cr

langchain/cache.py

@@ -14,7 +14,7 @@ try:
 except ImportError:
     from sqlalchemy.ext.declarative import declarative_base
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.schema import Generation
 from langchain.vectorstores.redis import Redis as RedisVectorstore
 
@@ -178,7 +178,10 @@ class RedisSemanticCache(BaseCache):
     # TODO - implement a TTL policy in Redis
 
     def __init__(
-        self, redis_url: str, embedding: Embeddings, score_threshold: float = 0.2
+        self,
+        redis_url: str,
+        embedding: TextEmbeddingModel,
+        score_threshold: float = 0.2,
     ):
         """Initialize by passing in the `init` GPTCache func
 

langchain/chains/hyde/base.py

@@ -14,16 +14,16 @@ 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.embeddings.base import TextEmbeddingModel
 
 
-class HypotheticalDocumentEmbedder(Chain, Embeddings):
+class HypotheticalDocumentEmbedder(Chain, TextEmbeddingModel):
     """Generate hypothetical document for query, and then embed that.
 
     Based on https://arxiv.org/abs/2212.10496
     """
 
-    base_embeddings: Embeddings
+    base_embeddings: TextEmbeddingModel
     llm_chain: LLMChain
 
     class Config:
@@ -71,7 +71,7 @@ class HypotheticalDocumentEmbedder(Chain, Embeddings):
     def from_llm(
         cls,
         llm: BaseLanguageModel,
-        base_embeddings: Embeddings,
+        base_embeddings: TextEmbeddingModel,
         prompt_key: str,
         **kwargs: Any,
     ) -> HypotheticalDocumentEmbedder:

langchain/chains/router/embedding_router.py

@@ -7,7 +7,7 @@ from pydantic import Extra
 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.embeddings.base import TextEmbeddingModel
 from langchain.vectorstores.base import VectorStore
 
 
@@ -45,7 +45,7 @@ class EmbeddingRouterChain(RouterChain):
         cls,
         names_and_descriptions: Sequence[Tuple[str, Sequence[str]]],
         vectorstore_cls: Type[VectorStore],
-        embeddings: Embeddings,
+        embeddings: TextEmbeddingModel,
         **kwargs: Any,
     ) -> EmbeddingRouterChain:
         """Convenience constructor."""

langchain/document_transformers.py

@@ -4,7 +4,7 @@ from typing import Any, Callable, List, Sequence
 import numpy as np
 from pydantic import BaseModel, Field
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.math_utils import cosine_similarity
 from langchain.schema import BaseDocumentTransformer, Document
 
@@ -50,7 +50,7 @@ def _filter_similar_embeddings(
 
 
 def _get_embeddings_from_stateful_docs(
-    embeddings: Embeddings, documents: Sequence[_DocumentWithState]
+    embeddings: TextEmbeddingModel, documents: Sequence[_DocumentWithState]
 ) -> List[List[float]]:
     if len(documents) and "embedded_doc" in documents[0].state:
         embedded_documents = [doc.state["embedded_doc"] for doc in documents]
@@ -66,7 +66,7 @@ def _get_embeddings_from_stateful_docs(
 class EmbeddingsRedundantFilter(BaseDocumentTransformer, BaseModel):
     """Filter that drops redundant documents by comparing their embeddings."""
 
-    embeddings: Embeddings
+    embeddings: TextEmbeddingModel
     """Embeddings to use for embedding document contents."""
     similarity_fn: Callable = cosine_similarity
     """Similarity function for comparing documents. Function expected to take as input

langchain/embeddings/aleph_alpha.py

@@ -2,11 +2,11 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, root_validator
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.utils import get_from_dict_or_env
 
 
-class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings):
+class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, TextEmbeddingModel):
     """
     Wrapper for Aleph Alpha's Asymmetric Embeddings
     AA provides you with an endpoint to embed a document and a query.

langchain/embeddings/base.py

@@ -3,8 +3,8 @@ from abc import ABC, abstractmethod
 from typing import List
 
 
-class Embeddings(ABC):
-    """Interface for embedding models."""
+class TextEmbeddingModel(ABC):
+    """Interface for text embedding models."""
 
     @abstractmethod
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
@@ -13,3 +13,7 @@ class Embeddings(ABC):
     @abstractmethod
     def embed_query(self, text: str) -> List[float]:
         """Embed query text."""
+
+
+# For backwards compatibility.
+Embedding = TextEmbeddingModel

langchain/embeddings/cohere.py

@@ -3,11 +3,11 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, Extra, root_validator
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.utils import get_from_dict_or_env
 
 
-class CohereEmbeddings(BaseModel, Embeddings):
+class CohereEmbeddings(BaseModel, TextEmbeddingModel):
     """Wrapper around Cohere embedding models.
 
     To use, you should have the ``cohere`` python package installed, and the

langchain/embeddings/fake.py

@@ -3,10 +3,10 @@ from typing import List
 import numpy as np
 from pydantic import BaseModel
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 
 
-class FakeEmbeddings(Embeddings, BaseModel):
+class FakeEmbeddings(TextEmbeddingModel, BaseModel):
     size: int
 
     def _get_embedding(self) -> List[float]:

langchain/embeddings/google_palm.py

@@ -13,7 +13,7 @@ from tenacity import (
     wait_exponential,
 )
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
@@ -54,7 +54,7 @@ def embed_with_retry(
     return _embed_with_retry(*args, **kwargs)
 
 
-class GooglePalmEmbeddings(BaseModel, Embeddings):
+class GooglePalmEmbeddings(BaseModel, TextEmbeddingModel):
     client: Any
     google_api_key: Optional[str]
     model_name: str = "models/embedding-gecko-001"

langchain/embeddings/huggingface.py

@@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, Extra, Field
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 
 DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
 DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
@@ -13,7 +13,7 @@ DEFAULT_QUERY_INSTRUCTION = (
 )
 
 
-class HuggingFaceEmbeddings(BaseModel, Embeddings):
+class HuggingFaceEmbeddings(BaseModel, TextEmbeddingModel):
     """Wrapper around sentence_transformers embedding models.
 
