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视频嵌入网站,厦门seo关键词优化运营,设计网站公司收费,民非企业网站建设费怎么记账目录 企业知识库 MCP Server 设计方案 一、需求分析与架构设计 核心需求 系统架构 二、工具#xff08;Tools#xff09;设计 1. 文档操作工具 2. 智能处理工具 3. 管理与集成工具 三、资源#xff08;Resources#xff09;设计 四、完整实现示例#xff08;Pyth…目录企业知识库 MCP Server 设计方案一、需求分析与架构设计核心需求系统架构二、工具Tools设计1. 文档操作工具2. 智能处理工具3. 管理与集成工具三、资源Resources设计四、完整实现示例Python五、配置文件示例六、部署与使用1. 启动服务器2. Claude Desktop 配置3. 使用示例七、扩展功能设计1. 实时协作2. 高级搜索3. 工作流集成4. 分析与洞察5. 移动端支持八、安全与合规企业知识库 MCP Server 设计方案一、需求分析与架构设计核心需求文档管理上传、搜索、检索、更新企业文档权限控制基于角色的访问控制RBAC多格式支持Markdown、PDF、Word、HTML、纯文本智能功能文档摘要、翻译、相似文档推荐审计与监控操作日志、使用统计集成能力与现有系统Confluence、Git、SharePoint集成系统架构┌─────────────────────────────────────┐ │ MCP Client (Claude) │ └───────────────┬─────────────────────┘ │ SSE/HTTP ┌───────────────▼─────────────────────┐ │ 企业知识库 MCP Server │ ├───────────────┬─────────────────────┤ │ API Gateway │ Auth Middleware │ │ Tool Router │ Rate Limiter │ │ Cache Layer │ Audit Logger │ └───────────────┴─────────────────────┘ │ ┌───────────────┬─────────────────────┐ │ Search Engine │ Vector Database │ │ (Elasticsearch)│ (Pinecone/Qdrant) │ └───────────────┴─────────────────────┘ │ ┌───────────────┬─────────────────────┐ │ Document Store│ External Systems │ │ (S3/MinIO) │ (Confluence/Git) │ └───────────────┴─────────────────────┘二、工具Tools设计1. 文档操作工具// 完整工具集定义 { tools: [ { name: knowledge_search, description: 搜索企业知识库文档, inputSchema: { type: object, properties: { query: { type: string, description: 搜索关键词 }, filters: { type: object, description: 过滤条件, properties: { department: { type: string, enum: [engineering, sales, hr, finance] }, doc_type: { type: string, enum: [policy, guide, api, meeting] }, author: { type: string }, date_range: { type: object, properties: { from: { type: string, format: date }, to: { type: string, format: date } } }, security_level: { type: string, enum: [public, internal, confidential] } } }, limit: { type: integer, minimum: 1, maximum: 50, default: 10 }, page: { type: integer, minimum: 1, default: 1 }, sort_by: { type: string, enum: [relevance, date_desc, date_asc, views], default: relevance } }, required: [query] } }, { name: knowledge_upload, description: 上传文档到知识库, inputSchema: { type: object, properties: { title: { type: string }, content: { type: string }, file_content: { type: string, description: Base64编码的文件内容 }, file_type: { type: string, enum: [text, markdown, pdf, docx, html] }, metadata: { type: object, properties: { department: { type: string }, tags: { type: array, items: { type: string } }, security_level: { type: string }, expires_at: { type: string, format: date } } } }, required: [title] } }, { name: get_document, description: 获取特定文档内容, inputSchema: { type: object, properties: { doc_id: { type: string }, include_metadata: { type: boolean, default: true }, include_embeddings: { type: boolean, default: false } }, required: [doc_id] } }, { name: update_document, description: 更新文档, inputSchema: { type: object, properties: { doc_id: { type: string }, content: { type: string }, metadata: { type: object }, update_reason: { type: string } }, required: [doc_id] } }, { name: delete_document, description: 删除文档需要确认, inputSchema: { type: object, properties: { doc_id: { type: string }, reason: { type: string } }, required: [doc_id, reason] } } ] }2. 