x

libs/community/langchain_community/vectorstores/pinecone.py

@@ -401,7 +401,7 @@ def from_texts(
         pool_threads: int = 4,
         embeddings_chunk_size: int = 1000,
         **kwargs: Any,
-    ) -> PineconeVectorStore:
+    ) -> Pinecone:
         """
         DEPRECATED: use langchain_pinecone.PineconeVectorStore.from_texts instead:
         Construct Pinecone wrapper from raw documents.
@@ -451,7 +451,7 @@ def from_existing_index(
         text_key: str = "text",
         namespace: Optional[str] = None,
         pool_threads: int = 4,
-    ) -> PineconeVectorStore:
+    ) -> Pinecone:
         """Load pinecone vectorstore from index name."""
         pinecone_index = cls.get_pinecone_index(index_name, pool_threads)
         return cls(pinecone_index, embedding, text_key, namespace)

libs/community/tests/integration_tests/vectorstores/test_pinecone.py

@@ -91,7 +91,7 @@ def test_from_texts(
         needs = f"foobuu {unique_id} booo"
         texts.insert(0, needs)
 
-        docsearch = PineconeVectorStore.from_texts(
+        docsearch = Pinecone.from_texts(
             texts=texts,
             embedding=embedding_openai,
             index_name=index_name,
@@ -111,7 +111,7 @@ def test_from_texts_with_metadatas(
         texts.insert(0, needs)
 
         metadatas = [{"page": i} for i in range(len(texts))]
-        docsearch = PineconeVectorStore.from_texts(
+        docsearch = Pinecone.from_texts(
             texts,
             embedding_openai,
             index_name=index_name,
@@ -128,7 +128,7 @@ def test_from_texts_with_scores(self, embedding_openai: OpenAIEmbeddings) -> Non
         """Test end to end construction and search with scores and IDs."""
         texts = ["foo", "bar", "baz"]
         metadatas = [{"page": i} for i in range(len(texts))]
-        docsearch = PineconeVectorStore.from_texts(
+        docsearch = Pinecone.from_texts(
             texts,
             embedding_openai,
             index_name=index_name,
@@ -157,7 +157,7 @@ def test_from_existing_index_with_namespaces(
         # Create two indexes with the same name but different namespaces
         texts_1 = ["foo", "bar", "baz"]
         metadatas = [{"page": i} for i in range(len(texts_1))]
-        PineconeVectorStore.from_texts(
+        Pinecone.from_texts(
             texts_1,
             embedding_openai,
             index_name=index_name,
@@ -168,7 +168,7 @@ def test_from_existing_index_with_namespaces(
         texts_2 = ["foo2", "bar2", "baz2"]
         metadatas = [{"page": i} for i in range(len(texts_2))]
 
-        PineconeVectorStore.from_texts(
+        Pinecone.from_texts(
             texts_2,
             embedding_openai,
             index_name=index_name,
@@ -177,7 +177,7 @@ def test_from_existing_index_with_namespaces(
         )
 
         # Search with namespace
-        docsearch = PineconeVectorStore.from_existing_index(
+        docsearch = Pinecone.from_existing_index(
             index_name=index_name,
             embedding=embedding_openai,
             namespace=f"{index_name}-1",
@@ -192,7 +192,7 @@ def test_add_documents_with_ids(
         self, texts: List[str], embedding_openai: OpenAIEmbeddings
     ) -> None:
         ids = [uuid.uuid4().hex for _ in range(len(texts))]
-        PineconeVectorStore.from_texts(
+        Pinecone.from_texts(
             texts=texts,
             ids=ids,
             embedding=embedding_openai,
@@ -203,7 +203,7 @@ def test_add_documents_with_ids(
         assert index_stats["namespaces"][index_name]["vector_count"] == len(texts)
 
         ids_1 = [uuid.uuid4().hex for _ in range(len(texts))]
-        PineconeVectorStore.from_texts(
+        Pinecone.from_texts(
             texts=texts,
             ids=ids_1,
             embedding=embedding_openai,
@@ -219,7 +219,7 @@ def test_relevance_score_bound(self, embedding_openai: OpenAIEmbeddings) -> None
         """Ensures all relevance scores are between 0 and 1."""
         texts = ["foo", "bar", "baz"]
         metadatas = [{"page": i} for i in range(len(texts))]
-        docsearch = PineconeVectorStore.from_texts(
+        docsearch = Pinecone.from_texts(
             texts,
             embedding_openai,
             index_name=index_name,
@@ -271,7 +271,7 @@ def test_from_texts_with_metadatas_benchmark(
         texts = [document.page_content for document in documents] * data_multiplier
         uuids = [uuid.uuid4().hex for _ in range(len(texts))]
         metadatas = [{"page": i} for i in range(len(texts))]
-        docsearch = PineconeVectorStore.from_texts(
+        docsearch = Pinecone.from_texts(
             texts,
             embedding_openai,
             ids=uuids,