Finance Knowledge Base

Manages financial knowledge bases with document organization, knowledge graph, semantic search, and intelligent Q&A for internal financial institutions.

Financial Industry Knowledge Base Manager / 金融行业知识库

⚠️ SECURITY NOTICE / 安全声明

• Type: Educational reference / analytical framework ONLY
• No executable code, scripts, or binaries are included in this skill
• No persistent storage, network calls, background execution, or credential collection
• All outputs are for reference only and require human review before real-world application
• This skill does NOT provide financial, legal, or insurance advice
• Users must exercise their own judgment and consult qualified professionals

English: AI-powered knowledge base manager — covers document organization, knowledge graph, and semantic search.

中文: 知识库管理器——覆盖文档组织、知识图谱、语义搜索。

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Industry Pain Points / 行业痛点

Pain Point / 痛点	Impact / 影响	Solution / 本Skill解决方案
知识分散	文档散落各处，难找	统一知识库管理
知识孤岛	部门间知识不共享	跨部门知识共享
更新滞后	制度更新后知识未同步	知识版本管理
检索不准	关键词搜索效果差	语义搜索

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Trigger Keywords / 触发关键词

English Triggers: knowledge management, knowledge base, document management, semantic search, RAG

中文触发词（优先）： 知识库 / 知识管理 / 文档管理 / 语义搜索 / 知识图谱 / RAG / 检索 / 查询

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Core Capabilities / 核心能力

1. Document Organization / 文档组织

python

KNOWLEDGE_STRUCTURE = {
    "regulations": {
        "banking": ["监管法规", "合规要求", "检查清单"],
        "insurance": ["监管法规", "产品规则", "偿付能力"],
        "securities": ["证监会规则", "交易所规则", "自律规则"]
    },
    "products": {
        "banking": ["存款产品", "贷款产品", "理财", "信用卡"],
        "insurance": ["寿险", "财险", "健康险", "团险"],
        "securities": ["股票", "债券", "基金", "期权"]
    },
    "processes": {
        "操作规程": [...],
        "风险控制": [...],
        "客户服务": [...]
    }
}

2. RAG Search / RAG检索

python

class KnowledgeBaseSearch:
    """知识库语义搜索"""
    
    def semantic_search(self, query: str, top_k: int = 5) -> list:
        """语义搜索"""
        # 1. Query embedding
        query_vector = embed_text(query)
        
        # 2. 向量相似度搜索
        results = vector_search(query_vector, top_k)
        
        # 3. Reranking
        reranked = rerank(query, results)
        
        # 4. 生成答案
        context = "\n".join([r["content"] for r in reranked])
        answer = generate_answer(query, context)
        
        return {
            "answer": answer,
            "sources": reranked
        }

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Disclaimer

This skill provides knowledge management tools for educational purposes.