---
sidebar_position: 20
slug: /faq
sidebar_custom_props: {
  sidebarIcon: LucideCircleQuestionMark
}
---
# FAQs

Answers to questions about general features, troubleshooting, usage, and more.

---

import TOCInline from '@theme/TOCInline';

<TOCInline toc={toc} />

## General features

---

### What sets RAGFlow apart from other RAG products?

The "garbage in garbage out" status quo remains unchanged despite the fact that LLMs have advanced Natural Language Processing (NLP) significantly. In its response, RAGFlow introduces two unique features compared to other Retrieval-Augmented Generation (RAG) products.

- Fine-grained document parsing: Document parsing involves images and tables, with the flexibility for you to intervene as needed.
- Traceable answers with reduced hallucinations: You can trust RAGFlow's responses as you can view the citations and references supporting them.

---

### Which embedding models can be deployed locally?

Starting from `v0.22.0`, we ship only the slim edition and no longer append the **-slim** suffix to the image tag.

---

### Where to find the version of RAGFlow? How to interpret it?

You can find the RAGFlow version number on the **System** page of the UI:

![Image](https://github.com/user-attachments/assets/20cf7213-2537-4e18-a88c-4dadf6228c6b)

If you build RAGFlow from source, the version number is also in the system log:

```
        ____   ___    ______ ______ __
       / __ \ /   |  / ____// ____// /____  _      __
      / /_/ // /| | / / __ / /_   / // __ \| | /| / /
     / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ /
    /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/

2025-02-18 10:10:43,835 INFO     1445658 RAGFlow version: v0.15.0-50-g6daae7f2
```

Where:

- `v0.15.0`: The officially published release.
- `50`: The number of git commits since the official release.
- `g6daae7f2`: `g` is the prefix, and `6daae7f2` is the first seven characters of the current commit ID.

---

### Why not use other open-source vector databases as the document engine?

Currently, only Elasticsearch and [Infinity](https://github.com/infiniflow/infinity) meet the hybrid search requirements of RAGFlow. Most open-source vector databases have limited support for full-text search, and sparse embedding is not an alternative to full-text search. Additionally, these vector databases lack critical features essential to RAGFlow, such as phrase search and advanced ranking capabilities.

These limitations led us to develop [Infinity](https://github.com/infiniflow/infinity), the AI-native database, from the ground up.

---

### Differences between cloud.ragflow.io and a locally deployed open-source RAGFlow service?

cloud.ragflow.io demonstrates the capabilities of RAGFlow Enterprise. Its DeepDoc models are pre-trained using proprietary data and it offers much more sophisticated team permission controls. Essentially, cloud.ragflow.io serves as a preview of RAGFlow's forthcoming SaaS (Software as a Service) offering.

You can deploy an open-source RAGFlow service and call it from a Python client or through RESTful APIs. However, this is not supported on cloud.ragflow.io.

---

### Why does it take longer for RAGFlow to parse a document than LangChain?

We put painstaking effort into document pre-processing tasks like layout analysis, table structure recognition, and OCR (Optical Character Recognition) using our vision models. This contributes to the additional time required.

---

### Why does RAGFlow require more resources than other projects?

RAGFlow has a number of built-in models for document structure parsing, which account for the additional computational resources.

---

### Which architectures or devices does RAGFlow support?

We officially support x86 CPU and nvidia GPU. While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM. If you are on an ARM platform, follow [this guide](./develop/build_docker_image.mdx) to build a RAGFlow Docker image.

---

### Do you offer an API for integration with third-party applications?

The corresponding APIs are now available. See the [RAGFlow HTTP API Reference](./references/http_api_reference.md) or the [RAGFlow Python API Reference](./references/python_api_reference.md) for more information.

---

### Do you support stream output?

Yes, we do. Stream output is enabled by default in the chat assistant and agent. Note that you cannot disable stream output via RAGFlow's UI. To disable stream output in responses, use RAGFlow's Python or RESTful APIs:

Python:

- [Create chat completion](./references/python_api_reference.md#create-chat-completion)
- [Converse with chat assistant](./references/python_api_reference.md#converse-with-chat-assistant)
- [Converse with agent](./references/python_api_reference.md#converse-with-agent)

RESTful:

- [Create chat completion](./references/http_api_reference.md#create-chat-completion)
- [Converse with chat assistant](./references/http_api_reference.md#converse-with-chat-assistant)
- [Converse with agent](./references/http_api_reference.md#converse-with-agent)

---

### Do you support sharing dialogue through URL?

No, this feature is not supported.

---

### Do you support multiple rounds of dialogues, referencing previous dialogues as context for the current query?

Yes, we support enhancing user queries based on existing context of an ongoing conversation:

1. On the **Chat** page, hover over the desired assistant and select **Edit**.
2. In the **Chat Configuration** popup, click the **Prompt engine** tab.
3. Switch on **Multi-turn optimization** to enable this feature.

