docs: update configuration docs (#1155)

* update configuration docs

* update configuration docs

* update configuration docs

Author: SunsetWolf

README.md

@@ -172,6 +172,9 @@ More details can be found in the [development setup](https://rdagent.readthedocs
 - **Using LiteLLM (Default)**: We now support LiteLLM as a backend for integration with multiple LLM providers. You can configure in multiple ways:
 
   **Option 1: Unified API base for both models**
+
+  *Configuration Example: `OpenAI` Setup :*
+
   ```bash
   cat << EOF  > .env
   # Set to any model supported by LiteLLM.
@@ -182,6 +185,19 @@ More details can be found in the [development setup](https://rdagent.readthedocs
   OPENAI_API_KEY=<replace_with_your_openai_api_key>
   ```
 
+  *Configuration Example: `Azure OpenAI` Setup :*
+
+  > Before using this configuration, please confirm in advance that your `Azure OpenAI API key` supports `embedded models`.
+
+  ```bash
+  cat << EOF  > .env
+  EMBEDDING_MODEL=azure/<Model deployment supporting embedding>
+  CHAT_MODEL=azure/<your deployment name>
+  AZURE_API_KEY=<replace_with_your_openai_api_key>
+  AZURE_API_BASE=<your_unified_api_base>
+  AZURE_API_VERSION=<azure api version>
+  ```
+
   **Option 2: Separate API bases for Chat and Embedding models**
   ```bash
   cat << EOF  > .env
@@ -201,7 +217,7 @@ More details can be found in the [development setup](https://rdagent.readthedocs
   LITELLM_PROXY_API_BASE=https://api.siliconflow.cn/v1
   ```
 
-  **Configuration Example: DeepSeek Setup**:
+  *Configuration Example: `DeepSeek` Setup :*
 
   >Since many users encounter configuration errors when setting up DeepSeek. Here's a complete working example for DeepSeek Setup:
   ```bash

docs/installation_and_configuration.rst

@@ -148,6 +148,17 @@ To align with the Python SDK example above, you can configure the `CHAT_MODEL` b
 
 This configuration allows you to call Azure OpenAI through LiteLLM while using an external provider (e.g., SiliconFlow) for embeddings.
 
+If your `Azure OpenAI API Key`` supports `embedding model`, you can refer to the following configuration example.
+
+   .. code-block:: Properties
+
+      cat << EOF  > .env
+      EMBEDDING_MODEL=azure/<Model deployment supporting embedding>
+      CHAT_MODEL=azure/<your deployment name>
+      AZURE_API_KEY=<replace_with_your_openai_api_key>
+      AZURE_API_BASE=<your_unified_api_base>
+      AZURE_API_VERSION=<azure api version>
+
 Configuration(deprecated)
 =========================
 

rdagent/scenarios/data_science/example/arf-12-hours-prediction-task/sample.py

@@ -66,7 +66,7 @@ def create_debug_data(
     min_frac: float = 0.02,
     min_num: int = 10,
 ):
-    dataset_root = Path(dataset_path) / "arf-12-hour-prediction-task"
+    dataset_root = Path(dataset_path) / "arf-12-hours-prediction-task"
     output_root = Path(output_path)
 
     for sub in ["train", "test"]:

rdagent/scenarios/data_science/example/eval/arf-12-hours-prediction-task/grade.py

@@ -60,7 +60,7 @@ def grade(submission: pd.DataFrame, answers: pd.DataFrame) -> float:
     print(
         json.dumps(
             {
-                "competition_id": "arf-12-hour-prediction-task",
+                "competition_id": "arf-12-hours-prediction-task",
                 "score": score,
             }
         )

rdagent/scenarios/data_science/example/source_data/arf-12-hours-prediction-task/prepare.py

@@ -11,8 +11,8 @@
 raw_feature_path = CURRENT_DIR / "X.npz"
 raw_label_path = CURRENT_DIR / "ARF_12h.csv"
 
-public = ROOT_DIR / "arf-12-hour-prediction-task"
-private = ROOT_DIR / "eval" / "arf-12-hour-prediction-task"
+public = ROOT_DIR / "arf-12-hours-prediction-task"
+private = ROOT_DIR / "eval" / "arf-12-hours-prediction-task"
 
 if not (public / "test").exists():
     (public / "test").mkdir(parents=True, exist_ok=True)