智能处理工具{ tools: [ { name: summarize_document, description: 生成文档摘要, inputSchema: { type: object, properties: { doc_id: { type: string }, length: { type: string, enum: [short, medium, detailed], default: medium }, language: { type: string, default: zh } }, required: [doc_id] } }, { name: translate_document, description: 翻译文档, inputSchema: { type: object, properties: { doc_id: { type: string }, target_language: { type: string }, include_original: { type: boolean, default: false } }, required: [doc_id, target_language] } }, { name: find_similar, description: 查找相似文档, inputSchema: { type: object, properties: { doc_id: { type: string }, query: { type: string }, top_k: { type: integer, default: 5 }, similarity_threshold: { type: number, minimum: 0, maximum: 1, default: 0.7 } } } }, { name: ask_question, description: 基于文档内容问答, inputSchema: { type: object, properties: { question: { type: string }, doc_ids: { type: array, items: { type: string }, description: 指定在哪些文档中搜索 }, scope: { type: string, enum: [all, department, personal], default: all }, include_sources: { type: boolean, default: true } }, required: [question] } } ] }3. 管理与集成工具{ tools: [ { name: sync_external, description: 同步外部系统文档, inputSchema: { type: object, properties: { source: { type: string, enum: [confluence, github, sharepoint, notion] }, config: { type: object }, full_sync: { type: boolean, default: false } }, required: [source] } }, { name: generate_report, description: 生成知识库使用报告, inputSchema: { type: object, properties: { report_type: { type: string, enum: [usage, coverage, freshness, popularity] }, time_range: { type: object, properties: { start: { type: string, format: date }, end: { type: string, format: date } } }, format: { type: string, enum: [markdown, json, html], default: markdown } }, required: [report_type] } }, { name: manage_permissions, description: 管理文档权限, inputSchema: { type: object, properties: { action: { type: string, enum: [grant, revoke, list] }, doc_id: { type: string }, user_or_group: { type: string }, permission: { type: string, enum: [read, write, admin] } }, required: [action] } } ] }三、资源Resources设计{ resources: [ { uri: knowledge://recent/{limit?}, name: 最近更新文档, description: 最近更新的知识库文档, mimeType: application/json }, { uri: knowledge://popular/{limit?}, name: 热门文档, description: 查看量最高的文档, mimeType: application/json }, { uri: knowledge://stats/overview, name: 知识库统计概览, description: 知识库使用统计信息, mimeType: application/json }, { uri: knowledge://category/{category}, name: 分类文档, description: 按分类浏览文档, mimeType: application/json }, { uri: knowledge://user/{user_id}/recent, name: 用户最近访问, description: 用户最近访问的文档, mimeType: application/json } ] }四、完整实现示例Pythonimport asyncio import json import base64 from typing import Dict, Any, List, Optional from datetime import datetime from enum import Enum from mcp import ClientSession, StdioServerParameters from mcp.client.stdio import stdio_client from pydantic import BaseModel, Field import aiohttp from elasticsearch import AsyncElasticsearch from qdrant_client import QdrantClient import hashlib import uuid # 数据模型 class DocumentMetadata(BaseModel): department: str doc_type: str Field(defaultdocument) security_level: str Field(defaultinternal) tags: List[str] Field(default_factorylist) author: str created_at: datetime Field(default_factorydatetime.now) updated_at: datetime Field(default_factorydatetime.now) expires_at: Optional[datetime] None views: int 0 permissions: Dict[str, List[str]] Field(default_factorydict) class Document(BaseModel): id: str Field(default_factorylambda: str(uuid.uuid4())) title: str content: str summary: Optional[str] None metadata: DocumentMetadata embeddings: Optional[List[float]] None file_hash: str # 用于去重 class SearchFilters(BaseModel): department: Optional[str] None doc_type: Optional[str] None security_level: Optional[str] None author: