Optimized program with code

optimize.py

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+import dspy
+from dspy import GEPA
+from modules import generate_cypher
+from datasets import load_dataset
+
+
+def process_dataset():
+    train_split = load_dataset("neo4j/text2cypher-2025v1")["train"]
+    train_split = [
+        dspy.Example(
+            {
+                "question": x["question"],
+                "neo4j_schema": x["schema"],
+                "expected_cypher": x["cypher"],
+            }
+        ).with_inputs("question", "neo4j_schema")
+        for x in train_split
+    ]
+    import random
+
+    random.Random(0).shuffle(train_split)
+    train_split = train_split[:200]
+    tot_num = len(train_split)
+
+    test_split = load_dataset("neo4j/text2cypher-2025v1")["test"]
+    test_split = [
+        dspy.Example(
+            {
+                "question": x["question"],
+                "neo4j_schema": x["schema"],
+                "expected_cypher": x["cypher"],
+            }
+        ).with_inputs("question", "neo4j_schema")
+        for x in test_split
+    ]
+
+    train_set = train_split[: int(0.5 * tot_num)]
+    val_set = train_split[int(0.5 * tot_num) :]
+    test_set = test_split[:200]
+
+    return train_set, val_set, test_set
+
+
+def metric(example, prediction, trace=None, pred_name=None, pred_trace=None):
+    from sacrebleu import sentence_bleu
+
+    expected_cypher = example["expected_cypher"]
+    generated_cypher = prediction.statement
+
+    # Calculate sentence-level BLEU (Google BLEU)
+    bleu_score = sentence_bleu(
+        generated_cypher,
+        [expected_cypher],  # Reference as a list
+    ).score / 100.0  # Normalize to 0-1
+
+    feedback = f"BLEU score: {bleu_score:.3f}"
+    return dspy.Prediction(score=bleu_score, feedback=feedback)
+
+
+train_set, val_set, test_set = process_dataset()
+
+optimizer = GEPA(
+    metric=metric,
+    auto="light",
+    num_threads=32,
+    track_stats=True,
+    reflection_minibatch_size=3,
+    reflection_lm=dspy.LM(model="gpt-5.2", temperature=1.0, max_tokens=32000),
+)
+
+
+if __name__ == "__main__":
+    optimized_program = optimizer.compile(
+        generate_cypher,
+        trainset=train_set,
+        valset=val_set,
+    )
+    optimized_program.push_to_hub("farouk1/text-to-cypher-gepa", tag="v1.0.1", commit_message="Optimized program with code")