refac

langchain/chains/hyde/base.py

@@ -42,9 +42,9 @@ class HypotheticalDocumentEmbedder(Chain, Embeddings):
         """Output keys for Hyde's LLM chain."""
         return self.llm_chain.output_keys
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         """Call the base embeddings."""
-        return self.base_embeddings.embed_documents(texts)
+        return self.base_embeddings.embed_texts(texts)
 
     def combine_embeddings(self, embeddings: List[List[float]]) -> List[float]:
         """Combine embeddings into final embeddings."""
@@ -55,7 +55,7 @@ class HypotheticalDocumentEmbedder(Chain, Embeddings):
         var_name = self.llm_chain.input_keys[0]
         result = self.llm_chain.generate([{var_name: text}])
         documents = [generation.text for generation in result.generations[0]]
-        embeddings = self.embed_documents(documents)
+        embeddings = self.embed_texts(documents)
         return self.combine_embeddings(embeddings)
 
     def _call(

langchain/document_transformers.py

@@ -55,9 +55,7 @@ def _get_embeddings_from_stateful_docs(
     if len(documents) and "embedded_doc" in documents[0].state:
         embedded_documents = [doc.state["embedded_doc"] for doc in documents]
     else:
-        embedded_documents = embeddings.embed_documents(
-            [d.page_content for d in documents]
-        )
+        embedded_documents = embeddings.embed_texts([d.page_content for d in documents])
         for doc, embedding in zip(documents, embedded_documents):
             doc.state["embedded_doc"] = embedding
     return embedded_documents

langchain/embeddings/aleph_alpha.py

@@ -65,7 +65,7 @@ class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings):
         values["client"] = Client(token=aleph_alpha_api_key)
         return values
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         """Call out to Aleph Alpha's asymmetric Document endpoint.
 
         Args:
@@ -186,7 +186,7 @@ class AlephAlphaSymmetricSemanticEmbedding(AlephAlphaAsymmetricSemanticEmbedding
 
         return query_response.embedding
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         """Call out to Aleph Alpha's Document endpoint.
 
         Args:

langchain/embeddings/base.py

@@ -6,9 +6,13 @@ from typing import List
 class Embeddings(ABC):
     """Interface for embedding models."""
 
-    @abstractmethod
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        """Embed search docs."""
+        """DEPRECATED. Kept for backwards compatibility."""
+        return self.embed_texts(texts)
+
+    @abstractmethod
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
+        """Embed search texts."""
 
     @abstractmethod
     def embed_query(self, text: str) -> List[float]:

langchain/embeddings/cohere.py

@@ -54,7 +54,7 @@ class CohereEmbeddings(BaseModel, Embeddings):
             )
         return values
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         """Call out to Cohere's embedding endpoint.
 
         Args:

langchain/embeddings/fake.py

@@ -12,7 +12,7 @@ class FakeEmbeddings(Embeddings, BaseModel):
     def _get_embedding(self) -> List[float]:
         return list(np.random.normal(size=self.size))
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         return [self._get_embedding() for _ in texts]
 
     def embed_query(self, text: str) -> List[float]:

langchain/embeddings/google_palm.py

@@ -77,7 +77,7 @@ class GooglePalmEmbeddings(BaseModel, Embeddings):
 
         return values
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         return [self.embed_query(text) for text in texts]
 
     def embed_query(self, text: str) -> List[float]:

langchain/embeddings/huggingface.py

@@ -60,7 +60,7 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
 
         extra = Extra.forbid
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         """Compute doc embeddings using a HuggingFace transformer model.
 
         Args:
@@ -135,7 +135,7 @@ class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
 
         extra = Extra.forbid
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         """Compute doc embeddings using a HuggingFace instruct model.
 
         Args:

langchain/embeddings/huggingface_hub.py

@@ -77,7 +77,7 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
             )
         return values
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         """Call out to HuggingFaceHub's embedding endpoint for embedding search docs.
 
