import dspy

# Check current config
print(dspy.settings.lm)  # Should show your LM, not None

# If None, configure it:
lm = dspy.LM("openai/gpt-4o-mini")
dspy.configure(lm=lm)

# Test the AI provider directly
lm = dspy.LM("openai/gpt-4o-mini")
response = lm("Hello, respond with just 'OK'")
print(response)

# Check your signature defines the right fields
class MySignature(dspy.Signature):
    """Clear task description here."""
    input_field: str = dspy.InputField(desc="what this contains")
    output_field: str = dspy.OutputField(desc="what to produce")

# Verify by inspecting
print(MySignature.fields)

# Check that input field names match
result = my_program(question="test")  # field name must match signature

# Wrong:
result = my_program(q="test")  # 'q' doesn't match 'question'
result = my_program("test")    # positional args don't work

result = my_program(question="test")
print(result)                    # see all fields
print(result.answer)             # access specific field
print(type(result.answer))       # check type

# Show the last 3 AI calls
dspy.inspect_history(n=3)

# Check retriever config
print(dspy.settings.rm)

# Test retriever directly
rm = dspy.ColBERTv2(url="http://...")
results = rm("test query", k=3)
print(results)

dspy.configure(lm=lm, trace=[])
# Now run your program — trace will be populated
result = my_program(question="test")

# Print the module tree
print(my_program)

# See all named predictors
for name, predictor in my_program.named_predictors():
    print(f"{name}: {predictor}")

class MyPipeline(dspy.Module):
    def __init__(self):
        self.step1 = dspy.ChainOfThought("question -> search_query")
        self.step2 = dspy.Retrieve(k=3)
        self.step3 = dspy.ChainOfThought("context, question -> answer")

    def forward(self, question):
        query = self.step1(question=question)
        print(f"Step 1 output: {query.search_query}")  # Debug

        context = self.step2(query.search_query)
        print(f"Step 2 retrieved: {len(context.passages)} passages")  # Debug

        answer = self.step3(context=context.passages, question=question)
        print(f"Step 3 output: {answer.answer}")  # Debug

        return answer

# Before optimization
baseline = MyProgram()
baseline(question="test")
print("=== BASELINE PROMPT ===")
dspy.inspect_history(n=1)

# After optimization
optimized = MyProgram()
optimized.load("optimized.json")
optimized(question="test")
print("=== OPTIMIZED PROMPT ===")
dspy.inspect_history(n=1)