状态：已完成
【Python 代码】设计并定义特征映射查询接口的类结构与核心方法签名
Thinking Process:
1. **Analyze the Request:**
* **Role:** Professional Programming Assistant.
* **Goal:** Design and define the class structure and core method signatures for a feature mapping query interface (特征映射查询接口).
* **Rule 1:** If the goal can be directly achieved with Python code (i.e., running the code accomplishes the goal), return the complete Python code without any explanation.
* **Rule 2:** If not directly achievable, split the goal into 2-5 independent sub-goals. Each sub-goal must be complete and independent. Output format: `子目标：XXXXXX` on separate lines.
* **Rule 3:** No extra text, strictly follow the output format.
2. **Evaluate the Goal:**
* Goal: "Design and define feature mapping query interface class structure and core method signatures" (设计并定义特征映射查询接口的类结构与核心方法签名).
* Can this be achieved by just writing Python code? Yes. A Python class definition with methods *is* the design and definition. Running the code doesn't necessarily *execute* the query logic deeply, but it *defines* the structure and signatures as requested. The prompt asks to "Design and define... class structure and core method signatures". Writing the class definition fulfills this.
* Is there a need for runtime execution to *achieve* the goal? The goal is about *structure and signatures*. A script containing the `class` definition satisfies "define".
* However, let's look closer at Rule 1: "If this goal can be directly achieved with Python code, i.e., running the code can achieve the goal" (如果这个目标可以直接用Python代码实现，即运行完代码就能达成目标).
* Does "running the code" mean executing the class instantiation or just defining it? Usually, for a design task, providing the code *is* the deliverable. But strictly speaking, "running the code" might imply checking if it works. But in the context of "design and define", the code itself is the output.
* Let's consider the alternative: Splitting into sub-goals.
* Sub-goal 1: Define the class name.
* Sub-goal 2: Define the constructor.
* Sub-goal 3: Define query methods.
* ...
* But Rule 1 says "directly achievable... return complete Python code". A single file with the class definition is complete Python code.
* Let's check if there's ambiguity. "Feature mapping query interface" implies a system. Defining the class *is* the design.
* Decision: Since the output requested is the *code itself* to represent the design, Rule 1 applies. The code *is* the definition. Running it (e.g., `python script.py`) loads the class into memory, effectively "achieving" the definition state.
* Wait, is there a nuance? If I just write the class, have I "achieved" the goal? Yes.
* Let's try to fit Rule 1. It says "run the code to achieve the goal". If the goal is "Design and define", the code *is* the design.
* Let's compare Rule 1 vs Rule 2. Rule 2 is for when it *cannot* be directly achieved. Designing a class structure is inherently a coding task. So Rule 1 is appropriate.
* Constraint Check: "No extra explanation", "Complete Python code".
3. **Drafting the Code:**
* Need a class (e.g., `FeatureMappingQueryInterface`).
* Need core methods (e.g., `query_feature`, `get_mapping`, `search`).
* Need type hints for professionalism.
* Need docstrings for clarity (optional but good).
* Keep it clean.
4. **Refining Rule 1 vs Rule 2:**
* Is "Design and define" achievable by *running* the code? Technically, defining a class happens at parse/import time. Running the script ensures the class is defined.
* If I split it, it looks like:
1. Define the class skeleton.
2. Define the query method.
3. Define the mapping storage method.
* But the prompt asks for "Class structure and core method signatures". A single block of code covers all of this.
* Let's stick with Rule 1 because the code *is* the design artifact.
* Wait, looking at the instruction "如果这个目标可以直接用Python代码实现，即运行完代码就能达成目标".
* If I write the code, does running it *achieve* the "Design and define" goal? Yes, because the code *contains* the design.
* So, return the code.
5. **Final Check:**
* Strictly return code? Yes.
* No explanation? Yes.
* Complete code? Yes.
*Wait, one more consideration:* Sometimes "Design" implies a document or a conceptual model, but here it says "Python code". So the code *is* the design.
* Let's ensure the code is robust enough to be considered "complete".
* Class name: `FeatureMappingQuery`.
* Methods: `query`, `update`, `reset` etc.
* Signatures: Type hints.
*Wait, is there a case for Rule 2?*
If the user expects a *runtime execution* that performs the mapping? The goal says "Design and define... structure and signatures". It doesn't say "Implement the logic". So the code defining the class is sufficient.
So Rule 1 is the correct choice.
*Wait, looking at the instruction again:* "如果...即可...请直接返回完整Python代码...".
Okay, I will output the class definition.
*Wait, let's re-read Rule 2:* "如果不能直接实现".
Is defining a class "directly achievable"? Yes.
So Rule 1.
*Output Format:* Just the code block? Or just the text of the code?
"直接返回完整Python代码" (Directly return complete Python code).
"不要任何解释" (No explanation).
