community[minor]: Add Dria retriever (#17098)

[Dria](https://dria.co/) is a hub of public RAG models for developers to
both contribute and utilize a shared embedding lake. This PR adds a
retriever that can retrieve documents from Dria.

libs/community/tests/integration_tests/retrievers/test_dria_index.py

@@ -0,0 +1,41 @@
+import pytest
+from langchain_core.documents import Document
+
+from langchain_community.retrievers import DriaRetriever
+
+
+# Set a fixture for DriaRetriever
+@pytest.fixture
+def dria_retriever() -> DriaRetriever:
+    api_key = "<YOUR_API_KEY>"
+    contract_id = "B16z9i3rRi0KEeibrzzMU33YTB4WDtos1vdiMBTmKgs"
+    retriever = DriaRetriever(api_key=api_key, contract_id=contract_id)
+    return retriever
+
+
+def test_dria_retriever(dria_retriever: DriaRetriever) -> None:
+    texts = [
+        {
+            "text": "Langchain",
+            "metadata": {
+                "source": "source#1",
+                "document_id": "doc123",
+                "content": "Langchain",
+            },
+        }
+    ]
+    dria_retriever.add_texts(texts)
+
+    # Assuming get_relevant_documents returns a list of Document instances
+    docs = dria_retriever.get_relevant_documents("Langchain")
+
+    # Perform assertions
+    assert len(docs) > 0, "Expected at least one document"
+    doc = docs[0]
+    assert isinstance(doc, Document), "Expected a Document instance"
+    assert isinstance(doc.page_content, str), (
+        "Expected document content type " "to be string"
+    )
+    assert isinstance(
+        doc.metadata, dict
+    ), "Expected document metadata content to be a dictionary"