# 标准库导入
import asyncio  # 异步IO支持
import json    # JSON数据处理
import re      # 正则表达式支持
from typing import Union  # 类型提示：联合类型
from collections import (
    Counter,     # 计数器集合类
    defaultdict  # 带默认值的字典
)
import warnings  # 警告控制

# 从本地utils模块导入工具函数
from .utils import (
    logger,                         # 日志记录器
    clean_str,                      # 字符串清理函数
    compute_mdhash_id,             # 计算MD5哈希ID
    decode_tokens_by_tiktoken,     # tiktoken解码函数
    encode_string_by_tiktoken,     # tiktoken编码函数
    is_float_regex,                # 浮点数检查函数
    list_of_list_to_csv,           # 列表转CSV函数
    pack_user_ass_to_openai_messages,  # OpenAI消息打包函数
    split_string_by_multi_markers,     # 多标记字符串分割函数
    truncate_list_by_token_size,       # 基于token大小截断列表
    process_combine_contexts,           # 上下文合并处理函数
)

# 从本地base模块导入基础类
from .base import (
    BaseGraphStorage,    # 图存储基类
    BaseKVStorage,       # 键值存储基类
    BaseVectorStorage,   # 向量存储基类
    TextChunkSchema,     # 文本块模式定义
    QueryParam,          # 查询参数类
)

# 从本地prompt模块导入提示相关常量
from .prompt import (
    GRAPH_FIELD_SEP,  # 图字段分隔符
    PROMPTS           # 提示模板集合
)


def chunking_by_token_size(
    content: str, 
    overlap_token_size=128,  # 重叠部分的token数量
    max_token_size=1024,     # 每个chunk的最大token数量 
    tiktoken_model="gpt-4o"  # 使用的tokenizer模型名称
):
    """将长文本按照token数量切分成多个重叠的chunk
    
    Args:
        content (str): 需要切分的原始文本内容
        overlap_token_size (int, optional): 相邻chunk之间的重叠token数. Defaults to 128.
        max_token_size (int, optional): 每个chunk的最大token数. Defaults to 1024.
        tiktoken_model (str, optional): 使用的tokenizer模型. Defaults to "gpt-4o".
    
    Returns:
        list[dict]: 包含切分后的chunk列表,每个chunk包含以下字段:
            - tokens: chunk实际包含的token数
            - content: chunk的文本内容
            - chunk_order_index: chunk的序号
    """
    # 使用指定的tokenizer模型将文本编码为tokens
    tokens = encode_string_by_tiktoken(content, model_name=tiktoken_model)
    
    results = []
    # 按照指定的步长(max_token_size - overlap_token_size)遍历tokens
    # 
    for index, start in enumerate(
        range(0, len(tokens), max_token_size - overlap_token_size)
    ):
        # 解码当前窗口范围内的tokens为文本
        chunk_content = decode_tokens_by_tiktoken(
            tokens[start : start + max_token_size], 
            model_name=tiktoken_model
        )
        
        # 将chunk添加到结果列表中
        results.append(
            {
                # 计算实际token数量(最后一个chunk可能不足max_token_size)
                "tokens": min(max_token_size, len(tokens) - start),
                # 去除chunk首尾的空白字符
                "content": chunk_content.strip(),
                # 记录chunk的序号
                "chunk_order_index": index,
            }
        )
    return results


async def _handle_entity_relation_summary(
    entity_or_relation_name: str,  # 实体名称或关系名称
    description: str,              # 需要总结的描述文本
    global_config: dict,           # 全局配置参数
) -> str:
    """对实体或关系的描述文本进行总结
    
    当描述文本的token数量超过指定阈值时,使用LLM对其进行总结,以减少token数量
    
    Args:
        entity_or_relation_name (str): 实体名称或关系名称(用于提示LLM)
        description (str): 需要总结的描述文本
        global_config (dict): 包含LLM配置的全局参数字典,必须包含:
            - llm_model_func: LLM调用函数
            - llm_model_max_token_size: LLM输入的最大token数
            - tiktoken_model_name: 使用的tokenizer模型名称
            - entity_summary_to_max_tokens: 总结后的最大token数
    
    Returns:
        str: 如果原文本较短则直接返回,否则返回LLM总结后的文本
    """
    # 从全局配置中获取必要的参数
    use_llm_func: callable = global_config["llm_model_func"]
    llm_max_tokens = global_config["llm_model_max_token_size"]
    tiktoken_model_name = global_config["tiktoken_model_name"]
    summary_max_tokens = global_config["entity_summary_to_max_tokens"]

    # 计算描述文本的token数量
    tokens = encode_string_by_tiktoken(description, model_name=tiktoken_model_name)
    
    # 如果token数量小于阈值,无需总结直接返回
    if len(tokens) < summary_max_tokens:
        return description
        
    # 获取总结用的提示模板
    prompt_template = PROMPTS["summarize_entity_descriptions"]
    
    # 截取LLM能处理的最大token数量
    use_description = decode_tokens_by_tiktoken(
        tokens[:llm_max_tokens], 
        model_name=tiktoken_model_name
    )
    
    # 构建提示上下文
    context_base = dict(
        entity_name=entity_or_relation_name,
        # 按字段分隔符分割描述文本
        description_list=use_description.split(GRAPH_FIELD_SEP),
    )
    
    # 格式化提示模板
    use_prompt = prompt_template.format(**context_base)
    
    # 记录调试日志
    logger.debug(f"Trigger summary: {entity_or_relation_name}")
    
    # 调用LLM生成总结
    summary = await use_llm_func(use_prompt, max_tokens=summary_max_tokens)
    
    return summary

async def _handle_single_entity_extraction(
    record_attributes: list[str],  # 记录的属性列表
    chunk_key: str,                # 当前chunk的唯一标识符
):
    """从记录属性中提取单个实体的信息
    
    Args:
        record_attributes (list[str]): 包含实体信息的属性列表
        chunk_key (str): 当前chunk的唯一标识符,用于追踪数据来源
    
    Returns:
        dict: 包含实体信息的字典,如果记录无效则返回None
    """
    # 检查记录是否为有效的实体记录
    if len(record_attributes) < 4 or record_attributes[0] != '"entity"':
        return None
    