         Args:
@@ -101,5 +101,5 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
         Returns:
             Embeddings for the text.
         """
-        response = self.embed_documents([text])[0]
+        response = self.embed_texts([text])[0]
         return response

langchain/embeddings/jina.py

@@ -71,7 +71,7 @@ class JinaEmbeddings(BaseModel, Embeddings):
         payload = dict(inputs=docs, metadata=self.request_headers, **kwargs)
         return self.client.post(on="/encode", **payload)
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         """Call out to Jina's embedding endpoint.
         Args:
             texts: The list of texts to embed.

langchain/embeddings/llamacpp.py

@@ -99,7 +99,7 @@ class LlamaCppEmbeddings(BaseModel, Embeddings):
 
         return values
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         """Embed a list of documents using the Llama model.
 
         Args:

langchain/embeddings/openai.py

@@ -271,7 +271,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 headers=self.headers,
             )["data"][0]["embedding"]
 
-    def embed_documents(
+    def embed_texts(
         self, texts: List[str], chunk_size: Optional[int] = 0
     ) -> List[List[float]]:
         """Call out to OpenAI's embedding endpoint for embedding search docs.

langchain/embeddings/sagemaker_endpoint.py

@@ -164,9 +164,7 @@ class SagemakerEndpointEmbeddings(BaseModel, Embeddings):
 
         return self.content_handler.transform_output(response["Body"])
 
-    def embed_documents(
-        self, texts: List[str], chunk_size: int = 64
-    ) -> List[List[float]]:
+    def embed_texts(self, texts: List[str], chunk_size: int = 64) -> List[List[float]]:
         """Compute doc embeddings using a SageMaker Inference Endpoint.
 
         Args:

langchain/embeddings/self_hosted.py

@@ -72,7 +72,7 @@ class SelfHostedEmbeddings(SelfHostedPipeline, Embeddings):
 
         extra = Extra.forbid
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         """Compute doc embeddings using a HuggingFace transformer model.
 
         Args:

langchain/embeddings/self_hosted_hugging_face.py

@@ -140,7 +140,7 @@ class SelfHostedHuggingFaceInstructEmbeddings(SelfHostedHuggingFaceEmbeddings):
         load_fn_kwargs["device"] = load_fn_kwargs.get("device", 0)
         super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         """Compute doc embeddings using a HuggingFace instruct model.
 
         Args:

langchain/embeddings/tensorflow_hub.py

@@ -43,7 +43,7 @@ class TensorflowHubEmbeddings(BaseModel, Embeddings):
 
         extra = Extra.forbid
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         """Compute doc embeddings using a TensorflowHub embedding model.
 
         Args:

langchain/retrievers/pinecone_hybrid_search.py

@@ -49,7 +49,7 @@ def create_index(
         ]
 
         # create dense vectors
-        dense_embeds = embeddings.embed_documents(context_batch)
+        dense_embeds = embeddings.embed_texts(context_batch)
         # create sparse vectors
         sparse_embeds = sparse_encoder.encode_documents(context_batch)
         for s in sparse_embeds:

langchain/vectorstores/analyticdb.py

@@ -202,7 +202,7 @@ class AnalyticDB(VectorStore):
         if ids is None:
             ids = [str(uuid.uuid1()) for _ in texts]
 
-        embeddings = self.embedding_function.embed_documents(list(texts))
+        embeddings = self.embedding_function.embed_texts(list(texts))
 
         if not metadatas:
             metadatas = [{} for _ in texts]

langchain/vectorstores/annoy.py

@@ -351,7 +351,7 @@ class Annoy(VectorStore):
                 embeddings = OpenAIEmbeddings()
                 index = Annoy.from_texts(texts, embeddings)
         """
-        embeddings = embedding.embed_documents(texts)
+        embeddings = embedding.embed_texts(texts)
         return cls.__from(
             texts, embeddings, embedding, metadatas, metric, trees, n_jobs, **kwargs
         )

langchain/vectorstores/atlas.py

@@ -119,7 +119,7 @@ class AtlasDB(VectorStore):
 
         # Embedding upload case
         if self._embedding_function is not None:
-            _embeddings = self._embedding_function.embed_documents(texts)
+            _embeddings = self._embedding_function.embed_texts(texts)
             embeddings = np.stack(_embeddings)
             if metadatas is None:
                 data = [
@@ -194,7 +194,7 @@ class AtlasDB(VectorStore):
                 "AtlasDB requires an embedding_function for text similarity search!"
             )
 
