COT_GENERATION_ZH = """根据给定的知识图谱原始信息及已生成的推理路径，产出一条符合模板要求、可直接用于下游训练或推理的 CoT 数据。\
CoT（Chain-of-Thought，思维链）指在回答复杂问题时，把中间推理步骤一步一步显式写出来，使推理过程透明、可追溯，而不是直接给出最终答案。

-输入格式-
[Entities:]
(实体名:实体描述)
...

[Relationships:]
(来源实体)-[关系描述]->(目标实体)
...

[Question and Reasoning Path:]
(问题)
(推理路径)

-输出要求-
1. 每一步只完成一个不可分割的子任务，并用自然语言衔接，但是要避免生硬的连接词。
2. 使用中文。
3. 不要使用有序列表或编号。
4. 请直接给出答案，不要生成无关信息。

-真实数据-
输入:
[Entities:]:
{entities}

[Relationships:]:
{relationships}

[Question:]:
{question}

[Reasoning_Template:]:
{reasoning_template}

输出：

"""

COT_GENERATION_EN = """Given the raw knowledge graph information and the provided reasoning-path, \
produce one Chain-of-Thought (CoT) sample that strictly follows the template \
and can be directly used for downstream training or inference.
CoT (Chain-of-Thought) means that when answering a complex question, the intermediate reasoning steps are \
explicitly written out one by one, making the reasoning process transparent and traceable instead of giving \
only the final answer.

-Input Format-
[Entities:]:
(ENTITY_NAME: ENTITY_DESCRIPTION)
...

[Relationships:]:
(ENTITY_SOURCE)-[RELATIONSHIP_DESCRIPTION]->(ENTITY_TARGET)
...

[Question and Reasoning Path:]:
(QUESTION)
(REASONING_PATH)

-Output Requirements-
1. Each step completes a single, indivisible sub-task and is naturally connected, avoiding abrupt transition words.
2. Use English.
3. Do not use ordered lists or numbering.
4. Do not generate extraneous information, just provide the answer.

-Real Data-
Input:
[Entities:]:
{entities}

[Relationships:]:
{relationships}

[Question:]:
{question}

[Reasoning_Template:]:
{reasoning_template}

Output:
"""

COT_TEMPLATE_DESIGN_ZH = """你是一位“元推理架构师”。你的任务不是回答问题，\
而是根据给定的知识图谱中的实体和关系的名称以及描述信息，设计一条可复用、可泛化的 CoT 推理路径模板。\

-步骤-
1. 实体识别
- 准确地识别[Entities:]章节中的实体信息，包括实体名、实体描述信息。
- 实体信息的一般格式为:
(实体名:实体描述)

2. 关系识别
- 准确地识别[Relationships:]章节中的关系信息，包括来源实体名、目标实体名、关系描述信息。
- 关系信息的一般格式为:
(来源实体名)-[关系描述]->(目标实体名)

3. 图结构理解
- 正确地将关系信息中的来源实体名与实体信息关联。
- 根据提供的关系信息还原出图结构。

4. 问题设计
- 围绕知识图谱所表达的“核心主题”设计一个问题。
- 问题必须能在图谱内部通过实体、关系或属性直接验证；避免主观判断。
- 问题应该能够模型足够的思考，充分利用图谱中的实体和关系，避免过于简单或无关的问题。

5. 推理路径生成
- 根据问题设计一个**可被后续模型直接执行的推理蓝图**。
- 保持步骤最小化：每一步只解决一个“不可分割”的子问题。 

-约束条件-
1. 不要在回答中描述你的思考过程，直接给出回复，只给出问题和推理路径设计，不要生成无关信息。
2. 如果提供的描述信息相互矛盾，请解决矛盾并提供一个单一、连贯的逻辑。
3. 避免使用停用词和过于常见的词汇。
4. 不要出现具体数值或结论，不要出现“识别实体”、“识别关系”这类无意义的操作描述。
5. 使用中文作为输出语言。
6. 输出格式为：
问题：
推理路径设计：

-真实数据-
输入:
[Entities:]:
{entities}

[Relationships:]:
{relationships}

输出:
"""


COT_TEMPLATE_DESIGN_EN = """You are a “meta-reasoning architect”. \
Your task is NOT to answer the question, but to design a reusable, generalizable CoT reasoning-path \
template based solely on the names and descriptions of entities and \
relationships in the provided knowledge graph.

- Steps -
1. Entity Recognition
- Accurately recognize entity information in the [Entities:] section, including entity names and descriptions.
- The general formats for entity information are:
(ENTITY_NAME: ENTITY_DESCRIPTION)

2. Relationship Recognition
- Accurately recognize relationship information in the [Relationships:] section, including source_entity_name, target_entity_name, and relationship descriptions.
- The general formats for relationship information are:
(SOURCE_ENTITY_NAME)-[RELATIONSHIP_DESCRIPTION]->(TARGET_ENTITY_NAME)

3. Graph Structure Understanding
- Correctly associate the source entity name in the relationship information with the entity information.
- Reconstruct the graph structure based on the provided relationship information.

4. Question Design
- Design a question around the "core theme" expressed by the knowledge graph.
- The question must be verifiable directly within the graph through entities, relationships, or attributes; avoid subjective judgments.
- The question should allow the model to think sufficiently, fully utilizing the entities and relationships in the graph, avoiding overly simple or irrelevant questions.

5. Reasoning-Path Design 
- Output a **blueprint that any later model can directly execute**.
- Keep steps minimal: each step solves one indivisible sub-problem.


- Constraints -
1. Do NOT describe your thinking; output only the reasoning-path design.
2. If the provided descriptions are contradictory, resolve conflicts and provide a single coherent logic.
3. Avoid using stop words and overly common words.
4. Do not include specific numerical values or conclusions, \
and DO NOT describing meaningless operations like "Identify the entity" or "Identify the relationship".
5. Use English as the output language.
6. The output format is:
Question:
Reasoning-Path Design:

Please summarize the information expressed by the knowledge graph based on the following [Entities:] and [Relationships:] provided.

- Real Data -
Input:
[Entities:]:
{entities}

[Relationships:]:
{relationships}

Output:
"""

COT_GENERATION_PROMPT = {
    "en": {
        "COT_GENERATION": COT_GENERATION_EN,
        "COT_TEMPLATE_DESIGN": COT_TEMPLATE_DESIGN_EN,
    },
    "zh": {
        "COT_GENERATION": COT_GENERATION_ZH,
        "COT_TEMPLATE_DESIGN": COT_TEMPLATE_DESIGN_ZH,
    },
}