【Python 代码】解析目标描述的结构与依赖项

Thinking Process: 1. **Analyze the Request:** * Role: Professional Programming Assistant. * Task: Analyze the structure and dependencies of the target description. * Rule 1: If the target can be directly achieved by running Python code, return the complete Python code only (no explanations). * Rule 2: If not directly achievable, break it down into 2-5 independently encodable sub-goals. Each sub-goal must be self-contained (no context dependency). Output format: one sub-goal per line, prefixed with "子目标：". * Rule 3: No extra text, strict output format. * Input: The prompt itself says "目标：解析目标描述的结构与依赖项" (Target: Parse the structure and dependencies of the target description). Wait, looking closely at the prompt, the *actual* target description provided within the prompt is "解析目标描述的结构与依赖项" (Parse the structure and dependencies of the target description). However, usually, there's a specific task description provided *after* the rules. In this case, the prompt ends with the rules, but the first line says "目标：解析目标描述的结构与依赖项". Let's re-read carefully. * The prompt starts with "你是专业编程助手。请严格按照要求判断并回答：" (You are a professional programming assistant. Please judge and answer strictly according to the requirements:). * Then "目标：解析目标描述的结构与依赖项" (Target: Parse the structure and dependencies of the target description). * Then "规则：..." (Rules: ...). * So the "Target" to be solved is "解析目标描述的结构与依赖项". * Wait, is this a meta-task? The user wants me to parse the structure and dependencies of the *current* target description provided in the prompt? Or is the target description missing? * Let's look at the structure. The prompt defines a target ("解析目标描述的结构与依赖项") and rules for how to respond. * If the target is "Parse the structure and dependencies of the target description", can this be done directly with Python code? * "Target description" usually refers to text input. * To "parse the structure and dependencies", I need to analyze text, perhaps using NLP or regex, or just logical analysis. * However, the output requirement is either *code* or *sub-goals*. * If I write code that parses a string, does it achieve the goal "Parse the structure and dependencies"? Yes, if the code takes the target description as input and outputs the parsed structure. * But the target description *is* the prompt itself. * Actually, usually in these few-shot or instruction-tuning scenarios, the "Target" is the task I need to execute. * Task: "解析目标描述的结构与依赖项" (Parse the structure and dependencies of the target description). * Can Python code do this directly? Python code can parse text, but "structure and dependencies" might be abstract. * However, Rule 1 says: "If this target can be directly achieved by running Python code, i.e., running the code achieves the target, return complete Python code". * If I write a script that analyzes the prompt string and outputs its structure (like finding rules, target, etc.), that fits. * But often, if the task is "Analyze X", the result is an analysis, not code. Unless the code *is* the analyzer. * Let's consider Rule 2: "If not directly achievable... break into 2-5 sub-goals". * "Parsing structure and dependencies" is often a cognitive task for an LLM, not just a script execution. A script might parse syntax, but "dependencies" usually implies logical flow. * However, if I treat this as a software engineering task where I need to write code that *performs* the parsing, Rule 1 might apply. * But wait, the prompt asks *me* to judge and answer. The output should be the *result* of the target, or the *code* to achieve it? * Rule 1: "return complete Python code". * Rule 2: "sub-goals". * If the target is "Parse the structure...", writing a script that prints the structure achieves the goal. * But is "the target description" the input to the script or the thing being analyzed? * Let's assume the target is the meta-task of analyzing the prompt's own structure. * Can a Python script run and print out the structure and dependencies? Yes, it can analyze the string. * However, "dependencies" might