     To use, you should have the ``sentence_transformers`` python package installed.
@@ -87,7 +87,7 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
         return embedding.tolist()
 
 
-class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
+class HuggingFaceInstructEmbeddings(BaseModel, TextEmbeddingModel):
     """Wrapper around sentence_transformers embedding models.
 
     To use, you should have the ``sentence_transformers``

langchain/embeddings/huggingface_hub.py

@@ -3,14 +3,14 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, Extra, root_validator
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.utils import get_from_dict_or_env
 
 DEFAULT_REPO_ID = "sentence-transformers/all-mpnet-base-v2"
 VALID_TASKS = ("feature-extraction",)
 
 
-class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
+class HuggingFaceHubEmbeddings(BaseModel, TextEmbeddingModel):
     """Wrapper around HuggingFaceHub embedding models.
 
     To use, you should have the ``huggingface_hub`` python package installed, and the

langchain/embeddings/jina.py

@@ -6,11 +6,11 @@ from typing import Any, Dict, List, Optional
 import requests
 from pydantic import BaseModel, root_validator
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.utils import get_from_dict_or_env
 
 
-class JinaEmbeddings(BaseModel, Embeddings):
+class JinaEmbeddings(BaseModel, TextEmbeddingModel):
     client: Any  #: :meta private:
 
     model_name: str = "ViT-B-32::openai"

langchain/embeddings/llamacpp.py

@@ -3,10 +3,10 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, Extra, Field, root_validator
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 
 
-class LlamaCppEmbeddings(BaseModel, Embeddings):
+class LlamaCppEmbeddings(BaseModel, TextEmbeddingModel):
     """Wrapper around llama.cpp embedding models.
 
     To use, you should have the llama-cpp-python library installed, and provide the

langchain/embeddings/openai.py

@@ -25,7 +25,7 @@ from tenacity import (
     wait_exponential,
 )
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
@@ -64,7 +64,7 @@ def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
     return _embed_with_retry(**kwargs)
 
 
-class OpenAIEmbeddings(BaseModel, Embeddings):
+class OpenAIEmbeddings(BaseModel, TextEmbeddingModel):
     """Wrapper around OpenAI embedding models.
 
     To use, you should have the ``openai`` python package installed, and the

langchain/embeddings/sagemaker_endpoint.py

@@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, Extra, root_validator
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.llms.sagemaker_endpoint import ContentHandlerBase
 
 
@@ -11,7 +11,7 @@ class EmbeddingsContentHandler(ContentHandlerBase[List[str], List[List[float]]])
     """Content handler for LLM class."""
 
 
-class SagemakerEndpointEmbeddings(BaseModel, Embeddings):
+class SagemakerEndpointEmbeddings(BaseModel, TextEmbeddingModel):
     """Wrapper around custom Sagemaker Inference Endpoints.
 
     To use, you must supply the endpoint name from your deployed

langchain/embeddings/self_hosted.py

@@ -3,7 +3,7 @@ from typing import Any, Callable, List
 
 from pydantic import Extra
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.llms import SelfHostedPipeline
 
 
@@ -16,7 +16,7 @@ def _embed_documents(pipeline: Any, *args: Any, **kwargs: Any) -> List[List[floa
     return pipeline(*args, **kwargs)
 
 
-class SelfHostedEmbeddings(SelfHostedPipeline, Embeddings):
+class SelfHostedEmbeddings(SelfHostedPipeline, TextEmbeddingModel):
     """Runs custom embedding models on self-hosted remote hardware.
 
     Supported hardware includes auto-launched instances on AWS, GCP, Azure,

langchain/embeddings/tensorflow_hub.py

@@ -3,12 +3,12 @@ from typing import Any, List
 
 from pydantic import BaseModel, Extra
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 
 DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
 
 
-class TensorflowHubEmbeddings(BaseModel, Embeddings):
+class TensorflowHubEmbeddings(BaseModel, TextEmbeddingModel):
     """Wrapper around tensorflow_hub embedding models.
 
     To use, you should have the ``tensorflow_text`` python package installed.

langchain/indexes/vectorstore.py

@@ -6,7 +6,7 @@ from langchain.base_language import BaseLanguageModel
 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.base import TextEmbeddingModel
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.llms.openai import OpenAI
 from langchain.schema import Document
@@ -55,7 +55,7 @@ class VectorstoreIndexCreator(BaseModel):
     """Logic for creating indexes."""
 
     vectorstore_cls: Type[VectorStore] = Chroma
-    embedding: Embeddings = Field(default_factory=OpenAIEmbeddings)
+    embedding: TextEmbeddingModel = Field(default_factory=OpenAIEmbeddings)
     text_splitter: TextSplitter = Field(default_factory=_get_default_text_splitter)
     vectorstore_kwargs: dict = Field(default_factory=dict)
 

langchain/prompts/example_selector/semantic_similarity.py

@@ -5,7 +5,7 @@ from typing import Any, Dict, List, Optional, Type
 
 from pydantic import BaseModel, Extra
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.prompts.example_selector.base import BaseExampleSelector
 from langchain.vectorstores.base import VectorStore
 
