# Create Your First Knowledge Base from a PDF

Turn a local English PDF into a knowledge base, ask a question, and open the cited passage to check the answer. MOI uses files stored in Catalog as knowledge-base sources and automatically processes them for document Q&A.

This tutorial uses *Scikit-learn: Machine Learning in Python*, a six-page paper publicly available from the Journal of Machine Learning Research (JMLR). You do not need to run any code from the paper.

## Before you begin

- Sign in to [MOI](https://moi.matrixorigin.cn/) and enter a workspace where you can import files and create knowledge bases.
- Set the interface language to **English** and select **Data** in the top navigation.
- Download the [sample PDF from JMLR](https://jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf) and save it as `scikit-learn-machine-learning-in-python.pdf`.

The screenshots use these resource names. You can use another writable Catalog location; select that same location when adding the file to the knowledge base.

| Resource | Example name |
| --- | --- |
| Catalog | `the_first_tutorial` |
| Database | `pdf` |
| Volume | `scikit_learn_machine_learning_in_python` |
| Knowledge base | `scikit_learn_pdf_knowledge` |

If you completed [your first AI Q&A](moi-quickstart.md) with this PDF, you can reuse the file already in Catalog. Confirm its location in step 1, then continue to step 2 without importing it again.

## Steps

### 1. Import the PDF into Catalog

1. Under **Data Connection**, open **Import Tasks** and start a new import task.
2. On **Create Import Task**, select **Unstructured**, then choose **Local Upload** as the **Data Source**.
3. In **Data Volume**, select a writable destination volume. For this example, use:

   ```text
   the_first_tutorial / pdf / scikit_learn_machine_learning_in_python
   ```

   If the destination does not exist, use the selector's creation options to create the required resources, or choose an existing writable volume. The **Data Volume** field must contain your destination before you start the import.

4. Keep the default **Unzip Strategy** and **Dedup Strategy**. Under **Processing Scope**, make sure **PDF** is included in **File Type Filter** and leave **Path Regex** empty.

   The following screenshot shows the local-upload configuration before a destination is selected:

   ```{image} ../assets/images/tutorials/pdf-knowledge-base-import-configuration.png
   :width: 100%
   :target: ../assets/images/tutorials/pdf-knowledge-base-import-configuration.png
   :alt: Create Import Task with Unstructured and Local Upload selected and Data Volume awaiting a destination
   ```

5. Scroll to **Load Files**, click **Select Files**, and select `scikit-learn-machine-learning-in-python.pdf`.
6. Confirm that the file list contains the PDF, then click **Create & Start Loading**.

   ```{image} ../assets/images/tutorials/pdf-knowledge-base-local-upload.png
   :width: 100%
   :target: ../assets/images/tutorials/pdf-knowledge-base-local-upload.png
   :alt: The English sample PDF selected in Load Files with the Create and Start Loading button highlighted
   ```

7. Wait for the import task to finish. Open **Resource Center > Catalog** and navigate to the destination volume. Confirm that the PDF appears in its file list.

   ```{image} ../assets/images/tutorials/pdf-knowledge-base-catalog-file.png
   :width: 100%
   :target: ../assets/images/tutorials/pdf-knowledge-base-catalog-file.png
   :alt: Catalog volume containing one scikit-learn PDF with a size of 41.32 KB
   ```

**Completion check:** The PDF is present in the selected Catalog volume. If it was already there, you can use that existing file; a second import is unnecessary.

### 2. Create the knowledge base

1. Under **Resource Center**, open **Knowledge Base**.
2. Click **Create Knowledge Base**.
3. Enter `scikit_learn_pdf_knowledge` in **Knowledge Base Name**.
4. Leave **Remark** empty, or add a short description such as `Answer questions about the scikit-learn paper with references to the original PDF.`
5. Keep **Advanced options** unchanged and click **Finish**.
6. Open the new knowledge base if its details page does not open automatically.

### 3. Add the PDF and wait for processing

1. In the knowledge-base details, click **Add Data**.
2. In **Select Data**, browse to the Catalog, database, and volume containing the PDF. For this example, select `the_first_tutorial / pdf / scikit_learn_machine_learning_in_python`.
3. Select `scikit-learn-machine-learning-in-python.pdf` and click **Append**.
4. On **Data Source**, wait until **Processing Status** shows **Completed**.

```{image} ../assets/images/tutorials/pdf-knowledge-base-processing-completed.png
:width: 100%
:target: ../assets/images/tutorials/pdf-knowledge-base-processing-completed.png
:alt: scikit_learn_pdf_knowledge containing one PDF with Processing Status shown as Completed
```

MOI automatically parses the document and builds its index after the file is added. You do not need to complete separate Data Sources and Knowledge Index wizard steps for this workflow.

**Completion check:** The knowledge base lists the intended PDF and its processing status is **Completed**.

### 4. Ask a question and check the reference

1. Return to the knowledge-base list and click **Chat** on the `scikit_learn_pdf_knowledge` card.
2. Confirm that this knowledge base is selected in the conversation input area.
3. Send the following question:

   ```text
   According to Section 2 of the paper "Scikit-learn: Machine Learning in Python", what unit-test coverage was reported for release 0.8, and which two static analysis tools were used?

   Answer in English using only this knowledge base, and cite the supporting passage.
   ```

4. Check that the answer identifies **81%**, **pyflakes**, and **pep8**, and includes a reference to the PDF. The wording of the generated answer can vary.

   ```{image} ../assets/images/tutorials/pdf-knowledge-base-answer.png
   :width: 100%
   :target: ../assets/images/tutorials/pdf-knowledge-base-answer.png
   :alt: English answer reporting 81 percent test coverage, pyflakes and pep8, with a reference to PDF page 3
   ```

5. Click the PDF filename under **References**. In the preview, check page **3 of 6**, under **2. Project Vision**. The highlighted code-quality paragraph contains all three facts.

   ```{image} ../assets/images/tutorials/pdf-knowledge-base-reference.png
   :width: 100%
   :target: ../assets/images/tutorials/pdf-knowledge-base-reference.png
   :alt: PDF preview on page 3 of 6 with the supporting Project Vision paragraph highlighted
   ```

These figures describe release 0.8 as reported in the 2011 paper. They are not claims about the current scikit-learn release. The viewer shows PDF page **3**; the printed journal page number on that page is **2827**.

**Completion check:** The answer includes the expected facts, and its reference opens the passage that supports them.

## Troubleshooting

**The import cannot start.** Check that **Data Volume** has a selected destination, that the PDF appears in **Load Files**, and that **File Type Filter** includes PDF. Keep **Path Regex** empty unless you intentionally want to filter paths.

**The PDF is missing from Select Data.** Check the workspace and Catalog location. Confirm that the file is visible inside the volume, rather than only checking that the volume exists.

**The knowledge-base file is still processing.** Wait and refresh its details. If processing fails, check that the PDF opens normally and review the error before retrying.

**The answer has no useful reference.** Check that the file status is **Completed** and that the intended knowledge base is selected in the conversation. Ask about a specific passage in the document, then inspect the returned reference.

## Tutorial complete

You have a knowledge base backed by a PDF in Catalog. You have also asked a question, received an English answer with a reference, and checked the answer against the original passage.