be semantic. * Let's look at the instruction again: "Target: Parse the structure and dependencies of the target description". * If I return code, the user runs the code, and the code parses the description. * If I return sub-goals, the user implements them to achieve the parsing. * Given the ambiguity of "dependencies" (logical vs syntactic), it's safer to treat this as a task that might require multiple steps (reading, analyzing, formatting). * But Rule 1 is strong: "If... directly... return code". * Let's try to see if a single script can do it. `import ast` or `re` to parse the text. * Actually, the most logical interpretation is that the *User* provides a target description, and I (the assistant) need to output either code or sub-goals based on *that* description. * Wait, the prompt *is* the description. The target is "解析目标描述的结构与依赖项". * This is recursive. * Let's look at the "Target" line: `目标：解析目标描述的结构与依赖项`. * This is the task I need to perform *right now*. * Can a Python script run and output the structure/dependencies of this specific text? * Yes. * But does "running the code" count as "achieving the target"? * If the target is "Parse...", the output of the parse is the answer. * If I give code, the code *does* the parsing when run. * If I give sub-goals, the sub-goals *lead* to the parsing. * Usually, for LLMs, "Parse the structure" is a text analysis task. Code might be overkill if the LLM does it natively. But the rule asks for code *if* it can be done by code. * Let's consider the complexity. Parsing structure (Target, Rules, Output format) and dependencies (Rules depend on Target, Output depends on Rules). * A Python script can define these as a dictionary and print them. * However, since the "Target Description" is the prompt itself, the code needs to read from stdin or a string. * Let's decide: Is it direct? * If I write a script that defines the prompt text, analyzes it, and prints the structure/dependencies, that is direct. * But "dependencies" might be hard to hardcode. * Maybe sub-goals are better. * Sub-goal 1: Extract the target definition. * Sub-goal 2: Extract the rules. * Sub-goal 3: Identify relationships between target and rules. * Sub-goal 4: Format the output. * Sub-goal 5: Generate final report. * This seems safer for "Structure and Dependencies". * Let's check the constraint: "2-5 个可独立编码的子目标" (2-5 independently encodable sub-goals). * "Sub-goals need to be independent complete goals, not dependent on context". * Example: "子目标：读取目标描述文本" (Read target description text). * Let's try to fit Rule 1 vs Rule 2. * If I provide code, the code *is* the solution. * If the prompt implies "What is the target?", the answer is the parsed structure. * If the prompt implies "Here is a task, solve it using code or sub-goals", then I need to choose. * Given "解析...结构与依赖项" (Parse structure and dependencies), this is often an analysis task. Code can do it, but is it "running code to achieve target"? Yes, running the analysis script achieves the parsing. * However, LLMs often treat "Parse" as text output. If I output code, the user expects to run it to get the parse. * Let's look for clues. "If this target can be directly achieved by Python code... return complete Python code". * "Parse the structure..." -> A function `parse(text)` returns structure. * So a script `if __name__ == "__main__": print(parse(sys.stdin.read()))` works. * Is it better than sub-goals? * Sub-goals are for when code *alone* isn't enough or it's a multi-step process. * Parsing text structure is code-able. * Let's go with Rule 1 (Code) because a script can easily extract the "Target", "Rules", and "Output Format" and print their relationships. * Wait, "Dependencies". Dependencies between what? Between parts of the description? * If I write a script that analyzes the string "目标：... 规则：... 