@@ -64,7 +64,7 @@ class SemanticSimilarityExampleSelector(BaseExampleSelector, BaseModel):
     def from_examples(
         cls,
         examples: List[dict],
-        embeddings: Embeddings,
+        embeddings: TextEmbeddingModel,
         vectorstore_cls: Type[VectorStore],
         k: int = 4,
         input_keys: Optional[List[str]] = None,
@@ -130,7 +130,7 @@ class MaxMarginalRelevanceExampleSelector(SemanticSimilarityExampleSelector):
     def from_examples(
         cls,
         examples: List[dict],
-        embeddings: Embeddings,
+        embeddings: TextEmbeddingModel,
         vectorstore_cls: Type[VectorStore],
         k: int = 4,
         input_keys: Optional[List[str]] = None,

langchain/retrievers/document_compressors/embeddings_filter.py

@@ -8,7 +8,7 @@ from langchain.document_transformers import (
     _get_embeddings_from_stateful_docs,
     get_stateful_documents,
 )
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.math_utils import cosine_similarity
 from langchain.retrievers.document_compressors.base import (
     BaseDocumentCompressor,
@@ -17,7 +17,7 @@ from langchain.schema import Document
 
 
 class EmbeddingsFilter(BaseDocumentCompressor):
-    embeddings: Embeddings
+    embeddings: TextEmbeddingModel
     """Embeddings to use for embedding document contents and queries."""
     similarity_fn: Callable = cosine_similarity
     """Similarity function for comparing documents. Function expected to take as input

langchain/retrievers/knn.py

@@ -10,17 +10,17 @@ from typing import Any, List, Optional
 import numpy as np
 from pydantic import BaseModel
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.schema import BaseRetriever, Document
 
 
-def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:
+def create_index(contexts: List[str], embeddings: TextEmbeddingModel) -> np.ndarray:
     with concurrent.futures.ThreadPoolExecutor() as executor:
         return np.array(list(executor.map(embeddings.embed_query, contexts)))
 
 
 class KNNRetriever(BaseRetriever, BaseModel):
-    embeddings: Embeddings
+    embeddings: TextEmbeddingModel
     index: Any
     texts: List[str]
     k: int = 4
@@ -34,7 +34,7 @@ class KNNRetriever(BaseRetriever, BaseModel):
 
     @classmethod
     def from_texts(
-        cls, texts: List[str], embeddings: Embeddings, **kwargs: Any
+        cls, texts: List[str], embeddings: TextEmbeddingModel, **kwargs: Any
     ) -> KNNRetriever:
         index = create_index(texts, embeddings)
         return cls(embeddings=embeddings, index=index, texts=texts, **kwargs)

langchain/retrievers/milvus.py

@@ -1,7 +1,7 @@
 """Milvus Retriever"""
 from typing import Any, Dict, List, Optional
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.schema import BaseRetriever, Document
 from langchain.vectorstores.milvus import Milvus
 
@@ -11,7 +11,7 @@ from langchain.vectorstores.milvus import Milvus
 class MilvusRetreiver(BaseRetriever):
     def __init__(
         self,
-        embedding_function: Embeddings,
+        embedding_function: TextEmbeddingModel,
         collection_name: str = "LangChainCollection",
         connection_args: Optional[Dict[str, Any]] = None,
         consistency_level: str = "Session",

langchain/retrievers/pinecone_hybrid_search.py

@@ -4,7 +4,7 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, Extra, root_validator
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.schema import BaseRetriever, Document
 
 
@@ -15,7 +15,7 @@ def hash_text(text: str) -> str:
 def create_index(
     contexts: List[str],
     index: Any,
-    embeddings: Embeddings,
+    embeddings: TextEmbeddingModel,
     sparse_encoder: Any,
     ids: Optional[List[str]] = None,
     metadatas: Optional[List[dict]] = None,
@@ -74,7 +74,7 @@ def create_index(
 
 
 class PineconeHybridSearchRetriever(BaseRetriever, BaseModel):
-    embeddings: Embeddings
+    embeddings: TextEmbeddingModel
     sparse_encoder: Any
     index: Any
     top_k: int = 4

langchain/retrievers/svm.py

@@ -10,17 +10,17 @@ from typing import Any, List, Optional
 import numpy as np
 from pydantic import BaseModel
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.schema import BaseRetriever, Document
 
 
-def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:
+def create_index(contexts: List[str], embeddings: TextEmbeddingModel) -> np.ndarray:
     with concurrent.futures.ThreadPoolExecutor() as executor:
         return np.array(list(executor.map(embeddings.embed_query, contexts)))
 
 
 class SVMRetriever(BaseRetriever, BaseModel):
-    embeddings: Embeddings
+    embeddings: TextEmbeddingModel
     index: Any
     texts: List[str]
     k: int = 4
@@ -34,7 +34,7 @@ class SVMRetriever(BaseRetriever, BaseModel):
 
     @classmethod
     def from_texts(
-        cls, texts: List[str], embeddings: Embeddings, **kwargs: Any
+        cls, texts: List[str], embeddings: TextEmbeddingModel, **kwargs: Any
     ) -> SVMRetriever:
         index = create_index(texts, embeddings)
         return cls(embeddings=embeddings, index=index, texts=texts, **kwargs)

langchain/retrievers/zilliz.py

@@ -1,7 +1,7 @@
 """Zilliz Retriever"""
 from typing import Any, Dict, List, Optional
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.schema import BaseRetriever, Document
 from langchain.vectorstores.zilliz import Zilliz
 
@@ -11,7 +11,7 @@ from langchain.vectorstores.zilliz import Zilliz
 class ZillizRetreiver(BaseRetriever):
     def __init__(
         self,
-        embedding_function: Embeddings,
+        embedding_function: TextEmbeddingModel,
         collection_name: str = "LangChainCollection",
         connection_args: Optional[Dict[str, Any]] = None,
         consistency_level: str = "Session",

langchain/vectorstores/analyticdb.py

@@ -13,7 +13,7 @@ from sqlalchemy.orm import Session, relationship
 from sqlalchemy.sql.expression import func
 
 from langchain.docstore.document import Document
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.utils import get_from_dict_or_env
 from langchain.vectorstores.base import VectorStore
 
