docs: add S3 configuration details for evaluation results in README and remote-provider documentation.

README.md

@@ -53,13 +53,23 @@ KUBEFLOW_LLAMA_STACK_URL=<your-llama-stack-url>
 KUBEFLOW_PIPELINES_ENDPOINT=<your-kfp-endpoint>
 KUBEFLOW_NAMESPACE=<your-namespace>
 KUBEFLOW_BASE_IMAGE=quay.io/diegosquayorg/my-ragas-provider-image:latest
+KUBEFLOW_RESULTS_S3_PREFIX=s3://my-bucket/ragas-results
+KUBEFLOW_S3_CREDENTIALS_SECRET_NAME=<secret-name>
 ```
 
 Where:
 - `KUBEFLOW_LLAMA_STACK_URL`: The URL of the llama stack server that the remote provider will use to run the evaluation (LLM generations and embeddings, etc.). If you are running Llama Stack locally, you can use [ngrok](https://ngrok.com/) to expose it to the remote provider.
 - `KUBEFLOW_PIPELINES_ENDPOINT`: You can get this via `kubectl get routes -A | grep -i pipeline` on your Kubernetes cluster.
 - `KUBEFLOW_NAMESPACE`: The name of the data science project where the Kubeflow Pipelines server is running.
 - `KUBEFLOW_BASE_IMAGE`: The image used to run the Ragas evaluation in the remote provider. See `Containerfile` for details. There is a public version of this image at `quay.io/diegosquayorg/my-ragas-provider-image:latest`.
+- `KUBEFLOW_RESULTS_S3_PREFIX`: S3 location (bucket and prefix folder) where evaluation results will be stored, e.g., `s3://my-bucket/ragas-results`.
+- `KUBEFLOW_S3_CREDENTIALS_SECRET_NAME`: Name of the Kubernetes secret containing AWS credentials with write access to the S3 bucket. Create with:
+  ```bash
+  oc create secret generic <secret-name> \
+    --from-literal=AWS_ACCESS_KEY_ID=your-access-key \
+    --from-literal=AWS_SECRET_ACCESS_KEY=your-secret-key \
+    --from-literal=AWS_DEFAULT_REGION=us-east-1
+  ```
 
 Run the server:
 ```bash

docs/modules/ROOT/pages/remote-provider.adoc

@@ -142,6 +142,10 @@ KUBEFLOW_NAMESPACE=<your-namespace>
 
 # Container image for remote execution
 KUBEFLOW_BASE_IMAGE=quay.io/diegosquayorg/my-ragas-provider-image:latest
+
+# S3 configuration for storing evaluation results
+KUBEFLOW_RESULTS_S3_PREFIX=s3://my-bucket/ragas-results
+KUBEFLOW_S3_CREDENTIALS_SECRET_NAME=<secret-name>
 ----
 
 === Environment Variable Details
@@ -166,6 +170,22 @@ The name of the data science project where the Kubeflow Pipelines server is runn
 `KUBEFLOW_BASE_IMAGE`::
 The container image used to run the Ragas evaluation in the remote provider. See the `Containerfile` in the repository root for details on building a custom image.
 
+`KUBEFLOW_RESULTS_S3_PREFIX`::
+The S3 location (bucket and prefix) where evaluation results will be stored. This should be a folder path, e.g., `s3://my-bucket/ragas-results`. The remote provider will write evaluation outputs to this location.
+
+`KUBEFLOW_S3_CREDENTIALS_SECRET_NAME`::
+The name of the Kubernetes secret containing AWS credentials with write access to the S3 bucket specified in `KUBEFLOW_RESULTS_S3_PREFIX`. This secret will be mounted as environment variables in the Kubeflow pipeline components.
++
+To create the secret:
++
+[,bash]
+----
+oc create secret generic <secret-name> \
+  --from-literal=AWS_ACCESS_KEY_ID=your-access-key \
+  --from-literal=AWS_SECRET_ACCESS_KEY=your-secret-key \
+  --from-literal=AWS_DEFAULT_REGION=us-east-1
+----
+
 === Distribution Configuration
 
 The repository includes a sample Llama Stack distribution configuration that uses Ollama as a provider for inference and embeddings.

src/llama_stack_provider_ragas/remote/ragas_remote_eval.py

@@ -43,7 +43,7 @@ class RagasEvaluationJob(Job):
 
     @property
     def result_s3_location(self) -> str:
-        return f"{self.runtime_config.kubeflow_config.results_s3_prefix}/{self.job_id}/results.jsonl"
+        return f"{self.runtime_config.kubeflow_config.results_s3_prefix.rstrip('/')}/{self.job_id}/results.jsonl"
 
 
 @json_schema_type