输出："... * Let's draft the code. * Wait, one more 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" (Sub-goals need to be independent complete goals, expression does not depend on any context). * If I use Rule 2, each line must be a goal like "子目标：解析目标描述中的核心任务". * If I use Rule 1, just code. * Which is more appropriate for "Parse structure and dependencies"? * If I write code, the code *performs* the parsing. * If I write sub-goals, the sub-goals *describe* the steps to parse. * Since the instruction asks *me* to judge, and the target is "Parse...", providing the code that *does* the parsing seems more direct than describing the steps. * However, "Dependencies" (依赖项) usually implies logical links (e.g., Rule 1 depends on Target). A simple script might not capture "dependencies" semantically without NLP. * Maybe Rule 2 is safer because "Dependencies" is abstract. * Let's try to find a middle ground. Can code parse dependencies? `import ast` parses code structure. Here the input is natural language. * Natural language dependency parsing is complex. * So maybe Rule 2 is better. * Sub-goal 1: Extract the target definition string. * Sub-goal 2: Extract all rule constraints. * Sub-goal 3: Analyze logical flow between rules. * Sub-goal 4: Identify output format requirements. * Sub-goal 5: Synthesize the dependency graph. * Wait, "Independently encodable". * "子目标：读取并存储目标描述文本" * "子目标：识别目标描述中的目标定义部分" * "子目标：识别目标描述中的规则定义部分" * "子目标：分析目标与规则之间的逻辑依赖" * "子目标：输出结构化的依赖关系描述" * This looks like 5 sub-goals. * Let's check Rule 1 again. "Directly achieved by running code". * If I write a script that prints the analysis, is that "achieving the target"? Yes. * But if the "Target" is "Parse...", and I give code, the user runs code to get the parse. * If I give sub-goals, the user writes code to fulfill them. * Usually, if the task is "Write a script to X", Rule 1 applies. * If the task is "Analyze X", Rule 2 might apply. * Here the task *is* "Parse...". * Let's assume Rule 1 is preferred if the code is concise. * Let's write a Python script that defines the prompt content, parses it using regex, and prints the structure/dependencies. * Wait, the "Target Description" is the prompt itself. * So the code should analyze `target_desc = "..."`. * Okay, let's try Rule 1. It's more "professional programming assistant" style to give the tool. * Wait, looking at the instruction "判断并回答" (Judge and answer). * If I judge it can be done by code -> Code. * If not -> Sub-goals. * Can a Python script parse the structure and dependencies of *this specific text*? * Yes. * So I will choose Rule 1. * Wait, re-reading Rule 1: "If this target can be directly achieved by running Python code... return complete Python code". * Target: "解析目标描述的结构与依赖项" (Parse structure and dependencies of the target description). * If I write: ```python import re text = """...""" # parse logic print(...) ``` * Running this produces the parsed structure. * So yes, Rule 1 applies. * Constraint: "不要任何解释" (No explanations). * Constraint: "严格按要求输出" (Strict output). * Wait, one nuance: "子目标需要是独立完整的目标，表述中不依赖任何上下文" (Sub-goals... not depend on context). This applies to Rule 2. * For Rule 1, it's just code. * Let's finalize the code. It needs to parse the prompt text provided in the variable. * But wait, the prompt *is* the instruction. The "Target Description" is "解析目标描述的结构与依赖项". * Does it mean "Parse the structure of the text '解析目标描述的结构与依赖项'"? Or "Parse the structure of the whole prompt"? * Contextually, "Target Description" refers to the task definition provided by the user. * So the code should analyze the provided text (the prompt content) and output its structure and dependencies. * To be safe and generic, the code should read from stdin or use a hardcoded string of the prompt. * Let's use a string variable containing the prompt content (minus the "Target" line maybe? No, the whole text is the description). * Actually, since the prompt is dynamic, the code should probably define the text it needs to parse. * Let's create a script that analyzes a string variable `target_description`. * To make it "complete", it should print the result. * Okay, I will write a script that parses a sample text representing the target description structure (Target, Rules, Output) and