[MRG] inject the project info into chat system prompt

mle/cli.py

@@ -17,7 +17,7 @@
 from mle.model import load_model
 from mle.agents import CodeAgent
 import mle.workflow as workflow
-from mle.utils import Memory
+from mle.utils import Memory, WorkflowCache
 from mle.utils.system import (
     get_config,
     write_config,
@@ -147,8 +147,17 @@ def chat():
         return
 
     model = load_model(os.getcwd())
+    cache = WorkflowCache(os.getcwd())
     coder = CodeAgent(model)
 
+    # read the project information
+    dataset = cache.resume_variable("dataset")
+    ml_requirement = cache.resume_variable("ml_requirement")
+    advisor_report = cache.resume_variable("advisor_report")
+
+    # inject the project information into prompts
+    coder.read_requirement(advisor_report or ml_requirement or dataset)
+
     while True:
         try:
             user_pmpt = questionary.text("[Exit/Ctrl+D]: ").ask()

mle/utils/cache.py

@@ -110,6 +110,26 @@ def current_step(self) -> int:
         """
         return max(self.cache.keys()) if self.cache else 0
 
+    def resume_variable(self, key: str, step: Optional[int] = None):
+        """
+        Resume the cached variable.
+
+        Args:
+            key (str): The key of the value to be resumed.
+            step (str): The step to be initialized.
+
+        Returns:
+            object: The resumed value, or None if the key does not exist.
+        """
+        if step is not None:
+            return self.__call__(step).resume(key)
+        else:
+            for step in range(self.current_step()):
+                value = self.resume_variable(key, step)
+                if value is not None:
+                    return value
+            return None
+
     def _load_cache_buffer(self) -> Dict[str, Any]:
         """
         Load the cache buffer from the configuration.
@@ -128,7 +148,7 @@ def _store_cache_buffer(self) -> None:
         """
         write_config(self.buffer)
 
-    def __call__(self, step: int, name: str) -> WorkflowCacheOperator:
+    def __call__(self, step: int, name: Optional[str] = None) -> WorkflowCacheOperator:
         """
         Initialize the cache content for a given step and name.