feat(adk): add native Bedrock embedding support for agent memory

python/packages/kagent-adk/src/kagent/adk/_memory_service.py

@@ -368,6 +368,8 @@ async def _call_embedding_provider(
             return await self._embed_ollama(model_name, texts, api_base)
         if provider in ("vertex_ai", "gemini"):
             return await self._embed_google(provider, model_name, texts)
+        if provider == "bedrock":
+            return await self._embed_bedrock(model_name, texts)
         # Unknown provider — try OpenAI-compatible as a fallback
         logger.warning("Unknown embedding provider '%s'; attempting OpenAI-compatible call.", provider)
         return await self._embed_openai("openai", model_name, texts, api_base)
@@ -437,6 +439,39 @@ async def _embed_google(
         )
         return [list(emb.values) for emb in response.embeddings]
 
+    async def _embed_bedrock(
+        self,
+        model_name: str,
+        texts: List[str],
+    ) -> List[List[float]]:
+        """Embed using the AWS Bedrock Titan Embedding API via boto3.
+
+        Uses the same credential chain (env vars, IRSA, instance profile) as
+        KAgentBedrockLlm.  Each text is embedded individually because the
+        Titan Embedding API accepts a single ``inputText`` per invocation.
+        """
+        import os
+
+        import boto3
+
+        region = os.environ.get("AWS_DEFAULT_REGION") or os.environ.get("AWS_REGION") or "us-east-1"
+        client = boto3.client("bedrock-runtime", region_name=region)
+
+        async def _invoke_single(text: str) -> List[float]:
+            body = json.dumps({"inputText": text})
+            response = await asyncio.to_thread(
+                client.invoke_model,
+                modelId=model_name,
+                body=body,
+                contentType="application/json",
+                accept="application/json",
+            )
+            result = json.loads(response["body"].read())
+            return result["embedding"]
+
+        embeddings = await asyncio.gather(*[_invoke_single(t) for t in texts])
+        return list(embeddings)
+
     async def _summarize_session_content_async(
         self,
         content: str,

python/packages/kagent-adk/tests/unittests/test_embedding.py

@@ -150,3 +150,43 @@ async def test_embedding_shorter_than_768_rejected(self):
             mock_cls.return_value = instance
             result = await svc._generate_embedding_async("test")
         assert result == []
+
+    @pytest.mark.asyncio
+    async def test_bedrock_embed(self):
+        svc = make_service(provider="bedrock", model="amazon.titan-embed-text-v1")
+        vec = [0.1] * 1536
+        mock_client = mock.MagicMock()
+        mock_client.invoke_model = mock.MagicMock(
+            return_value={"body": mock.MagicMock(read=lambda: b'{"embedding": ' + str(vec).encode() + b"}")}
+        )
+
+        with mock.patch("boto3.client", return_value=mock_client):
+            result = await svc._generate_embedding_async("hello world")
+        assert len(result) == 768
+        mock_client.invoke_model.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_bedrock_embed_uses_region_from_env(self):
+        svc = make_service(provider="bedrock", model="amazon.titan-embed-text-v1")
+        vec = [0.5] * 1536
+        mock_client = mock.MagicMock()
+        mock_client.invoke_model = mock.MagicMock(
+            return_value={"body": mock.MagicMock(read=lambda: b'{"embedding": ' + str(vec).encode() + b"}")}
+        )
+
+        with (
+            mock.patch.dict("os.environ", {"AWS_REGION": "eu-west-1", "AWS_DEFAULT_REGION": ""}),
+            mock.patch("boto3.client", return_value=mock_client) as mock_boto,
+        ):
+            await svc._generate_embedding_async("test")
+        mock_boto.assert_called_once_with("bedrock-runtime", region_name="eu-west-1")
+
+    @pytest.mark.asyncio
+    async def test_bedrock_embed_error_returns_empty(self):
+        svc = make_service(provider="bedrock", model="amazon.titan-embed-text-v1")
+        mock_client = mock.MagicMock()
+        mock_client.invoke_model = mock.MagicMock(side_effect=Exception("Bedrock API error"))
+
+        with mock.patch("boto3.client", return_value=mock_client):
+            result = await svc._generate_embedding_async("test")
+        assert result == []