeddings import Embeddings
from langchain.utils.math import cosine_similarity

@ -1,7 +1,7 @@
from typing import Any, Dict, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env

@ -1,7 +1,7 @@
from typing import Any, Dict, List
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.embeddings import Embeddings
class AwaEmbeddings(BaseModel, Embeddings):

@ -3,8 +3,8 @@ from __future__ import annotations
import logging
from typing import Any, Dict, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)

@ -4,8 +4,8 @@ import os
from functools import partial
from typing import Any, Dict, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
class BedrockEmbeddings(BaseModel, Embeddings):

@ -14,8 +14,8 @@ import uuid
from functools import partial
from typing import Callable, List, Sequence, Union, cast
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseStore
from langchain.schema.embeddings import Embeddings
from langchain.storage.encoder_backed import EncoderBackedStore
NAMESPACE_UUID = uuid.UUID(int=1985)

@ -1,8 +1,8 @@
import logging
from typing import Any, Dict, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)

@ -1,7 +1,7 @@
from typing import Any, Dict, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env

@ -18,8 +18,8 @@ from tenacity import (
wait_exponential,
)
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)

@ -2,8 +2,8 @@ from typing import Any, Dict, List, Mapping, Optional
import requests
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_MODEL_ID = "sentence-transformers/clip-ViT-B-32"

@ -1,8 +1,8 @@
from typing import Any, Dict, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
from langchain.requests import Requests
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env

@ -8,7 +8,7 @@ if TYPE_CHECKING:
from elasticsearch import Elasticsearch
from elasticsearch.client import MlClient
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
class ElasticsearchEmbeddings(Embeddings):

@ -3,8 +3,8 @@ from typing import Any, Dict, List, Mapping, Optional
import requests
from typing_extensions import NotRequired, TypedDict
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
# Currently supported maximum batch size for embedding requests

@ -6,8 +6,8 @@ from typing import Dict, List, Optional
import requests
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)

@ -3,8 +3,8 @@ from typing import List
import numpy as np
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel
from langchain.schema.embeddings import Embeddings
class FakeEmbeddings(Embeddings, BaseModel):

@ -11,8 +11,8 @@ from tenacity import (
wait_exponential,
)
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)

@ -1,7 +1,7 @@
from typing import Any, Dict, List
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.embeddings import Embeddings
class GPT4AllEmbeddings(BaseModel, Embeddings):

@ -2,8 +2,8 @@ from typing import Any, Dict, List, Optional
import requests
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra, Field
from langchain.schema.embeddings import Embeddings
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"

@ -1,7 +1,7 @@
from typing import Any, Dict, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_REPO_ID = "sentence-transformers/all-mpnet-base-v2"

@ -3,8 +3,8 @@ from typing import Any, Dict, List, Optional
import requests
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env

@ -1,7 +1,7 @@
from typing import Any, Dict, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
from langchain.schema.embeddings import Embeddings
class LlamaCppEmbeddings(BaseModel, Embeddings):

@ -24,8 +24,8 @@ from tenacity import (
wait_exponential,
)
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env, get_pydantic_field_names
logger = logging.getLogger(__name__)

@ -11,8 +11,8 @@ from tenacity import (
wait_exponential,
)
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)

@ -2,8 +2,8 @@ from __future__ import annotations
from typing import Any, Iterator, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel
from langchain.schema.embeddings import Embeddings
def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:

@ -1,7 +1,7 @@
from typing import Any, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra
from langchain.schema.embeddings import Embeddings
class ModelScopeEmbeddings(BaseModel, Embeddings):

@ -2,8 +2,8 @@ from typing import Any, Dict, List, Mapping, Optional, Tuple
import requests
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env

@ -1,7 +1,7 @@
from typing import Any, Dict, List
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env

@ -1,7 +1,7 @@
from typing import Any, Dict, List, Mapping, Optional
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_EMBED_INSTRUCTION = "Represent this input: "

@ -2,8 +2,8 @@ from typing import Any, Dict, List, Mapping, Optional
import requests
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra
from langchain.schema.embeddings import Embeddings
class OllamaEmbeddings(BaseModel, Embeddings):