    # 提取并清理实体名称
    entity_name = clean_str(record_attributes[1].upper())
    if not entity_name.strip():  # 如果实体名称为空,返回None
        return None
    
    # 提取并清理实体类型和描述
    entity_type = clean_str(record_attributes[2].upper())
    entity_description = clean_str(record_attributes[3])
    entity_source_id = chunk_key  # 记录数据来源
    
    # 返回包含实体信息的字典
    return dict(
        entity_name=entity_name,
        entity_type=entity_type,
        description=entity_description,
        source_id=entity_source_id,
    )


async def _handle_single_relationship_extraction(
    record_attributes: list[str],  # 记录的属性列表
    chunk_key: str,                # 当前chunk的唯一标识符
):
    """从记录属性中提取单个关系的信息
    
    Args:
        record_attributes (list[str]): 包含关系信息的属性列表
        chunk_key (str): 当前chunk的唯一标识符,用于追踪数据来源
    
    Returns:
        dict: 包含关系信息的字典,如果记录无效则返回None
    """
    # 检查记录是否为有效的关系记录
    if len(record_attributes) < 5 or record_attributes[0] != '"relationship"':
        return None
    
    # 提取并清理关系的源节点和目标节点
    source = clean_str(record_attributes[1].upper())
    target = clean_str(record_attributes[2].upper())
    
    # 提取并清理关系的描述和关键词
    edge_description = clean_str(record_attributes[3])
    edge_keywords = clean_str(record_attributes[4])
    edge_source_id = chunk_key  # 记录数据来源
    
    # 提取并计算关系的权重,默认为1.0
    weight = (
        float(record_attributes[-1]) if is_float_regex(record_attributes[-1]) else 1.0
    )
    
    # 返回包含关系信息的字典
    return dict(
        src_id=source,
        tgt_id=target,
        weight=weight,
        description=edge_description,
        keywords=edge_keywords,
        source_id=edge_source_id,
    )

async def _merge_nodes_then_upsert(
    entity_name: str,  # 实体名称
    nodes_data: list[dict],  # 待合并的节点数据列表
    knowledge_graph_inst: BaseGraphStorage,  # 知识图谱实例
    global_config: dict,  # 全局配置参数
):
    """合并节点数据并更新到知识图谱中
    
    Args:
        entity_name (str): 实体名称
        nodes_data (list[dict]): 待合并的节点数据列表
        knowledge_graph_inst (BaseGraphStorage): 知识图谱实例
        global_config (dict): 全局配置参数
    
    Returns:
        dict: 更新后的节点数据
    """
    already_entitiy_types = []
    already_source_ids = []
    already_description = []

    # 检查知识图谱中是否已存在该节点
    already_node = await knowledge_graph_inst.get_node(entity_name)
    if already_node is not None:
        # 如果存在,提取已有的实体类型、来源ID和描述
        already_entitiy_types.append(already_node["entity_type"])
        already_source_ids.extend(
            split_string_by_multi_markers(already_node["source_id"], [GRAPH_FIELD_SEP])
        )
        already_description.append(already_node["description"])

    # 计算合并后的实体类型(选择出现频率最高的类型)
    entity_type = sorted(
        Counter(
            [dp["entity_type"] for dp in nodes_data] + already_entitiy_types
        ).items(),
        key=lambda x: x[1],
        reverse=True,
    )[0][0]
    
    # 合并描述和来源ID
    description = GRAPH_FIELD_SEP.join(
        sorted(set([dp["description"] for dp in nodes_data] + already_description))
    )
    source_id = GRAPH_FIELD_SEP.join(
        set([dp["source_id"] for dp in nodes_data] + already_source_ids)
    )
    
    # 使用LLM对合并后的描述进行总结
    description = await _handle_entity_relation_summary(
        entity_name, description, global_config
    )
    
    # 构建节点数据字典
    node_data = dict(
        entity_type=entity_type,
        description=description,
        source_id=source_id,
    )
    
    # 更新或插入节点到知识图谱中
    await knowledge_graph_inst.upsert_node(
        entity_name,
        node_data=node_data,
    )
    
    node_data["entity_name"] = entity_name
    return node_data


async def _merge_edges_then_upsert(
    src_id: str,  # 源节点ID
    tgt_id: str,  # 目标节点ID
    edges_data: list[dict],  # 待合并的边数据列表
    knowledge_graph_inst: BaseGraphStorage,  # 知识图谱实例
    global_config: dict,  # 全局配置参数
):
    """合并边数据并更新到知识图谱中
    
    Args:
        src_id (str): 源节点ID
        tgt_id (str): 目标节点ID
        edges_data (list[dict]): 待合并的边数据列表
        knowledge_graph_inst (BaseGraphStorage): 知识图谱实例
        global_config (dict): 全局配置参数
    
    Returns:
        dict: 更新后的边数据
    """
    already_weights = []
    already_source_ids = []
    already_description = []
    already_keywords = []

    # 检查知识图谱中是否已存在该边
    if await knowledge_graph_inst.has_edge(src_id, tgt_id):
        already_edge = await knowledge_graph_inst.get_edge(src_id, tgt_id)
        # 如果存在,提取已有的权重、来源ID、描述和关键词
        already_weights.append(already_edge["weight"])
        already_source_ids.extend(
            split_string_by_multi_markers(already_edge["source_id"], [GRAPH_FIELD_SEP])
        )
        already_description.append(already_edge["description"])
        already_keywords.extend(
            split_string_by_multi_markers(already_edge["keywords"], [GRAPH_FIELD_SEP])
        )

    # 计算合并后的权重(累加)
    weight = sum([dp["weight"] for dp in edges_data] + already_weights)
    
    # 合并描述、关键词和来源ID
    description = GRAPH_FIELD_SEP.join(
        sorted(set([dp["description"] for dp in edges_data] + already_description))
    )
    keywords = GRAPH_FIELD_SEP.join(
        sorted(set([dp["keywords"] for dp in edges_data] + already_keywords))
    )
    source_id = GRAPH_FIELD_SEP.join(
        set([dp["source_id"] for dp in edges_data] + already_source_ids)
    )
    