-        _embedding = self._embedding_function.embed_documents([query])[0]
+        _embedding = self._embedding_function.embed_texts([query])[0]
         embedding = np.array(_embedding).reshape(1, -1)
         with self.project.wait_for_project_lock():
             neighbors, _ = self.project.projections[0].vector_search(

langchain/vectorstores/chroma.py

@@ -92,7 +92,7 @@ class Chroma(VectorStore):
         self._persist_directory = persist_directory
         self._collection = self._client.get_or_create_collection(
             name=collection_name,
-            embedding_function=self._embedding_function.embed_documents
+            embedding_function=self._embedding_function.embed_texts
             if self._embedding_function is not None
             else None,
             metadata=collection_metadata,
@@ -156,7 +156,7 @@ class Chroma(VectorStore):
             ids = [str(uuid.uuid1()) for _ in texts]
         embeddings = None
         if self._embedding_function is not None:
-            embeddings = self._embedding_function.embed_documents(list(texts))
+            embeddings = self._embedding_function.embed_texts(list(texts))
         self._collection.add(
             metadatas=metadatas, embeddings=embeddings, documents=texts, ids=ids
         )

langchain/vectorstores/deeplake.py

@@ -224,7 +224,7 @@ class DeepLake(VectorStore):
             embeds: Sequence[Optional[np.ndarray]] = []
 
             if self._embedding_function is not None:
-                embeddings = self._embedding_function.embed_documents(text_list)
+                embeddings = self._embedding_function.embed_texts(text_list)
                 embeds = [np.array(e, dtype=np.float32) for e in embeddings]
             else:
                 embeds = [None] * len(text_list)

langchain/vectorstores/docarray/base.py

@@ -77,7 +77,7 @@ class DocArrayIndex(VectorStore, ABC):
             List of ids from adding the texts into the vectorstore.
         """
         ids: List[str] = []
-        embeddings = self.embedding.embed_documents(list(texts))
+        embeddings = self.embedding.embed_texts(list(texts))
         for i, (t, e) in enumerate(zip(texts, embeddings)):
             m = metadatas[i] if metadatas else {}
             doc = self.doc_cls(text=t, embedding=e, metadata=m)

langchain/vectorstores/elastic_vector_search.py

@@ -167,7 +167,7 @@ class ElasticVectorSearch(VectorStore, ABC):
             )
         requests = []
         ids = []
-        embeddings = self.embedding.embed_documents(list(texts))
+        embeddings = self.embedding.embed_texts(list(texts))
         dim = len(embeddings[0])
         mapping = _default_text_mapping(dim)
 

langchain/vectorstores/faiss.py

@@ -417,7 +417,7 @@ class FAISS(VectorStore):
                 embeddings = OpenAIEmbeddings()
                 faiss = FAISS.from_texts(texts, embeddings)
         """
-        embeddings = embedding.embed_documents(texts)
+        embeddings = embedding.embed_texts(texts)
         return cls.__from(
             texts,
             embeddings,

langchain/vectorstores/lancedb.py

@@ -71,7 +71,7 @@ class LanceDB(VectorStore):
         # Embed texts and create documents
         docs = []
         ids = ids or [str(uuid.uuid4()) for _ in texts]
-        embeddings = self._embedding.embed_documents(list(texts))
+        embeddings = self._embedding.embed_texts(list(texts))
         for idx, text in enumerate(texts):
             embedding = embeddings[idx]
             metadata = metadatas[idx] if metadatas else {}

langchain/vectorstores/milvus.py

@@ -397,7 +397,7 @@ class Milvus(VectorStore):
         texts = list(texts)
 
         try:
-            embeddings = self.embedding_func.embed_documents(texts)
+            embeddings = self.embedding_func.embed_texts(texts)
         except NotImplementedError:
             embeddings = [self.embedding_func.embed_query(x) for x in texts]
 

