feat: add DocDev for auto-generating workspace documentation (#781)

* feat: add DocDev for auto-generating workspace documentation

* fix: update markdown instructions in tpl.yaml

* feat: add enable_doc_dev flag and conditionally call DocDev

rdagent/components/coder/data_science/share/doc.py

@@ -0,0 +1,37 @@
+"""
+Developers concentrating on writing documents for a workspace
+"""
+
+from rdagent.core.developer import Developer
+from rdagent.core.experiment import Experiment, FBWorkspace
+from rdagent.oai.llm_utils import APIBackend
+from rdagent.utils.agent.ret import MarkdownAgentOut
+from rdagent.utils.agent.workflow import T
+
+
+class DocDev(Developer[Experiment]):
+    """
+    The developer is responsible for writing documents for a workspace.
+    """
+
+    def develop(self, exp: Experiment) -> None:
+        """
+        Write documents for the workspace.
+        """
+        ws: FBWorkspace = exp.experiment_workspace
+
+        file_li = [str(file.relative_to(ws.workspace_path)) for file in ws.workspace_path.iterdir() if file.is_file()]
+
+        key_file_list = ["main.py", "scores.csv"]
+
+        system_prompt = T(".prompts:dump_model_eval.system").r()
+        user_prompt = T(".prompts:dump_model_eval.user").r(
+            file_li=file_li,
+            key_files={f: (ws.workspace_path / f).read_text() for f in key_file_list},
+        )
+
+        resp = APIBackend().build_messages_and_create_chat_completion(
+            user_prompt=user_prompt, system_prompt=system_prompt
+        )
+        markdown = MarkdownAgentOut.extract_output(resp)
+        ws.inject_files({"README.md": markdown})

rdagent/components/coder/data_science/share/prompts.yaml

@@ -52,3 +52,36 @@ dump_model_eval:
     # Inference:
     {{scores_content_after}}
 
+
+docdev:
+  system: |-
+    You are a skilled developer and a Kaggle grandmaster. Your task is to create documentation for a data science solution.
+
+    You will be given:
+    - a list of files in the folder.
+    - content from some important files.
+
+    Please explain the trained models in the "models/" folder. The training and inference processes are detailed in the `main.py` file. The models' evaluation results are in `scores.csv`. Please respond with a markdown file that includes the following information:
+    - Explain the purpose of each model. If some models are part of a group (like those from cross-validation), describe them together.
+    - Provide key details for each model group:
+      - Important training parameters
+      - Model details
+      - Performance of each model
+    - Ensemble
+
+    {% include "rdagent.utils.agent.tpl:MarkdownOut" %}
+
+  user: |-
+    --------------- The file list in the workspace ---------------
+    {% for f in file_li %}
+    - {{ f }}
+    {% endfor %}
+
+    --------------- File content of each file ---------------
+    {% for fname, content in key_files.items() %}
+    File Path: {{fname}}
+    ```
+    {{content}}
+    ```
+    {% endfor %}
+