    # 确保源节点和目标节点存在于知识图谱中
    for need_insert_id in [src_id, tgt_id]:
        if not (await knowledge_graph_inst.has_node(need_insert_id)):
            await knowledge_graph_inst.upsert_node(
                need_insert_id,
                node_data={
                    "source_id": source_id,
                    "description": description,
                    "entity_type": '"UNKNOWN"',
                },
            )
    
    # 使用LLM对合并后的描述进行总结
    description = await _handle_entity_relation_summary(
        (src_id, tgt_id), description, global_config
    )
    
    # 更新或插入边到知识图谱中
    await knowledge_graph_inst.upsert_edge(
        src_id,
        tgt_id,
        edge_data=dict(
            weight=weight,
            description=description,
            keywords=keywords,
            source_id=source_id,
        ),
    )

    edge_data = dict(
        src_id=src_id,
        tgt_id=tgt_id,
        description=description,
        keywords=keywords,
    )

    return edge_data

async def extract_entities(
    chunks: dict[str, TextChunkSchema],  # 文本块字典,键为chunk的唯一标识符
    knowledge_graph_inst: BaseGraphStorage,  # 知识图谱实例
    entity_vdb: BaseVectorStorage,  # 实体向量数据库
    relationships_vdb: BaseVectorStorage,  # 关系向量数据库
    global_config: dict,  # 全局配置参数
) -> Union[BaseGraphStorage, None]:
    """从文本块中提取实体和关系,并更新到知识图谱中
    
    Args:
        chunks: 待处理的文本块字典
        knowledge_graph_inst: 用于存储实体和关系的知识图谱实例
        entity_vdb: 用于存储实体向量的数据库
        relationships_vdb: 用于存储关系向量的数据库
        global_config: 包含LLM配置等全局参数
    
    Returns:
        更新后的知识图谱实例,如果提取失败则返回None
    """
    # 从全局配置中获取LLM函数和最大提取次数
    use_llm_func: callable = global_config["llm_model_func"]
    entity_extract_max_gleaning = global_config["entity_extract_max_gleaning"]

    # 将chunks字典转换为有序列表
    ordered_chunks = list(chunks.items())

    # 获取提示模板和分隔符配置
    entity_extract_prompt = PROMPTS["entity_extraction"]
    context_base = dict(
        tuple_delimiter=PROMPTS["DEFAULT_TUPLE_DELIMITER"],
        record_delimiter=PROMPTS["DEFAULT_RECORD_DELIMITER"],
        completion_delimiter=PROMPTS["DEFAULT_COMPLETION_DELIMITER"],
        entity_types=",".join(PROMPTS["DEFAULT_ENTITY_TYPES"]),
    )
    continue_prompt = PROMPTS["entiti_continue_extraction"]
    if_loop_prompt = PROMPTS["entiti_if_loop_extraction"]

    # 初始化处理计数器
    already_processed = 0
    already_entities = 0
    already_relations = 0

    async def _process_single_content(chunk_key_dp: tuple[str, TextChunkSchema]):
        """处理单个文本块,提取实体和关系
        
        Args:
            chunk_key_dp: (chunk_key, chunk_data)元组
            
        Returns:
            (nodes_dict, edges_dict): 提取的实体和关系字典
        """
        nonlocal already_processed, already_entities, already_relations
        chunk_key = chunk_key_dp[0]
        chunk_dp = chunk_key_dp[1]
        content = chunk_dp["content"]
        
        # 构建初始提示并获取LLM响应
        hint_prompt = entity_extract_prompt.format(**context_base, input_text=content)
        final_result = await use_llm_func(hint_prompt)

        # 记录对话历史
        history = pack_user_ass_to_openai_messages(hint_prompt, final_result)
        
        # 多轮提取循环
        for now_glean_index in range(entity_extract_max_gleaning):
            # 继续提取新的实体和关系
            glean_result = await use_llm_func(continue_prompt, history_messages=history)
            history += pack_user_ass_to_openai_messages(continue_prompt, glean_result)
            final_result += glean_result
            
            if now_glean_index == entity_extract_max_gleaning - 1:
                break

            # 询问是否需要继续提取
            if_loop_result: str = await use_llm_func(
                if_loop_prompt, history_messages=history
            )
            if_loop_result = if_loop_result.strip().strip('"').strip("'").lower()
            if if_loop_result != "yes":
                break

        # 分割提取结果为独立记录
        records = split_string_by_multi_markers(
            final_result,
            [context_base["record_delimiter"], context_base["completion_delimiter"]],
        )

        # 用于收集可能的实体和关系
        maybe_nodes = defaultdict(list)
        maybe_edges = defaultdict(list)
        
        # 处理每条记录
        for record in records:
            # 提取括号内的属性内容
            record = re.search(r"\((.*)\)", record)
            if record is None:
                continue
            record = record.group(1)
            
            # 分割属性列表
            record_attributes = split_string_by_multi_markers(
                record, [context_base["tuple_delimiter"]]
            )
            
            # 尝试提取实体
            if_entities = await _handle_single_entity_extraction(
                record_attributes, chunk_key
            )
            if if_entities is not None:
                maybe_nodes[if_entities["entity_name"]].append(if_entities)
                continue

            # 尝试提取关系
            if_relation = await _handle_single_relationship_extraction(
                record_attributes, chunk_key
            )
            if if_relation is not None:
                maybe_edges[(if_relation["src_id"], if_relation["tgt_id"])].append(
                    if_relation
                )
                
        # 更新处理计数
        already_processed += 1
        already_entities += len(maybe_nodes)
        already_relations += len(maybe_edges)
        
        # 显示处理进度
        now_ticks = PROMPTS["process_tickers"][
            already_processed % len(PROMPTS["process_tickers"])
        ]
        print(
            f"{now_ticks} Processed {already_processed} chunks, {already_entities} entities(duplicated), {already_relations} relations(duplicated)\r",
            end="",
            flush=True,
        )
        return dict(maybe_nodes), dict(maybe_edges)

    # 并发处理所有文本块
    results = await asyncio.gather(
        *[_process_single_content(c) for c in ordered_chunks]
    )
    print()  # 清除进度条
    