langchain/vectorstores/opensearch_vector_search.py

@@ -327,7 +327,7 @@ class OpenSearchVectorSearch(VectorStore):
             text_field: Document field the text of the document is stored in. Defaults
             to "text".
         """
-        embeddings = self.embedding_function.embed_documents(list(texts))
+        embeddings = self.embedding_function.embed_texts(list(texts))
         _validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
         text_field = _get_kwargs_value(kwargs, "text_field", "text")
         dim = len(embeddings[0])
@@ -560,7 +560,7 @@ class OpenSearchVectorSearch(VectorStore):
             "ef_construction",
             "m",
         ]
-        embeddings = embedding.embed_documents(texts)
+        embeddings = embedding.embed_texts(texts)
         _validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
         dim = len(embeddings[0])
         # Get the index name from either from kwargs or ENV Variable

langchain/vectorstores/pgvector.py

@@ -212,7 +212,7 @@ class PGVector(VectorStore):
         if ids is None:
             ids = [str(uuid.uuid1()) for _ in texts]
 
-        embeddings = self.embedding_function.embed_documents(list(texts))
+        embeddings = self.embedding_function.embed_texts(list(texts))
 
         if not metadatas:
             metadatas = [{} for _ in texts]

langchain/vectorstores/pinecone.py

@@ -229,7 +229,7 @@ class Pinecone(VectorStore):
             else:
                 ids_batch = [str(uuid.uuid4()) for n in range(i, i_end)]
             # create embeddings
-            embeds = embedding.embed_documents(lines_batch)
+            embeds = embedding.embed_texts(lines_batch)
             # prep metadata and upsert batch
             if metadatas:
                 metadata = metadatas[i:i_end]

langchain/vectorstores/qdrant.py

@@ -139,7 +139,7 @@ class Qdrant(VectorStore):
             List of floats representing the texts embedding.
         """
         if self.embeddings is not None:
-            embeddings = self.embeddings.embed_documents(list(texts))
+            embeddings = self.embeddings.embed_texts(list(texts))
             if hasattr(embeddings, "tolist"):
                 embeddings = embeddings.tolist()
         elif self._embeddings_function is not None:
@@ -388,7 +388,7 @@ class Qdrant(VectorStore):
         from qdrant_client.http import models as rest
 
         # Just do a single quick embedding to get vector size
-        partial_embeddings = embedding.embed_documents(texts[:1])
+        partial_embeddings = embedding.embed_texts(texts[:1])
         vector_size = len(partial_embeddings[0])
 
         collection_name = collection_name or uuid.uuid4().hex
@@ -418,7 +418,7 @@ class Qdrant(VectorStore):
         )
 
         # Now generate the embeddings for all the texts
-        embeddings = embedding.embed_documents(texts)
+        embeddings = embedding.embed_texts(texts)
 
         client.upsert(
             collection_name=collection_name,

langchain/vectorstores/redis.py

@@ -408,7 +408,7 @@ class Redis(VectorStore):
         )
 
         # Create embeddings over documents
-        embeddings = embedding.embed_documents(texts)
+        embeddings = embedding.embed_texts(texts)
 
         # Create the search index
         instance._create_index(dim=len(embeddings[0]), distance_metric=distance_metric)

langchain/vectorstores/supabase.py

@@ -74,7 +74,7 @@ class SupabaseVectorStore(VectorStore):
     ) -> List[str]:
         docs = self._texts_to_documents(texts, metadatas)
 
-        vectors = self._embedding.embed_documents(list(texts))
+        vectors = self._embedding.embed_texts(list(texts))
         return self.add_vectors(vectors, docs)
 
     @classmethod
@@ -96,7 +96,7 @@ class SupabaseVectorStore(VectorStore):
         if not table_name:
             raise ValueError("Supabase document table_name is required.")
 
-        embeddings = embedding.embed_documents(texts)
+        embeddings = embedding.embed_texts(texts)
         docs = cls._texts_to_documents(texts, metadatas)
         _ids = cls._add_vectors(client, table_name, embeddings, docs)
 
