How to Build an Efficient Knowledge Base for AI Models
How to Build an Efficient Knowledge Base for AI Models
如何为 AI 模型构建高效的知识库
Building a knowledge base for AI models isn’t a one-time task but an iterative process of refinement. 为 AI 模型构建知识库并非一劳永逸的任务，而是一个不断迭代和优化的过程。
AI models are only as strong as their knowledge base. An accurate and curated knowledge base improves both model speed and accuracy—areas where current models often fall short. In fact, a recent study shows that major AI chatbots are wrong for almost every second query. In this article, I’ll cover how you can build a reliable knowledge base with detailed steps and mistakes to avoid. AI 模型的强大程度取决于其知识库的质量。一个准确且经过精心整理的知识库可以同时提升模型的速度和准确性——而这正是当前模型往往表现不足的地方。事实上，最近的一项研究表明，主流 AI 聊天机器人几乎每两次查询中就有一次是错误的。在本文中，我将介绍如何通过详细的步骤构建可靠的知识库，并指出需要避免的误区。
6 steps to build an effective knowledge base
构建高效知识库的 6 个步骤
Taking a systematic approach to building a knowledge base helps you create one that is standardized, scalable, and self-explanatory. Any new developer can easily add or update the knowledge base over time to keep it up to date and reliable. To ensure you get there, you can follow these six steps whenever you start creating a knowledge base: 采用系统化的方法构建知识库，有助于创建标准化、可扩展且易于理解的系统。任何新加入的开发人员都能轻松地随时间推移添加或更新知识库，以保持其时效性和可靠性。为了确保达成这一目标，你在开始创建知识库时可以遵循以下六个步骤：
1. Collect data
1. 收集数据
A main misconception with collecting data for a knowledge base is assuming more is better. It makes you fall into the classic “garbage in, garbage out” issue. Prioritize value over volume and collect all data that is relevant for your model. 在收集知识库数据时，一个主要的误区是认为“越多越好”。这会让你陷入经典的“垃圾进，垃圾出”（Garbage In, Garbage Out）问题。请优先考虑价值而非数量，并收集所有与你的模型相关的数据。
It could be in the form of: 它可以是以下形式：
- Factual and tutorial content covering facts and procedures
- 涵盖事实和流程的事实性及教程内容
- Problem-solving content in the form of an instructive text or videos
- 以指导性文本或视频形式呈现的问题解决内容
- Historical data showing past issues or execution logs
- 展示过往问题或执行日志的历史数据
- Real-time data covering live system status or recent news feeds
- 涵盖实时系统状态或最新新闻源的实时数据
- Domain data for the model to get more context
- 用于让模型获取更多上下文的领域数据
It’s important to understand that your system doesn’t need every information. For example, if you are building a customer support chatbot, then your model may need only factual and tutorial content explaining company policy and procedures. It ensures your model doesn’t invent an invalid or out-of-scope response and sticks to what is provided to it. 必须明白，你的系统并不需要所有信息。例如，如果你正在构建一个客户支持聊天机器人，那么你的模型可能只需要解释公司政策和流程的事实性及教程内容。这能确保模型不会编造无效或超出范围的回答，并严格遵循所提供的内容。
Tip: There is an increasing trend to feed AI-generated data while building a knowledge base of new AI models. I feel this practice is a bit of a double-edged sword. It does offer speed, but you must check the output for reliability and fluff. Always optimize the content for crisp responses and verify the output before adding it to the knowledge base. 提示：在构建新 AI 模型的知识库时，使用 AI 生成的数据已成为一种趋势。我认为这种做法是一把双刃剑。它确实提供了速度，但你必须检查输出内容的可靠性和冗余度。在将其添加到知识库之前，务必优化内容以确保回答简洁明了，并对输出进行验证。
2. Clean and segment data into chunks
2. 清理数据并将其分块
After you have the raw data ready, you can clean it first. The cleaning process would typically include: 在准备好原始数据后，首先需要进行清理。清理过程通常包括：
- Removing duplicate and outdated content
- 删除重复和过时的内容
- Deleting irrelevant details such as headers, footers, and page numbers
- 删除页眉、页脚和页码等无关细节
- Standardizing content, both format and content-wise (consistent terminology)
- 标准化内容，包括格式和内容（术语一致性）
This cleaned data is then divided into logical chunks, where each chunk contains one clear idea or topic. Every chunk is also assigned metadata that provides quick context about the content in it. This metadata helps AI models to browse through knowledge bases faster and quickly reach chunks that have relevant details. 这些清理后的数据随后被划分为逻辑块，每个块包含一个清晰的观点或主题。每个块还会被分配元数据，以提供关于其中内容的快速上下文。这些元数据有助于 AI 模型更快地浏览知识库，并迅速定位到包含相关细节的块。
You can also set role-based access in chunks to ensure which roles get access to information in that chunk. While many roles may have access to a model, not everyone can access all the data. Chunking is where you can set security and access control within the model. 你还可以在块中设置基于角色的访问权限，以确保哪些角色可以访问该块中的信息。虽然许多角色可能都有权访问模型，但并非每个人都能访问所有数据。分块正是你可以在模型内部设置安全和访问控制的地方。
Tip: A best practice I always follow is to chunk data based on user queries instead of document structure. For example, you have a document on login and access management. You can chunk it on common user questions like ‘How to change password?’, ‘What is the password policy?’, etc. You can then validate these chunks by testing against real queries. A safe set can be 10-12 questions. 提示：我一直遵循的最佳实践是根据用户查询而非文档结构来对数据进行分块。例如，你有一份关于登录和访问管理的文档，你可以根据常见的用户问题（如“如何修改密码？”、“密码策略是什么？”等）对其进行分块。然后，你可以通过针对真实查询进行测试来验证这些分块。一个安全的测试集可以是 10-12 个问题。
3. Organize and index data
3. 组织并索引数据
The text chunks are converted into numbers called vectors using an embedding model like OpenAI v3-Large, BGE-M3, etc. AI models can skim through vectors faster than a huge block of text. After vectorization, the metadata attached to the chunk is then attached to the vector. The final chunk will look like this: 文本块会使用嵌入模型（如 OpenAI v3-Large、BGE-M3 等）转换为称为向量的数字。AI 模型浏览向量的速度比浏览大段文本要快得多。向量化后，附加到块上的元数据会随之附加到向量上。最终的块结构如下所示：
[ Vector (numbers) ] + [ Original text ] + [ Metadata ] [ 向量（数字）] + [ 原始文本 ] + [ 元数据 ]
4. Choose a platform to store data
4. 选择存储数据的平台
You can store this vector output in a vector database such as Pinecone, Milvus, or Weaviate for retrieval. You can upload the vector data by writing a simple python code. 你可以将这些向量输出存储在向量数据库（如 Pinecone、Milvus 或 Weaviate）中以便检索。你可以通过编写简单的 Python 代码来上传向量数据。
(Code snippet omitted for brevity) (代码片段略)