Remove from task eval and use env config variables instead

src/fhda/config.py

@@ -20,6 +20,4 @@
 else:
     DATA_STORAGE_PATH = Path("/storage")
 
-EVAL = bool(os.getenv("EVAL", "false").lower() == "true")
-
 VALID_FROM_TASK_KWARGS = ["run_notebook_on_edit"]

src/fhda/data_analysis_env.py

@@ -1,7 +1,6 @@
 import hashlib
 import logging
 import shutil
-import json
 from typing import Any, cast
 import time
 from aviary.core import (
@@ -100,55 +99,12 @@ def export_frame(self) -> Frame:
             },
         )
 
-    @classmethod
-    def eval_from_task(
-        cls, task: str, gcs_artifact_path: str, environment_config: str | None = None
-    ) -> "DataAnalysisEnv":
-        """
-        Used for evaluations via crow jobs.
-
-        Args:
-            task: The user query structured as <data_path> | <query>
-            gcs_artifact_path: The path to the GCS artifact – required for evaluation on crow jobs
-        """
-        logger.info("Using the eval_from_task method")
-        # Create temporary directory in GCP mounted storage volume
-        task_hash = hashlib.sha256(task.encode()).hexdigest()
-        trajectory_path = cfg.DATA_STORAGE_PATH / f"{task_hash}-{time.time()}"
-        trajectory_path.mkdir(parents=True, exist_ok=True)
-        logger.info("Trajectory path: %s", trajectory_path)
-        nb_path = trajectory_path / NBEnvironment.NOTEBOOK_NAME
-        # Copy task data to trajectory path
-        for item in (cfg.DATA_STORAGE_PATH / gcs_artifact_path).iterdir():
-            if item.is_file():
-                shutil.copy2(item, trajectory_path)
-            elif item.is_dir():
-                shutil.copytree(item, trajectory_path / item.name, dirs_exist_ok=True)
-
-        language = NBLanguage.PYTHON  # In future, this should be a hyperparameter
-        if trajectory_path.exists():
-            logger.info(
-                "Files in directory: %s", [f.name for f in trajectory_path.iterdir()]
-            )
-
-        return cls(
-            problem_id=f"data-analysis-task-{task_hash}",
-            problem=task,
-            # Using exact just because I won't ultimately be using env evaluation
-            eval_mode=EvalAnswerMode.EXACT,
-            nb_path=nb_path,
-            work_dir=trajectory_path,
-            language=language,
-            system_prompt=prompts.CAPSULE_SYSTEM_PROMPT_OPEN,
-            use_tmp_work_dir=False,
-        )
-
     @classmethod
     def from_task(
         cls,
         task: str,
         gcs_artifact_path: str | None = None,
-        environment_config: str | None = None,
+        environment_config: dict[str, Any] | None = None,
     ) -> "DataAnalysisEnv":
         """
         Perform data analysis on a user query.
@@ -161,74 +117,67 @@ def from_task(
         logger.info("User task: %s", task)
         logger.info("GCS artifact path: %s", gcs_artifact_path)
         logger.info("environment_config: %s", environment_config)
-        if cfg.EVAL:
-            return cls.eval_from_task(task, gcs_artifact_path)  # type: ignore
 
