Solutions

import dspy

class CustomerSupportRAG(dspy.Module):
    def __init__(self, knowledge_base=None):
        super().__init__()
        # In a real scenario, knowledge_base would be indexed in a vector DB
        # Here we simulate retrieval
        self.knowledge_base = knowledge_base or []
        self.retrieve = dspy.Retrieve(k=3)
        self.generate_answer = dspy.ChainOfThought(
            "question, context -> answer, sources, confidence"
        )
    
    def forward(self, question):
        # 1. Retrieve relevant info
        # context = self.retrieve(question) 
        # Simulating retrieval for this example:
        context = [doc for doc in self.knowledge_base if any(kw in doc.lower() for kw in question.lower().split())]
        context_str = "\n".join(context) if context else "No relevant documents found."

        # 2. Generate answer
        prediction = self.generate_answer(
            question=question,
            context=context_str
        )
        
        return dspy.Prediction(
            answer=prediction.answer,
            reasoning=prediction.rationale,
            sources=context,
            confidence=prediction.confidence
        )

# Example Usage
kb = [
    "Product returns must be initiated within 30 days of purchase.",
    "Free shipping is available for orders over $50.",
    "Customer support is available 24/7 via phone and chat."
]

rag = CustomerSupportRAG(knowledge_base=kb)
result = rag("What is the return policy?")
print(f"Answer: {result.answer}")
print(f"Confidence: {result.confidence}")

class ResearchAssistant(dspy.Module):
    def __init__(self):
        super().__init__()
        self.retrieve = dspy.Retrieve(k=3)
        self.generate_subquestions = dspy.Predict(
            "question -> subquestions"
        )
        self.synthesize = dspy.ChainOfThought(
            "question, findings -> answer"
        )

    def forward(self, research_question):
        # 1. Break down complex question
        plan = self.generate_subquestions(question=research_question)
        subquestions = plan.subquestions.split("\n")
        
        findings = []
        for subq in subquestions:
            # 2. Retrieve info for each step
            docs = self.retrieve(subq).passages
            findings.append(f"Q: {subq}\nFound: {docs}")
            
        # 3. Synthesize final answer
        result = self.synthesize(
            question=research_question,
            findings="\n\n".join(findings)
        )
        
        return dspy.Prediction(
            answer=result.answer,
            reasoning=result.rationale
        )

class MultiLabelClassifier(dspy.Module):
    def __init__(self, possible_labels):
        super().__init__()
        self.labels = ", ".join(possible_labels)
        self.classify = dspy.Predict(
            f"document, possible_labels[{self.labels}] -> applied_labels, confidence_scores"
        )

    def forward(self, document):
        prediction = self.classify(
            document=document,
            possible_labels=self.labels
        )
        
        # Parse labels (assuming comma-separated output)
        labels = [l.strip() for l in prediction.applied_labels.split(',')]
        
        return dspy.Prediction(
            labels=labels,
            scores=prediction.confidence_scores
        )

class ContractExtractor(dspy.Module):
    def __init__(self):
        super().__init__()
        self.extract_parties = dspy.Predict("contract -> parties")
        self.extract_dates = dspy.Predict("contract -> effective_date, termination_date")
        self.extract_terms = dspy.Predict("contract -> payment_terms, obligations")

    def forward(self, contract_text):
        parties = self.extract_parties(contract=contract_text)
        dates = self.extract_dates(contract=contract_text)
        terms = self.extract_terms(contract=contract_text)

        return dspy.Prediction(
            parties=parties.parties,
            dates={
                "effective": dates.effective_date,
                "termination": dates.termination_date
            },
            terms={
                "payment": terms.payment_terms,
                "obligations": terms.obligations
            }
        )

class CustomerServiceAgent(dspy.Module):
    def __init__(self):
        super().__init__()
        self.classify_intent = dspy.Predict("message -> intent")
        self.handle_inquiry = dspy.ChainOfThought("message, history -> response, action")
        self.escalate = dspy.Predict("message, reason -> department")
        self.history = []

    def forward(self, customer_message):
        # 1. Identify Intent
        intent = self.classify_intent(message=customer_message).intent
        
        # 2. Add to history
        self.history.append(f"User: {customer_message}")
        
        if "urgent" in intent.lower() or "complaint" in intent.lower():
            # Escalate
            dept = self.escalate(message=customer_message, reason=intent)
            response = f"I am escalating this issue to {dept.department}."
            action = "ESCALATE"
        else:
            # Handle normally
            result = self.handle_inquiry(
                message=customer_message, 
                history="\n".join(self.history[-5:])
            )
            response = result.response
            action = result.action

        self.history.append(f"Agent: {response}")
        
        return dspy.Prediction(
            response=response,
            action=action,
            intent=intent
        )

class CodeReviewAssistant(dspy.Module):
    def __init__(self):
        super().__init__()
        self.analyze = dspy.ChainOfThought(
            "code, language -> bugs, security_issues, style_suggestions, overall_score"
        )
    
    def forward(self, code, language="python"):
        review = self.analyze(code=code, language=language)
        
        return dspy.Prediction(
            bugs=review.bugs,
            security=review.security_issues,
            style=review.style_suggestions,
            score=review.overall_score,
            summary=review.rationale
        )