# How to Create Knowledge Base: Basic Settings ## Basic Setup ### General Information You can define the name of your knowledge base and add a description on the following page:
#### Retrieval Chunks The number of retrieval chunks represents the maximum number of data chunks the AI assistant will reference when answering. The system default is "12", meaning the AI assistant will retrieve the 12 most relevant chunks for each response. Therefore, you can increase or decrease the number of retrieval chunks to adjust the amount of information the AI assistant references when answering. ### What is a Parser? A Parser allows the system to "understand" the content within PDF documents, making it searchable, editable, or convertible to other formats. When uploading PDF or Word format files, the parser provides three options: * MaiAgent Parser (default) * MaiAgent Parser (Online) * MaiAgent Parser (OCR beta) The system currently uses MaiAgent Parser by default, which is particularly suitable for extracting structured data from complex professional documents for AI system analysis. {% hint style="info" %} To understand the differences between the three parsers, please refer to: [Technical Manual - Parser Tool](https://docs.maiagent.ai/tech/quickstart/parser#maiagent-ti-gong-de-parser-jie-xi-qi) {% endhint %} If problems occur during data parsing, you can also click the \[Reparse] icon to have the parser reorganize the data.
### Retrieval Model Settings In the knowledge base settings, you can select the Embedding model and Reranker model you wish to use.
#### Embedding Model Embedding is like translating human language into "numerical language" that AI can understand, allowing computers to comprehend the true meaning of text. This process is called "vectorization". Different Embedding models have different characteristics, such as languages they excel at processing and supported deployment environments. Different model settings within the knowledge base can be used to adjust the vectorization processing effect when knowledge base documents are uploaded. You can select the most suitable Embedding model for different scenarios. You can freely choose from multiple Embedding models: {% hint style="info" %} To understand the differences between Embedding models, please refer to: [Technical Manual - Embedding Models](https://docs.maiagent.ai/tech/quickstart/embedding#maiagent-zhi-yuan-de-embedding-mo-xing) {% endhint %}
#### Reranker Model Reranker is like a professional judge who re-evaluates which data in the initial search results can best answer the customer's question. What are the differences in effectiveness when using or not using a Reranker? When a customer asks: "What tent is suitable for beginners? Budget under 8000 dollars" **Without Reranker:** ``` AI might respond: "We have tents at various price points, products at 8000 dollars include..." (May mention advanced models, not focused enough on beginner needs) ``` **With Reranker:** ``` AI responds: "Specifically recommended for beginners are these tents under 8000 dollars..." (Precisely targets beginners + budget + product recommendations) ``` When search result reranking is enabled, the AI assistant will reorder the knowledge base content chunks retrieved, responding based on the most relevant documents. {% hint style="info" %} To understand Reranker models, please refer to: [Technical Manual - Reranker Models](https://docs.maiagent.ai/tech/quickstart/reranker#maiagent-ti-gong-de-reranker-mo-xing) {% endhint %}
In summary, using Embedding combined with Reranker allows the AI assistant to understand the knowledge you provide and, after retrieving chunks, review the importance of content again, responding with knowledge most relevant to the question. *** ### Associate AI Assistants #### Multiple AI Assistants Sharing a Knowledge Base Associating AI assistants means authorizing which AI assistants you want to use this knowledge base. If you have two AI assistants: * **Product Customer Service AI** * **Order Customer Service AI** When both need to answer questions related to returns, you can associate both AI assistants at once in the settings of the "Return Policy" knowledge base:

Diagram of associating multiple AI assistants

1. Select the AI assistants to associate
2. Click Add AI Assistant After adding, it will appear in the selected AI assistants area. Click "Save" in the lower right corner to complete the association.
Once associated, both AI assistants can share the "Return Policy" knowledge base and respond based on the same content. For subsequent maintenance, you only need to **update one knowledge base** to ensure the AI assistants use the latest data. #### One AI Assistant Using Multiple Knowledge Bases In addition to sharing knowledge bases, one AI assistant can also use multiple knowledge bases. 1. Enter the AI Assistant page, select the AI assistant you want to configure, and click Settings
2. Enter Model Settings, click "Select Knowledge Base"
3. Select the knowledge bases to use and click Confirm. The selected knowledge bases will appear in the list
4. Finally, click "Save", and the AI assistant can use multiple knowledge bases --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.maiagent.ai/maiagent-user-guide/maiagent-user-guide-en/km/km-basic-settings.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.