In the digital age, many companies have accumulated large amounts of documents, such as meeting minutes, product manuals, research reports, customer contracts, and more. But have you ever thought that these "bunch of files" scattered in hard drives or cloud folders are not actually equivalent to a "knowledge base" that can truly bring value? Without integration, summary and application, these files can only lie quietly in the folder and cannot function. This article will take you to understand why we need to evolve from "files" to "knowledge bases" and explore in depth the differences between databases and knowledge bases, so that companies can make more effective use of their own resources.

1. First understand three concepts: database, knowledge base, and archives

1. Database

databaseIt is usually a well-standardized and structured system used to store "organized and well-defined" data, such as employee resumes, rental information, order management, etc. These data can often be stored in relational databases (such as MySQL, PostgreSQL) or NoSQL (such as MongoDB), making it easy to quickly obtain, update and maintain through SQL or query languages.

• feature: Clear field structure and efficient retrieval
• Scope of application: Trading systems, backend management, applications that require instant writing and querying

2. Knowledge Base

knowledge baseIt is mainly used to store relatively unstructured or semi-structured information such as "content, process, method", such as FAQs, technical white papers, teaching procedures, design manuals, working documents, etc.

• feature: Information is relatively free and requires additional annotation or vectorization tools to retrieve it at the semantic level.
• Common implementation: The "vector database" that has become popular in recent years can convert documents into embedded vectors (embeddings) and find the most relevant content through similarity searches.

It is worth noting that vector databases do not necessarily require the use of GPUs to perform searches, but if large-scale data needs to be queried in real time, GPUs will greatly improve computing performance. In other words, the process of establishing a knowledge base often includesData cleaning, semantic segmentation, vectorizationand other steps to achieve truly “searchable and usable” knowledge management. The use of AI Agents falls into this category.

3. File

fileIt can be regarded as the most basic data carrier, that is, all documents, reports, photos, videos, design drawings, etc. initially collected by an enterprise are classified as "files". If these files have never been organized or summarized, but are only scattered in various folders or cloud spaces, they cannot be directly referenced and retrieved by databases or knowledge bases.

• feature: Most of them are only used for storage.
• Application level: Without structuring or semantic processing, the application level is low and it is difficult to achieve efficient retrieval.

2. Why can’t “a pile of files” be considered a “knowledge base”?

Although an enterprise may store a large number of files, without process and systematic management, these files will be just static files. To truly realize their value, at least the following steps are required:

1. Classification and labeling: Categorize the documents according to their subject or purpose, and mark them with keywords and tags, or convert them into a machine-readable format.
2. Cleaning and cutting: Cut lengthy files into appropriately sized pieces for easier retrieval and exclude duplicate or useless content.
3. vectorization: Use language models or tools to extract feature vectors of text or images to facilitate the establishment of a vector database.
4. Integrate into workflow: Allow the knowledge in these files to be successfully retrieved and cited by the team in daily work, for example:
   • Search for company specifications or technical white papers
   • Inquire about past project practices or experiences
   • Get the best solution instantly

Only by completing the above process and cooperating with the continuous updating and maintenance of files and documents can a truly usable "knowledge base" be formed, instead of just a pile of scattered files.

3. Structured data vs. unstructured data?

1. Database (structured data): Traditional relational databases use SQL queries, which rely heavily on the computing power of the CPU, because queries and index retrieval are often column- or row-based comparisons or associations.
2. Knowledge base (vectorized data): Most of them use vector databases, because the document contents are mostly unstructured and need to be converted into vectors before performing "semantic similarity search".
   • Small-scale or non-immediate needs: can be handled with only CPU.
   • Large-scale or high-speed requirements: GPU can provide better parallel computing performance and significantly accelerate high-dimensional vector retrieval.

4. How to upgrade "Files" to "Knowledge Base"?

1. Develop document management process: Develop standards for file types, version control, review processes, and permissions management.
2. Import tools for cleaning and segmentation: For example, using natural language processing technology to split large documents into entries or paragraphs and eliminate duplicate or useless information.
3. Create vector database: Convert important document contents or media into semantically searchable vectors and store them in the vector database.
4. Combined with front-end applications: Customer service robots or intelligent search functions can be provided within the company or on the website platform to facilitate employees or users to quickly locate the required knowledge.
5. Regular maintenance and updates: The usability of the knowledge base must be constantly updated and maintained to ensure that new and expired files can be processed correctly to maintain knowledge quality. (Dynamic updatesexhibit)

5. Conclusion: Just putting files has limited value; building a knowledge base has infinite value.

In the era of information explosion, "more" data and files collected by companies does not necessarily mean "strong". If you simply throw all your files into the cloud, you can barely access them, but you can't actually use them for daily processes or decision-making. To truly unlock the potential of these documents, we need toClassification, annotation, vectorizationand other steps, and with the help ofdatabaseAndknowledge baseThese two completely different storage methods, each with their own advantages, are used to create the “AI new world” for enterprises. Only when "knowledge" can be retrieved, quoted and learned can it become the most precious intangible asset of an enterprise.

If you are faced with the problem of massive piles of documents, or want to upgrade your existing data management model, you may wish to consider planning a complete "knowledge management” mechanism to implement the transformation from “file” to “knowledge base”. Through good knowledge base operation, your company no longer just has a bunch of documents, but a set of intelligent resources that can be referenced at any time and bring substantial benefits to the business.

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