@ -25,8 +25,8 @@ from tenacity import (
wait_exponential,
)
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env, get_pydantic_field_names
logger = logging.getLogger(__name__)

@ -1,8 +1,8 @@
from typing import Any, Dict, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.llms.sagemaker_endpoint import ContentHandlerBase
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
class EmbeddingsContentHandler(ContentHandlerBase[List[str], List[List[float]]]):

@ -1,8 +1,8 @@
from typing import Any, Callable, List
from langchain.embeddings.base import Embeddings
from langchain.llms import SelfHostedPipeline
from langchain.pydantic_v1 import Extra
from langchain.schema.embeddings import Embeddings
def _embed_documents(pipeline: Any, *args: Any, **kwargs: Any) -> List[List[float]]:

@ -1,8 +1,8 @@
import importlib.util
from typing import Any, Dict, List
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.schema.embeddings import Embeddings
class SpacyEmbeddings(BaseModel, Embeddings):

@ -1,7 +1,7 @@
from typing import Any, List
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseModel, Extra
from langchain.schema.embeddings import Embeddings
DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"

@ -1,8 +1,8 @@
from typing import Dict, List
from langchain.embeddings.base import Embeddings
from langchain.llms.vertexai import _VertexAICommon
from langchain.pydantic_v1 import root_validator
from langchain.schema.embeddings import Embeddings
from langchain.utilities.vertexai import raise_vertex_import_error

@ -1,7 +1,7 @@
"""Wrapper around Xinference embedding models."""
from typing import Any, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
class XinferenceEmbeddings(Embeddings):

@ -10,11 +10,11 @@ from langchain.callbacks.manager import (
Callbacks,
)
from langchain.chains.base import Chain
from langchain.embeddings.base import Embeddings
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.evaluation.schema import PairwiseStringEvaluator, StringEvaluator
from langchain.pydantic_v1 import Field, root_validator
from langchain.schema import RUN_KEY
from langchain.schema.embeddings import Embeddings
from langchain.utils.math import cosine_similarity

@ -3,11 +3,11 @@ from typing import Any, Dict, List, Optional, Type
from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain
from langchain.chains.retrieval_qa.base import RetrievalQA
from langchain.document_loaders.base import BaseLoader
from langchain.embeddings.base import Embeddings
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms.openai import OpenAI
from langchain.pydantic_v1 import BaseModel, Extra, Field
from langchain.schema import Document
from langchain.schema.embeddings import Embeddings
from langchain.schema.language_model import BaseLanguageModel
from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
from langchain.vectorstores.base import VectorStore

@ -3,9 +3,9 @@ from __future__ import annotations
from typing import Any, Dict, List, Optional, Type
from langchain.embeddings.base import Embeddings
from langchain.prompts.example_selector.base import BaseExampleSelector
from langchain.pydantic_v1 import BaseModel, Extra
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore

@ -4,8 +4,8 @@ from typing import Any, Dict, List, Optional, Union
import numpy as np
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRetriever, Document
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.utils import maximal_marginal_relevance

@ -7,12 +7,12 @@ from langchain.document_transformers.embeddings_redundant_filter import (
_get_embeddings_from_stateful_docs,
get_stateful_documents,
)
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import root_validator
from langchain.retrievers.document_compressors.base import (
BaseDocumentCompressor,
)
from langchain.schema import Document
from langchain.schema.embeddings import Embeddings
from langchain.utils.math import cosine_similarity

@ -10,8 +10,8 @@ from typing import Any, List, Optional
import numpy as np
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRetriever, Document
from langchain.schema.embeddings import Embeddings
def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:

@ -3,9 +3,9 @@ import warnings
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import root_validator
from langchain.schema import BaseRetriever, Document
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.milvus import Milvus
# TODO: Update to MilvusClient + Hybrid Search when available

@ -4,9 +4,9 @@ import hashlib
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import Extra, root_validator
from langchain.schema import BaseRetriever, Document
from langchain.schema.embeddings import Embeddings
def hash_text(text: str) -> str:

@ -6,8 +6,8 @@ from typing import Any, Iterable, List, Optional
import numpy as np
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRetriever, Document
from langchain.schema.embeddings import Embeddings
def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:

@ -2,9 +2,9 @@ import warnings
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import root_validator
from langchain.schema import BaseRetriever, Document
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.zilliz import Zilliz
# TODO: Update to ZillizClient + Hybrid Search when available

@ -4,7 +4,6 @@ from typing import Any, Dict, List, Optional, Union
from langsmith import RunEvaluator
from langchain.embeddings.base import Embeddings
from langchain.evaluation.criteria.eval_chain import CRITERIA_TYPE
from langchain.evaluation.embedding_distance.base import (
EmbeddingDistance as EmbeddingDistanceEnum,
@ -14,6 +13,7 @@ from langchain.evaluation.string_distance.base import (
StringDistance as StringDistanceEnum,
)
from langchain.pydantic_v1 import BaseModel, Field
from langchain.schema.embeddings import Embeddings
from langchain.schema.language_model import BaseLanguageModel
from langchain.schema.prompt_template import BasePromptTemplate

@ -4,8 +4,8 @@ import numbers
from hashlib import sha1
from typing import Any, Dict, Iterable, List, Optional, Tuple
from langchain.embeddings.base import Embeddings
from langchain.schema import Document
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
logger = logging.getLogger()

@ -13,7 +13,7 @@ except ImportError:
from sqlalchemy.ext.declarative import declarative_base
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
from langchain.vectorstores.base import VectorStore

@ -12,7 +12,7 @@ import numpy as np
from langchain.docstore.base import Docstore
from langchain.docstore.document import Document
from langchain.docstore.in_memory import InMemoryDocstore
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance

@ -7,7 +7,7 @@ from typing import Any, Iterable, List, Optional, Type
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
logger = logging.getLogger(__name__)

@ -7,7 +7,7 @@ from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Set, Tupl
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance

@ -23,9 +23,9 @@ from langchain.callbacks.manager import (
CallbackManagerForRetrieverRun,
)
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import root_validator
from langchain.schema import BaseRetriever
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_env
from langchain.vectorstores.base import VectorStore

@ -19,7 +19,7 @@ if TYPE_CHECKING:
from bagel.api.types import ID, OneOrMany, Where, WhereDocument
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.utils import xor_args
from langchain.vectorstores.base import VectorStore

@ -25,9 +25,9 @@ from langchain.callbacks.manager import (
CallbackManagerForRetrieverRun,
)
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import Field, root_validator
from langchain.schema import BaseRetriever
from langchain.schema.embeddings import Embeddings
logger = logging.getLogger(__name__)

@ -21,7 +21,7 @@ if typing.TYPE_CHECKING:
from cassandra.cluster import Session
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance

@ -17,7 +17,7 @@ from typing import (
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.utils import xor_args
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance

@ -9,7 +9,7 @@ from typing import Any, Iterable, List, Optional, Tuple
import requests
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
logger = logging.getLogger(__name__)

@ -7,8 +7,8 @@ from threading import Thread
from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseSettings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
logger = logging.getLogger()

@ -13,7 +13,7 @@ from typing import (
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_env
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance

@ -15,7 +15,7 @@ except ImportError:
_DEEPLAKE_INSTALLED = False
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance

@ -7,7 +7,7 @@ from typing import Any, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance

@ -3,9 +3,9 @@ from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Type
import numpy as np
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import Field
from langchain.schema import Document
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance

@ -2,7 +2,7 @@ from __future__ import annotations
from typing import Any, List, Literal, Optional
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.docarray.base import (
DocArrayIndex,
_check_docarray_import,

@ -3,7 +3,7 @@ from __future__ import annotations
from typing import Any, Dict, List, Literal, Optional
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.docarray.base import (
DocArrayIndex,
_check_docarray_import,

@ -17,7 +17,7 @@ from typing import (
from langchain._api import deprecated
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
from langchain.vectorstores.base import VectorStore

@ -15,7 +15,7 @@ from typing import (
)
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import DistanceStrategy

