citu_vanna/custompgvector/pgvector.py

import ast
import json
import logging
import uuid

import pandas as pd
from langchain_core.documents import Document
from langchain_postgres.vectorstores import PGVector
from sqlalchemy import create_engine, text

from vanna.exceptions import ValidationError
from vanna.base import VannaBase
from vanna.types import TrainingPlan, TrainingPlanItem

# 导入embedding缓存管理器
from common.embedding_cache_manager import get_embedding_cache_manager


class PG_VectorStore(VannaBase):
    def __init__(self, config=None):
        if not config or "connection_string" not in config:
            raise ValueError(
                "A valid 'config' dictionary with a 'connection_string' is required.")

        VannaBase.__init__(self, config=config)

        if config and "connection_string" in config:
            self.connection_string = config.get("connection_string")
            self.n_results = config.get("n_results", 10)

        if config and "embedding_function" in config:
            self.embedding_function = config.get("embedding_function")
        else:
            raise ValueError("No embedding_function was found.")
            # from langchain_huggingface import HuggingFaceEmbeddings
            # self.embedding_function = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")

        self.sql_collection = PGVector(
            embeddings=self.embedding_function,
            collection_name="sql",
            connection=self.connection_string,
        )
        self.ddl_collection = PGVector(
            embeddings=self.embedding_function,
            collection_name="ddl",
            connection=self.connection_string,
        )
        self.documentation_collection = PGVector(
            embeddings=self.embedding_function,
            collection_name="documentation",
            connection=self.connection_string,
        )
        self.error_sql_collection = PGVector(
            embeddings=self.embedding_function,
            collection_name="error_sql",
            connection=self.connection_string,
        )

    def add_question_sql(self, question: str, sql: str, **kwargs) -> str:
        question_sql_json = json.dumps(
            {
                "question": question,
                "sql": sql,
            },
            ensure_ascii=False,
        )
        id = str(uuid.uuid4()) + "-sql"
        createdat = kwargs.get("createdat")
        doc = Document(
            page_content=question_sql_json,
            metadata={"id": id, "createdat": createdat},
        )
        self.sql_collection.add_documents([doc], ids=[doc.metadata["id"]])

        return id

    def add_ddl(self, ddl: str, **kwargs) -> str:
        _id = str(uuid.uuid4()) + "-ddl"
        doc = Document(
            page_content=ddl,
            metadata={"id": _id},
        )
        self.ddl_collection.add_documents([doc], ids=[doc.metadata["id"]])
        return _id

    def add_documentation(self, documentation: str, **kwargs) -> str:
        _id = str(uuid.uuid4()) + "-doc"
        doc = Document(
            page_content=documentation,
            metadata={"id": _id},
        )
        self.documentation_collection.add_documents([doc], ids=[doc.metadata["id"]])
        return _id

    def get_collection(self, collection_name):
        match collection_name:
            case "sql":
                return self.sql_collection
            case "ddl":
                return self.ddl_collection
            case "documentation":
                return self.documentation_collection
            case "error_sql":
                return self.error_sql_collection
            case _:
                raise ValueError("Specified collection does not exist.")

    # def get_similar_question_sql(self, question: str) -> list:
    #     documents = self.sql_collection.similarity_search(query=question, k=self.n_results)
    #     return [ast.literal_eval(document.page_content) for document in documents]

    # 在原来的基础之上，增加相似度的值。
    def get_similar_question_sql(self, question: str) -> list:
        # 尝试使用embedding缓存
        embedding_cache = get_embedding_cache_manager()
        cached_embedding = embedding_cache.get_cached_embedding(question)
        
        if cached_embedding is not None:
            # 使用缓存的embedding进行向量搜索
            docs_with_scores = self.sql_collection.similarity_search_with_score_by_vector(
                embedding=cached_embedding,
                k=self.n_results
            )
        else:
            # 执行常规的向量搜索（会自动生成embedding）
            docs_with_scores = self.sql_collection.similarity_search_with_score(
                query=question,
                k=self.n_results
            )
            
            # 获取刚生成的embedding并缓存
            try:
                # 通过embedding_function生成向量并缓存
                generated_embedding = self.embedding_function.generate_embedding(question)
                if generated_embedding:
                    embedding_cache.cache_embedding(question, generated_embedding)
            except Exception as e:
                print(f"[WARNING] 缓存embedding失败: {e}")

        results = []
        for doc, score in docs_with_scores:
            # 将文档内容转换为 dict
            base = ast.literal_eval(doc.page_content)

