AIDeveloper-PC/requirements_generator/core/requirement_analyzer.py

# core/requirement_analyzer.py - 需求分解 & 函数签名生成
import json
from typing import List, Optional, Callable

import config
from core.llm_client import LLMClient
from database.models import FunctionalRequirement


class RequirementAnalyzer:
    """负责需求分解、模块分类、函数签名生成"""

    def __init__(self, llm: LLMClient):
        self.llm = llm

    # ══════════════════════════════════════════════════
    #  需求分解
    # ══════════════════════════════════════════════════

    def decompose(
        self,
        raw_requirement: str,
        project_id:      int,
        raw_req_id:      int,
        knowledge:       str = "",
    ) -> List[FunctionalRequirement]:
        """
        将原始需求文本分解为功能需求列表（含模块分类）。

        Args:
            raw_requirement: 原始需求文本
            project_id:      所属项目 ID
            raw_req_id:      原始需求记录 ID
            knowledge:       知识库文本（可选）

        Returns:
            FunctionalRequirement 对象列表（未持久化，id=None）
        """
        knowledge_section = (
            f"【参考知识库】\n{knowledge}\n" if knowledge else ""
        )
        prompt = config.DECOMPOSE_PROMPT_TEMPLATE.format(
            raw_requirement   = raw_requirement,
            knowledge_section = knowledge_section,
        )

        try:
            items = self.llm.chat_json(prompt)
            if not isinstance(items, list):
                raise ValueError("LLM 返回结果不是数组")
        except Exception as e:
            raise RuntimeError(f"需求分解失败: {e}")

        reqs = []
        for i, item in enumerate(items, 1):
            req = FunctionalRequirement(
                project_id    = project_id,
                raw_req_id    = raw_req_id,
                index_no      = i,
                title         = item.get("title", f"功能{i}"),
                description   = item.get("description", ""),
                function_name = item.get("function_name", f"function_{i}"),
                priority      = item.get("priority", "medium"),
                module        = item.get("module", config.DEFAULT_MODULE),
                status        = "pending",
                is_custom     = False,
            )
            reqs.append(req)
        return reqs

    # ══════════════════════════════════════════════════
    #  模块分类（独立步骤，可对已有需求列表重新分类）
    # ══════════════════════════════════════════════════

    def classify_modules(
        self,
        func_reqs: List[FunctionalRequirement],
        knowledge: str = "",
    ) -> List[dict]:
        """
        对功能需求列表进行模块分类，返回 {function_name: module} 映射列表。

        Args:
            func_reqs: 功能需求列表
            knowledge: 知识库文本（可选）

        Returns:
            [{"function_name": "...", "module": "..."}, ...]
        """
        req_list = [
            {
                "index_no":      r.index_no,
                "title":         r.title,
                "description":   r.description,
                "function_name": r.function_name,
            }
            for r in func_reqs
        ]
        knowledge_section = f"【参考知识库】\n{knowledge}\n" if knowledge else ""
        prompt = config.MODULE_CLASSIFY_PROMPT_TEMPLATE.format(
            requirements_json = json.dumps(req_list, ensure_ascii=False, indent=2),
            knowledge_section = knowledge_section,
        )
        try:
            result = self.llm.chat_json(prompt)
            if not isinstance(result, list):
                raise ValueError("LLM 返回结果不是数组")
            return result
        except Exception as e:
            raise RuntimeError(f"模块分类失败: {e}")

    # ══════════════════════════════════════════════════
    #  函数签名生成
    # ══════════════════════════════════════════════════

    def build_function_signature(
        self,
        func_req:       FunctionalRequirement,
        requirement_id: str  = "",
        knowledge:      str  = "",
    ) -> dict:
        """
        为单个功能需求生成函数签名 dict。

        Args:
            func_req:       功能需求对象
            requirement_id: 需求编号字符串（如 "REQ.01"）
            knowledge:      知识库文本

        Returns:
            函数签名 dict

        Raises:
            RuntimeError: LLM 调用或解析失败
        """
        knowledge_section = f"【参考知识库】\n{knowledge}\n" if knowledge else ""
        prompt = config.FUNC_SIGNATURE_PROMPT_TEMPLATE.format(
            requirement_id    = requirement_id or f"REQ.{func_req.index_no:02d}",
            title             = func_req.title,
            description       = func_req.description,
            function_name     = func_req.function_name,
            module            = func_req.module or config.DEFAULT_MODULE,
            knowledge_section = knowledge_section,
        )
        try:
            sig = self.llm.chat_json(prompt)
            if not isinstance(sig, dict):
                raise ValueError("LLM 返回结果不是 dict")
            # 确保 module 字段存在
            if "module" not in sig:
                sig["module"] = func_req.module or config.DEFAULT_MODULE
            return sig
        except Exception as e:
            raise RuntimeError(f"签名生成失败 [{func_req.function_name}]: {e}")

    def build_function_signatures_batch(
        self,
        func_reqs:   List[FunctionalRequirement],
        knowledge:   str = "",
        on_progress: Optional[Callable] = None,
    ) -> List[dict]:
        """
        批量生成函数签名，失败时使用降级结构。

        Args:
            func_reqs:   功能需求列表
            knowledge:   知识库文本
            on_progress: 进度回调 fn(index, total, req, signature, error)

        Returns:
            与 func_reqs 等长的签名 dict 列表（索引一一对应）
        """
        signatures = []
        total      = len(func_reqs)

        for i, req in enumerate(func_reqs, 1):
            req_id = f"REQ.{req.index_no:02d}"
            try:
                sig   = self.build_function_signature(req, req_id, knowledge)
                error = None
            except Exception as e:
                sig   = self._fallback_signature(req, req_id)
                error = e

            signatures.append(sig)
            if on_progress:
                on_progress(i, total, req, sig, error)

        return signatures

    @staticmethod
    def _fallback_signature(
        req:            FunctionalRequirement,
        requirement_id: str,
    ) -> dict:
        """生成降级签名结构（LLM 失败时使用）"""
        return {
            "name":           req.function_name,
            "requirement_id": requirement_id,
            "description":    req.description,
            "type":           "function",
            "module":         req.module or config.DEFAULT_MODULE,
            "parameters":     {},
            "return": {
                "type":       "any",
                "on_success": {"value": "...", "description": "成功时返回值"},
                "on_failure": {"value": "None", "description": "失败时返回 None"},
            },
        }