---

### Key differences between AI search and chat?

- **AI search**: This is a single-turn AI conversation using a predefined retrieval strategy (a hybrid search of weighted keyword similarity and weighted vector similarity) and the system's default chat model. It does not involve advanced RAG strategies like knowledge graph, auto-keyword, or auto-question. Retrieved chunks will be listed below the chat model's response.
- **AI chat**: This is a multi-turn AI conversation where you can define your retrieval strategy (a weighted reranking score can be used to replace the weighted vector similarity in a hybrid search) and choose your chat model. In an AI chat, you can configure advanced RAG strategies, such as knowledge graphs, auto-keyword, and auto-question, for your specific case. Retrieved chunks are not displayed along with the answer.

When debugging your chat assistant, you can use AI search as a reference to verify your model settings and retrieval strategy.

---

## Troubleshooting

---

### Get a `Request error 404: undefined` when upgrading to v0.26.2

To resolve this issue, do either of the following:

- Pull the latest source code from the [main branch](https://github.com/infiniflow/ragflow), then pull and start the v0.26.2 image.
- Update `RAGFLOW_IMAGE` from `infiniflow/ragflow:latest` to `infiniflow/ragflow:v0.26.2` in the [.env file](https://github.com/infiniflow/ragflow/blob/main/docker/.env), then restart the service.

### How to build the RAGFlow image from scratch?

See [Build a RAGFlow Docker image](./develop/build_docker_image.mdx).

---

### Cannot access https://huggingface.co

A locally deployed RAGFlow downloads OCR models from [Huggingface website](https://huggingface.co) by default. If your machine is unable to access this site, the following error occurs and PDF parsing fails:

```
FileNotFoundError: [Errno 2] No such file or directory: '/root/.cache/huggingface/hub/models--InfiniFlow--deepdoc/snapshots/be0c1e50eef6047b412d1800aa89aba4d275f997/ocr.res'
```

To fix this issue, use https://hf-mirror.com instead:

1. Stop all containers and remove all related resources:

   ```bash
   cd ragflow/docker/
   docker compose down
   ```

2. Uncomment the following line in **ragflow/docker/.env**:

   ```
   # HF_ENDPOINT=https://hf-mirror.com
   ```

3. Start up the server:

   ```bash
   docker compose up -d
   ```

---

### `Fail to access model(Ollama/xxxxx)`

Ollama may time out or fail during its first model load due to memory constraints or out-of-memory (OOM). It is best to test your local model in isolation first. If sharing hardware with other services, memory exhaustion is likely. To resolve this, switch to a smaller model or increase RAM.

---

### `MaxRetryError: HTTPSConnectionPool(host='hf-mirror.com', port=443)`

This error suggests that you do not have Internet access or are unable to connect to hf-mirror.com. Try the following:

1. Manually download the resource files from [huggingface.co/InfiniFlow/deepdoc](https://huggingface.co/InfiniFlow/deepdoc) to your local folder **~/deepdoc**.
2. Add a volumes to **docker-compose.yml**, for example:

   ```
   - ~/deepdoc:/ragflow/rag/res/deepdoc
   ```

---

### `RuntimeError: Unable to start Tika server.`

This error is almost always caused by Java not being installed or not accessible in the environment. See [here](https://github.com/infiniflow/ragflow/issues/13194) for detailed instructions.

---

### `Cannot stat '/etc/nginx/conf.d/ragflow.conf.python': No such file or directory`

To resolve this, either download the missing file from the corresponding tag on [GitHub](https://github.com/infiniflow/ragflow) or update `~/ragflow/docker/docker-compose.yml` as follows: 

![](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/docker-compose_missing.jpg)

---

### `WARNING: can't find /ragflow/rag/res/borker.tm`

Ignore this warning and continue. All system warnings can be ignored.

---

### `network anomaly There is an abnormality in your network and you cannot connect to the server.`

![anomaly](https://github.com/infiniflow/ragflow/assets/93570324/beb7ad10-92e4-4a58-8886-bfb7cbd09e5d)

You will not log in to RAGFlow unless the server is fully initialized. Run `docker logs -f docker-ragflow-cpu-1`.

*The server is successfully initialized, if your system displays the following:*

```
     ____   ___    ______ ______ __
    / __ \ /   |  / ____// ____// /____  _      __
   / /_/ // /| | / / __ / /_   / // __ \| | /| / /
  / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ /
 /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/

 * Running on all addresses (0.0.0.0)
 * Running on http://127.0.0.1:9380
 * Running on http://x.x.x.x:9380
 INFO:werkzeug:Press CTRL+C to quit
```

---

### `Realtime synonym is disabled, since no redis connection`

Ignore this warning and continue. All system warnings can be ignored.

![](https://github.com/infiniflow/ragflow/assets/93570324/ef5a6194-084a-4fe3-bdd5-1c025b40865c)

---

### Why does my document parsing stall at under one percent?

![stall](https://github.com/infiniflow/ragflow/assets/93570324/3589cc25-c733-47d5-bbfc-fedb74a3da50)

Click the red cross beside the 'parsing status' bar, then restart the parsing process to see if the issue remains. If the issue persists and your RAGFlow is deployed locally, try the following:

1. Check the log of your RAGFlow server to see if it is running properly:

   ```bash
   docker logs -f docker-ragflow-cpu-1
   ```

2. Check if the **task_executor.py** process exists.
3. Check if your RAGFlow server can access hf-mirror.com or huggingface.com.