@@ -126,7 +126,7 @@ class AnalyticDB(VectorStore):
     def __init__(
         self,
         connection_string: str,
-        embedding_function: Embeddings,
+        embedding_function: TextEmbeddingModel,
         collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
         collection_metadata: Optional[dict] = None,
         pre_delete_collection: bool = False,
@@ -343,7 +343,7 @@ class AnalyticDB(VectorStore):
     def from_texts(
         cls,
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
         ids: Optional[List[str]] = None,
@@ -390,7 +390,7 @@ class AnalyticDB(VectorStore):
     def from_documents(
         cls,
         documents: List[Document],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
         ids: Optional[List[str]] = None,
         pre_delete_collection: bool = False,

langchain/vectorstores/annoy.py

@@ -13,7 +13,7 @@ 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.embeddings.base import TextEmbeddingModel
 from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
@@ -282,7 +282,7 @@ class Annoy(VectorStore):
         cls,
         texts: List[str],
         embeddings: List[List[float]],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         metric: str = DEFAULT_METRIC,
         trees: int = 100,
@@ -319,7 +319,7 @@ class Annoy(VectorStore):
     def from_texts(
         cls,
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         metric: str = DEFAULT_METRIC,
         trees: int = 100,
@@ -360,7 +360,7 @@ class Annoy(VectorStore):
     def from_embeddings(
         cls,
         text_embeddings: List[Tuple[str, List[float]]],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         metric: str = DEFAULT_METRIC,
         trees: int = 100,
@@ -424,7 +424,7 @@ class Annoy(VectorStore):
     def load_local(
         cls,
         folder_path: str,
-        embeddings: Embeddings,
+        embeddings: TextEmbeddingModel,
     ) -> Annoy:
         """Load Annoy index, docstore, and index_to_docstore_id to disk.
 

langchain/vectorstores/atlas.py

@@ -8,7 +8,7 @@ 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.embeddings.base import TextEmbeddingModel
 from langchain.vectorstores.base import VectorStore
 
 logger = logging.getLogger(__name__)
@@ -34,7 +34,7 @@ class AtlasDB(VectorStore):
     def __init__(
         self,
         name: str,
-        embedding_function: Optional[Embeddings] = None,
+        embedding_function: Optional[TextEmbeddingModel] = None,
         api_key: Optional[str] = None,
         description: str = "A description for your project",
         is_public: bool = True,
@@ -212,7 +212,7 @@ class AtlasDB(VectorStore):
     def from_texts(
         cls: Type[AtlasDB],
         texts: List[str],
-        embedding: Optional[Embeddings] = None,
+        embedding: Optional[TextEmbeddingModel] = None,
         metadatas: Optional[List[dict]] = None,
         ids: Optional[List[str]] = None,
         name: Optional[str] = None,
@@ -229,7 +229,7 @@ class AtlasDB(VectorStore):
             texts (List[str]): The list of texts to ingest.
             name (str): Name of the project to create.
             api_key (str): Your nomic API key,
-            embedding (Optional[Embeddings]): Embedding function. Defaults to None.
+            embedding (Optional[TextEmbeddingModel]): Embedding function. Defaults to None.
             metadatas (Optional[List[dict]]): List of metadatas. Defaults to None.
             ids (Optional[List[str]]): Optional list of document IDs. If None,
                 ids will be auto created
@@ -272,7 +272,7 @@ class AtlasDB(VectorStore):
     def from_documents(
         cls: Type[AtlasDB],
         documents: List[Document],
-        embedding: Optional[Embeddings] = None,
+        embedding: Optional[TextEmbeddingModel] = None,
         ids: Optional[List[str]] = None,
         name: Optional[str] = None,
         api_key: Optional[str] = None,
@@ -289,7 +289,7 @@ class AtlasDB(VectorStore):
             name (str): Name of the collection to create.
             api_key (str): Your nomic API key,
             documents (List[Document]): List of documents to add to the vectorstore.
-            embedding (Optional[Embeddings]): Embedding function. Defaults to None.
+            embedding (Optional[TextEmbeddingModel]): Embedding function. Defaults to None.
             ids (Optional[List[str]]): Optional list of document IDs. If None,
                 ids will be auto created
             description (str): A description for your project.

langchain/vectorstores/base.py

@@ -10,7 +10,7 @@ from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, TypeVar
 from pydantic import BaseModel, Field, root_validator
 
 from langchain.docstore.document import Document
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.schema import BaseRetriever
 
 VST = TypeVar("VST", bound="VectorStore")
@@ -298,7 +298,7 @@ class VectorStore(ABC):
     def from_documents(
         cls: Type[VST],
         documents: List[Document],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         **kwargs: Any,
     ) -> VST:
         """Return VectorStore initialized from documents and embeddings."""
@@ -310,7 +310,7 @@ class VectorStore(ABC):
     async def afrom_documents(
         cls: Type[VST],
         documents: List[Document],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         **kwargs: Any,
     ) -> VST:
         """Return VectorStore initialized from documents and embeddings."""
@@ -323,7 +323,7 @@ class VectorStore(ABC):
     def from_texts(
         cls: Type[VST],
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         **kwargs: Any,
     ) -> VST:
@@ -333,7 +333,7 @@ class VectorStore(ABC):
     async def afrom_texts(
         cls: Type[VST],
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         **kwargs: Any,
     ) -> VST:

langchain/vectorstores/chroma.py

@@ -8,7 +8,7 @@ from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Ty
 import numpy as np
 