@@ -115,7 +115,7 @@ class SupabaseVectorStore(VectorStore):
     def similarity_search(
         self, query: str, k: int = 4, **kwargs: Any
     ) -> List[Document]:
-        vectors = self._embedding.embed_documents([query])
+        vectors = self._embedding.embed_texts([query])
         return self.similarity_search_by_vector(vectors[0], k)
 
     def similarity_search_by_vector(
@@ -130,7 +130,7 @@ class SupabaseVectorStore(VectorStore):
     def similarity_search_with_relevance_scores(
         self, query: str, k: int = 4, **kwargs: Any
     ) -> List[Tuple[Document, float]]:
-        vectors = self._embedding.embed_documents([query])
+        vectors = self._embedding.embed_texts([query])
         return self.similarity_search_by_vector_with_relevance_scores(vectors[0], k)
 
     def similarity_search_by_vector_with_relevance_scores(
@@ -328,7 +328,7 @@ class SupabaseVectorStore(VectorStore):
         END;
         $$;```
         """
-        embedding = self._embedding.embed_documents([query])
+        embedding = self._embedding.embed_texts([query])
         docs = self.max_marginal_relevance_search_by_vector(
             embedding[0], k, fetch_k, lambda_mult=lambda_mult
         )

langchain/vectorstores/tair.py

@@ -82,7 +82,7 @@ class Tair(VectorStore):
         keys = kwargs.get("keys", None)
         # Write data to tair
         pipeline = self.client.pipeline(transaction=False)
-        embeddings = self.embedding_function.embed_documents(list(texts))
+        embeddings = self.embedding_function.embed_texts(list(texts))
         for i, text in enumerate(texts):
             # Use provided key otherwise use default key
             key = keys[i] if keys else _uuid_key()
@@ -191,7 +191,7 @@ class Tair(VectorStore):
             raise ValueError(f"tair failed to connect: {e}")
 
         # Create embeddings for documents
-        embeddings = embedding.embed_documents(texts)
+        embeddings = embedding.embed_texts(texts)
 
         tair_vector_store.create_index_if_not_exist(
             len(embeddings[0]),

langchain/vectorstores/weaviate.py

@@ -142,7 +142,7 @@ class Weaviate(VectorStore):
                 )
 
                 if self._embedding is not None:
-                    vector = self._embedding.embed_documents([text])[0]
+                    vector = self._embedding.embed_texts([text])[0]
                 else:
                     vector = None
                 batch.add_data_object(
@@ -391,7 +391,7 @@ class Weaviate(VectorStore):
         from weaviate.util import get_valid_uuid
 
         index_name = kwargs.get("index_name", f"LangChain_{uuid4().hex}")
-        embeddings = embedding.embed_documents(texts) if embedding else None
+        embeddings = embedding.embed_texts(texts) if embedding else None
         text_key = "text"
         schema = _default_schema(index_name)
         attributes = list(metadatas[0].keys()) if metadatas else None

tests/integration_tests/embeddings/test_cohere.py

@@ -6,7 +6,7 @@ def test_cohere_embedding_documents() -> None:
     """Test cohere embeddings."""
     documents = ["foo bar"]
     embedding = CohereEmbeddings()
-    output = embedding.embed_documents(documents)
+    output = embedding.embed_texts(documents)
     assert len(output) == 1
     assert len(output[0]) == 2048
 

tests/integration_tests/embeddings/test_google_palm.py

@@ -10,7 +10,7 @@ def test_google_palm_embedding_documents() -> None:
     """Test Google PaLM embeddings."""
     documents = ["foo bar"]
     embedding = GooglePalmEmbeddings()
-    output = embedding.embed_documents(documents)
+    output = embedding.embed_texts(documents)
     assert len(output) == 1
     assert len(output[0]) == 768
 
@@ -19,7 +19,7 @@ def test_google_palm_embedding_documents_multiple() -> None:
     """Test Google PaLM embeddings."""
     documents = ["foo bar", "bar foo", "foo"]
     embedding = GooglePalmEmbeddings()
-    output = embedding.embed_documents(documents)
+    output = embedding.embed_texts(documents)
     assert len(output) == 3
     assert len(output[0]) == 768
     assert len(output[1]) == 768

tests/integration_tests/embeddings/test_huggingface.py

@@ -10,7 +10,7 @@ def test_huggingface_embedding_documents() -> None:
     """Test huggingface embeddings."""
     documents = ["foo bar"]
     embedding = HuggingFaceEmbeddings()
-    output = embedding.embed_documents(documents)
+    output = embedding.embed_texts(documents)
     assert len(output) == 1
     assert len(output[0]) == 768
 