            # 计算相似度
            similarity = round(1 - score, 4)

            # 每条记录单独打印
            print(f"[DEBUG] SQL Match: {base.get('question', '')} | similarity: {similarity}")

            # 添加 similarity 字段
            base["similarity"] = similarity
            results.append(base)

        # 应用阈值过滤
        filtered_results = self._apply_score_threshold_filter(
            results, 
            "RESULT_VECTOR_SQL_SCORE_THRESHOLD",
            "SQL"
        )

        return filtered_results

    def get_related_ddl(self, question: str, **kwargs) -> list:
        # 尝试使用embedding缓存
        embedding_cache = get_embedding_cache_manager()
        cached_embedding = embedding_cache.get_cached_embedding(question)
        
        if cached_embedding is not None:
            # 使用缓存的embedding进行向量搜索
            docs_with_scores = self.ddl_collection.similarity_search_with_score_by_vector(
                embedding=cached_embedding,
                k=self.n_results
            )
        else:
            # 执行常规的向量搜索（会自动生成embedding）
            docs_with_scores = self.ddl_collection.similarity_search_with_score(
                query=question,
                k=self.n_results
            )
            
            # 获取刚生成的embedding并缓存
            try:
                # 通过embedding_function生成向量并缓存
                generated_embedding = self.embedding_function.generate_embedding(question)
                if generated_embedding:
                    embedding_cache.cache_embedding(question, generated_embedding)
            except Exception as e:
                print(f"[WARNING] 缓存embedding失败: {e}")

        results = []
        for doc, score in docs_with_scores:
            # 计算相似度
            similarity = round(1 - score, 4)

            # 每条记录单独打印
            print(f"[DEBUG] DDL Match: {doc.page_content[:50]}... | similarity: {similarity}")

            # 添加 similarity 字段
            result = {
                "content": doc.page_content,
                "similarity": similarity
            }
            results.append(result)

        # 应用阈值过滤
        filtered_results = self._apply_score_threshold_filter(
            results, 
            "RESULT_VECTOR_DDL_SCORE_THRESHOLD",
            "DDL"
        )

        return filtered_results

    def get_related_documentation(self, question: str, **kwargs) -> list:
        # 尝试使用embedding缓存
        embedding_cache = get_embedding_cache_manager()
        cached_embedding = embedding_cache.get_cached_embedding(question)
        
        if cached_embedding is not None:
            # 使用缓存的embedding进行向量搜索
            docs_with_scores = self.documentation_collection.similarity_search_with_score_by_vector(
                embedding=cached_embedding,
                k=self.n_results
            )
        else:
            # 执行常规的向量搜索（会自动生成embedding）
            docs_with_scores = self.documentation_collection.similarity_search_with_score(
                query=question,
                k=self.n_results
            )
            
            # 获取刚生成的embedding并缓存
            try:
                # 通过embedding_function生成向量并缓存
                generated_embedding = self.embedding_function.generate_embedding(question)
                if generated_embedding:
                    embedding_cache.cache_embedding(question, generated_embedding)
            except Exception as e:
                print(f"[WARNING] 缓存embedding失败: {e}")

        results = []
        for doc, score in docs_with_scores:
            # 计算相似度
            similarity = round(1 - score, 4)

            # 每条记录单独打印
            print(f"[DEBUG] Doc Match: {doc.page_content[:50]}... | similarity: {similarity}")

            # 添加 similarity 字段
            result = {
                "content": doc.page_content,
                "similarity": similarity
            }
            results.append(result)

        # 应用阈值过滤
        filtered_results = self._apply_score_threshold_filter(
            results, 
            "RESULT_VECTOR_DOC_SCORE_THRESHOLD",
            "DOC"
        )

        return filtered_results

    def _apply_score_threshold_filter(self, results: list, threshold_config_key: str, result_type: str) -> list:
        """
        应用相似度阈值过滤逻辑
        
        Args:
            results: 原始结果列表，每个元素包含 similarity 字段
            threshold_config_key: 配置中的阈值参数名
            result_type: 结果类型（用于日志）
            
        Returns:
            过滤后的结果列表
        """
        if not results:
            return results
            