Optional[str] None tags: Optional[List[str]] None date_range: Optional[Dict[str, datetime]] None # 权限控制 class PermissionManager: ROLES { admin: [read, write, delete, manage], editor: [read, write], viewer: [read], guest: [read_public] } def __init__(self): self.user_roles: Dict[str, str] {} def check_permission(self, user: str, doc: Document, action: str) - bool: 检查用户对文档的权限 role self.user_roles.get(user, guest) # 检查角色权限 if action not in self.ROLES.get(role, []): return False # 检查文档级别的权限 if doc.metadata.security_level confidential: return role in [admin, editor] # 检查自定义权限 if user in doc.metadata.permissions.get(action, []): return True return role in [admin, editor] # 知识库 MCP Server class KnowledgeBaseMCPServer: def __init__(self): # 存储 self.documents: Dict[str, Document] {} self.permission_mgr PermissionManager() # 搜索和向量数据库 self.es AsyncElasticsearch([localhost:9200]) self.qdrant QdrantClient(localhost, port6333) # 缓存 self.cache {} # 审计日志 self.audit_log [] # 工具实现 async def handle_knowledge_search(self, params: Dict[str, Any]) - Dict[str, Any]: 搜索文档实现 try: query params.get(query, ) filters params.get(filters, {}) limit params.get(limit, 10) page params.get(page, 1) # 构建 ES 查询 es_query { query: { bool: { must: [{ multi_match: { query: query, fields: [title^3, content, summary^2], type: best_fields } }], filter: [] } }, from: (page - 1) * limit, size: limit } # 添加过滤器 if filters: filter_clauses [] if department : filters.get(department): filter_clauses.append({term: {department: department}}) if doc_type : filters.get(doc_type): filter_clauses.append({term: {doc_type: doc_type}}) if security_level : filters.get(security_level): filter_clauses.append({term: {security_level: security_level}}) if date_range : filters.get(date_range): filter_clauses.append({ range: { created_at: { gte: date_range.get(from), lte: date_range.get(to) } } }) if filter_clauses: es_query[query][bool][filter] filter_clauses # 执行搜索 response await self.es.search( indexknowledge_docs, bodyes_query ) # 处理结果 results [] for hit in response[hits][hits]: doc hit[_source] results.append({ id: doc[id], title: doc[title], summary: doc.get(summary, ), score: hit[_score], metadata: { department: doc[department], doc_type: doc[doc_type], created_at: doc[created_at], author: doc[author] } }) return { content: [{ type: text, text: f找到 {response[hits][total][value]} 个结果: }, { type: text, text: self._format_search_results(results) }], metadata: { total: response[hits][total][value], page: page, page_size: limit } } except Exception as e: return { content: [{ type: text, text: f搜索失败: {str(e)} }], isError: True } async def handle_knowledge_upload(self, params: Dict[str, Any]) - Dict[str, Any]: 上传文档实现 try: title params[title] content params.get(content, ) metadata params.get(metadata, {}) # 检查权限 user metadata.get(author, unknown) if not self.permission_mgr.check_permission(user, None, write): return self._error_response(权限不足) # 创建文档 doc Document( titletitle, contentcontent, metadataDocumentMetadata( departmentmetadata.get(department, general), security_levelmetadata.get(security_level, internal), tagsmetadata.get(tags, []), authoruser ), file_hashself._calculate_hash(content) ) # 生成摘要 doc.summary await self._generate_summary(content) # 生成嵌入向量 doc.embeddings await self._generate_embeddings(content) # 存储文档 self.documents[doc.id] doc # 索引到搜索引擎 await self._index_document(doc) # 存储到向量数据库 await self._store_embeddings(doc) # 审计日志 self._log_audit(upload, user, doc.id) return { content: [{ type: text, text: f文档上传成功\nID: {doc.id}\n标题: {title}\n安全等级: {doc.metadata.security_level} }], suggestedToolCalls: [{ toolName: summarize_document, arguments: {doc_id: doc.id} }] } except Exception as e: return self._error_response(f上传失败: {str(e)}) async def handle_ask_question(self, params: Dict[str, Any]) - Dict[str, Any]: 文档问答实现 try: question params[question] scope params.get(scope, all) include_sources params.get(include_sources, True) # 向量搜索相关文档 query_embedding await self._generate_embeddings(question) similar_docs await self._vector_search(query_embedding, top_k5) if not similar_docs: return { content: [{ type: text, text: 没有找到相关文档来回答这个问题。 }] } # 构建上下文 context \n\n.join([ f文档: {doc.title}\n内容: {doc.content[:1000]} for doc in similar_docs ]) # 调用 LLM 生成答案 answer await self._call_llm_for_qa(question, context) # 构建响应 response_content [{ type: text, text: f**问题**: {question}\n\n**答案**: {answer} }] if include_sources: sources_text \n.join([ f- {doc.title} (相关度: {score:.2f}) for doc, score in similar_docs ]) response_content.append({ type: text, text: f\n**参考文档**:\n{sources_text} }) return {content: response_content} except Exception as e: return self._error_response(f问答失败: {str(e)}) # 资源处理 async def handle_resource_read(self, uri: str) - Dict[str, Any]: 处理资源读取请求 if uri.startswith(knowledge://recent/): limit int(uri.split(/)[-1]) if uri.split(/)[-1].isdigit() else 10 recent_docs sorted( self.documents.values(), keylambda x: x.metadata.updated_at, reverseTrue )[:limit] return { contents: [{ uri: uri, mimeType: application/json, text: json.dumps([ { id: doc.id, title: doc.title, updated_at: doc.metadata.updated_at.isoformat(), author: doc.metadata.author } for doc in recent_docs ], ensure_asciiFalse, indent2) }] } elif uri.startswith(knowledge://stats/overview): stats self._generate_statistics() return { contents: [{ uri: uri, mimeType: application/json, text: json.dumps(stats, ensure_asciiFalse, indent2) }] } return {error: f未知资源: {uri}} # 辅助方法 def _format_search_results(self, results: List[Dict]) - str: 格式化搜索结果 formatted [] for i, result in enumerate(results, 1): formatted.append( f{i}. **{result[title]}**\n f 概要: {result[summary][:100]}...\n f 部门: {result[metadata][department]} | f类型: {result[metadata][doc_type]} | f作者: {result[metadata][author]}\n ) return \n.join(formatted) def _generate_statistics(self) - Dict: 生成统计信息 total_docs len(self.documents) departments {} doc_types {} for doc in self.documents.values(): dept doc.metadata.department doc_type doc.metadata.doc_type departments[dept] departments.get(dept, 0) 1 doc_types[doc_type] doc_types.get(doc_type, 0) 1 return { total_documents: total_docs, by_department: departments, by_type: doc_types, last_updated: max( [doc.metadata.updated_at for doc in self.documents.values()], defaultdatetime.now() ).isoformat() } async def _generate_summary(self, content: str, length: str medium) - str: 生成文档摘要简化版 # 实际实现应该调用 LLM API sentences content.split(.) if len(sentences) 3: return content if length short: return ..join(sentences[:2]) . elif length detailed: return ..join(sentences[:10]) . else: # medium return ..join(sentences[:5]) . async def _generate_embeddings(self, text: str) - List[float]: 生成文本向量简化版 # 实际实现应该调用嵌入模型 API import numpy as np # 使用简单的哈希作为模拟嵌入 hash_val int(hashlib.md5(text.encode()).hexdigest()[:8], 16) np.random.seed(hash_val) return np.random.randn(384).tolist() # 384维向量 async def _vector_search(self, query_embedding: List[float], top_k: int 5): 向量搜索 # 简化实现 results [] for doc in self.documents.values(): if doc.embeddings: # 计算余弦相似度 similarity self._cosine_similarity(query_embedding, doc.embeddings) if similarity 