@@ -27,7 +27,7 @@ def test_huggingface_instructor_embedding_documents() -> None:
     """Test huggingface embeddings."""
     documents = ["foo bar"]
     embedding = HuggingFaceInstructEmbeddings()
-    output = embedding.embed_documents(documents)
+    output = embedding.embed_texts(documents)
     assert len(output) == 1
     assert len(output[0]) == 768
 

tests/integration_tests/embeddings/test_huggingface_hub.py

@@ -8,7 +8,7 @@ def test_huggingfacehub_embedding_documents() -> None:
     """Test huggingfacehub embeddings."""
     documents = ["foo bar"]
     embedding = HuggingFaceHubEmbeddings()
-    output = embedding.embed_documents(documents)
+    output = embedding.embed_texts(documents)
     assert len(output) == 1
     assert len(output[0]) == 768
 

tests/integration_tests/embeddings/test_jina.py

@@ -6,7 +6,7 @@ def test_jina_embedding_documents() -> None:
     """Test jina embeddings for documents."""
     documents = ["foo bar", "bar foo"]
     embedding = JinaEmbeddings()
-    output = embedding.embed_documents(documents)
+    output = embedding.embed_texts(documents)
     assert len(output) == 2
     assert len(output[0]) == 512
 

tests/integration_tests/embeddings/test_llamacpp.py

@@ -32,7 +32,7 @@ def test_llamacpp_embedding_documents() -> None:
     documents = ["foo bar"]
     model_path = get_model()
     embedding = LlamaCppEmbeddings(model_path=model_path)
-    output = embedding.embed_documents(documents)
+    output = embedding.embed_texts(documents)
     assert len(output) == 1
     assert len(output[0]) == 512
 

tests/integration_tests/embeddings/test_openai.py

@@ -9,7 +9,7 @@ def test_openai_embedding_documents() -> None:
     """Test openai embeddings."""
     documents = ["foo bar"]
     embedding = OpenAIEmbeddings()
-    output = embedding.embed_documents(documents)
+    output = embedding.embed_texts(documents)
     assert len(output) == 1
     assert len(output[0]) == 1536
 
@@ -19,7 +19,7 @@ def test_openai_embedding_documents_multiple() -> None:
     documents = ["foo bar", "bar foo", "foo"]
     embedding = OpenAIEmbeddings(chunk_size=2)
     embedding.embedding_ctx_length = 8191
-    output = embedding.embed_documents(documents)
+    output = embedding.embed_texts(documents)
     assert len(output) == 3
     assert len(output[0]) == 1536
     assert len(output[1]) == 1536
@@ -38,7 +38,7 @@ def test_openai_embedding_with_empty_string() -> None:
     """Test openai embeddings with empty string."""
     document = ["", "abc"]
     embedding = OpenAIEmbeddings()
-    output = embedding.embed_documents(document)
+    output = embedding.embed_texts(document)
     assert len(output) == 2
     assert len(output[0]) == 1536
     expected_output = openai.Embedding.create(input="", model="text-embedding-ada-002")[

tests/integration_tests/embeddings/test_self_hosted.py

@@ -24,7 +24,7 @@ def test_self_hosted_huggingface_embedding_documents() -> None:
     documents = ["foo bar"]
     gpu = get_remote_instance()
     embedding = SelfHostedHuggingFaceEmbeddings(hardware=gpu)
-    output = embedding.embed_documents(documents)
+    output = embedding.embed_texts(documents)
     assert len(output) == 1
     assert len(output[0]) == 768
 
@@ -43,7 +43,7 @@ def test_self_hosted_huggingface_instructor_embedding_documents() -> None:
     documents = ["foo bar"]
     gpu = get_remote_instance()
     embedding = SelfHostedHuggingFaceInstructEmbeddings(hardware=gpu)
-    output = embedding.embed_documents(documents)
+    output = embedding.embed_texts(documents)
     assert len(output) == 1
     assert len(output[0]) == 768
 