 from langchain.docstore.document import Document
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.utils import xor_args
 from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
@@ -58,7 +58,7 @@ class Chroma(VectorStore):
     def __init__(
         self,
         collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
-        embedding_function: Optional[Embeddings] = None,
+        embedding_function: Optional[TextEmbeddingModel] = None,
         persist_directory: Optional[str] = None,
         client_settings: Optional[chromadb.config.Settings] = None,
         collection_metadata: Optional[Dict] = None,
@@ -354,7 +354,7 @@ class Chroma(VectorStore):
     def from_texts(
         cls: Type[Chroma],
         texts: List[str],
-        embedding: Optional[Embeddings] = None,
+        embedding: Optional[TextEmbeddingModel] = None,
         metadatas: Optional[List[dict]] = None,
         ids: Optional[List[str]] = None,
         collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
@@ -372,7 +372,7 @@ class Chroma(VectorStore):
             texts (List[str]): List of texts to add to the collection.
             collection_name (str): Name of the collection to create.
             persist_directory (Optional[str]): Directory to persist the collection.
-            embedding (Optional[Embeddings]): Embedding function. Defaults to None.
+            embedding (Optional[TextEmbeddingModel]): Embedding function. Defaults to None.
             metadatas (Optional[List[dict]]): List of metadatas. Defaults to None.
             ids (Optional[List[str]]): List of document IDs. Defaults to None.
             client_settings (Optional[chromadb.config.Settings]): Chroma client settings
@@ -394,7 +394,7 @@ class Chroma(VectorStore):
     def from_documents(
         cls: Type[Chroma],
         documents: List[Document],
-        embedding: Optional[Embeddings] = None,
+        embedding: Optional[TextEmbeddingModel] = None,
         ids: Optional[List[str]] = None,
         collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
         persist_directory: Optional[str] = None,
@@ -412,7 +412,7 @@ class Chroma(VectorStore):
             persist_directory (Optional[str]): Directory to persist the collection.
             ids (Optional[List[str]]): List of document IDs. Defaults to None.
             documents (List[Document]): List of documents to add to the vectorstore.
-            embedding (Optional[Embeddings]): Embedding function. Defaults to None.
+            embedding (Optional[TextEmbeddingModel]): Embedding function. Defaults to None.
             client_settings (Optional[chromadb.config.Settings]): Chroma client settings
         Returns:
             Chroma: Chroma vectorstore.

langchain/vectorstores/deeplake.py

@@ -9,7 +9,7 @@ from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tupl
 import numpy as np
 
 from langchain.docstore.document import Document
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
@@ -96,7 +96,7 @@ class DeepLake(VectorStore):
         self,
         dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
         token: Optional[str] = None,
-        embedding_function: Optional[Embeddings] = None,
+        embedding_function: Optional[TextEmbeddingModel] = None,
         read_only: Optional[bool] = False,
         ingestion_batch_size: int = 1024,
         num_workers: int = 0,
@@ -494,7 +494,7 @@ class DeepLake(VectorStore):
     def from_texts(
         cls,
         texts: List[str],
-        embedding: Optional[Embeddings] = None,
+        embedding: Optional[TextEmbeddingModel] = None,
         metadatas: Optional[List[dict]] = None,
         ids: Optional[List[str]] = None,
         dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
@@ -522,7 +522,7 @@ class DeepLake(VectorStore):
                     save the dataset, but keeps it in memory instead.
                     Should be used only for testing as it does not persist.
             documents (List[Document]): List of documents to add.
-            embedding (Optional[Embeddings]): Embedding function. Defaults to None.
+            embedding (Optional[TextEmbeddingModel]): Embedding function. Defaults to None.
             metadatas (Optional[List[dict]]): List of metadatas. Defaults to None.
             ids (Optional[List[str]]): List of document IDs. Defaults to None.
 

langchain/vectorstores/docarray/base.py

@@ -4,7 +4,7 @@ from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Type
 import numpy as np
 from pydantic import Field
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.schema import Document
 from langchain.vectorstores import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
@@ -36,7 +36,7 @@ class DocArrayIndex(VectorStore, ABC):
     def __init__(
         self,
         doc_index: "BaseDocIndex",
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
     ):
         """Initialize a vector store from DocArray's DocIndex."""
         self.doc_index = doc_index

langchain/vectorstores/docarray/hnsw.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 
 from typing import Any, List, Literal, Optional
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.vectorstores.docarray.base import (
     DocArrayIndex,
     _check_docarray_import,
@@ -20,7 +20,7 @@ class DocArrayHnswSearch(DocArrayIndex):
     @classmethod
     def from_params(
         cls,
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         work_dir: str,
         n_dim: int,
         dist_metric: Literal["cosine", "ip", "l2"] = "cosine",
@@ -36,7 +36,7 @@ class DocArrayHnswSearch(DocArrayIndex):
         """Initialize DocArrayHnswSearch store.
 
         Args:
-            embedding (Embeddings): Embedding function.
+            embedding (TextEmbeddingModel): Embedding function.
             work_dir (str): path to the location where all the data will be stored.
             n_dim (int): dimension of an embedding.
             dist_metric (str): Distance metric for DocArrayHnswSearch can be one of:
@@ -78,7 +78,7 @@ class DocArrayHnswSearch(DocArrayIndex):
     def from_texts(
         cls,
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         work_dir: Optional[str] = None,
         n_dim: Optional[int] = None,
@@ -89,7 +89,7 @@ class DocArrayHnswSearch(DocArrayIndex):
 
         Args:
             texts (List[str]): Text data.
-            embedding (Embeddings): Embedding function.
+            embedding (TextEmbeddingModel): Embedding function.
             metadatas (Optional[List[dict]]): Metadata for each text if it exists.
                 Defaults to None.
             work_dir (str): path to the location where all the data will be stored.

langchain/vectorstores/docarray/in_memory.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Literal, Optional
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.vectorstores.docarray.base import (
     DocArrayIndex,
     _check_docarray_import,
@@ -20,7 +20,7 @@ class DocArrayInMemorySearch(DocArrayIndex):
     @classmethod
     def from_params(
         cls,
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metric: Literal[
             "cosine_sim", "euclidian_dist", "sgeuclidean_dist"
         ] = "cosine_sim",
@@ -29,7 +29,7 @@ class DocArrayInMemorySearch(DocArrayIndex):
         """Initialize DocArrayInMemorySearch store.
 