@ -6,7 +6,7 @@ import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
if TYPE_CHECKING:

@ -22,7 +22,7 @@ import numpy as np
from langchain.docstore.base import AddableMixin, Docstore
from langchain.docstore.document import Document
from langchain.docstore.in_memory import InMemoryDocstore
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import DistanceStrategy, maximal_marginal_relevance

@ -6,7 +6,7 @@ import uuid
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
from langchain.vectorstores.base import VectorStore

@ -4,7 +4,7 @@ import uuid
from typing import Any, Iterable, List, Optional
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore

@ -16,7 +16,7 @@ from typing import (
)
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
if TYPE_CHECKING:

@ -8,7 +8,7 @@ from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Type
from langchain.docstore.document import Document
from langchain.embeddings import TensorflowHubEmbeddings
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
if TYPE_CHECKING:

@ -4,7 +4,7 @@ import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_env
from langchain.vectorstores.base import VectorStore

@ -7,7 +7,7 @@ from uuid import uuid4
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance

@ -17,7 +17,7 @@ from typing import (
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance

@ -7,8 +7,8 @@ from threading import Thread
from typing import Any, Dict, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseSettings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
logger = logging.getLogger()

@ -15,7 +15,7 @@ from typing import (
)
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_env
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import DistanceStrategy

@ -1,8 +1,8 @@
import os
from typing import Any, Dict, Iterable, List, Optional, Type
from langchain.embeddings.base import Embeddings
from langchain.schema.document import Document
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VST, VectorStore
FIELD_TYPES = {

@ -6,8 +6,8 @@ from typing import Any, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.embeddings.base import Embeddings
from langchain.schema import Document
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance

@ -10,7 +10,7 @@ from sqlalchemy.dialects.postgresql import JSON, UUID
from sqlalchemy.orm import Session, declarative_base, relationship
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
from langchain.vectorstores.base import VectorStore

@ -26,7 +26,7 @@ from sqlalchemy.dialects.postgresql import UUID
from sqlalchemy.orm import Session, declarative_base
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance

@ -8,7 +8,7 @@ from typing import TYPE_CHECKING, Any, Callable, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.utils.iter import batch_iterate
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import DistanceStrategy, maximal_marginal_relevance

@ -25,7 +25,7 @@ from typing import (
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance

@ -29,7 +29,7 @@ from langchain.callbacks.manager import (
CallbackManagerForRetrieverRun,
)
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.utilities.redis import (
_array_to_buffer,
_buffer_to_array,

@ -5,7 +5,7 @@ from enum import Enum
from typing import Any, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
logger = logging.getLogger(__name__)

@ -11,7 +11,7 @@ import numpy as np
from langchain.docstore.base import AddableMixin, Docstore
from langchain.docstore.document import Document
from langchain.docstore.in_memory import InMemoryDocstore
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import DistanceStrategy

@ -20,7 +20,7 @@ from langchain.callbacks.manager import (
CallbackManagerForRetrieverRun,
)
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore, VectorStoreRetriever
from langchain.vectorstores.utils import DistanceStrategy

@ -11,7 +11,7 @@ from typing import Any, Dict, Iterable, List, Literal, Optional, Tuple, Type
from uuid import uuid4
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.utils import guard_import
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance

@ -14,7 +14,7 @@ from typing import (
)
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
if TYPE_CHECKING:

@ -7,8 +7,8 @@ from threading import Thread
from typing import Any, Dict, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.pydantic_v1 import BaseSettings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
logger = logging.getLogger()

@ -17,7 +17,7 @@ from typing import (
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance

@ -6,7 +6,7 @@ import uuid
from typing import Any, Iterable, List, Optional, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.utils import get_from_dict_or_env
from langchain.vectorstores.base import VectorStore

@ -9,7 +9,7 @@ from typing import Any, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.schema.embeddings import Embeddings
from langchain.utils import guard_import
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance

@ -3,8 +3,8 @@ from __future__ import annotations
import itertools
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple
from langchain.embeddings.base import Embeddings
from langchain.schema import Document
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores import VectorStore
if TYPE_CHECKING:

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