community[minor]: Rememberizer retriever (#20052)

**Description:**
This pull request introduces a new feature for LangChain: the
integration with the Rememberizer API through a custom retriever.
This enables LangChain applications to allow users to load and sync
their data from Dropbox, Google Drive, Slack, their hard drive into a
vector database that LangChain can query. Queries involve sending text
chunks generated within LangChain and retrieving a collection of
semantically relevant user data for inclusion in LLM prompts.
User knowledge dramatically improved AI applications.
The Rememberizer integration will also allow users to access general
purpose vectorized data such as Reddit channel discussions and US
patents.

**Issue:**
N/A

**Dependencies:**
N/A

**Twitter handle:**
https://twitter.com/Rememberizer

libs/community/tests/unit_tests/utilities/test_rememberizer.py

@@ -0,0 +1,75 @@
+import unittest
+from typing import Any
+from unittest.mock import patch
+
+import responses
+
+from langchain_community.utilities import RememberizerAPIWrapper
+
+
+class TestRememberizerAPIWrapper(unittest.TestCase):
+    @responses.activate
+    def test_search_successful(self) -> None:
+        responses.add(
+            responses.GET,
+            "https://api.rememberizer.ai/api/v1/documents/search?q=test&n=10",
+            json={
+                "matched_chunks": [
+                    {
+                        "chunk_id": "chunk",
+                        "matched_content": "content",
+                        "document": {"id": "id", "name": "name"},
+                    }
+                ]
+            },
+        )
+        wrapper = RememberizerAPIWrapper(rememberizer_api_key="dummy_key", n=10)
+        result = wrapper.search("test")
+        self.assertEqual(
+            result,
+            [
+                {
+                    "chunk_id": "chunk",
+                    "matched_content": "content",
+                    "document": {"id": "id", "name": "name"},
+                }
+            ],
+        )
+
+    @responses.activate
+    def test_search_fail(self) -> None:
+        responses.add(
+            responses.GET,
+            "https://api.rememberizer.ai/api/v1/documents/search?q=test&n=10",
+            status=400,
+            json={"detail": "Incorrect authentication credentials."},
+        )
+        wrapper = RememberizerAPIWrapper(rememberizer_api_key="dummy_key", n=10)
+        with self.assertRaises(ValueError) as e:
+            wrapper.search("test")
+            self.assertEqual(
+                str(e.exception),
+                "API Error: {'detail': 'Incorrect authentication credentials.'}",
+            )
+
+    @patch("langchain_community.utilities.rememberizer.RememberizerAPIWrapper.search")
+    def test_load(self, mock_search: Any) -> None:
+        mock_search.return_value = [
+            {
+                "chunk_id": "chunk1",
+                "matched_content": "content1",
+                "document": {"id": "id1", "name": "name1"},
+            },
+            {
+                "chunk_id": "chunk2",
+                "matched_content": "content2",
+                "document": {"id": "id2", "name": "name2"},
+            },
+        ]
+        wrapper = RememberizerAPIWrapper(rememberizer_api_key="dummy_key", n=10)
+        result = wrapper.load("test")
+        self.assertEqual(len(result), 2)
+        self.assertEqual(result[0].page_content, "content1")
+        self.assertEqual(result[0].metadata, {"id": "id1", "name": "name1"})
+        self.assertEqual(result[1].page_content, "content2")
+        self.assertEqual(result[1].metadata, {"id": "id2", "name": "name2"})