fix: use self.embeddings.aembed_* in async methods

langchain_postgres/vectorstores.py

@@ -842,7 +842,7 @@ def similarity_search(
             List of Documents most similar to the query.
         """
         assert not self._async_engine, "This method must be called without async_mode"
-        embedding = self.embedding_function.embed_query(text=query)
+        embedding = self.embeddings.embed_query(query)
         return self.similarity_search_by_vector(
             embedding=embedding,
             k=k,
@@ -867,7 +867,7 @@ async def asimilarity_search(
             List of Documents most similar to the query.
         """
         await self.__apost_init__()  # Lazy async init
-        embedding = self.embedding_function.embed_query(text=query)
+        embedding = await self.embeddings.aembed_query(query)
         return await self.asimilarity_search_by_vector(
             embedding=embedding,
             k=k,
@@ -891,7 +891,7 @@ def similarity_search_with_score(
             List of Documents most similar to the query and score for each.
         """
         assert not self._async_engine, "This method must be called without async_mode"
-        embedding = self.embedding_function.embed_query(query)
+        embedding = self.embeddings.embed_query(query)
         docs = self.similarity_search_with_score_by_vector(
             embedding=embedding, k=k, filter=filter
         )
@@ -914,7 +914,7 @@ async def asimilarity_search_with_score(
             List of Documents most similar to the query and score for each.
         """
         await self.__apost_init__()  # Lazy async init
-        embedding = self.embedding_function.embed_query(query)
+        embedding = await self.embeddings.aembed_query(query)
         docs = await self.asimilarity_search_with_score_by_vector(
             embedding=embedding, k=k, filter=filter
         )
@@ -968,7 +968,7 @@ def _results_to_docs_and_scores(self, results: Any) -> List[Tuple[Document, floa
                     page_content=result.EmbeddingStore.document,
                     metadata=result.EmbeddingStore.cmetadata,
                 ),
-                result.distance if self.embedding_function is not None else None,
+                result.distance if self.embeddings is not None else None,
             )
             for result in results
         ]
@@ -1472,7 +1472,7 @@ async def afrom_texts(
         **kwargs: Any,
     ) -> PGVector:
         """Return VectorStore initialized from documents and embeddings."""
-        embeddings = embedding.embed_documents(list(texts))
+        embeddings = await embedding.aembed_documents(list(texts))
         return await cls.__afrom(
             texts,
             embeddings,
@@ -1895,7 +1895,7 @@ def max_marginal_relevance_search(
         Returns:
             List[Document]: List of Documents selected by maximal marginal relevance.
         """
-        embedding = self.embedding_function.embed_query(query)
+        embedding = self.embeddings.embed_query(query)
         return self.max_marginal_relevance_search_by_vector(
             embedding,
             k=k,
@@ -1934,7 +1934,7 @@ async def amax_marginal_relevance_search(
             List[Document]: List of Documents selected by maximal marginal relevance.
         """
         await self.__apost_init__()  # Lazy async init
-        embedding = self.embedding_function.embed_query(query)
+        embedding = await self.embeddings.aembed_query(query)
         return await self.amax_marginal_relevance_search_by_vector(
             embedding,
             k=k,
@@ -1973,7 +1973,7 @@ def max_marginal_relevance_search_with_score(
             List[Tuple[Document, float]]: List of Documents selected by maximal marginal
                 relevance to the query and score for each.
         """
-        embedding = self.embedding_function.embed_query(query)
+        embedding = self.embeddings.embed_query(query)
         docs = self.max_marginal_relevance_search_with_score_by_vector(
             embedding=embedding,
             k=k,
@@ -2014,7 +2014,7 @@ async def amax_marginal_relevance_search_with_score(
                 relevance to the query and score for each.
         """
         await self.__apost_init__()  # Lazy async init
-        embedding = self.embedding_function.embed_query(query)
+        embedding = await self.embeddings.aembed_query(query)
         docs = await self.amax_marginal_relevance_search_with_score_by_vector(
             embedding=embedding,
             k=k,
@@ -2145,7 +2145,7 @@ def upsert(self, items: Sequence[Document], /, **kwargs: Any) -> UpsertResponse:
         texts = [item.page_content for item in items]
         metadatas = [item.metadata for item in items]
         ids = [item.id if item.id is not None else str(uuid.uuid4()) for item in items]
-        embeddings = self.embedding_function.embed_documents(list(texts))
+        embeddings = self.embeddings.embed_documents(list(texts))
         added_ids = self.add_embeddings(
             texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
         )
@@ -2175,7 +2175,7 @@ async def aupsert(
         texts = [item.page_content for item in items]
         metadatas = [item.metadata for item in items]
         ids = [item.id if item.id is not None else str(uuid.uuid4()) for item in items]
-        embeddings = await self.embedding_function.aembed_documents(list(texts))
+        embeddings = await self.embeddings.aembed_documents(list(texts))
         added_ids = await self.aadd_embeddings(
             texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
         )