> 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. Content can be added later, but the naming boundary and retrieval method will affect future maintenance.

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

## Make two decisions before creating

### The name should describe the scope of the content

Prefer using “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 tell the content scope later.

### Choose the embedding method

| Choose                | Suitable scenarios                                            | Retrieval method               | Prerequisites                                          |
| --------------------- | ------------------------------------------------------------- | ------------------------------ | ------------------------------------------------------ |
| Do not use            | First try, keywords are clear                                 | 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 | Want vectorization to be done locally                         | BM25 + local vector retrieval  | First complete the download in \[Local Models]         |

## Creation steps

{% stepper %}
{% step %}

### 1. Open the creation window

Open the \[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 indicates 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, select \[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 content; 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 creation is complete, 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, and wait for processing to complete.
{% endstep %}
{% endstepper %}

## Use a local embedding model

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

<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 this model.</p></figcaption></figure>

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

## Change the model after materials already exist

When enabling an embedding model for a knowledge base that previously used only BM25, you can build a vector index. 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 the chunks 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 | Suggested starting point                      | Effect                                       | Applicable scenario                                 | Notes                                                                     |
| --------------------- | --------------- | --------------------------------------------- | -------------------------------------------- | --------------------------------------------------- | ------------------------------------------------------------------------- |
| Name                  | Empty           | Object + purpose                              | Differentiate the scope of 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 try               | Determines whether vector retrieval is added | Colloquial phrasing and many synonymous expressions | Billing and data handling 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 higher privacy requirements              | Parsing, reranking, and chat models still need to be checked separately   |

## Expected result

* 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 finished downloading.

## 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 policy documents, and completed the recall test. After the keyword queries became stable, he configured an embedding model and compared the results of colloquial queries using the same questions.

The completion standard is: after upgrading the retrieval method, the original fixed questions do not degrade, and colloquial queries can more consistently find the same policy.

## Common questions

<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 mean I can’t search anything at all?</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 “should these be retrieved together when in use?” as the criterion. Materials with completely different permissions, lifecycles, or topics are more suitable to keep separate.

</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 same-name conflicts.</td><td><a href="/pages/53062e948442e1c25a4e6ad7b4ec9baf548deafc">/pages/53062e948442e1c25a4e6ad7b4ec9baf548deafc</a></td></tr><tr><td><strong>Check materials and recall</strong></td><td>Validate 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 embeddings, reranking, and rebuilding.</td><td><a href="/pages/244bae136e1bc54a35816d43dc1b6b0e3b8c7e3e">/pages/244bae136e1bc54a35816d43dc1b6b0e3b8c7e3e</a></td></tr></tbody></table>


---

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