         if (
             not gcs_artifact_path
-        ):  # The files are already in the GCS bucket in a job-specific directory
+        ):  # Platform jobs should always be associated with data from a GCS bucket
             raise NotImplementedError(
                 "Running crow jobs without gcs_artifact_path is not supported"
             )
-        trajectory_path = cfg.DATA_STORAGE_PATH / gcs_artifact_path
-        nb_path = trajectory_path / NBEnvironment.NOTEBOOK_NAME
-        query = task
-        task_hash = gcs_artifact_path
+
         if environment_config:
             kwargs = {
                 k: v
-                for k, v in json.loads(environment_config).items()
+                for k, v in environment_config.items()
                 if k in cfg.VALID_FROM_TASK_KWARGS
             }
         else:
             kwargs = {}
         logger.info("Filtered kwargs: %s", kwargs)
-
-        # Augment incoming task with CoT instructions
-        augmented_task = f"""\
-Here is the user query to address:
-
-<query>
-{query}
-</query>
-
-{prompts.CHAIN_OF_THOUGHT_AGNOSTIC}
-{prompts.GENERAL_NOTEBOOK_GUIDELINES}"""
+        task_hash = hashlib.sha256(task.encode()).hexdigest()
+        if kwargs.get("eval", False):
+            # Create a temporary directory in GCP mounted storage volume
+            trajectory_path = cfg.DATA_STORAGE_PATH / f"{task_hash}-{time.time()}"
+            trajectory_path.mkdir(parents=True, exist_ok=True)
+            for item in (cfg.DATA_STORAGE_PATH / gcs_artifact_path).iterdir():
+                if item.is_file():
+                    shutil.copy2(item, trajectory_path)
+                elif item.is_dir():
+                    shutil.copytree(
+                        item, trajectory_path / item.name, dirs_exist_ok=True
+                    )
+        else:
+            # Use the GCP folder created when uploading the data via the platform
+            trajectory_path = cfg.DATA_STORAGE_PATH / gcs_artifact_path
+            # Augment incoming user query with CoT instructions
+            task = (
+                f"Here is the user query to address:\n"
+                f"<query>\n"
+                f"{task}\n"
+                f"</query>\n"
+                f"{prompts.CHAIN_OF_THOUGHT_AGNOSTIC}\n"
+                f"{prompts.GENERAL_NOTEBOOK_GUIDELINES}"
+            )
+        logger.info("Trajectory path: %s", trajectory_path)
+        nb_path = trajectory_path / NBEnvironment.NOTEBOOK_NAME
 
         language = NBLanguage.PYTHON  # In future, this should be a hyperparameter
         if language == NBLanguage.R:
-            augmented_task += f"\n{prompts.R_OUTPUT_RECOMMENDATION_PROMPT}"
-
-        # Log all parameters being passed to constructor
-        logger.info(
-            "Creating DataAnalysisEnv with parameters: "
-            "problem_id=data-analysis-task-%s, "
-            "problem=%s, "
-            "eval_mode=%s, "
-            "nb_path=%s, "
-            "work_dir=%s, "
-            "language=%s, "
-            "system_prompt=%s, "
-            "use_tmp_work_dir=%s, "
-            "gcs_artifact_path=%s",
-            task_hash,
-            augmented_task,
-            EvalAnswerMode.LLM,
-            nb_path,
-            trajectory_path,
-            language,
-            prompts.CAPSULE_SYSTEM_PROMPT_QUERY,
-            False,
-            gcs_artifact_path,
-        )
+            task += f"\n{prompts.R_OUTPUT_RECOMMENDATION_PROMPT}"
+
         if trajectory_path.exists():
-            logger.info(
-                "Files in directory: %s", [f.name for f in trajectory_path.iterdir()]
-            )
+            files = list(trajectory_path.iterdir())
+            logger.info("Files in directory: %s", [f.name for f in files])
+            if not files:
+                raise ValueError(
+                    f"No files found in trajectory path: {trajectory_path}"
+                )
+        else:
+            raise ValueError(f"Trajectory path does not exist: {trajectory_path}")
 
         return cls(
             problem_id=f"data-analysis-task-{task_hash}",
-            problem=augmented_task,
+            problem=task,
             eval_mode=EvalAnswerMode.LLM,
             nb_path=nb_path,
             work_dir=trajectory_path,

src/scripts/deploy.py

@@ -18,8 +18,7 @@
     "ANTHROPIC_API_KEY": os.environ["ANTHROPIC_API_KEY"],
     "USE_R": "false",
     "USE_DOCKER": "false",
-    "STAGE": "DEV",
-    "EVAL": "true" if EVAL else "false",
+    "STAGE": "PROD",
 }
 
 CONTAINER_CONFIG = DockerContainerConfiguration(cpu="2", memory="4Gi")
@@ -33,7 +32,7 @@
     CrowDeploymentConfig(
         requirements_path=Path("pyproject.toml"),
         path=Path("src"),
-        name="bixbench-crow2" if EVAL else "data-analysis-crow",
+        name="data-analysis-crow",
         environment="src.fhda.data_analysis_env.DataAnalysisEnv",
         environment_variables=ENV_VARS,
         agent="ldp.agent.ReActAgent",
@@ -42,7 +41,7 @@
         frame_paths=frame_paths,
         timeout=3600,
         task_queues_config=TaskQueuesConfig(
-            name="bixbench-crow2" if EVAL else "data-analysis-crow",
+            name="data-analysis-crow",
             max_running_jobs=300,
         ),
     ),