构建一个写作助手Agent：提升创作效率的实践

在上一篇文章中,我们讨论了如何构建一个翻译助手Agent。今天,我想分享另一个实际项目:如何构建一个写作助手Agent。这个项目源于我们一个内容团队的真实需求 - 提升创作效率,保障内容质量。

从创作痛点说起

记得和内容团队讨论时的场景：

小王：每天要写很多文章,有时候会卡壳
小李：是啊,而且要保持文风一致也很难
我：主要是哪些写作场景？
小王：技术博客、产品文案、运营文章这些
我：这些场景很适合用AI Agent来协助

经过需求分析,我们确定了几个核心功能：

1. 智能创作
2. 风格管理
3. 内容优化
4. 素材管理

技术方案设计

首先是整体架构：

from typing import List, Dict, Any, Optional
from enum import Enum
from pydantic import BaseModel
import asyncio

class WritingTask(Enum):
    CREATE = "create"
    STYLE = "style"
    OPTIMIZE = "optimize"
    MATERIAL = "material"

class WritingContext(BaseModel):
    task_type: WritingTask
    topic: str
    style: str
    length: Optional[int]
    keywords: Optional[List[str]]
    references: Optional[List[Dict[str, Any]]]

class WritingAssistant:
    def __init__(
        self,
        config: Dict[str, Any]
    ):
        # 1. 初始化写作模型
        self.writing_model = WritingLLM(
            model="gpt-4",
            temperature=0.7,
            context_length=8000
        )

        # 2. 初始化工具集
        self.tools = {
            "creator": ContentCreator(),
            "stylist": StyleManager(),
            "optimizer": ContentOptimizer(),
            "material": MaterialManager()
        }

        # 3. 初始化知识库
        self.knowledge_base = VectorStore(
            embeddings=WritingEmbeddings(),
            collection="writing_knowledge"
        )

    async def process_task(
        self,
        context: WritingContext
    ) -> Dict[str, Any]:
        # 1. 分析任务
        task_info = await self._analyze_task(
            context
        )

        # 2. 准备资源
        resources = await self._prepare_resources(
            context,
            task_info
        )

        # 3. 生成方案
        plan = await self._generate_plan(
            task_info,
            resources
        )

        # 4. 执行任务
        result = await self._execute_task(
            plan,
            context
        )

        return result

    async def _analyze_task(
        self,
        context: WritingContext
    ) -> Dict[str, Any]:
        # 1. 识别写作类型
        writing_type = await self._identify_writing_type(
            context.topic
        )

        # 2. 评估难度
        difficulty = await self._evaluate_difficulty(
            context
        )

        # 3. 确定策略
        strategy = await self._determine_strategy(
            writing_type,
            difficulty
        )

        return {
            "type": writing_type,
            "difficulty": difficulty,
            "strategy": strategy
        }

智能创作功能

首先实现智能创作功能：

class ContentCreator:
    def __init__(
        self,
        model: WritingLLM
    ):
        self.model = model

    async def create_content(
        self,
        context: WritingContext
    ) -> Dict[str, Any]:
        # 1. 构思内容
        outline = await self._brainstorm_outline(
            context
        )

        # 2. 撰写内容
        content = await self._write_content(
            outline,
            context
        )

        # 3. 优化内容
        optimized = await self._optimize_content(
            content,
            context
        )

        return optimized

    async def _brainstorm_outline(
        self,
        context: WritingContext
    ) -> Dict[str, Any]:
        # 1. 收集素材
        materials = await self._collect_materials(
            context.topic,
            context.references
        )

        # 2. 生成大纲
        outline = await self._generate_outline(
            materials,
            context
        )

        # 3. 优化结构
        structure = await self._optimize_structure(
            outline
        )

        return {
            "materials": materials,
            "outline": outline,
            "structure": structure
        }

    async def _write_content(
        self,
        outline: Dict[str, Any],
        context: WritingContext
    ) -> Dict[str, Any]:
        sections = []

        for section in outline["structure"]:
            # 1. 准备素材
            section_materials = await self._prepare_section_materials(
                section,
                outline["materials"]
            )

            # 2. 撰写章节
            content = await self.model.write(
                section,
                section_materials,
                context.style
            )

            # 3. 优化表达
            optimized = await self._optimize_expression(
                content,
                context.style
            )

            sections.append({
                "title": section["title"],
                "content": optimized,
                "materials": section_materials
            })

        return sections

风格管理功能

接下来是风格管理功能：

class StyleManager:
    def __init__(
        self,
        model: WritingLLM
    ):
        self.model = model

    async def manage_style(
        self,
        context: WritingContext,
        content: Dict[str, Any]
    ) -> Dict[str, Any]:
        # 1. 分析风格
        style_info = await self._analyze_style(
            content
        )