    # 合并所有提取结果
    maybe_nodes = defaultdict(list)
    maybe_edges = defaultdict(list)
    for m_nodes, m_edges in results:
        for k, v in m_nodes.items():
            maybe_nodes[k].extend(v)
        for k, v in m_edges.items():
            maybe_edges[tuple(sorted(k))].extend(v)
            
    # 合并并更新实体到知识图谱
    all_entities_data = await asyncio.gather(
        *[
            _merge_nodes_then_upsert(k, v, knowledge_graph_inst, global_config)
            for k, v in maybe_nodes.items()
        ]
    )
    
    # 合并并更新关系到知识图谱
    all_relationships_data = await asyncio.gather(
        *[
            _merge_edges_then_upsert(k[0], k[1], v, knowledge_graph_inst, global_config)
            for k, v in maybe_edges.items()
        ]
    )
    
    # 检查提取结果
    if not len(all_entities_data):
        logger.warning("Didn't extract any entities, maybe your LLM is not working")
        return None
    if not len(all_relationships_data):
        logger.warning(
            "Didn't extract any relationships, maybe your LLM is not working"
        )
        return None

    # 更新实体向量数据库
    if entity_vdb is not None:
        data_for_vdb = {
            compute_mdhash_id(dp["entity_name"], prefix="ent-"): {
                "content": dp["entity_name"] + dp["description"],
                "entity_name": dp["entity_name"],
            }
            for dp in all_entities_data
        }
        await entity_vdb.upsert(data_for_vdb)

    # 更新关系向量数据库
    if relationships_vdb is not None:
        data_for_vdb = {
            compute_mdhash_id(dp["src_id"] + dp["tgt_id"], prefix="rel-"): {
                "src_id": dp["src_id"],
                "tgt_id": dp["tgt_id"],
                "content": dp["keywords"]
                + dp["src_id"]
                + dp["tgt_id"]
                + dp["description"],
            }
            for dp in all_relationships_data
        }
        await relationships_vdb.upsert(data_for_vdb)

    return knowledge_graph_inst

async def local_query(
    query,  # 用户的查询文本
    knowledge_graph_inst: BaseGraphStorage,  # 知识图谱存储实例
    entities_vdb: BaseVectorStorage,  # 实体的向量数据库
    relationships_vdb: BaseVectorStorage,  # 关系的向量数据库
    text_chunks_db: BaseKVStorage[TextChunkSchema],  # 原始文本块的存储
    query_param: QueryParam,  # 查询参数配置
    global_config: dict,  # 全局配置字典
) -> str:
    """本地查询函数,从知识图谱中检索相关信息并生成回答
    
    Args:
        query: 用户输入的查询文本
        knowledge_graph_inst: 用于存储和查询知识图谱的实例
        entities_vdb: 用于向量检索实体的数据库
        relationships_vdb: 用于向量检索关系的数据库
        text_chunks_db: 存储原始文本块的键值数据库
        query_param: 包含查询相关参数的配置对象
        global_config: 包含全局配置的字典
    
    Returns:
        str: 查询的响应文本
    """
    context = None
    # 从全局配置中获取语言模型函数
    use_model_func = global_config["llm_model_func"]

    # 构建关键词提取的提示
    kw_prompt_temp = PROMPTS["keywords_extraction"]
    kw_prompt = kw_prompt_temp.format(query=query)
    result = await use_model_func(kw_prompt)

    # 尝试解析语言模型返回的关键词JSON
    try:
        keywords_data = json.loads(result)
        keywords = keywords_data.get("low_level_keywords", [])
        keywords = ", ".join(keywords)
    except json.JSONDecodeError:
        # 第一次解析失败,清理响应文本后重试
        try:
            result = (
                result.replace(kw_prompt[:-1], "")
                .replace("user", "")
                .replace("model", "")
                .strip()
            )
            result = "{" + result.split("{")[1].split("}")[0] + "}"

            keywords_data = json.loads(result)
            keywords = keywords_data.get("low_level_keywords", [])
            keywords = ", ".join(keywords)
        except json.JSONDecodeError as e:
            # 解析彻底失败,返回错误响应
            print(f"JSON解析错误: {e}")
            return PROMPTS["fail_response"]
            
    # 如果成功提取到关键词,构建查询上下文
    if keywords:
        context = await _build_local_query_context(
            keywords,
            knowledge_graph_inst,
            entities_vdb,
            text_chunks_db,
            query_param,
        )
        
    # 如果只需要上下文,直接返回
    if query_param.only_need_context:
        return context
    if context is None:
        return PROMPTS["fail_response"]
        
    # 构建RAG系统提示并获取语言模型响应
    sys_prompt_temp = PROMPTS["rag_response"]
    sys_prompt = sys_prompt_temp.format(
        context_data=context, response_type=query_param.response_type
    )
    response = await use_model_func(
        query,
        system_prompt=sys_prompt,
    )
    
    # 清理语言模型的响应文本
    if len(response) > len(sys_prompt):
        response = (
            response.replace(sys_prompt, "")
            .replace("user", "")
            .replace("model", "")
            .replace(query, "")
            .replace("<system>", "")
            .replace("</system>", "")
            .strip()
        )

    return response


async def _build_local_query_context(
    query,  # 查询关键词
    knowledge_graph_inst: BaseGraphStorage,  # 知识图谱实例
    entities_vdb: BaseVectorStorage,  # 实体向量数据库
    text_chunks_db: BaseKVStorage[TextChunkSchema],  # 文本块存储
    query_param: QueryParam,  # 查询参数
):
    """构建本地查询的上下文信息
    
    基于查询关键词,从知识图谱和向量数据库中检索相关的实体、关系和原文信息,
    并将它们组织成结构化的上下文
    """
    # 从实体向量数据库中检索最相关的实体
    results = await entities_vdb.query(query, top_k=query_param.top_k)

    if not len(results):
        return None
        
    # 并发获取检索到的实体节点数据
    node_datas = await asyncio.gather(
        *[knowledge_graph_inst.get_node(r["entity_name"]) for r in results]
    )
    if not all([n is not None for n in node_datas]):
        logger.warning("部分节点数据缺失,存储可能损坏")
        