prints the analysis. * Wait, the instruction says "Target: Parse the structure...". * So the *code* must perform the parsing. * Let's draft the code. * Wait, is "Parse structure and dependencies" better suited for Sub-goals? * If I just give code, does it *show* the dependencies? * It prints them. * Okay, I'll go with Code (Rule 1) because it's a deterministic task. * Wait, looking at the instruction again: "目标：解析目标描述的结构与依赖项". * This is the task *I* am executing. * If I return code, the code *is* the answer. * If I return sub-goals, the sub-goals *are* the answer. * Usually, for "Parse", Code is good. * Let's write a concise script. * Wait, one more check on Rule 1: "运行完代码就能达成目标" (Running the code achieves the target). * Target: Parse structure and dependencies. * Code: Analyzes text, prints structure and dependencies. * Yes, running it achieves the parsing. * Wait, what if the "Target Description" is the input to the code? * The code needs to know *what* to parse. * I will embed the prompt text in the code for demonstration, or read from stdin. * To be "complete", reading from stdin is better for a utility. * But to be self-contained, embedding the text is safer. * Let's embed the text found in the prompt (excluding the "You are..." intro maybe? Or the whole thing). * Let's just define a string variable with the content after "目标：". * Actually, to make it robust, I'll write a script that defines the target description string and parses it. * Wait, if I choose Rule 1, I need to make sure the code is self-contained. * Code structure: 1. Define text. 2. Regex/Logic to find Target, Rules, Output. 3. Print analysis. * This seems valid. * Wait, is there a risk? "Dependencies" (依赖项) might be hard for code to infer without semantic understanding. * E.g., Rule 1 depends on Target. * Code can output "Rule 1 depends on Target". * Okay, let's try to make the code output logical dependencies based on keywords. * Alternative: Rule 2 (Sub-goals). * If I choose sub-goals, I need 2-5 lines. * Line 1: 子目标：提取目标描述中的核心任务定义。 * Line 2: 子目标：识别目标描述中列出的所有约束规则。 * Line 3: 子目标：分析任务定义与规则之间的逻辑依赖关系。 * Line 4: 子目标：识别输出格式要求及其对前序步骤的依赖。 * Line 5: 子目标：汇总并输出结构化的依赖图谱。 * This looks very safe and fits the "Parse structure" task which is often meta. * But Rule 1 is preferred if possible. * Is it "Directly achievable"? Yes, a script can print the analysis. * Let's go with Rule 1 (Code) because it's more "Programmer" like. * Wait, the prompt says "If... directly... return code... If not... sub-goals". * Since a script *can* do it, Rule 1 is triggered. * Wait, does "running the code" mean *executing* it? Yes. * So I provide the script. * Let's refine the code. * It should parse the text provided in the prompt (the "Target Description"). * To make it executable and meaningful, I'll include the text in the script. * Text: ``` 目标：解析目标描述的结构与依赖项 规则： 1. 如果这个目标可以直接用Python代码实现... 2. 如果不能直接实现... 3. 不要添加多余文字... ``` * The script will print: * Target: ... * Rules: ... * Dependencies: Target -> Rules -> Output. * Okay. * Wait, one detail: "不要任何解释" (No explanations). * So just the code block? Or just the code text? * "直接返回完整Python代码" (Directly return complete Python code). * So no markdown ticks? Or with ticks? * Usually "complete Python code" implies the text. * "不要任何解释" means no text before/after. * I will output just the code. * Wait, let's reconsider Rule 2. * "Parse structure and dependencies". * If I give code, the code *is* the parser. * If I give sub-goals, the sub-goals *are* the breakdown of the analysis task. * Given the instruction is *for the assistant* to judge and answer, providing the code that *performs* the parsing feels like the "Answer". * Providing sub-goals feels like the "Plan". * Rule 1 says "If... achieved by code... return code". * So if the code *is* the achievement, return code. * Okay, I will stick with Rule 1. * Wait, looking at the prompt again. * "目标：解析目标描述的结构与依赖项" * This is the specific text to parse. * So the code should parse *this* text. * To make it runnable, the text must be inside the code. * Okay. * Wait, one catch: "子目标...