(no commit message)

compile.py

@@ -1,88 +0,0 @@
-from agent.agent import PersanaSearcher, PersanaConfig
-from dotenv import load_dotenv
-import os
-import dspy
-import json
-from dspy import Prediction, Example
-
-searcher = PersanaSearcher(config=PersanaConfig(), api_key=os.getenv("PERSANA_KEY"))
-feedback_creator = searcher.feedback_creator
-searcher.push_to_hub(
-    "swagginty/persana-lead-gen", with_code=True, commit_message="Uncompiled"
-)
-
-
-class SearchExample(Example):
-    company_description: str
-    target_customer: str
-    selected_profiles: list[dict]
-
-
-class SearchPrediction(Prediction):
-    profiles: list[dict]
-    search_parameters: dict
-
-
-def evaluate_results(
-    target: SearchExample,
-    predictied: SearchPrediction,
-    trace=None,
-    pred_name=None,
-    pred_trace=None,
-) -> Prediction:
-    """
-    Evaluates the search results target results were retrieved
-    """
-    # How many of the target profiles were retrieved
-    pred_ids = {result["profile_id"] for result in predictied.profiles}
-    count = 0
-    for t_result in target.selected_profiles:
-        if t_result["profile_id"] in pred_ids:
-            count += 1
-
-    score = count / len(target.selected_profiles)
-
-    target_ids = {result["profile_id"] for result in target.selected_profiles}
-    # Which retrieved profiles were target profiles
-    selected_preds = [
-        result for result in predictied.profiles if result["profile_id"] in target_ids
-    ]
-    # Which retrieved profiles were not target profiles
-    unselected_preds = [
-        result
-        for result in predictied.profiles
-        if result["profile_id"] not in target_ids
-    ]
-    # Resuse feedback creator to get feedback for prompt creation
-    feedback = feedback_creator(
-        search_parameters=predictied.search_parameters,
-        selected_profiles=selected_preds,
-        unselected_profiles=unselected_preds,
-        user_feedback=None,
-    ).feedback
-    return Prediction(
-        score=score,
-        feedback=feedback,
-    )
-
-
-if __name__ == "__main__":
-    load_dotenv()
-
-    data = [json.loads(line) for line in open("dataset.jsonl", "r")]
-    trainset = [
-        dspy.Example(
-            company_description=e["company_description"],
-            target_customer=e["target_customer"],
-            selected_profiles=e["selected_profiles"],
-        ).with_inputs("company_description", "target_customer")
-        for e in data
-    ]
-    compiler = dspy.GEPA(
-        metric=evaluate_results,
-        auto="light",
-        reflection_lm=dspy.LM("openai/gpt-5", temperature=1.0, max_tokens=32000),
-    )
-    compiled_searcher = compiler.compile(searcher, trainset=trainset)
-    compiled_searcher.save("compiled_searcher.json")
-    compiled_searcher.push_to_hub("swagginty/persana-lead-gen")