# -*- coding: utf-8 -*-
"""Self-RAG - self-reflection on retrieval and generation quality."""
from .base import BaseRAG
from loguru import logger


class SelfRAG(BaseRAG):
    def __init__(self, reflection_threshold=0.5, max_reflections=2, **kwargs):
        super().__init__(**kwargs)
        self.reflection_threshold = reflection_threshold
        self.max_reflections = max_reflections

    def _assess_retrieval(self, query, docs):
        context = "\n\n".join([doc.page_content[:200] for doc, _ in docs[:3]])
        prompt = (
            "评估以下检索到的文档对于回答问题的相关性和完整性。给出0-1的分数。\n\n"
            "问题：{}\n\n"
            "检索到的文档（前3个）：\n{}\n\n"
            "相关性分数：".format(query, context)
        )
        try:
            response = self._call_llm(prompt).strip()
            return float(response)
        except:
            return 0.5

    def _assess_generation(self, query, answer):
        prompt = (
            "评估以下答案对于问题的准确性和完整性。给出0-1的分数。\n\n"
            "问题：{}\n\n"
            "答案：\n{}\n\n"
            "质量分数：".format(query, answer[:300])
        )
        try:
            response = self._call_llm(prompt).strip()
            return float(response)
        except:
            return 0.5

    def _generate_critique(self, query, answer):
        prompt = (
            "以下答案有哪些不足之处？请指出需要改进的地方。\n\n"
            "问题：{}\n\n"
            "答案：\n{}\n\n"
            "不足：".format(query, answer[:300])
        )
        return self._call_llm(prompt)

    def retrieve(self, query, k=10):
        query_embedding = self.embedding_model.embed_query(query)
        initial_results = self.vector_store.similarity_search(query_embedding, k)
        
        relevance_score = self._assess_retrieval(query, initial_results)
        logger.info("SelfRAG retrieval relevance: {:.3f}".format(relevance_score))
        
        return self._deduplicate_results(initial_results, k)

    def generate(self, query, context):
        answer = self._call_llm(
            "根据以下参考文档，回答问题。\n\n"
            "参考文档：\n{}\n\n"
            "问题：{}\n\n"
            "请详细回答。".format(context, query)
        )
        
        quality_score = self._assess_generation(query, answer)
        logger.info("SelfRAG generation quality: {:.3f}".format(quality_score))
        
        if quality_score < self.reflection_threshold:
            critique = self._generate_critique(query, answer)
            refined_prompt = (
                "之前的答案有以下不足：{}\n\n"
                "请重新回答以下问题，弥补这些不足。\n\n"
                "参考文档：\n{}\n\n"
                "问题：{}\n\n"
                "改进后的答案：".format(critique, context, query)
            )
            answer = self._call_llm(refined_prompt)
        
        return answer