        # 导入配置
        try:
            import app_config
            enable_threshold = getattr(app_config, 'ENABLE_RESULT_VECTOR_SCORE_THRESHOLD', False)
            threshold = getattr(app_config, threshold_config_key, 0.65)
        except (ImportError, AttributeError) as e:
            print(f"[WARNING] 无法加载阈值配置: {e}，使用默认值")
            enable_threshold = False
            threshold = 0.65
        
        # 如果未启用阈值过滤，直接返回原结果
        if not enable_threshold:
            print(f"[DEBUG] {result_type} 阈值过滤未启用，返回全部 {len(results)} 条结果")
            return results
        
        total_count = len(results)
        min_required = max((total_count + 1) // 2, 1)
        
        print(f"[DEBUG] {result_type} 阈值过滤: 总数={total_count}, 阈值={threshold}, 最少保留={min_required}")
        
        # 按相似度降序排序（确保最相似的在前面）
        sorted_results = sorted(results, key=lambda x: x.get('similarity', 0), reverse=True)
        
        # 找出满足阈值的结果
        above_threshold = [r for r in sorted_results if r.get('similarity', 0) >= threshold]
        
        # 应用过滤逻辑
        if len(above_threshold) >= min_required:
            # 情况1: 满足阈值的结果数量 >= 最少保留数量，返回满足阈值的结果
            filtered_results = above_threshold
            filtered_count = len(above_threshold)
            print(f"[DEBUG] {result_type} 过滤结果: 保留 {filtered_count} 条, 过滤掉 {total_count - filtered_count} 条 (全部满足阈值)")
        else:
            # 情况2: 满足阈值的结果数量 < 最少保留数量，强制保留前 min_required 条
            filtered_results = sorted_results[:min_required]
            above_count = len(above_threshold)
            below_count = min_required - above_count
            filtered_count = min_required
            print(f"[DEBUG] {result_type} 过滤结果: 保留 {filtered_count} 条, 过滤掉 {total_count - filtered_count} 条 (满足阈值: {above_count}, 强制保留: {below_count})")
        
        # 打印过滤详情
        for i, result in enumerate(filtered_results):
            similarity = result.get('similarity', 0)
            status = "✓" if similarity >= threshold else "✗"
            print(f"[DEBUG] {result_type} 保留 {i+1}: similarity={similarity} {status}")
        
        return filtered_results

    def _apply_error_sql_threshold_filter(self, results: list) -> list:
        """
        应用错误SQL特有的相似度阈值过滤逻辑
        
        与其他方法不同，错误SQL的过滤逻辑是：
        - 只返回相似度高于阈值的结果
        - 不设置最低返回数量
        - 如果都低于阈值，返回空列表
        
        Args:
            results: 原始结果列表，每个元素包含 similarity 字段
            
        Returns:
            过滤后的结果列表
        """
        if not results:
            return results
            
        # 导入配置
        try:
            import app_config
            enable_threshold = getattr(app_config, 'ENABLE_RESULT_VECTOR_SCORE_THRESHOLD', False)
            threshold = getattr(app_config, 'RESULT_VECTOR_ERROR_SQL_SCORE_THRESHOLD', 0.5)
        except (ImportError, AttributeError) as e:
            print(f"[WARNING] 无法加载错误SQL阈值配置: {e}，使用默认值")
            enable_threshold = False
            threshold = 0.5
        
        # 如果未启用阈值过滤，直接返回原结果
        if not enable_threshold:
            print(f"[DEBUG] Error SQL 阈值过滤未启用，返回全部 {len(results)} 条结果")
            return results
        
        total_count = len(results)
        print(f"[DEBUG] Error SQL 阈值过滤: 总数={total_count}, 阈值={threshold}")
        
        # 按相似度降序排序（确保最相似的在前面）
        sorted_results = sorted(results, key=lambda x: x.get('similarity', 0), reverse=True)
        
        # 只保留满足阈值的结果，不设置最低返回数量
        filtered_results = [r for r in sorted_results if r.get('similarity', 0) >= threshold]
        
        filtered_count = len(filtered_results)
        filtered_out_count = total_count - filtered_count
        
        if filtered_count > 0:
            print(f"[DEBUG] Error SQL 过滤结果: 保留 {filtered_count} 条, 过滤掉 {filtered_out_count} 条")
            # 打印保留的结果详情
            for i, result in enumerate(filtered_results):
                similarity = result.get('similarity', 0)
                print(f"[DEBUG] Error SQL 保留 {i+1}: similarity={similarity} ✓")
        else:
            print(f"[DEBUG] Error SQL 过滤结果: 所有 {total_count} 条结果都低于阈值 {threshold}，返回空列表")
        
        return filtered_results

    def train(
        self,
        question: str | None = None,
        sql: str | None = None,
        ddl: str | None = None,
        documentation: str | None = None,
        plan: TrainingPlan | None = None,
        createdat: str | None = None,
    ):
        if question and not sql:
            raise ValidationError("Please provide a SQL query.")