        # 2. 调整风格
        adjusted = await self._adjust_style(
            content,
            context.style
        )

        # 3. 统一风格
        unified = await self._unify_style(
            adjusted
        )

        return unified

    async def _analyze_style(
        self,
        content: Dict[str, Any]
    ) -> Dict[str, Any]:
        # 1. 语言风格分析
        language = await self._analyze_language(
            content
        )

        # 2. 表达特点分析
        expression = await self._analyze_expression(
            content
        )

        # 3. 结构特点分析
        structure = await self._analyze_structure(
            content
        )

        return {
            "language": language,
            "expression": expression,
            "structure": structure
        }

    async def _adjust_style(
        self,
        content: Dict[str, Any],
        target_style: str
    ) -> Dict[str, Any]:
        # 1. 加载风格模板
        template = await self._load_style_template(
            target_style
        )

        # 2. 调整语言
        language_adjusted = await self._adjust_language(
            content,
            template
        )

        # 3. 调整表达
        expression_adjusted = await self._adjust_expression(
            language_adjusted,
            template
        )

        return expression_adjusted

内容优化功能

再来实现内容优化功能：

class ContentOptimizer:
    def __init__(
        self,
        model: WritingLLM
    ):
        self.model = model

    async def optimize_content(
        self,
        context: WritingContext,
        content: Dict[str, Any]
    ) -> Dict[str, Any]:
        # 1. 分析内容
        analysis = await self._analyze_content(
            content
        )

        # 2. 优化内容
        optimized = await self._optimize_content(
            content,
            analysis
        )

        # 3. 验证质量
        validated = await self._validate_quality(
            optimized,
            context
        )

        return validated

    async def _analyze_content(
        self,
        content: Dict[str, Any]
    ) -> Dict[str, Any]:
        # 1. 结构分析
        structure = await self._analyze_structure(
            content
        )

        # 2. 逻辑分析
        logic = await self._analyze_logic(
            content
        )

        # 3. 表达分析
        expression = await self._analyze_expression(
            content
        )

        return {
            "structure": structure,
            "logic": logic,
            "expression": expression
        }

    async def _optimize_content(
        self,
        content: Dict[str, Any],
        analysis: Dict[str, Any]
    ) -> Dict[str, Any]:
        # 1. 优化结构
        structure_optimized = await self._optimize_structure(
            content,
            analysis["structure"]
        )

        # 2. 优化逻辑
        logic_optimized = await self._optimize_logic(
            structure_optimized,
            analysis["logic"]
        )

        # 3. 优化表达
        expression_optimized = await self._optimize_expression(
            logic_optimized,
            analysis["expression"]
        )

        return expression_optimized

素材管理功能

最后是素材管理功能：

class MaterialManager:
    def __init__(
        self,
        model: WritingLLM
    ):
        self.model = model

    async def manage_materials(
        self,
        context: WritingContext
    ) -> Dict[str, Any]:
        # 1. 收集素材
        materials = await self._collect_materials(
            context
        )

        # 2. 整理素材
        organized = await self._organize_materials(
            materials
        )

        # 3. 推荐素材
        recommended = await self._recommend_materials(
            organized,
            context
        )

        return recommended

    async def _collect_materials(
        self,
        context: WritingContext
    ) -> Dict[str, Any]:
        # 1. 搜索相关内容
        search_results = await self._search_content(
            context.topic,
            context.keywords
        )

        # 2. 提取关键信息
        key_info = await self._extract_key_info(
            search_results
        )

        # 3. 验证可用性
        validated = await self._validate_materials(
            key_info
        )

        return validated

    async def _organize_materials(
        self,
        materials: Dict[str, Any]
    ) -> Dict[str, Any]:
        # 1. 分类整理
        categorized = await self._categorize_materials(
            materials
        )

        # 2. 标记重要性
        marked = await self._mark_importance(
            categorized
        )

        # 3. 建立索引
        indexed = await self._build_index(
            marked
        )

        return indexed

实际效果

经过两个月的使用,这个写作助手Agent带来了显著的改善：

1. 效率提升

   • 创作速度提升70%
   • 素材整理更快
   • 修改优化更便捷

2. 质量改善

   • 内容更专业
   • 风格更统一
   • 表达更流畅

3. 体验优化

   • 创作更轻松
   • 协作更顺畅
   • 管理更便捷

实践心得

在开发这个写作助手Agent的过程中,我总结了几点经验：

1. 理解需求

   • 把握写作目标
   • 理解受众特点
   • 注重实用性

2. 保持风格

   • 风格要统一
   • 表达要自然
   • 内容要专业

3. 持续优化

   • 收集反馈
   • 更新知识库
   • 改进算法

写在最后

一个好的写作助手Agent不仅要能生成内容,更要理解写作的本质,帮助创作者提升内容质量。它就像一个经验丰富的编辑,在合适的时候给出恰当的建议。

在下一篇文章中,我会讲解如何开发一个产品助手Agent。如果你对写作助手Agent的开发有什么想法,欢迎在评论区交流。