Pinecone: Add V4 support (#7473)

tests/integration_tests/vectorstores/test_pinecone.py

@@ -1,5 +1,6 @@
 import importlib
 import os
+import time
 import uuid
 from typing import List
 
@@ -11,7 +12,6 @@ from langchain.embeddings import OpenAIEmbeddings
 from langchain.vectorstores.pinecone import Pinecone
 
 index_name = "langchain-test-index"  # name of the index
-namespace_name = "langchain-test-namespace"  # name of the namespace
 dimension = 1536  # dimension of the embeddings
 
 
@@ -39,40 +39,28 @@ class TestPinecone:
         cls.index = pinecone.Index(index_name)
 
         if index_name in pinecone.list_indexes():
-            index_stats = cls.index.describe_index_stats()
-            if index_stats["dimension"] == dimension:
-                # delete all the vectors in the index if the dimension is the same
-                # from all namespaces
-                index_stats = cls.index.describe_index_stats()
-                for _namespace_name in index_stats["namespaces"].keys():
-                    cls.index.delete(delete_all=True, namespace=_namespace_name)
+            pinecone.delete_index(index_name)
 
-            else:
-                pinecone.delete_index(index_name)
-                pinecone.create_index(name=index_name, dimension=dimension)
-        else:
-            pinecone.create_index(name=index_name, dimension=dimension)
+        pinecone.create_index(name=index_name, dimension=dimension)
 
         # insure the index is empty
         index_stats = cls.index.describe_index_stats()
         assert index_stats["dimension"] == dimension
-        if index_stats["namespaces"].get(namespace_name) is not None:
-            assert index_stats["namespaces"][namespace_name]["vector_count"] == 0
+        assert index_stats["total_vector_count"] == 0
 
     @classmethod
     def teardown_class(cls) -> None:
-        index_stats = cls.index.describe_index_stats()
-        for _namespace_name in index_stats["namespaces"].keys():
-            cls.index.delete(delete_all=True, namespace=_namespace_name)
+        if index_name in pinecone.list_indexes():
+            pinecone.delete_index(index_name)
+            pinecone.create_index(index_name, dimension=dimension)
 
         reset_pinecone()
 
     @pytest.fixture(autouse=True)
     def setup(self) -> None:
-        # delete all the vectors in the index
-        index_stats = self.index.describe_index_stats()
-        for _namespace_name in index_stats["namespaces"].keys():
-            self.index.delete(delete_all=True, namespace=_namespace_name)
+        if index_name in pinecone.list_indexes():
+            pinecone.delete_index(index_name)
+            pinecone.create_index(index_name, dimension=dimension)
 
         reset_pinecone()
 
@@ -86,12 +74,11 @@ class TestPinecone:
         texts.insert(0, needs)
 
         docsearch = Pinecone.from_texts(
-            texts=texts,
-            embedding=embedding_openai,
-            index_name=index_name,
-            namespace=namespace_name,
+            texts=texts, embedding=embedding_openai, index_name=index_name
         )
-        output = docsearch.similarity_search(unique_id, k=1, namespace=namespace_name)
+        # wait for the index to be ready
+        time.sleep(20)
+        output = docsearch.similarity_search(unique_id, k=1)
         assert output == [Document(page_content=needs)]
 
     @pytest.mark.vcr()
@@ -110,9 +97,10 @@ class TestPinecone:
             embedding_openai,
             index_name=index_name,
             metadatas=metadatas,
-            namespace=namespace_name,
         )
-        output = docsearch.similarity_search(needs, k=1, namespace=namespace_name)
+        # wait for the index to be ready
+        time.sleep(20)
+        output = docsearch.similarity_search(needs, k=1)
 
         # TODO: why metadata={"page": 0.0}) instead of {"page": 0}?
         assert output == [Document(page_content=needs, metadata={"page": 0.0})]
@@ -127,11 +115,10 @@ class TestPinecone:
             embedding_openai,
             index_name=index_name,
             metadatas=metadatas,
-            namespace=namespace_name,
-        )
-        output = docsearch.similarity_search_with_score(
-            "foo", k=3, namespace=namespace_name
         )
+        # wait for the index to be ready
+        time.sleep(20)
+        output = docsearch.similarity_search_with_score("foo", k=3)
         docs = [o[0] for o in output]
         scores = [o[1] for o in output]
         sorted_documents = sorted(docs, key=lambda x: x.metadata["page"])
@@ -144,57 +131,17 @@ class TestPinecone:
         ]
         assert scores[0] > scores[1] > scores[2]
 
-    def test_from_existing_index_with_namespaces(
-        self, embedding_openai: OpenAIEmbeddings
-    ) -> None:
-        """Test that namespaces are properly handled."""
-        # 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))]
-        Pinecone.from_texts(
-            texts_1,
-            embedding_openai,
-            index_name=index_name,
-            metadatas=metadatas,
-            namespace=f"{index_name}-1",
-        )
-
-        texts_2 = ["foo2", "bar2", "baz2"]
-        metadatas = [{"page": i} for i in range(len(texts_2))]
-
-        Pinecone.from_texts(
-            texts_2,
-            embedding_openai,
-            index_name=index_name,
-            metadatas=metadatas,
-            namespace=f"{index_name}-2",
-        )
-
-        # Search with namespace
-        docsearch = Pinecone.from_existing_index(
-            index_name=index_name,
-            embedding=embedding_openai,
-            namespace=f"{index_name}-1",
-        )
-        output = docsearch.similarity_search("foo", k=20, namespace=f"{index_name}-1")
-        # check that we don't get results from the other namespace
-        page_contents = sorted(set([o.page_content for o in output]))
-        assert all(content in ["foo", "bar", "baz"] for content in page_contents)
-        assert all(content not in ["foo2", "bar2", "baz2"] for content in page_contents)
-
     def test_add_documents_with_ids(
         self, texts: List[str], embedding_openai: OpenAIEmbeddings
     ) -> None:
         ids = [uuid.uuid4().hex for _ in range(len(texts))]
         Pinecone.from_texts(
-            texts=texts,
-            ids=ids,
-            embedding=embedding_openai,
-            index_name=index_name,
-            namespace=index_name,
+            texts=texts, ids=ids, embedding=embedding_openai, index_name=index_name
         )
+        # wait for the index to be ready
+        time.sleep(20)
         index_stats = self.index.describe_index_stats()
-        assert index_stats["namespaces"][index_name]["vector_count"] == len(texts)
+        assert index_stats["total_vector_count"] == len(texts)
 
         ids_1 = [uuid.uuid4().hex for _ in range(len(texts))]
         Pinecone.from_texts(
@@ -202,7 +149,8 @@ class TestPinecone:
             ids=ids_1,
             embedding=embedding_openai,
             index_name=index_name,
-            namespace=index_name,
         )
+        # wait for the index to be ready
+        time.sleep(20)
         index_stats = self.index.describe_index_stats()
-        assert index_stats["namespaces"][index_name]["vector_count"] == len(texts) * 2
+        assert index_stats["total_vector_count"] == len(texts) * 2