@@ -80,7 +80,7 @@ def test_self_hosted_embedding_documents() -> None:
     embedding = SelfHostedEmbeddings(
         model_load_fn=get_pipeline, hardware=gpu, inference_fn=inference_fn
     )
-    output = embedding.embed_documents(documents)
+    output = embedding.embed_texts(documents)
     assert len(output) == 2
     assert len(output[0]) == 50265
 

tests/integration_tests/embeddings/test_sentence_transformer.py

@@ -9,7 +9,7 @@ def test_sentence_transformer_embedding_documents() -> None:
     """Test sentence_transformer embeddings."""
     embedding = SentenceTransformerEmbeddings()
     documents = ["foo bar"]
-    output = embedding.embed_documents(documents)
+    output = embedding.embed_texts(documents)
     assert len(output) == 1
     assert len(output[0]) == 384
 

tests/integration_tests/embeddings/test_tensorflow_hub.py

@@ -6,7 +6,7 @@ def test_tensorflowhub_embedding_documents() -> None:
     """Test tensorflowhub embeddings."""
     documents = ["foo bar"]
     embedding = TensorflowHubEmbeddings()
-    output = embedding.embed_documents(documents)
+    output = embedding.embed_texts(documents)
     assert len(output) == 1
     assert len(output[0]) == 512
 

tests/integration_tests/vectorstores/fake_embeddings.py

@@ -9,7 +9,7 @@ fake_texts = ["foo", "bar", "baz"]
 class FakeEmbeddings(Embeddings):
     """Fake embeddings functionality for testing."""
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         """Return simple embeddings.
         Embeddings encode each text as its index."""
         return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))]

tests/integration_tests/vectorstores/test_analyticdb.py

@@ -24,7 +24,7 @@ ADA_TOKEN_COUNT = 1536
 class FakeEmbeddingsWithAdaDimension(FakeEmbeddings):
     """Fake embeddings functionality for testing."""
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         """Return simple embeddings."""
         return [
             [float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(i)] for i in range(len(texts))

tests/integration_tests/vectorstores/test_deeplake.py

@@ -143,7 +143,7 @@ def test_similarity_search_by_vector(
     deeplake_datastore: DeepLake, distance_metric: str
 ) -> None:
     """Test similarity search by vector."""
-    embeddings = FakeEmbeddings().embed_documents(["foo", "bar", "baz"])
+    embeddings = FakeEmbeddings().embed_texts(["foo", "bar", "baz"])
     output = deeplake_datastore.similarity_search_by_vector(
         embeddings[1], k=1, distance_metric=distance_metric
     )
@@ -185,7 +185,7 @@ def test_max_marginal_relevance_search(deeplake_datastore: DeepLake) -> None:
 
     assert output == [Document(page_content="foo", metadata={"page": "0"})]
 
-    embeddings = FakeEmbeddings().embed_documents(["foo", "bar", "baz"])
+    embeddings = FakeEmbeddings().embed_texts(["foo", "bar", "baz"])
     output = deeplake_datastore.max_marginal_relevance_search_by_vector(
         embeddings[0], k=1, fetch_k=2
     )

tests/integration_tests/vectorstores/test_lancedb.py

@@ -8,7 +8,7 @@ def test_lancedb() -> None:
     embeddings = FakeEmbeddings()
     db = lancedb.connect("/tmp/lancedb")
     texts = ["text 1", "text 2", "item 3"]
-    vectors = embeddings.embed_documents(texts)
+    vectors = embeddings.embed_texts(texts)
     table = db.create_table(
         "my_table",
         data=[
@@ -27,7 +27,7 @@ def test_lancedb_add_texts() -> None:
     embeddings = FakeEmbeddings()
     db = lancedb.connect("/tmp/lancedb")
     texts = ["text 1"]
-    vectors = embeddings.embed_documents(texts)
+    vectors = embeddings.embed_texts(texts)
     table = db.create_table(
         "my_table",
         data=[

tests/integration_tests/vectorstores/test_pgvector.py

@@ -24,7 +24,7 @@ ADA_TOKEN_COUNT = 1536
 class FakeEmbeddingsWithAdaDimension(FakeEmbeddings):
     """Fake embeddings functionality for testing."""
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         """Return simple embeddings."""
         return [
             [float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(i)] for i in range(len(texts))

tests/unit_tests/chains/test_hyde.py

@@ -17,7 +17,7 @@ from langchain.schema import Generation, LLMResult
 class FakeEmbeddings(Embeddings):
     """Fake embedding class for tests."""
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
         """Return random floats."""
         return [list(np.random.uniform(0, 1, 10)) for _ in range(10)]