from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.vectorstores.faiss import FAISS
from langchain.schema import Document
import sentence_transformers
import json
import os

from qwen_agent.utils.util import get_data_from_jsons

os.environ["TOKENIZERS_PARALLELISM"] = "false"

embedding_model_dict = {
    # "text2vec": "/data/m3e-base",
    "text2vec": "E:\项目临时\AI大模型\m3e-base",
    # "text2vec": r"E:\项目临时\AI大模型\bge_large_zh_v1.5",#使用bge-large-zh-v3模型也可以进行相似度搜索
}
EMBEDDING_MODEL = "text2vec"  # embedding 模型，对应 embedding_model_dict
DEVICE = "cpu"

embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[EMBEDDING_MODEL])
embeddings.client = sentence_transformers.SentenceTransformer(embeddings.model_name, device=DEVICE)


# embeddings = DashScopeEmbeddings(model="text-embedding-v1",
#                                  dashscope_api_key="sk-cb5c097eb78f4dae8daa6a833590d757")


class SqlRetriever():
    def __init__(self, query_type='bidding') -> None:

        few_shot_docs = []
        self.data = get_data_from_jsons(os.path.join(os.path.abspath(os.path.dirname(__file__)), 'data'),
                                        'sql_examples')
        for line in self.data:
            if line['query_type'] == query_type:
                few_shot_docs.append(Document(page_content=line['query'], metadata={'sql_code': line['sql_code']}))
        # page_content='帮我在萧山区推荐几块50亩左右的工业用地，数据表是控制性详细规划' metadata={'sql_code': "select id from sde.kzxxxgh where xzqmc = '萧山区' and ydxz like '%工业%' and abs(ydmj - 50*0.0667) <= 1 and shape is not null order by ydmj nulls last limit 5"}
        # page_content是query，metadata是sql
        self.vector_db = FAISS.from_documents(few_shot_docs, embeddings)

    # 以前没有用向量数据库进行相似度搜索，用的是find_most_similar_queries进行字符串匹配实现的这些功能
    # 现在这2个方法已经被废弃调了，使用get_relevant_documents方法进行替代
    def longest_common_substring(self, str1, str2):
        m, n = len(str1), len(str2)
        dp = [[0] * (n + 1) for _ in range(m + 1)]
        max_length = 0
        for i in range(1, m + 1):
            for j in range(1, n + 1):
                if str1[i - 1] == str2[j - 1]:
                    dp[i][j] = dp[i - 1][j - 1] + 1
                    max_length = max(max_length, dp[i][j])
        return max_length

    def find_most_similar_queries(self, data, text, top_n=3):
        similarity_scores = [(item, self.longest_common_substring(item['query'], text)) for item in data]
        similarity_scores.sort(key=lambda x: x[1], reverse=True)
        return [item[0] for item in similarity_scores[:top_n]]

    def get_relevant_documents(self, query, top_k=4):
        results = []
        for r in self.vector_db.similarity_search(query, k=top_k):
            results.append((r.page_content, r.metadata['sql_code']))
        return results


if __name__ == "__main__":

    # def longest_common_substring(str1, str2):
    #     m, n = len(str1), len(str2)
    #     dp = [[0] * (n + 1) for _ in range(m + 1)]
    #     max_length = 0
    #     for i in range(1, m + 1):
    #         for j in range(1, n + 1):
    #             if str1[i - 1] == str2[j - 1]:
    #                 dp[i][j] = dp[i - 1][j - 1] + 1
    #                 max_length = max(max_length, dp[i][j])
    #     return max_length
    #
    #
    # def find_most_similar_queries(data, text, top_n=3):
    #     similarity_scores = [(item, longest_common_substring(item['query'], text)) for item in data]
    #     similarity_scores.sort(key=lambda x: x[1], reverse=True)
    #     return [item[0] for item in similarity_scores[:top_n]]
    #
    #
    # # data = [{"query":"example1", "sql_code": "sql1"},{"query":"example2", "sql_code": "sql2"}]
    # # text = "Some input text"
    # # print(find_most_similar_queries(data, text))
    #
    # records = []
    # data = json.load(open(os.path.join(os.path.abspath(os.path.dirname(__file__)), 'data/sql_examples.jsonl'), 'r'))
    # for line in data:
    #     records.append(line)
    # results = []
    # for item in find_most_similar_queries(records, '浙江万维今年中了几个标？'):
    #     results.append((item['query'], item['sql_code']))
    # print(results)
    # print(find_most_similar_queries(records, '浙江万维今年中了几个标？'))

    sql_retrieval = SqlRetriever("land_site_selection")
    results = sql_retrieval.get_relevant_documents("萧山区推荐几块工业用地", top_k=2)
    for r in results:
        print(r)
        # ('帮我在萧山区推荐几块50亩左右的工业用地，数据表是公告地块', "select id from sde.ecgap_klyzy where xzqmc = '萧山区' and tdyt like '%工业%' and abs(dkmj-5) <= 1 and shape is not null and sfsj=1 order by dkmj nulls last limit 5")
        # ('帮我在萧山区推荐几块50亩左右的工业用地，数据表是控制性详细规划', "select id from sde.kzxxxgh where xzqmc = '萧山区' and ydxz like '%工业%' and abs(ydmj - 50*0.0667) <= 1 and shape is not null order by ydmj nulls last limit 5")