        if documentation:
            logging.info(f"Adding documentation: {documentation}")
            return self.add_documentation(documentation)

        if sql and question:
            return self.add_question_sql(question=question, sql=sql, createdat=createdat)

        if ddl:
            logging.info(f"Adding ddl: {ddl}")
            return self.add_ddl(ddl)

        if plan:
            for item in plan._plan:
                if item.item_type == TrainingPlanItem.ITEM_TYPE_DDL:
                    self.add_ddl(item.item_value)
                elif item.item_type == TrainingPlanItem.ITEM_TYPE_IS:
                    self.add_documentation(item.item_value)
                elif item.item_type == TrainingPlanItem.ITEM_TYPE_SQL and item.item_name:
                    self.add_question_sql(question=item.item_name, sql=item.item_value)

    def get_training_data(self, **kwargs) -> pd.DataFrame:
        # Establishing the connection
        engine = create_engine(self.connection_string)

        # Querying the 'langchain_pg_embedding' table
        query_embedding = "SELECT cmetadata, document FROM langchain_pg_embedding"
        df_embedding = pd.read_sql(query_embedding, engine)

        # List to accumulate the processed rows
        processed_rows = []

        # Process each row in the DataFrame
        for _, row in df_embedding.iterrows():
            custom_id = row["cmetadata"]["id"]
            document = row["document"]
            
            # 处理不同类型的ID后缀
            if custom_id.endswith("-doc"):
                training_data_type = "documentation"
            elif custom_id.endswith("-error_sql"):
                training_data_type = "error_sql"
            elif custom_id.endswith("-sql"):
                training_data_type = "sql"
            elif custom_id.endswith("-ddl"):
                training_data_type = "ddl"
            else:
                # If the suffix is not recognized, skip this row
                logging.info(f"Skipping row with custom_id {custom_id} due to unrecognized training data type.")
                continue

            if training_data_type in ["sql", "error_sql"]:
                # Convert the document string to a dictionary
                try:
                    doc_dict = ast.literal_eval(document)
                    question = doc_dict.get("question")
                    content = doc_dict.get("sql")
                except (ValueError, SyntaxError):
                    logging.info(f"Skipping row with custom_id {custom_id} due to parsing error.")
                    continue
            elif training_data_type in ["documentation", "ddl"]:
                question = None  # Default value for question
                content = document

            # Append the processed data to the list
            processed_rows.append(
                {"id": custom_id, "question": question, "content": content, "training_data_type": training_data_type}
            )

        # Create a DataFrame from the list of processed rows
        df_processed = pd.DataFrame(processed_rows)

        return df_processed

    def remove_training_data(self, id: str, **kwargs) -> bool:
        # Create the database engine
        engine = create_engine(self.connection_string)

        # SQL DELETE statement
        delete_statement = text(
            """
            DELETE FROM langchain_pg_embedding
            WHERE cmetadata ->> 'id' = :id
        """
        )

        # Connect to the database and execute the delete statement
        with engine.connect() as connection:
            # Start a transaction
            with connection.begin() as transaction:
                try:
                    result = connection.execute(delete_statement, {"id": id})
                    # Commit the transaction if the delete was successful
                    transaction.commit()
                    # Check if any row was deleted and return True or False accordingly
                    return result.rowcount > 0
                except Exception as e:
                    # Rollback the transaction in case of error
                    logging.error(f"An error occurred: {e}")
                    transaction.rollback()
                    return False

    def remove_collection(self, collection_name: str) -> bool:
        engine = create_engine(self.connection_string)

        # Determine the suffix to look for based on the collection name
        suffix_map = {"ddl": "ddl", "sql": "sql", "documentation": "doc", "error_sql": "error_sql"}
        suffix = suffix_map.get(collection_name)

        if not suffix:
            logging.info("Invalid collection name. Choose from 'ddl', 'sql', 'documentation', or 'error_sql'.")
            return False