0.7: # 阈值 results.append((doc, similarity)) # 按相似度排序 results.sort(keylambda x: x[1], reverseTrue) return results[:top_k] def _cosine_similarity(self, a: List[float], b: List[float]) - float: 计算余弦相似度 import numpy as np a_np np.array(a) b_np np.array(b) return np.dot(a_np, b_np) / (np.linalg.norm(a_np) * np.linalg.norm(b_np)) def _calculate_hash(self, content: str) - str: 计算内容哈希值 return hashlib.md5(content.encode()).hexdigest() def _log_audit(self, action: str, user: str, doc_id: str): 记录审计日志 self.audit_log.append({ timestamp: datetime.now().isoformat(), action: action, user: user, doc_id: doc_id, ip: 127.0.0.1 # 实际应该从请求获取 }) def _error_response(self, message: str) - Dict[str, Any]: 错误响应 return { content: [{type: text, text: f错误: {message}}], isError: True } async def _call_llm_for_qa(self, question: str, context: str) - str: 调用 LLM 进行问答简化版 # 实际实现应该调用 LLM API prompt f 基于以下文档内容回答问题 文档内容 {context} 问题{question} 答案 # 这里应该调用实际的 LLM API return 这是基于文档内容生成的答案。 # MCP Server 主程序 async def main(): server KnowledgeBaseMCPServer() # 这里应该实现 MCP 协议的具体通信 # 包括 initialize, tools/list, tools/call, resources/list, resources/read 等 print(知识库 MCP Server 已启动) print(可用工具:) print(- knowledge_search: 搜索文档) print(- knowledge_upload: 上传文档) print(- get_document: 获取文档) print(- ask_question: 文档问答) print(- summarize_document: 文档摘要) print(- find_similar: 查找相似文档) if __name__ __main__: asyncio.run(main())五、配置文件示例# config.yaml server: name: enterprise-knowledge-base version: 1.0.0 host: 0.0.0.0 port: 8000 auth_required: true database: elasticsearch: hosts: [localhost:9200] index: knowledge_docs qdrant: host: localhost port: 6333 collection: doc_embeddings redis: host: localhost port: 6379 db: 0 embeddings: model: text-embedding-ada-002 api_key: ${OPENAI_API_KEY} dimensions: 1536 llm: model: gpt-4-turbo api_key: ${OPENAI_API_KEY} temperature: 0.1 security: jwt_secret: ${JWT_SECRET} token_expiry: 86400 rate_limit: requests_per_minute: 60 allowed_origins: - https://claude.ai - http://localhost:* storage: document_store: s3 s3: endpoint: s3.amazonaws.com bucket: knowledge-docs local_backup: /var/backups/knowledge integrations: confluence: enabled: true base_url: https://your-company.atlassian.net/wiki github: enabled: false sharepoint: enabled: false logging: level: INFO file: /var/log/knowledge-mcp.log audit_log: /var/log/knowledge-audit.log六、部署与使用1. 启动服务器# 安装依赖 pip install mcp elasticsearch qdrant-client aiohttp pydantic # 启动 python knowledge_mcp_server.py2. Claude Desktop 配置// ~/Library/Application Support/Claude/claude_desktop_config.json { mcpServers: { knowledge-base: { command: python, args: [ /path/to/knowledge_mcp_server.py ], env: { OPENAI_API_KEY: sk-..., ELASTICSEARCH_HOSTS: localhost:9200 } } } }3. 使用示例# 客户端调用示例 async with ClientSession(stdio_client(StdioServerParameters( commandpython, args[knowledge_mcp_server.py] ))) as session: # 初始化 await session.initialize() # 列出工具 tools await session.list_tools() print(可用工具:, [t.name for t in tools.tools]) # 搜索文档 response await session.call_tool( knowledge_search, arguments{ query: Q4 销售报告, filters: { department: sales, security_level: internal } } ) # 文档问答 response await session.call_tool( ask_question, arguments{ question: 公司今年的销售目标是多少, scope: sales } )七、扩展功能设计1. 实时协作文档协同编辑评论和批注系统变更历史追踪2. 高级搜索语义搜索增强混合搜索关键词向量自然语言查询转换3. 工作流集成文档审批流程自动化文档分类过期文档清理4. 分析与洞察知识图谱构建文档关联分析知识缺口识别5. 移动端支持响应式设计离线访问移动端优化界面八、安全与合规数据加密传输层TLS 1.3存储加密AES-256密钥管理HSM/KMS访问控制基于角色的访问控制RBAC属性基础的访问控制ABAC多因素认证合规性GDPR 数据主体权利SOX 文档保留策略HIPAA 医疗文档保护审计日志保留 7 年监控与告警异常访问检测数据泄露防护实时告警系统这个设计提供了一个完整的企业级知识库 MCP Server 解决方案可以根据实际需求进行调整和扩展。