         Args:
-            embedding (Embeddings): Embedding function.
+            embedding (TextEmbeddingModel): Embedding function.
             metric (str): metric for exact nearest-neighbor search.
                 Can be one of: "cosine_sim", "euclidean_dist" and "sqeuclidean_dist".
                 Defaults to "cosine_sim".
@@ -46,7 +46,7 @@ class DocArrayInMemorySearch(DocArrayIndex):
     def from_texts(
         cls,
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[Dict[Any, Any]]] = None,
         **kwargs: Any,
     ) -> DocArrayInMemorySearch:
@@ -54,7 +54,7 @@ class DocArrayInMemorySearch(DocArrayIndex):
 
         Args:
             texts (List[str]): Text data.
-            embedding (Embeddings): Embedding function.
+            embedding (TextEmbeddingModel): Embedding function.
             metadatas (Optional[List[Dict[Any, Any]]]): Metadata for each text
                 if it exists. Defaults to None.
             metric (str): metric for exact nearest-neighbor search.

langchain/vectorstores/elastic_vector_search.py

@@ -6,7 +6,7 @@ from abc import ABC
 from typing import Any, Dict, Iterable, List, Optional, Tuple
 
 from langchain.docstore.document import Document
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.utils import get_from_env
 from langchain.vectorstores.base import VectorStore
 
@@ -106,7 +106,7 @@ class ElasticVectorSearch(VectorStore, ABC):
     Args:
         elasticsearch_url (str): The URL for the Elasticsearch instance.
         index_name (str): The name of the Elasticsearch index for the embeddings.
-        embedding (Embeddings): An object that provides the ability to embed text.
+        embedding (TextEmbeddingModel): An object that provides the ability to embed text.
                 It should be an instance of a class that subclasses the Embeddings
                 abstract base class, such as OpenAIEmbeddings()
 
@@ -118,7 +118,7 @@ class ElasticVectorSearch(VectorStore, ABC):
         self,
         elasticsearch_url: str,
         index_name: str,
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         *,
         ssl_verify: Optional[Dict[str, Any]] = None,
     ):
@@ -244,7 +244,7 @@ class ElasticVectorSearch(VectorStore, ABC):
     def from_texts(
         cls,
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         elasticsearch_url: Optional[str] = None,
         index_name: Optional[str] = None,

langchain/vectorstores/faiss.py

@@ -13,7 +13,7 @@ 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.embeddings.base import TextEmbeddingModel
 from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
@@ -364,7 +364,7 @@ class FAISS(VectorStore):
         cls,
         texts: List[str],
         embeddings: List[List[float]],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         normalize_L2: bool = False,
         **kwargs: Any,
@@ -396,7 +396,7 @@ class FAISS(VectorStore):
     def from_texts(
         cls,
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         **kwargs: Any,
     ) -> FAISS:
@@ -430,7 +430,7 @@ class FAISS(VectorStore):
     def from_embeddings(
         cls,
         text_embeddings: List[Tuple[str, List[float]]],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         **kwargs: Any,
     ) -> FAISS:
@@ -486,7 +486,7 @@ class FAISS(VectorStore):
 
     @classmethod
     def load_local(
-        cls, folder_path: str, embeddings: Embeddings, index_name: str = "index"
+        cls, folder_path: str, embeddings: TextEmbeddingModel, index_name: str = "index"
     ) -> FAISS:
         """Load FAISS index, docstore, and index_to_docstore_id to disk.
 

langchain/vectorstores/lancedb.py

@@ -5,7 +5,7 @@ import uuid
 from typing import Any, Iterable, List, Optional
 
 from langchain.docstore.document import Document
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.vectorstores.base import VectorStore
 
 
@@ -27,7 +27,7 @@ class LanceDB(VectorStore):
     def __init__(
         self,
         connection: Any,
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         vector_key: Optional[str] = "vector",
         id_key: Optional[str] = "id",
         text_key: Optional[str] = "text",
@@ -113,7 +113,7 @@ class LanceDB(VectorStore):
     def from_texts(
         cls,
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         connection: Any = None,
         vector_key: Optional[str] = "vector",

langchain/vectorstores/milvus.py

@@ -8,7 +8,7 @@ from uuid import uuid4
 import numpy as np
 
 from langchain.docstore.document import Document
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
@@ -28,7 +28,7 @@ class Milvus(VectorStore):
 
     def __init__(
         self,
-        embedding_function: Embeddings,
+        embedding_function: TextEmbeddingModel,
         collection_name: str = "LangChainCollection",
         connection_args: Optional[dict[str, Any]] = None,
         consistency_level: str = "Session",
@@ -77,7 +77,7 @@ class Milvus(VectorStore):
             server_name (str): If use tls, need to write the common name.
 
         Args:
-            embedding_function (Embeddings): Function used to embed the text.
+            embedding_function (TextEmbeddingModel): Function used to embed the text.
             collection_name (str): Which Milvus collection to use. Defaults to
                 "LangChainCollection".
             connection_args (Optional[dict[str, any]]): The arguments for connection to
@@ -754,7 +754,7 @@ class Milvus(VectorStore):
     def from_texts(
         cls,
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         collection_name: str = "LangChainCollection",
         connection_args: dict[str, Any] = DEFAULT_MILVUS_CONNECTION,
@@ -768,7 +768,7 @@ class Milvus(VectorStore):
 
         Args:
             texts (List[str]): Text data.
-            embedding (Embeddings): Embedding function.
+            embedding (TextEmbeddingModel): Embedding function.
             metadatas (Optional[List[dict]]): Metadata for each text if it exists.
                 Defaults to None.
             collection_name (str, optional): Collection name to use. Defaults to

langchain/vectorstores/myscale.py

@@ -10,7 +10,7 @@ from typing import Any, Dict, Iterable, List, Optional, Tuple
 from pydantic import BaseSettings
 
 from langchain.docstore.document import Document
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.vectorstores.base import VectorStore
 
 logger = logging.getLogger()
@@ -98,7 +98,7 @@ class MyScale(VectorStore):
 
     def __init__(
         self,
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         config: Optional[MyScaleSettings] = None,
         **kwargs: Any,
     ) -> None:
@@ -259,7 +259,7 @@ class MyScale(VectorStore):
     def from_texts(
         cls,
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[Dict[Any, Any]]] = None,
         config: Optional[MyScaleSettings] = None,
         text_ids: Optional[Iterable[str]] = None,
@@ -269,7 +269,7 @@ class MyScale(VectorStore):
         """Create Myscale wrapper with existing texts
 
