fix(google_genai): use dataclass replace to avoid modifying input documents (#2762)

This PR fixes the Google GenAI document embedder to not modify input
Documents in place when setting embeddings.

Instead of mutating the original documents:
  doc.embedding = embeddings

We now create new document instances using dataclass replace:
  replace(doc, embedding=embeddings)

This follows the established pattern from haystack-ai/haystack#9693 and
aligns with other integrations (FastEmbed, Optimum, Nvidia, Bedrock, Cohere).

Related to: #2174

Co-authored-by: David S. Batista <dsbatista@gmail.com>

Author: GunaPalanivel

integrations/google_genai/src/haystack_integrations/components/embedders/google_genai/document_embedder.py

@@ -2,6 +2,7 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
+from dataclasses import replace
 from typing import Any, Literal
 
 from google.genai import types
@@ -281,10 +282,11 @@ def run(self, documents: list[Document]) -> dict[str, list[Document]] | dict[str
         meta: dict[str, Any]
         embeddings, meta = self._embed_batch(texts_to_embed=texts_to_embed, batch_size=self._batch_size)
 
+        new_documents = []
         for doc, emb in zip(documents, embeddings, strict=True):
-            doc.embedding = emb
+            new_documents.append(replace(doc, embedding=emb))
 
-        return {"documents": documents, "meta": meta}
+        return {"documents": new_documents, "meta": meta}
 
     @component.output_types(documents=list[Document], meta=dict[str, Any])
     async def run_async(self, documents: list[Document]) -> dict[str, list[Document]] | dict[str, Any]:
@@ -310,7 +312,8 @@ async def run_async(self, documents: list[Document]) -> dict[str, list[Document]
 
         embeddings, meta = await self._embed_batch_async(texts_to_embed=texts_to_embed, batch_size=self._batch_size)
 
+        new_documents = []
         for doc, emb in zip(documents, embeddings, strict=True):
-            doc.embedding = emb
+            new_documents.append(replace(doc, embedding=emb))
 
-        return {"documents": documents, "meta": meta}
+        return {"documents": new_documents, "meta": meta}

integrations/google_genai/tests/test_document_embedder.py

@@ -201,6 +201,61 @@ def test_run_on_empty_list(self):
         assert result["documents"] is not None
         assert not result["documents"]  # empty list
 
+    def test_run_does_not_modify_original_documents(self, monkeypatch):
+        monkeypatch.setenv("GOOGLE_API_KEY", "fake-api-key")
+        embedder = GoogleGenAIDocumentEmbedder()
+
+        docs = [
+            Document(content="I love cheese", meta={"topic": "Cuisine"}),
+            Document(content="A transformer is a deep learning architecture", meta={"topic": "ML"}),
+        ]
+
+        # Mock the _embed_batch method to return fake embeddings
+        def mock_embed_batch(texts_to_embed, batch_size):
+            embeddings = [[0.1, 0.2, 0.3] for _ in texts_to_embed]
+            meta = {"model": "text-embedding-004"}
+            return embeddings, meta
+
+        embedder._embed_batch = mock_embed_batch
+
+        result = embedder.run(documents=docs)
+
+        # Check that the original documents are not modified
+        for doc in docs:
+            assert doc.embedding is None
+
+        # Check that the returned documents have embeddings
+        for doc_with_embedding in result["documents"]:
+            assert doc_with_embedding.embedding == [0.1, 0.2, 0.3]
+
+    @pytest.mark.asyncio
+    async def test_run_async_does_not_modify_original_documents(self, monkeypatch):
+        monkeypatch.setenv("GOOGLE_API_KEY", "fake-api-key")
+        embedder = GoogleGenAIDocumentEmbedder()
+
+        docs = [
+            Document(content="I love cheese", meta={"topic": "Cuisine"}),
+            Document(content="A transformer is a deep learning architecture", meta={"topic": "ML"}),
+        ]
+
+        # Mock the _embed_batch_async method to return fake embeddings
+        async def mock_embed_batch_async(texts_to_embed, batch_size):
+            embeddings = [[0.1, 0.2, 0.3] for _ in texts_to_embed]
+            meta = {"model": "text-embedding-004"}
+            return embeddings, meta
+
+        embedder._embed_batch_async = mock_embed_batch_async
+
+        result = await embedder.run_async(documents=docs)
+
+        # Check that the original documents are not modified
+        for doc in docs:
+            assert doc.embedding is None
+
+        # Check that the returned documents have embeddings
+        for doc_with_embedding in result["documents"]:
+            assert doc_with_embedding.embedding == [0.1, 0.2, 0.3]
+
     @pytest.mark.skipif(
         not os.environ.get("GOOGLE_API_KEY", None),
         reason="Export an env var called GOOGLE_API_KEY containing the Google API key to run this test.",