    # 获取每个实体节点的度数(连接数)
    node_degrees = await asyncio.gather(
        *[knowledge_graph_inst.node_degree(r["entity_name"]) for r in results]
    )
    
    # 合并节点信息,添加实体名称和排名
    node_datas = [
        {**n, "entity_name": k["entity_name"], "rank": d}
        for k, n, d in zip(results, node_datas, node_degrees)
        if n is not None
    ]
    
    # 查找与实体最相关的文本单元和关系
    use_text_units = await _find_most_related_text_unit_from_entities(
        node_datas, query_param, text_chunks_db, knowledge_graph_inst
    )
    use_relations = await _find_most_related_edges_from_entities(
        node_datas, query_param, knowledge_graph_inst
    )
    
    # 记录使用的数据量
    logger.info(
        f"本地查询使用了 {len(node_datas)} 个实体, {len(use_relations)} 个关系, {len(use_text_units)} 个文本单元"
    )
    
    # 构建实体信息的CSV格式数据
    entites_section_list = [["id", "entity", "type", "description", "rank"]]
    for i, n in enumerate(node_datas):
        entites_section_list.append(
            [
                i,
                n["entity_name"],
                n.get("entity_type", "UNKNOWN"),
                n.get("description", "UNKNOWN"),
                n["rank"],
            ]
        )
    entities_context = list_of_list_to_csv(entites_section_list)

    # 构建关系信息的CSV格式数据
    relations_section_list = [
        ["id", "source", "target", "description", "keywords", "weight", "rank"]
    ]
    for i, e in enumerate(use_relations):
        relations_section_list.append(
            [
                i,
                e["src_tgt"][0],
                e["src_tgt"][1],
                e["description"],
                e["keywords"],
                e["weight"],
                e["rank"],
            ]
        )
    relations_context = list_of_list_to_csv(relations_section_list)

    # 构建文本单元的CSV格式数据
    text_units_section_list = [["id", "content"]]
    for i, t in enumerate(use_text_units):
        text_units_section_list.append([i, t["content"]])
    text_units_context = list_of_list_to_csv(text_units_section_list)
    
    # 返回格式化的上下文信息
    return f"""
-----Entities-----
```csv
{entities_context}
```
-----Relationships-----
```csv
{relations_context}
```
-----Sources-----
```csv
{text_units_context}
```
"""

async def _find_most_related_text_unit_from_entities(
    node_datas: list[dict],  # 实体节点数据列表
    query_param: QueryParam,  # 查询参数配置
    text_chunks_db: BaseKVStorage[TextChunkSchema],  # 文本块存储
    knowledge_graph_inst: BaseGraphStorage,  # 知识图谱实例
):
    """查找与给定实体最相关的文本单元
    
    通过分析实体的一跳邻居和关系,找出最相关的原始文本单元
    """
    # 获取每个实体节点关联的文本单元ID
    text_units = [
        split_string_by_multi_markers(dp["source_id"], [GRAPH_FIELD_SEP])
        for dp in node_datas
    ]
    
    # 获取每个实体的边(关系)信息
    edges = await asyncio.gather(
        *[knowledge_graph_inst.get_node_edges(dp["entity_name"]) for dp in node_datas]
    )
    
    # 收集所有一跳邻居节点
    all_one_hop_nodes = set()
    for this_edges in edges:
        if not this_edges:
            continue
        all_one_hop_nodes.update([e[1] for e in this_edges])

    # 获取一跳邻居节点的数据
    all_one_hop_nodes = list(all_one_hop_nodes)
    all_one_hop_nodes_data = await asyncio.gather(
        *[knowledge_graph_inst.get_node(e) for e in all_one_hop_nodes]
    )

    # 构建一跳邻居节点的文本单元查找表
    all_one_hop_text_units_lookup = {
        k: set(split_string_by_multi_markers(v["source_id"], [GRAPH_FIELD_SEP]))
        for k, v in zip(all_one_hop_nodes, all_one_hop_nodes_data)
        if v is not None and "source_id" in v  # 检查节点数据有效性
    }

    # 构建所有文本单元的查找表,包含顺序和关系计数信息
    all_text_units_lookup = {}
    for index, (this_text_units, this_edges) in enumerate(zip(text_units, edges)):
        for c_id in this_text_units:
            if c_id in all_text_units_lookup:
                continue
            # 计算文本单元与一跳邻居的关系数量
            relation_counts = 0
            if this_edges:  # 检查边是否存在
                for e in this_edges:
                    if (
                        e[1] in all_one_hop_text_units_lookup
                        and c_id in all_one_hop_text_units_lookup[e[1]]
                    ):
                        relation_counts += 1

            # 获取文本单元数据
            chunk_data = await text_chunks_db.get_by_id(c_id)
            if chunk_data is not None and "content" in chunk_data:  # 检查内容有效性
                all_text_units_lookup[c_id] = {
                    "data": chunk_data,
                    "order": index,
                    "relation_counts": relation_counts,
                }

    # 过滤无效数据并确保包含内容
    all_text_units = [
        {"id": k, **v}
        for k, v in all_text_units_lookup.items()
        if v is not None and v.get("data") is not None and "content" in v["data"]
    ]

    if not all_text_units:
        logger.warning("No valid text units found")
        return []

    # 按顺序和关系数量排序
    all_text_units = sorted(
        all_text_units, key=lambda x: (x["order"], -x["relation_counts"])
    )

    # 根据token数量限制截断文本单元列表
    all_text_units = truncate_list_by_token_size(
        all_text_units,
        key=lambda x: x["data"]["content"],
        max_token_size=query_param.max_token_for_text_unit,
    )

    # 只返回文本单元数据
    all_text_units = [t["data"] for t in all_text_units]
    return all_text_units


async def _find_most_related_edges_from_entities(
    node_datas: list[dict],  # 实体节点数据列表
    query_param: QueryParam,  # 查询参数配置
    knowledge_graph_inst: BaseGraphStorage,  # 知识图谱实例
):
    """查找与给定实体最相关的边(关系)
    
    获取实体的所有关系,并根据度数和权重进行排序
    """
    # 获取所有相关边
    all_related_edges = await asyncio.gather(
        *[knowledge_graph_inst.get_node_edges(dp["entity_name"]) for dp in node_datas]
    )
    