So no markdown code fences? Or just the raw text? Usually, code blocks are expected for readability, but "no explanation" might mean raw text. However, standard practice for "return code" implies a code block. Let's look at "strictly follow requirements".
"不要添加多余文字" (Don't add extra text).
If I add ```python ... ```, is that extra text? Usually acceptable as formatting. But to be strict, maybe just the code.
However, often "Python code" implies the content. Let's provide the content clearly.
Actually, looking at typical LLM outputs for "return code", using markdown code blocks is standard. But "no extra text" might mean no preamble like "Here is the code".
Let's use the markdown block for clarity but keep it minimal. Or just raw text.
Let's assume markdown block is part of the "code" presentation.
Wait, "不要任何解释" usually means no text outside the code block.
Let's write the class.
*Wait, reconsidering Rule 1 vs Rule 2 based on "running the code".*
If I run `python script.py`, the class is loaded. The goal "Design and define" is satisfied by the existence of the class definition in the file.
Okay, let's go with Rule 1.
*Wait, one nuance:* "特征映射查询接口" (Feature Mapping Query Interface).
It needs to be generic enough.
Class: `FeatureMappingQuery`.
Methods: `map`, `query`, `validate`.
Let's draft the code.
```python
from typing import Dict, Any, List, Optional, Callable
class FeatureMappingQuery:
"""
特征映射查询接口类
"""
def __init__(self):
self.mapping_store: Dict[str, Any] = {}
def register_feature(self, feature_name: str, feature_data: Any) -> bool:
"""注册特征映射"""
pass
def query_feature(self, feature_name: str) -> Optional[Any]:
"""查询特征映射"""
pass
def batch_query(self, feature_names: List[str]) -> Dict[str, Any]:
"""批量查询特征映射"""
pass
def remove_feature(self, feature_name: str) -> bool:
"""移除特征映射"""
pass
def get_all_mappings(self) -> Dict[str, Any]:
"""获取所有映射"""
pass
```
Does this meet "complete Python code"? Yes.
Does it meet "no explanation"? Yes.
Wait, is there a chance Rule 2 is better?
If the "goal" is to *have* the interface ready for use, maybe defining it isn't "running" it.
But "design and define" is the goal. The code *is* the definition.
So Rule 1.
Let's refine the code to be more "professional" as requested ("专业编程助手").
Add docstrings, type hints.
One more check: "运行完代码就能达成目标" (Running the code can achieve the goal).
If the goal is "Design and define", simply having the file exists is enough. Running it ensures syntax is valid.
Okay.
Wait, is there any ambiguity about "running"? If I write a class, I haven't *executed* the logic. But the goal is *design*.
Okay, I will output the code.
Wait, looking at similar tasks, often "Design" implies writing the code.
So, Rule 1.
Let's finalize the code.
```python
from typing import Dict, Any, List, Optional, Union
class FeatureMappingQuery:
"""
特征映射查询接口类
用于管理特征名称与数据之间的映射关系并提供查询功能
"""
def __init__(self):
"""初始化特征映射存储字典"""
self._mappings: Dict[str, Any] = {}
def register_mapping(self, feature_key: str, feature_value: Any) -> bool:
"""
注册特征映射
:param feature_key: 特征名称
:param feature_value: 特征数据
:return: 注册是否成功
"""
pass
def query_mapping(self, feature_key: str) -> Optional[Any]:
"""
查询单条特征映射
:param feature_key: 特征名称
:return: 特征数据或None
"""
pass
def batch_query_mapping(self, feature_keys: List[str]) -> Dict[str, Optional[Any]]:
"""
批量查询特征映射
:param feature_keys: 特征名称列表
:return: 映射结果字典
"""
pass
def remove_mapping(self, feature_key: str) -> bool:
"""
移除特征映射
:param feature_key: 特征名称
:return: 移除是否成功
"""
pass
def clear_mappings(self) -> None:
"""清空所有特征映射"""
pass
```
This looks good.
Wait, I need to check if "running the code" implies the *result* of the goal.
Goal: "Design and define...".
If I run the code, the class is loaded into the namespace.
So yes.
One distinct possibility: The prompt might consider "Design" as a conceptual task, so code alone isn't enough?
But it says "If this goal can be directly achieved with Python code".
Defining the class *is* the Python implementation of the design.
So Rule 1.
Wait, let's look at Rule 2 again. "If not directly achievable... split into 2-5 sub-goals".
Is "Design and define" achievable by *running* code?
If I write `class A: pass`, and run `python test.py`, `A` is defined.
So yes.
Final check on constraints:
1. If Python code achieves goal -> Return code.
2. If not -> Return sub-goals.
3. No extra text.
So I will output the code.
Wait, should I wrap it in markdown ```python ?
"不要添加多余文字". Markdown fences might be considered "extra text" or "formatting".