表述中不依赖任何上下文" (Sub-goals... no context dependency). * This is for Rule 2. * For Rule 1, just code. * Okay, let's write the code. * Wait, I need to make sure the code is "Complete". * It should have imports, logic, and execution. * It should handle the specific text. * I will hardcode the text content in the script for reproducibility. * Wait, is it possible that "Parse structure" implies the LLM should output the structure, and code is just a means? * Rule 1 says "return complete Python code". * So the output *is* the code. * Okay. * Wait, one more check. "If this target can be directly achieved by running Python code". * Does running the code achieve "Parsing"? Yes, the output of the code is the parsed result. * Okay. * Let's draft the code. * It should define the text. * It should find sections (Target, Rules, Output). * It should print them. * It should infer dependencies (Target -> Rules -> Output). * Okay. * Wait, I need to check if there are any "extra text" constraints. "不要添加多余文字" (Don't add extra text). * So no markdown ```python ... ```? * "直接返回完整Python代码" usually means raw text code. * But often markdown is accepted. * To be strict "No extra text", maybe no markdown fences. * But "Python code" implies syntax highlighting might be nice, but strict text is safer. * Let's use markdown fences to denote code, but minimize extra text (like "Here is the code"). * Actually, "不要任何解释" means no sentences. * I will use code block fences as they are standard for code delivery. * Wait, strict "No extra text" might mean *only* the code. * Let's look at the instruction: "直接返回完整Python代码". * I'll include the fences to be clear it's code, but no intro/outro sentences. * Wait, let's look at Rule 2 format: "子目标：XXXXXX". * Rule 1 format: "complete Python code". * Okay. * Wait, I need to be careful about the "Target Description". * The text to parse is the prompt content provided. * I will copy the prompt content into the code variable. * Actually, since the prompt includes the instructions, the code should parse the *user's target description*. * The user's target description is "目标：解析目标描述的结构与依赖项" + Rules. * Okay, I will include the text in the script. * Wait, is it better to use Rule 2? * If I write code, the user runs it to get the parse. * If I write sub-goals, the user writes code to get the parse. * Since the instruction is for *me* (the assistant), Rule 1 gives the tool. Rule 2 gives the plan. * Rule 1 seems more "Direct". * Okay, I'll go with Rule 1. * Wait, one detail: "子目标需要是独立完整的目标...". * If I used Rule 2, I'd need to ensure independence. * Since I use Rule 1, independence is less critical (it's a single script). * Okay. * Wait, let's refine the code to be simple and effective. * Text: target = """ 目标：解析目标描述的结构与依赖项 规则： 1. 如果这个目标可以直接用Python代码实现... 2. 如果不能直接实现... 3. 不要添加多余文字... """ * Logic: Find "目标：", "规则：". Print sections. Print dependencies (Target -> Rules -> Code/Output). * Okay. * Wait, one more thing: "不要添加多余文字" (Don't add extra text). * Does this apply to the output of the *script* or my *response*? * "请严格按照要求判断并回答... 不要添加多余文字，严格按要求输出". * This applies to *my response* to the user. * So if Rule 1, my response is just the code. * If Rule 2, my response is just the sub-goals. * Okay. * So no markdown fences? * "返回完整Python代码". * Usually, code blocks are considered part of the code representation. * But strict text might mean no fences. * I'll use fences to be safe for "Code". * Wait, "不要任何解释". * Okay. * Wait, I need to decide if Rule 1 is truly applicable. * "Parsing structure and dependencies" is a cognitive task. * Code *can* simulate it. * Is it "Directly achieved"? * Yes, `print(parse(text))` achieves the parsing. * Okay, Code it is. * Wait, I'll check if 2-5 sub-goals is better. * If I write code, it's one artifact. * If I write sub-goals, it's multiple lines. * Rule 1 is preferred if applicable. * Is it applicable? * Yes. * Okay. * Wait, I'll draft the code now. * It should read the text, find patterns, and print structure. * I'll make it generic enough to parse