    # 收集所有唯一的边(确保边的方向一致)
    all_edges = set()
    for this_edges in all_related_edges:
        all_edges.update([tuple(sorted(e)) for e in this_edges])
    all_edges = list(all_edges)
    
    # 获取边的详细信息和度数
    all_edges_pack = await asyncio.gather(
        *[knowledge_graph_inst.get_edge(e[0], e[1]) for e in all_edges]
    )
    all_edges_degree = await asyncio.gather(
        *[knowledge_graph_inst.edge_degree(e[0], e[1]) for e in all_edges]
    )
    
    # 合并边的信息
    all_edges_data = [
        {"src_tgt": k, "rank": d, **v}
        for k, v, d in zip(all_edges, all_edges_pack, all_edges_degree)
        if v is not None
    ]
    
    # 按度数和权重排序
    all_edges_data = sorted(
        all_edges_data, key=lambda x: (x["rank"], x["weight"]), reverse=True
    )
    
    # 根据token数量限制截断边列表
    all_edges_data = truncate_list_by_token_size(
        all_edges_data,
        key=lambda x: x["description"],
        max_token_size=query_param.max_token_for_global_context,
    )
    return all_edges_data
async def global_query(
    query,  # 用户查询文本
    knowledge_graph_inst: BaseGraphStorage,  # 知识图谱实例
    entities_vdb: BaseVectorStorage,  # 实体向量数据库
    relationships_vdb: BaseVectorStorage,  # 关系向量数据库
    text_chunks_db: BaseKVStorage[TextChunkSchema],  # 文本块存储
    query_param: QueryParam,  # 查询参数配置
    global_config: dict,  # 全局配置
) -> str:
    """执行全局查询,基于关系的向量检索
    
    通过分析高层概念关键词,检索相关的关系和实体,构建查询上下文
    """
    context = None
    # 获取语言模型函数
    use_model_func = global_config["llm_model_func"]

    # 提取查询关键词
    kw_prompt_temp = PROMPTS["keywords_extraction"]
    kw_prompt = kw_prompt_temp.format(query=query)
    result = await use_model_func(kw_prompt)

    # 解析关键词JSON结果
    try:
        keywords_data = json.loads(result)
        keywords = keywords_data.get("high_level_keywords", [])
        keywords = ", ".join(keywords)
    except json.JSONDecodeError:
        # 清理响应文本后重试解析
        try:
            result = (
                result.replace(kw_prompt[:-1], "")
                .replace("user", "")
                .replace("model", "")
                .strip()
            )
            result = "{" + result.split("{")[1].split("}")[0] + "}"

            keywords_data = json.loads(result)
            keywords = keywords_data.get("high_level_keywords", [])
            keywords = ", ".join(keywords)

        except json.JSONDecodeError as e:
            print(f"JSON解析错误: {e}")
            return PROMPTS["fail_response"]
            
    # 基于关键词构建查询上下文
    if keywords:
        context = await _build_global_query_context(
            keywords,
            knowledge_graph_inst,
            entities_vdb,
            relationships_vdb,
            text_chunks_db,
            query_param,
        )

    # 如果只需要上下文,直接返回
    if query_param.only_need_context:
        return context
    if context is None:
        return PROMPTS["fail_response"]

    # 构建系统提示并获取语言模型响应
    sys_prompt_temp = PROMPTS["rag_response"]
    sys_prompt = sys_prompt_temp.format(
        context_data=context, response_type=query_param.response_type
    )
    response = await use_model_func(
        query,
        system_prompt=sys_prompt,
    )
    
    # 清理响应文本
    if len(response) > len(sys_prompt):
        response = (
            response.replace(sys_prompt, "")
            .replace("user", "")
            .replace("model", "")
            .replace(query, "")
            .replace("<system>", "")
            .replace("</system>", "")
            .strip()
        )

    return response


async def _build_global_query_context(
    keywords,  # 查询关键词
    knowledge_graph_inst: BaseGraphStorage,  # 知识图谱实例
    entities_vdb: BaseVectorStorage,  # 实体向量数据库
    relationships_vdb: BaseVectorStorage,  # 关系向量数据库
    text_chunks_db: BaseKVStorage[TextChunkSchema],  # 文本块存储
    query_param: QueryParam,  # 查询参数
):
    """构建全局查询的上下文信息
    
    基于关系的向量检索,找出最相关的关系、实体和文本单元
    """
    # 从关系向量数据库检索相关关系
    results = await relationships_vdb.query(keywords, top_k=query_param.top_k)

    if not len(results):
        return None

    # 获取关系的详细数据
    edge_datas = await asyncio.gather(
        *[knowledge_graph_inst.get_edge(r["src_id"], r["tgt_id"]) for r in results]
    )

    # 检查数据完整性
    if not all([n is not None for n in edge_datas]):
        logger.warning("部分边数据缺失,存储可能损坏")
        
    # 获取关系的度数
    edge_degree = await asyncio.gather(
        *[knowledge_graph_inst.edge_degree(r["src_id"], r["tgt_id"]) for r in results]
    )
    
    # 合并关系信息
    edge_datas = [
        {"src_id": k["src_id"], "tgt_id": k["tgt_id"], "rank": d, **v}
        for k, v, d in zip(results, edge_datas, edge_degree)
        if v is not None
    ]
    
    # 按度数和权重排序
    edge_datas = sorted(
        edge_datas, key=lambda x: (x["rank"], x["weight"]), reverse=True
    )
    
    # 根据token限制截断关系列表
    edge_datas = truncate_list_by_token_size(
        edge_datas,
        key=lambda x: x["description"],
        max_token_size=query_param.max_token_for_global_context,
    )

    # 获取相关的实体和文本单元
    use_entities = await _find_most_related_entities_from_relationships(
        edge_datas, query_param, knowledge_graph_inst
    )
    use_text_units = await _find_related_text_unit_from_relationships(
        edge_datas, query_param, text_chunks_db, knowledge_graph_inst
    )
    
    # 记录使用的数据量
    logger.info(
        f"全局查询使用了 {len(use_entities)} 个实体, {len(edge_datas)} 个关系, {len(use_text_units)} 个文本单元"
    )
    