         Args:
-            embedding_function (Embeddings): Function to extract text embedding
+            embedding_function (TextEmbeddingModel): Function to extract text embedding
             texts (Iterable[str]): List or tuple of strings to be added
             config (MyScaleSettings, Optional): Myscale configuration
             text_ids (Optional[Iterable], optional): IDs for the texts.

langchain/vectorstores/opensearch_vector_search.py

@@ -5,7 +5,7 @@ import uuid
 from typing import Any, Dict, Iterable, List, Optional, Tuple
 
 from langchain.docstore.document import Document
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.utils import get_from_dict_or_env
 from langchain.vectorstores.base import VectorStore
 
@@ -295,7 +295,7 @@ class OpenSearchVectorSearch(VectorStore):
         self,
         opensearch_url: str,
         index_name: str,
-        embedding_function: Embeddings,
+        embedding_function: TextEmbeddingModel,
         **kwargs: Any,
     ):
         """Initialize with necessary components."""
@@ -494,7 +494,7 @@ class OpenSearchVectorSearch(VectorStore):
     def from_texts(
         cls,
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         bulk_size: int = 500,
         **kwargs: Any,

langchain/vectorstores/pgvector.py

@@ -12,7 +12,7 @@ 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.embeddings.base import TextEmbeddingModel
 from langchain.utils import get_from_dict_or_env
 from langchain.vectorstores.base import VectorStore
 
@@ -122,7 +122,7 @@ class PGVector(VectorStore):
     def __init__(
         self,
         connection_string: str,
-        embedding_function: Embeddings,
+        embedding_function: TextEmbeddingModel,
         collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
         collection_metadata: Optional[dict] = None,
         distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
@@ -363,7 +363,7 @@ class PGVector(VectorStore):
     def from_texts(
         cls: Type[PGVector],
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
         distance_strategy: DistanceStrategy = DistanceStrategy.COSINE,
@@ -412,7 +412,7 @@ class PGVector(VectorStore):
     def from_documents(
         cls: Type[PGVector],
         documents: List[Document],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
         distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
         ids: Optional[List[str]] = None,

langchain/vectorstores/pinecone.py

@@ -6,7 +6,7 @@ import uuid
 from typing import Any, Callable, Iterable, List, Optional, Tuple
 
 from langchain.docstore.document import Document
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.vectorstores.base import VectorStore
 
 logger = logging.getLogger(__name__)
@@ -161,7 +161,7 @@ class Pinecone(VectorStore):
     def from_texts(
         cls,
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         ids: Optional[List[str]] = None,
         batch_size: int = 32,
@@ -247,7 +247,7 @@ class Pinecone(VectorStore):
     def from_existing_index(
         cls,
         index_name: str,
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         text_key: str = "text",
         namespace: Optional[str] = None,
     ) -> Pinecone:

langchain/vectorstores/qdrant.py

@@ -21,7 +21,7 @@ from typing import (
 import numpy as np
 
 from langchain.docstore.document import Document
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.vectorstores import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
@@ -55,7 +55,7 @@ class Qdrant(VectorStore):
         self,
         client: Any,
         collection_name: str,
-        embeddings: Optional[Embeddings] = None,
+        embeddings: Optional[TextEmbeddingModel] = None,
         content_payload_key: str = CONTENT_KEY,
         metadata_payload_key: str = METADATA_KEY,
         embedding_function: Optional[Callable] = None,  # deprecated
@@ -99,7 +99,7 @@ class Qdrant(VectorStore):
                 "Pass `Embeddings` instance to `embeddings` instead."
             )
 
-        if not isinstance(embeddings, Embeddings):
+        if not isinstance(embeddings, TextEmbeddingModel):
             warnings.warn(
                 "`embeddings` should be an instance of `Embeddings`."
                 "Using `embeddings` as `embedding_function` which is deprecated"
@@ -292,7 +292,7 @@ class Qdrant(VectorStore):
     def from_texts(
         cls: Type[Qdrant],
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         location: Optional[str] = None,
         url: Optional[str] = None,

langchain/vectorstores/redis.py

@@ -22,7 +22,7 @@ import numpy as np
 from pydantic import BaseModel, root_validator
 
 from langchain.docstore.document import Document
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.utils import get_from_dict_or_env
 from langchain.vectorstores.base import VectorStore, VectorStoreRetriever
 
@@ -361,7 +361,7 @@ class Redis(VectorStore):
     def from_texts_return_keys(
         cls,
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         index_name: Optional[str] = None,
         content_key: str = "content",
@@ -421,7 +421,7 @@ class Redis(VectorStore):
     def from_texts(
         cls: Type[Redis],
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         index_name: Optional[str] = None,
         content_key: str = "content",
@@ -502,7 +502,7 @@ class Redis(VectorStore):
     @classmethod
     def from_existing_index(
         cls,
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         index_name: str,
         content_key: str = "content",
         metadata_key: str = "metadata",

langchain/vectorstores/supabase.py

@@ -15,7 +15,7 @@ from typing import (
 import numpy as np
 
 from langchain.docstore.document import Document
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
@@ -41,14 +41,14 @@ class SupabaseVectorStore(VectorStore):
     # This is the embedding function. Don't confuse with the embedding vectors.
     # We should perhaps rename the underlying Embedding base class to EmbeddingFunction
     # or something
-    _embedding: Embeddings
+    _embedding: TextEmbeddingModel
     table_name: str
     query_name: str
 
     def __init__(
         self,
         client: supabase.client.Client,
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         table_name: str,
         query_name: Union[str, None] = None,
     ) -> None:
@@ -62,7 +62,7 @@ class SupabaseVectorStore(VectorStore):
             )
 
         self._client = client
-        self._embedding: Embeddings = embedding
+        self._embedding: TextEmbeddingModel = embedding
         self.table_name = table_name or "documents"
         self.query_name = query_name or "match_documents"
 