        # SQL query to delete rows based on the condition
        query = text(
            f"""
            DELETE FROM langchain_pg_embedding
            WHERE cmetadata->>'id' LIKE '%{suffix}'
        """
        )

        # Execute the deletion within a transaction block
        with engine.connect() as connection:
            with connection.begin() as transaction:
                try:
                    result = connection.execute(query)
                    transaction.commit()  # Explicitly commit the transaction
                    if result.rowcount > 0:
                        logging.info(
                            f"Deleted {result.rowcount} rows from "
                            f"langchain_pg_embedding where collection is {collection_name}."
                        )
                        return True
                    else:
                        logging.info(f"No rows deleted for collection {collection_name}.")
                        return False
                except Exception as e:
                    logging.error(f"An error occurred: {e}")
                    transaction.rollback()  # Rollback in case of error
                    return False

    def generate_embedding(self, *args, **kwargs):
        pass

    # 增加错误SQL的训练和查询功能
    # 1. 确保error_sql集合存在
    def _ensure_error_sql_collection(self):
        """确保error_sql集合存在"""
        # 集合已在 __init__ 中初始化，这里只是为了保持方法的一致性
        pass
    
    # 2. 将错误的question-sql对存储到error_sql集合中
    def train_error_sql(self, question: str, sql: str, **kwargs) -> str:
        """
        将错误的question-sql对存储到error_sql集合中
        """
        # 确保集合存在
        self._ensure_error_sql_collection()
        
        # 创建文档内容，格式与现有SQL训练数据一致
        question_sql_json = json.dumps(
            {
                "question": question,
                "sql": sql,
                "type": "error_sql"
            },
            ensure_ascii=False,
        )
        
        # 生成ID，使用与现有方法一致的格式
        id = str(uuid.uuid4()) + "-error_sql"
        createdat = kwargs.get("createdat")
        
        # 创建Document对象
        doc = Document(
            page_content=question_sql_json,
            metadata={"id": id, "createdat": createdat},
        )
        
        # 添加到error_sql集合
        self.error_sql_collection.add_documents([doc], ids=[doc.metadata["id"]])
        
        return id
    
    # 3. 获取相关的错误SQL示例
    def get_related_error_sql(self, question: str, **kwargs) -> list:
        """
        获取相关的错误SQL示例
        """
        # 确保集合存在
        self._ensure_error_sql_collection()
        
        try:
            # 尝试使用embedding缓存
            embedding_cache = get_embedding_cache_manager()
            cached_embedding = embedding_cache.get_cached_embedding(question)
            
            if cached_embedding is not None:
                # 使用缓存的embedding进行向量搜索
                docs_with_scores = self.error_sql_collection.similarity_search_with_score_by_vector(
                    embedding=cached_embedding,
                    k=self.n_results
                )
            else:
                # 执行常规的向量搜索（会自动生成embedding）
                docs_with_scores = self.error_sql_collection.similarity_search_with_score(
                    query=question,
                    k=self.n_results
                )
                
                # 获取刚生成的embedding并缓存
                try:
                    # 通过embedding_function生成向量并缓存
                    generated_embedding = self.embedding_function.generate_embedding(question)
                    if generated_embedding:
                        embedding_cache.cache_embedding(question, generated_embedding)
                except Exception as e:
                    print(f"[WARNING] 缓存embedding失败: {e}")
            
            results = []
            for doc, score in docs_with_scores:
                try:
                    # 将文档内容转换为 dict，与现有方法保持一致
                    base = ast.literal_eval(doc.page_content)
                    
                    # 计算相似度
                    similarity = round(1 - score, 4)
                    
                    # 每条记录单独打印
                    print(f"[DEBUG] Error SQL Match: {base.get('question', '')} | similarity: {similarity}")
                    
                    # 添加 similarity 字段
                    base["similarity"] = similarity
                    results.append(base)
                    
                except (ValueError, SyntaxError) as e:
                    print(f"Error parsing error SQL document: {e}")
                    continue
            
            # 应用错误SQL特有的阈值过滤逻辑
            filtered_results = self._apply_error_sql_threshold_filter(results)
            
            return filtered_results
            
        except Exception as e:
            print(f"Error retrieving error SQL examples: {e}")
            return []