    # 构建关系的CSV格式数据
    relations_section_list = [
        ["id", "source", "target", "description", "keywords", "weight", "rank"]
    ]
    for i, e in enumerate(edge_datas):
        relations_section_list.append(
            [
                i,
                e["src_id"],
                e["tgt_id"],
                e["description"],
                e["keywords"],
                e["weight"],
                e["rank"],
            ]
        )
    relations_context = list_of_list_to_csv(relations_section_list)

    # 构建实体的CSV格式数据
    entites_section_list = [["id", "entity", "type", "description", "rank"]]
    for i, n in enumerate(use_entities):
        entites_section_list.append(
            [
                i,
                n["entity_name"],
                n.get("entity_type", "UNKNOWN"),
                n.get("description", "UNKNOWN"),
                n["rank"],
            ]
        )
    entities_context = list_of_list_to_csv(entites_section_list)

    # 构建文本单元的CSV格式数据
    text_units_section_list = [["id", "content"]]
    for i, t in enumerate(use_text_units):
        text_units_section_list.append([i, t["content"]])
    text_units_context = list_of_list_to_csv(text_units_section_list)

    # 返回格式化的上下文信息
    return f"""
-----Entities-----
```csv
{entities_context}
```
-----Relationships-----
```csv
{relations_context}
```
-----Sources-----
```csv
{text_units_context}
```
"""

async def _find_most_related_entities_from_relationships(
    edge_datas: list[dict],  # 关系数据列表
    query_param: QueryParam,  # 查询参数配置
    knowledge_graph_inst: BaseGraphStorage,  # 知识图谱实例
):
    """从关系中找出最相关的实体节点
    
    通过分析关系的源节点和目标节点,获取所有相关实体的详细信息
    """
    # 收集所有关系中涉及的实体名称
    entity_names = set()
    for e in edge_datas:
        entity_names.add(e["src_id"])
        entity_names.add(e["tgt_id"])

    # 并发获取所有实体节点的详细数据
    node_datas = await asyncio.gather(
        *[knowledge_graph_inst.get_node(entity_name) for entity_name in entity_names]
    )

    # 并发获取所有实体节点的度数(连接数)
    node_degrees = await asyncio.gather(
        *[knowledge_graph_inst.node_degree(entity_name) for entity_name in entity_names]
    )
    
    # 合并实体信息,添加实体名称和排名
    node_datas = [
        {**n, "entity_name": k, "rank": d}
        for k, n, d in zip(entity_names, node_datas, node_degrees)
    ]

    # 根据token数量限制截断实体列表
    node_datas = truncate_list_by_token_size(
        node_datas,
        key=lambda x: x["description"],
        max_token_size=query_param.max_token_for_local_context,
    )

    return node_datas


async def _find_related_text_unit_from_relationships(
    edge_datas: list[dict],  # 关系数据列表
    query_param: QueryParam,  # 查询参数配置
    text_chunks_db: BaseKVStorage[TextChunkSchema],  # 文本块存储
    knowledge_graph_inst: BaseGraphStorage,  # 知识图谱实例
):
    """从关系中找出相关的文本单元
    
    通过分析关系的源文本,获取所有相关的原始文本单元
    """
    # 从每个关系中提取文本单元ID
    text_units = [
        split_string_by_multi_markers(dp["source_id"], [GRAPH_FIELD_SEP])
        for dp in edge_datas
    ]

    # 构建文本单元查找表,记录顺序信息
    all_text_units_lookup = {}

    # 获取每个文本单元的详细数据
    for index, unit_list in enumerate(text_units):
        for c_id in unit_list:
            if c_id not in all_text_units_lookup:
                all_text_units_lookup[c_id] = {
                    "data": await text_chunks_db.get_by_id(c_id),
                    "order": index,
                }

    # 检查数据完整性
    if any([v is None for v in all_text_units_lookup.values()]):
        logger.warning("Text chunks are missing, maybe the storage is damaged")
        
    # 过滤无效数据并添加ID信息
    all_text_units = [
        {"id": k, **v} for k, v in all_text_units_lookup.items() if v is not None
    ]
    
    # 按出现顺序排序
    all_text_units = sorted(all_text_units, key=lambda x: x["order"])
    
    # 根据token数量限制截断文本单元列表
    all_text_units = truncate_list_by_token_size(
        all_text_units,
        key=lambda x: x["data"]["content"],
        max_token_size=query_param.max_token_for_text_unit,
    )
    
    # 只返回文本单元数据
    all_text_units: list[TextChunkSchema] = [t["data"] for t in all_text_units]

    return all_text_units


async def hybrid_query(
    query,  # 用户查询文本
    knowledge_graph_inst: BaseGraphStorage,  # 知识图谱实例
    entities_vdb: BaseVectorStorage,  # 实体向量数据库
    relationships_vdb: BaseVectorStorage,  # 关系向量数据库
    text_chunks_db: BaseKVStorage[TextChunkSchema],  # 文本块存储
    query_param: QueryParam,  # 查询参数配置
    global_config: dict,  # 全局配置
) -> str:
    """混合查询函数,结合局部和全局查询的优势
    
    同时提取高层和低层关键词,分别构建上下文后合并
    """
    # 初始化上下文和模型函数
    low_level_context = None
    high_level_context = None
    use_model_func = global_config["llm_model_func"]

    # 构建关键词提取提示
    kw_prompt_temp = PROMPTS["keywords_extraction"]
    kw_prompt = kw_prompt_temp.format(query=query)

    # 获取语言模型响应
    result = await use_model_func(kw_prompt)
    try:
        # 尝试解析关键词JSON
        keywords_data = json.loads(result)
        hl_keywords = keywords_data.get("high_level_keywords", [])
        ll_keywords = keywords_data.get("low_level_keywords", [])
        hl_keywords = ", ".join(hl_keywords)
        ll_keywords = ", ".join(ll_keywords)
    except json.JSONDecodeError:
        # 清理响应文本后重试解析
        try:
            result = (
                result.replace(kw_prompt[:-1], "")
                .replace("user", "")
                .replace("model", "")
                .strip()
            )
            result = "{" + result.split("{")[1].split("}")[0] + "}"
            keywords_data = json.loads(result)
            hl_keywords = keywords_data.get("high_level_keywords", [])
            ll_keywords = keywords_data.get("low_level_keywords", [])
            hl_keywords = ", ".join(hl_keywords)
            ll_keywords = ", ".join(ll_keywords)
        except json.JSONDecodeError as e:
            print(f"JSON parsing error: {e}")
            return PROMPTS["fail_response"]