@@ -81,7 +81,7 @@ class SupabaseVectorStore(VectorStore):
     def from_texts(
         cls: Type["SupabaseVectorStore"],
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         client: Optional[supabase.client.Client] = None,
         table_name: Optional[str] = "documents",

langchain/vectorstores/tair.py

@@ -7,7 +7,7 @@ import uuid
 from typing import Any, Iterable, List, Optional, Type
 
 from langchain.docstore.document import Document
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.utils import get_from_dict_or_env
 from langchain.vectorstores.base import VectorStore
 
@@ -21,7 +21,7 @@ def _uuid_key() -> str:
 class Tair(VectorStore):
     def __init__(
         self,
-        embedding_function: Embeddings,
+        embedding_function: TextEmbeddingModel,
         url: str,
         index_name: str,
         content_key: str = "content",
@@ -140,7 +140,7 @@ class Tair(VectorStore):
     def from_texts(
         cls: Type[Tair],
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         index_name: str = "langchain",
         content_key: str = "content",
@@ -208,7 +208,7 @@ class Tair(VectorStore):
     def from_documents(
         cls,
         documents: List[Document],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         index_name: str = "langchain",
         content_key: str = "content",
@@ -262,7 +262,7 @@ class Tair(VectorStore):
     @classmethod
     def from_existing_index(
         cls,
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         index_name: str = "langchain",
         content_key: str = "content",
         metadata_key: str = "metadata",

langchain/vectorstores/weaviate.py

@@ -8,7 +8,7 @@ from uuid import uuid4
 import numpy as np
 
 from langchain.docstore.document import Document
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.utils import get_from_dict_or_env
 from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
@@ -89,7 +89,7 @@ class Weaviate(VectorStore):
         client: Any,
         index_name: str,
         text_key: str,
-        embedding: Optional[Embeddings] = None,
+        embedding: Optional[TextEmbeddingModel] = None,
         attributes: Optional[List[str]] = None,
         relevance_score_fn: Optional[
             Callable[[float], float]
@@ -364,7 +364,7 @@ class Weaviate(VectorStore):
     def from_texts(
         cls: Type[Weaviate],
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         **kwargs: Any,
     ) -> Weaviate:

langchain/vectorstores/zilliz.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 import logging
 from typing import Any, List, Optional
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.vectorstores.milvus import Milvus
 
 logger = logging.getLogger(__name__)
@@ -59,7 +59,7 @@ class Zilliz(Milvus):
     def from_texts(
         cls,
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         collection_name: str = "LangChainCollection",
         connection_args: dict[str, Any] = {},
@@ -73,7 +73,7 @@ class Zilliz(Milvus):
 
         Args:
             texts (List[str]): Text data.
-            embedding (Embeddings): Embedding function.
+            embedding (TextEmbeddingModel): Embedding function.
             metadatas (Optional[List[dict]]): Metadata for each text if it exists.
                 Defaults to None.
             collection_name (str, optional): Collection name to use. Defaults to

tests/integration_tests/vectorstores/fake_embeddings.py

@@ -1,12 +1,12 @@
 """Fake Embedding class for testing purposes."""
 from typing import List
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 
 fake_texts = ["foo", "bar", "baz"]
 
 
-class FakeEmbeddings(Embeddings):
+class FakeEmbeddings(TextEmbeddingModel):
     """Fake embeddings functionality for testing."""
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:

tests/integration_tests/vectorstores/test_qdrant.py

@@ -4,7 +4,7 @@ from typing import Callable, Optional
 import pytest
 
 from langchain.docstore.document import Document
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.vectorstores import Qdrant
 from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
 
@@ -139,7 +139,7 @@ def test_qdrant_max_marginal_relevance_search(
     ],
 )
 def test_qdrant_embedding_interface(
-    embeddings: Optional[Embeddings], embedding_function: Optional[Callable]
+    embeddings: Optional[TextEmbeddingModel], embedding_function: Optional[Callable]
 ) -> None:
     from qdrant_client import QdrantClient
 
@@ -162,7 +162,7 @@ def test_qdrant_embedding_interface(
     ],
 )
 def test_qdrant_embedding_interface_raises(
-    embeddings: Optional[Embeddings], embedding_function: Optional[Callable]
+    embeddings: Optional[TextEmbeddingModel], embedding_function: Optional[Callable]
 ) -> None:
     from qdrant_client import QdrantClient
 

tests/unit_tests/chains/test_hyde.py

@@ -9,12 +9,12 @@ from langchain.callbacks.manager import (
 )
 from langchain.chains.hyde.base import HypotheticalDocumentEmbedder
 from langchain.chains.hyde.prompts import PROMPT_MAP
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.llms.base import BaseLLM
 from langchain.schema import Generation, LLMResult
 
 
-class FakeEmbeddings(Embeddings):
+class FakeEmbeddings(TextEmbeddingModel):
     """Fake embedding class for tests."""
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:

tests/unit_tests/retrievers/test_time_weighted_retriever.py

@@ -5,7 +5,7 @@ from typing import Any, Iterable, List, Optional, Tuple, Type
 
 import pytest
 
-from langchain.embeddings.base import Embeddings
+from langchain.embeddings.base import TextEmbeddingModel
 from langchain.retrievers.time_weighted_retriever import (
     TimeWeightedVectorStoreRetriever,
     _get_hours_passed,
@@ -67,7 +67,7 @@ class MockVectorStore(VectorStore):
     def from_documents(
         cls: Type["MockVectorStore"],
         documents: List[Document],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         **kwargs: Any,
     ) -> "MockVectorStore":
         """Return VectorStore initialized from documents and embeddings."""
@@ -79,7 +79,7 @@ class MockVectorStore(VectorStore):
     def from_texts(
         cls: Type["MockVectorStore"],
         texts: List[str],
-        embedding: Embeddings,
+        embedding: TextEmbeddingModel,
         metadatas: Optional[List[dict]] = None,
         **kwargs: Any,
     ) -> "MockVectorStore":