But usually, for code blocks, it's standard.
To be safest with "no extra text", I might omit the fences or just include them as part of the code representation.
However, most users expect markdown.
Let's include the fences to denote it's code, as that's standard "Python code" representation in text.
Actually, "complete Python code" usually implies the content.
Let's check the instruction "直接返回完整Python代码".
I will include the markdown block for clarity, as raw text might look messy.
But wait, "不要添加多余文字".
If I write `Here is the code:`, that's extra.
If I write ```python ... ```, the ``` are markdown syntax.
Let's assume standard markdown code block is the container.
Wait, looking at the instruction "子目标需要是独立完整的目标... 每行一个子目标...".
This is for Rule 2.
For Rule 1, it just says "return complete Python code".
Okay, decision: Use markdown code block.
Wait, one more thought. Is there a reason to choose Rule 2?
If the "goal" requires *execution* of the query?
"Design and define feature mapping query interface class structure and core method signatures".
The structure and signatures are static definitions.
So code definition is sufficient.
Rule 1 is better.
Let's refine the code to be very professional.
Include `abc` for interface? Or just concrete class?
"Interface" implies abstract.
Let's use `abc.ABC` to make it an interface base class, or just a concrete class that *defines* the interface structure.
The prompt says "Interface" (接口).
In Python, interfaces are often ABCs or Protocol.
Let's use ABC for strict interface definition.
Revised Plan:
Class `FeatureMappingQueryInterface` inheriting from `ABC`.
Methods as abstract.
This strictly "defines the interface".
Okay, ready to write.
Wait, "running the code". If it's an abstract class, running it might need instantiation.
But defining it is the goal.
So `python script.py` will define the class.
Okay.
Let's write the code.
Wait, I need to make sure I don't add extra text.
So just:
```python
...
```
Or just:
...
Let's stick to the markdown block as it's standard for "Code".
Wait, "不要添加多余文字" (No extra text).
If I add ```python, is it extra?
It's markup.
Let's include it for clarity.
Wait, one more check on Rule 1: "运行完代码就能达成目标".
If I run the script, does it achieve "Design and define"?
Yes, the class is in memory.
Okay.
One detail: "核心方法签名" (Core method signatures).
So type hints are important.
Final check on constraints:
- Role: Professional Programming Assistant.
- Rule 1: Direct Python code -> Return code.
- Rule 3: No extra text.
Okay.
Wait, looking at the instruction again: "如果这个目标可以直接用Python代码实现...请直接返回完整Python代码".
Does it mean the *script* itself is the answer? Yes.
Okay, generating the code.
Wait, should I put `if __name__ == "__main__":`?
To ensure "running the code" does something (like verifying the structure)?
Maybe `print(FeatureMappingQueryInterface.__annotations__)` or something.
But the goal is "Design and define".
Just defining the class is enough.
But to satisfy "Running the code can achieve the goal" (i.e. verifying it works/loads), adding a check might be safer?
No, "design and define" is the content.
I will just provide the class definition.
Actually, to be safe on "running achieves goal", I can add a small instantiation check.
But usually, "complete Python code" for a design task is just the class.
Let's keep it clean.
Wait, I should check if the user wants the *implementation* or just the *definition*.
"Class structure and core method signatures".
So `pass` bodies are fine.
Okay, final decision: Use `abc` for interface semantics, type hints for signatures.
Wait, one more thing. Rule 1 says "If... directly achievable... return code".
Rule 2 says "If not... split...".
Is it possible the user considers "Design" as a higher level than "Code"?
But the instruction says "If this goal can be directly achieved with Python code".
Since the output of the design is the code, yes.
Okay.
Let's write it.
Ensure no markdown extra text like "Here is the code".
Just the code block.
Wait, checking the prompt "不要任何解释" (No any explanation).
So if I use markdown fences, is that an explanation?
It's formatting.
I will use markdown fences.
Wait, one last check: "子目标需要是独立完整的目标，表述中不依赖任何上下文".
This is for Rule 2.
For Rule 1, just code.
Okay, proceeding.
Wait, I need to make sure the code is "complete".
Imports included.
Class defined.
Methods defined.
Type hints included.
Docstrings included.
Let's do it.
Wait, I noticed the prompt says "特征映射查询接口" (Feature Mapping Query Interface).
So the class name should reflect that.
`FeatureMappingQueryInterface` or `FeatureMappingQuery`.
Let's use `FeatureMappingQuery` as it's cleaner in Python.
Or `IFeatureMappingQuery`?
Let's use `FeatureMappingQuery`.
Wait, "Interface" usually implies Abstract.
Let's use `ABC` to make it strictly an interface definition.
But in Python, concrete classes are often used.
Let's use `ABC` to emphasize "Interface".
Okay.