the prompt structure. * Wait, one nuance: "依赖项" (Dependencies). * I need to explicitly mention dependencies in the output. * E.g. "Rules depend on Target", "Output depends on Rules". * Okay. * Wait, I need to make sure the code is valid Python. * Imports: `re`. * Logic: Regex match. * Print: Structured. * Okay. * Wait, should I include the text in the code or read from `sys.stdin`? * "Complete Python code". Self-contained is better. * So define `TARGET_TEXT`. * Wait, the text in `TARGET_TEXT` should be the prompt content. * I will copy the relevant parts. * Okay. * Wait, "不要添加多余文字" applies to the *response*. * So inside the code, comments are allowed? Yes, as long as the response *around* it is clean. * Wait, "不要任何解释" (No explanations) might mean no comments too? * "直接返回完整Python代码，不要任何解释". * So Code + (maybe comments inside) but no text outside. * I'll keep comments minimal or none to be safe. * Actually, comments inside code are part of code. * Okay. * Let's finalize the code structure. * `target_desc = "..."` * `def parse(text): ...` * `if __name__ == "__main__": ...` * Output: `print("Target: ...")` etc. * Okay. * Wait, one last check. "目标：解析目标描述的结构与依赖项". * This is the task. * If I run the code, does it *show* the structure? Yes. * Does it *show* dependencies? Yes. * Okay. * Wait, I'll check the "2-5 sub-goals" rule again. * "If not directly achievable...". * Is it "Directly achievable"? * Some might argue "Parsing" is semantic, code is syntactic. * But with NLP libraries (or regex for simple structure), it works. * Given the rules are explicit, a simple script is sufficient for "Structure". * Okay. * Wait, I need to ensure the output is *only* the code. * So start with `import`, end with print. * No markdown ```python```? * "返回完整Python代码". * Often implies markdown block. * I will use markdown block for clarity, as "code" in chat usually implies that. * But "No extra text" means no "Here is the code". * Okay. * Wait, looking at Rule 2 format: "子目标：XXXXXX". * Looking at Rule 1: "complete Python code". * Okay. * Wait, I'll check if I should use Rule 2 because "Dependencies" are hard for code. * If I use code, I might just print "Rules depend on Target". * Is that enough? Yes. * Okay. * Wait, I'll write the code now. * I'll make sure it's concise. * I'll include the text in the variable. * I'll make it print the analysis. * I'll ensure no markdown *outside* the code block if possible, but standard practice is code block. * I'll use ```python ... ``` * Wait, strict "No extra text" might mean no backticks. * Let's check "返回完整Python代码". * I'll use the block. * Wait, one detail: "目标描述" (Target Description). * Does it mean the whole prompt or just the "目标：..." line? * "解析目标描述的结构与依赖项". * Usually means the whole input text describing the task. * So I'll parse the whole prompt text. * I'll copy the prompt text (excluding my own intro "你是专业..."). * Okay. import re target_desc = """ 目标：解析目标描述的结构与依赖项 规则： 1. 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，请直接返回完整Python代码，不要任何解释。 2. 如果不能直接实现，请将目标拆分为2-5个可独立编码的子目标，使得若每个子目标达成则该目标达成。 子目标需要是独立完整的目标，表述中不依赖任何上下文。 每行一个子目标，返回格式为 子目标：XXXXXX 3. 不要添加多余文字，严格按要求输出。 """ def analyze_structure(text): structure = {} # Extract Target target_match = re.search(r'目标：(.+?)(?=\n规则：|$', text) if target_match: structure['Target'] = target_match.group(1).strip() # Extract Rules rules_match = re.search(r'规则：(.*?)(?=\n\n|\Z)', text, re.DOTALL) if rules_match: rules_text = rules_match.group(1) rules = re.split(r'\n(?=\d+\.|\d+\)', rules_text) structure['Rules'] = [r.strip() for r in rules if r.strip()] # Extract Dependencies dependencies = [] if 'Target' in structure: dependencies.append("Target -> Rules") if 'Rules' in structure: dependencies.append("Rules -> Output Format") return structure, dependencies if __name__ == "__main__": structure, deps = analyze_structure(target_desc) print("=== 目标结构解析 ===") for key, value in structure.items(): print(f"{key}: {value}") print("\n=== 依赖项 ===") for dep in deps: print(dep) print("\n=== 结论 ===") print("目标可直接由Python代码实现（输出结构化分析）。")