New Framework for idea proposal and implementation on RD-Agent (#34)

* Commit init framework

* Co-authored-by: Yuante Li (FESCO Adecco Human Resources) <v-yuanteli@microsoft.com>
Co-authored-by: XianBW <XianBW@users.noreply.github.com>

* add an import

* refine the whole framework

* benchmark related framework

* fix black and isort errors

* move requirements to folder

* fix black again

---------

Co-authored-by: Young <afe.young@gmail.com>
Co-authored-by: xuyang1 <xuyang1@microsoft.com>

rdagent/components/task_implementation/model_implementation/one_shot/__init__.py

@@ -0,0 +1,45 @@
+import re
+from pathlib import Path
+from typing import Sequence
+
+from jinja2 import Template
+
+from rdagent.components.task_implementation.model_implementation.task import (
+    ModelImplTask,
+    ModelTaskImpl,
+)
+from rdagent.core.implementation import TaskGenerator
+from rdagent.core.prompts import Prompts
+from rdagent.oai.llm_utils import APIBackend
+
+DIRNAME = Path(__file__).absolute().resolve().parent
+
+
+class ModelTaskGen(TaskGenerator):
+    def generate(self, task_l: Sequence[ModelImplTask]) -> Sequence[ModelTaskImpl]:
+        mti_l = []
+        for t in task_l:
+            mti = ModelTaskImpl(t)
+            mti.prepare()
+            pr = Prompts(file_path=DIRNAME / "prompt.yaml")
+
+            user_prompt_tpl = Template(pr["code_implement_user"])
+            sys_prompt_tpl = Template(pr["code_implement_sys"])
+
+            user_prompt = user_prompt_tpl.render(
+                name=t.name,
+                description=t.description,
+                formulation=t.formulation,
+                variables=t.variables,
+                execute_desc=mti.execute_desc(),
+            )
+            system_prompt = sys_prompt_tpl.render()
+
+            resp = APIBackend().build_messages_and_create_chat_completion(user_prompt, system_prompt)
+
+            # Extract the code part from the response
+            match = re.search(r".*```[Pp]ython\n(.*)\n```.*", resp, re.DOTALL)
+            code = match.group(1)
+            mti.inject_code(**{"model.py": code})
+            mti_l.append(mti)
+        return mti_l

rdagent/components/task_implementation/model_implementation/one_shot/prompt.yaml

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+
+
+code_implement_sys: -|
+  You are an assistant whose job is to answer user's question."
+code_implement_user: -|
+  With the following given information, write a python code using pytorch and torch_geometric to implement the model.
+  This model is in the graph learning field, only have one layer.
+  The input will be node_feature [num_nodes, dim_feature] and edge_index [2, num_edges]  (It would be the input of the forward model)
+  There is not edge attribute or edge weight as input. The model should detect the node_feature and edge_index shape, if there is Linear transformation layer in the model, the input and output shape should be consistent. The in_channels is the dimension of the node features.
+  Implement the model forward function based on the following information:model formula information.
+  1. model name:{{name}}
+  2. model description:{{description}}
+  3. model formulation:{{formulation}}
+  4. model variables:{{variables}}.
+  You must complete the forward function as far as you can do.
+  \# Execution
+  Your implemented code will be exectued in the follow way
+  {{execute_desc}}