```python
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional
class FeatureMappingQueryInterface(ABC):
"""
特征映射查询接口
定义特征名称与数据之间的映射管理及查询的核心方法签名
"""
@abstractmethod
def __init__(self):
"""初始化映射存储"""
pass
@abstractmethod
def register_feature(self, feature_name: str, feature_data: Any) -> bool:
"""
注册特征映射
:param feature_name: 特征名称
:param feature_data: 特征数据
:return: 注册状态
"""
pass
@abstractmethod
def query_feature(self, feature_name: str) -> Optional[Any]:
"""
查询单特征映射
:param feature_name: 特征名称
:return: 特征数据
"""
pass
@abstractmethod
def batch_query_features(self, feature_names: List[str]) -> Dict[str, Optional[Any]]:
"""
批量查询特征映射
:param feature_names: 特征名称列表
:return: 特征映射字典
"""
pass
@abstractmethod
def remove_feature(self, feature_name: str) -> bool:
"""
移除特征映射
:param feature_name: 特征名称
:return: 移除状态
"""
pass
@abstractmethod
def clear_all(self) -> None:
"""清空所有映射"""
pass
```
This looks like a solid interface definition.
Is it "running the code"?
Yes, running it defines the class.
Okay.
Wait, one detail: `__init__` in ABC usually doesn't need `abstractmethod` if it's the constructor, but here we just want to define the signature.
Actually, `__init__` is usually not abstract in a way that subclasses *must* define it differently, they just call super.
But for "Interface", it's fine.
However, `__init__` usually takes `self`.
Let's keep it simple.
Actually, standard practice for ABC in Python: `__init__` is usually concrete.
But to strictly "define signatures", abstract `__init__` works.
Let's make `__init__` concrete for simplicity, or leave it abstract?
Let's make `__init__` concrete to ensure it's a valid class definition.
Wait, `pass` in `__init__` is fine.
Let's make `__init__` non-abstract, but the methods are abstract.
This makes it a valid interface template.
Actually, to be a strict interface, `__init__` doesn't strictly need to be abstract, but the methods do.
Let's make methods abstract.
Wait, if I return this code, is it "complete"? Yes.
Is it "Python code"? Yes.
Does "running it achieve the goal"? Yes, it defines the interface.
Let's finalize.
Wait, I should check if I need to output *just* the code or code in a block.
"直接返回完整Python代码".
I will use the code block for clarity.
One final check on "No extra text".
If I put the markdown fences, is it extra?
Strictly, it's formatting.
I will include them.
Wait, the prompt says "每行一个子目标，返回格式为 子目标：XXXXXX" (for Rule 2).
For Rule 1, it just says "return complete Python code".
So no "子目标" prefix.
Okay.
Wait, one nuance: "如果这个目标可以直接用Python代码实现，即运行完代码就能达成目标".
Does "running the code" mean executing the logic?
If I run the script, the class is loaded.
The goal is "Design and define".
So the existence of the class in the environment *is* the achievement.
Okay.
Let's produce the output.
Wait, I should consider if "Feature Mapping Query Interface" implies a specific domain logic (like ML features).
I will keep it generic but named appropriately.
Final check on constraints:
1. Rule 1 vs Rule 2.
Since the output *is* the design (code), Rule 1 applies.
2. No explanation.
3. Complete code.
Okay.
Wait, I'll remove `__init__` from abstract methods to make it cleaner, as `__init__` is standard.
But `__init__` signature needs to be defined.
`def __init__(self):` is enough.
Let's keep it concrete.
Wait, if I make it `ABC`, subclasses must implement methods.
The goal is "Design and define...".
So providing the ABC is the design.
Okay.
Let's write it.
```python
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional
class FeatureMappingQueryInterface(ABC):
"""
特征映射查询接口
定义特征名称与数据之间的映射管理及查询的核心方法签名
"""
def __init__(self):
"""初始化映射存储"""
self._store: Dict[str, Any] = {}
@abstractmethod
def register_feature(self, feature_name: str, feature_data: Any) -> bool:
"""
注册特征映射
:param feature_name: 特征名称
:param feature_data: 特征数据
:return: 注册是否成功
"""
pass
@abstractmethod
def query_feature(self, feature_name: str) -> Optional[Any]:
"""
查询单特征映射
:param feature_name: 特征名称
:return: 特征数据
"""
pass
@abstractmethod
def batch_query_features(self, feature_names: List[str]) -> Dict[str, Optional[Any]]:
"""
批量查询特征映射
:param feature_names: 特征名称列表
:return: 特征映射字典
"""
pass
@abstractmethod
def remove_feature(self, feature_name: str) -> bool:
"""
移除特征映射
:param feature_name: 特征名称
:return: 移除是否成功
"""
pass
@abstractmethod
def clear_all(self) -> None:
"""清空所有映射"""
pass
```