    # 如果有低层关键词,构建局部查询上下文
    if ll_keywords:
        low_level_context = await _build_local_query_context(
            ll_keywords,
            knowledge_graph_inst,
            entities_vdb,
            text_chunks_db,
            query_param,
        )

    # 如果有高层关键词,构建全局查询上下文
    if hl_keywords:
        high_level_context = await _build_global_query_context(
            hl_keywords,
            knowledge_graph_inst,
            entities_vdb,
            relationships_vdb,
            text_chunks_db,
            query_param,
        )

    # 合并两种上下文
    context = combine_contexts(high_level_context, low_level_context)

    # 如果只需要上下文,直接返回
    if query_param.only_need_context:
        return context
    if context is None:
        return PROMPTS["fail_response"]

    # 构建系统提示并获取语言模型响应
    sys_prompt_temp = PROMPTS["rag_response"]
    sys_prompt = sys_prompt_temp.format(
        context_data=context, response_type=query_param.response_type
    )
    response = await use_model_func(
        query,
        system_prompt=sys_prompt,
    )
    
    # 清理响应文本
    if len(response) > len(sys_prompt):
        response = (
            response.replace(sys_prompt, "")
            .replace("user", "")
            .replace("model", "")
            .replace(query, "")
            .replace("<system>", "")
            .replace("</system>", "")
            .strip()
        )
    return response


def combine_contexts(high_level_context, low_level_context):
    """合并高层和低层上下文
    
    从两种上下文中提取并合并实体、关系和来源信息
    """
    # 定义从上下文字符串中提取各部分的内部函数
    def extract_sections(context):
        # 使用正则表达式匹配实体部分
        entities_match = re.search(
            r"-----Entities-----\s*```csv\s*(.*?)\s*```", context, re.DOTALL
        )
        # 匹配关系部分
        relationships_match = re.search(
            r"-----Relationships-----\s*```csv\s*(.*?)\s*```", context, re.DOTALL
        )
        # 匹配来源部分
        sources_match = re.search(
            r"-----Sources-----\s*```csv\s*(.*?)\s*```", context, re.DOTALL
        )

        # 提取匹配内容或返回空字符串
        entities = entities_match.group(1) if entities_match else ""
        relationships = relationships_match.group(1) if relationships_match else ""
        sources = sources_match.group(1) if sources_match else ""

        return entities, relationships, sources

    # 从高层上下文提取内容
    if high_level_context is None:
        warnings.warn(
            "High Level context is None. Return empty High entity/relationship/source"
        )
        hl_entities, hl_relationships, hl_sources = "", "", ""
    else:
        hl_entities, hl_relationships, hl_sources = extract_sections(high_level_context)

    # 从低层上下文提取内容
    if low_level_context is None:
        warnings.warn(
            "Low Level context is None. Return empty Low entity/relationship/source"
        )
        ll_entities, ll_relationships, ll_sources = "", "", ""
    else:
        ll_entities, ll_relationships, ll_sources = extract_sections(low_level_context)

    # 合并并去重实体信息
    combined_entities = process_combine_contexts(hl_entities, ll_entities)

    # 合并并去重关系信息
    combined_relationships = process_combine_contexts(
        hl_relationships, ll_relationships
    )

    # 合并并去重来源信息
    combined_sources = process_combine_contexts(hl_sources, ll_sources)

    # 返回格式化的合并上下文
    return f"""
-----Entities-----
```csv
{combined_entities}
```
-----Relationships-----
```csv
{combined_relationships}
```
-----Sources-----
```csv
{combined_sources}
```
"""


async def naive_query(
    query,  # 用户查询文本
    chunks_vdb: BaseVectorStorage,  # 文本块向量数据库
    text_chunks_db: BaseKVStorage[TextChunkSchema],  # 文本块存储
    query_param: QueryParam,  # 查询参数配置
    global_config: dict,  # 全局配置
):
    """简单查询函数,直接基于文本块的向量检索
    
    不使用知识图谱,仅通过向量相似度检索相关文本块
    """
    # 获取语言模型函数
    use_model_func = global_config["llm_model_func"]
    
    # 从向量数据库检索相关文本块
    results = await chunks_vdb.query(query, top_k=query_param.top_k)
    if not len(results):
        return PROMPTS["fail_response"]
        
    # 获取检索结果的ID列表
    chunks_ids = [r["id"] for r in results]
    
    # 从文本块存储获取完整内容
    chunks = await text_chunks_db.get_by_ids(chunks_ids)

    # 根据token数量限制截断文本块列表
    maybe_trun_chunks = truncate_list_by_token_size(
        chunks,
        key=lambda x: x["content"],
        max_token_size=query_param.max_token_for_text_unit,
    )
    
    # 记录截断信息
    logger.info(f"Truncate {len(chunks)} to {len(maybe_trun_chunks)} chunks")
    
    # 将文本块内容拼接成一个字符串
    section = "--New Chunk--\n".join([c["content"] for c in maybe_trun_chunks])
    
    # 如果只需要上下文,直接返回拼接的文本
    if query_param.only_need_context:
        return section
        
    # 构建系统提示并获取语言模型响应
    sys_prompt_temp = PROMPTS["naive_rag_response"]
    sys_prompt = sys_prompt_temp.format(
        content_data=section, response_type=query_param.response_type
    )
    response = await use_model_func(
        query,
        system_prompt=sys_prompt,
    )

    # 清理响应文本
    if len(response) > len(sys_prompt):
        response = (
            response[len(sys_prompt) :]
            .replace(sys_prompt, "")
            .replace("user", "")
            .replace("model", "")
            .replace(query, "")
            .replace("<system>", "")
            .replace("</system>", "")
            .strip()
        )

    return response