> For the complete documentation index, see [llms.txt](https://docs.cherryai.com.cn/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.cherryai.com.cn/docs/en-us/knowledge-base/create.md).

# Create a Knowledge Base

The two most important choices when creating are the name and the embedding model. Materials can be added later, but the name boundary and retrieval method will affect future maintenance.

{% hint style="info" %}
For a first-time experience, you can set the \[Embedding Model] to \[Do Not Use]. The knowledge base will still use BM25 keyword retrieval; first just get the import and recall flow working.
{% endhint %}

## Make two decisions before creating

### The name should indicate the scope of the materials

Prefer “object + purpose,” such as \[Employee Travel Policy], \[Product After-Sales Manual], or \[Market Research Materials]. Avoid names like \[Materials] or \[Test], which make it impossible to judge the content scope later.

### Choose an embedding method

| Choice                | Applicable scenarios                                          | Retrieval method               | Prerequisites                                          |
| --------------------- | ------------------------------------------------------------- | ------------------------------ | ------------------------------------------------------ |
| Do not use            | First-time experience, clear keywords                         | BM25 keyword retrieval         | None                                                   |
| Cloud embedding model | The user’s wording differs greatly from the original material | BM25 + vector hybrid retrieval | The corresponding model service can be called normally |
| Local embedding model | You want vectorization to be completed on this machine        | BM25 + local vector retrieval  | First download it in \[Local Models]                   |

## Creation steps

{% stepper %}
{% step %}

### 1. Open the creation window

Open \[Knowledge Base] in the left navigation, then click the Add button above the knowledge base list.
{% endstep %}

{% step %}

### 2. Enter a name

Enter a name that can indicate the scope, such as \[Employee Travel Policy].
{% endstep %}

{% step %}

### 3. Select an embedding model

Choose an available cloud or local embedding model; if semantic retrieval is not needed for now, choose \[Do Not Use].

<figure><img src="/files/a11537e0fdcf35f490b2090f74ef80babf6a6712" alt="填写员工差旅制度名称并选择嵌入模型的知识库创建表单"><figcaption><p>The name determines the scope of the materials; the embedding model determines whether vector retrieval is included.</p></figcaption></figure>
{% endstep %}

{% step %}

### 4. Click Create

After confirming the name and model, click \[Create]. Once created, you will enter an empty knowledge base.
{% endstep %}

{% step %}

### 5. Add the first batch of materials

Click the Add Materials button, import one or two files or notes with clear answers, then wait for processing to complete.
{% endstep %}
{% endstepper %}

## Use a local embedding model

Open \[Settings] → \[Local Models], and download an available model in the \[Embedding Models] section. The models and download sizes shown in the interface may vary depending on the installation environment; please refer to the current list.

<figure><img src="/files/9085804a861312f65d0eca07b55ebb6d1c05b554" alt="本地模型设置中的嵌入模型下载入口"><figcaption><p>After the download is complete, return to the knowledge base creation or settings page and select that model.</p></figcaption></figure>

{% hint style="warning" %}
Local embedding only means that vectorization is completed on this machine. Whether document parsing, reranking, and chat use the cloud still depends on the selected services and models.
{% endhint %}

## Changing the model after materials already exist

When enabling an embedding model for a knowledge base that previously used only BM25, a vector index can be built. When changing the embedding model for a knowledge base that already has vectors, the interface will enter the \[Rebuild Knowledge Base] process.

{% hint style="danger" %}
Before starting the rebuild, make sure the new model can be called normally. After rebuilding, run the recall test again; do not change the model and modify chunking in the same round, or you won't be able to tell where the result changes came from.
{% endhint %}

## Configuration notes

| Configuration item    | Product default | Recommended starting point                    | Function                                        | Applicable scenario                    | Notes                                                                       |
| --------------------- | --------------- | --------------------------------------------- | ----------------------------------------------- | -------------------------------------- | --------------------------------------------------------------------------- |
| Name                  | Empty           | Object + purpose                              | Distinguish the scope of the materials          | All knowledge bases                    | Materials with different permissions or lifecycles should be separated      |
| Embedding model       | Do not use      | Do not use it for the first-time experience   | Determines whether vector retrieval is included | Colloquial questions, many paraphrases | Billing and data processing for cloud models depend on the service provider |
| Local embedding model | Not downloaded  | Download only when local processing is needed | Complete vectorization on this machine          | Offline or high privacy requirements   | You still need to separately check parsing, reranking, and the chat model   |

## Expected results

* The new knowledge base appears in the list, and its name can be distinguished from other knowledge bases.
* You know whether the current retrieval is keyword retrieval or hybrid retrieval.
* The selected cloud model can be called, or the local model has already been downloaded.

## User case

Xiaolin created an \[Employee Travel Policy] knowledge base for the first time. He first chose \[Do Not Use] for the embedding model, imported three policies, and completed the recall test. After keyword queries became stable, he configured an embedding model and compared the results of colloquial questions using the same questions.

The completion criterion is: after upgrading the retrieval method, the original fixed questions do not regress, and colloquial questions can more stably find the same policy.

## FAQ

<details>

<summary>What should I do if the Create button is unavailable?</summary>

Check whether the name is empty and whether the selected model is still available. If the model service is not configured, you can first switch to \[Do Not Use] to complete creation.

</details>

<details>

<summary>Will not using an embedding model make it completely impossible to search?</summary>

No. The knowledge base will still use BM25 keyword retrieval; the closer the question wording is to the materials, the more stable the results usually are.

</details>

<details>

<summary>Do I need to create a separate knowledge base for each topic?</summary>

Use whether they should be retrieved together at the time of use as the criterion. Materials with completely different permissions, lifecycles, or topics are more suitable to be separated.

</details>

## Continue reading

<table data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>Add and organize materials</strong></td><td>Import content and handle name conflicts.</td><td><a href="/pages/53062e948442e1c25a4e6ad7b4ec9baf548deafc">/pages/53062e948442e1c25a4e6ad7b4ec9baf548deafc</a></td></tr><tr><td><strong>Check materials and recall</strong></td><td>Validate the results with real questions.</td><td><a href="/pages/7c7a6d04d1e906eb7a89261dabeedb48874a9b8e">/pages/7c7a6d04d1e906eb7a89261dabeedb48874a9b8e</a></td></tr><tr><td><strong>Model and retrieval settings</strong></td><td>Learn about embedding, reranking, and rebuilding.</td><td><a href="/pages/244bae136e1bc54a35816d43dc1b6b0e3b8c7e3e">/pages/244bae136e1bc54a35816d43dc1b6b0e3b8c7e3e</a></td></tr></tbody></table>


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