---

### Why does my pdf parsing stall near completion, while the log does not show any error?

Click the red cross beside the 'parsing status' bar, then restart the parsing process to see if the issue remains. If the issue persists and your RAGFlow is deployed locally, the parsing process is likely killed due to insufficient RAM. Try increasing your memory allocation by increasing the `MEM_LIMIT` value in **docker/.env**.

:::note
Ensure that you restart up your RAGFlow server for your changes to take effect!

```bash
docker compose stop
```

```bash
docker compose up -d
```

:::

![nearcompletion](https://github.com/infiniflow/ragflow/assets/93570324/563974c3-f8bb-4ec8-b241-adcda8929cbb)

---

### `Index failure`

An index failure usually indicates an unavailable Elasticsearch service.

---

### How to check the log of RAGFlow?

```bash
tail -f ragflow/docker/ragflow-logs/*.log
```

---

### How to check the status of each component in RAGFlow?

1. Check the status of the Elasticsearch Docker container:

   ```bash
   $ docker ps
   ```

   *The following is an example result:*

   ```bash
   5bc45806b680   infiniflow/ragflow:latest     "./entrypoint.sh"        11 hours ago   Up 11 hours               0.0.0.0:80->80/tcp, :::80->80/tcp, 0.0.0.0:443->443/tcp, :::443->443/tcp, 0.0.0.0:9380->9380/tcp, :::9380->9380/tcp   docker-ragflow-cpu-1
   91220e3285dd   docker.elastic.co/elasticsearch/elasticsearch:8.11.3   "/bin/tini -- /usr/l…"   11 hours ago   Up 11 hours (healthy)     9300/tcp, 0.0.0.0:9200->9200/tcp, :::9200->9200/tcp           ragflow-es-01
   d8c86f06c56b   mysql:5.7.18        "docker-entrypoint.s…"   7 days ago     Up 16 seconds (healthy)   0.0.0.0:3306->3306/tcp, :::3306->3306/tcp     ragflow-mysql
   cd29bcb254bc   quay.io/minio/minio:RELEASE.2023-12-20T01-00-02Z       "/usr/bin/docker-ent…"   2 weeks ago    Up 11 hours      0.0.0.0:9001->9001/tcp, :::9001->9001/tcp, 0.0.0.0:9000->9000/tcp, :::9000->9000/tcp     ragflow-minio
   ```

2. Follow [this document](./guides/run_health_check.md) to check the health status of the Elasticsearch service.

:::danger IMPORTANT
The status of a Docker container status does not necessarily reflect the status of the service. You may find that your services are unhealthy even when the corresponding Docker containers are up running. Possible reasons for this include network failures, incorrect port numbers, or DNS issues.
:::

---

### `Exception: Can't connect to ES cluster`

1. Check the status of the Elasticsearch Docker container:

   ```bash
   $ docker ps
   ```

   *The status of a healthy Elasticsearch component should look as follows:*

   ```
   91220e3285dd   docker.elastic.co/elasticsearch/elasticsearch:8.11.3   "/bin/tini -- /usr/l…"   11 hours ago   Up 11 hours (healthy)     9300/tcp, 0.0.0.0:9200->9200/tcp, :::9200->9200/tcp           ragflow-es-01
   ```

2. Follow [this document](./guides/run_health_check.md) to check the health status of the Elasticsearch service.

   :::danger IMPORTANT
   The status of a Docker container status does not necessarily reflect the status of the service. You may find that your services are unhealthy even when the corresponding Docker containers are up running. Possible reasons for this include network failures, incorrect port numbers, or DNS issues.
   :::

3. If your container keeps restarting, ensure `vm.max_map_count` >= 262144 as per [this README](https://github.com/infiniflow/ragflow?tab=readme-ov-file#-start-up-the-server). Updating the `vm.max_map_count` value in **/etc/sysctl.conf** is required, if you wish to keep your change permanent. Note that this configuration works only for Linux.

---

### Can't start ES container and get `Elasticsearch did not exit normally`

This is because you forgot to update the `vm.max_map_count` value in **/etc/sysctl.conf** and your change to this value was reset after a system reboot.

---

### `{"data":null,"code":100,"message":"<NotFound '404: Not Found'>"}`

Your IP address or port number may be incorrect. If you are using the default configurations, enter `http://<IP_OF_YOUR_MACHINE>` (**NOT 9380, AND NO PORT NUMBER REQUIRED!**) in your browser. This should work.

---

### `Ollama - Mistral instance running at 127.0.0.1:11434 but cannot add Ollama as model in RagFlow`

A correct Ollama IP address and port is crucial to adding models to Ollama:

- If you are on cloud.ragflow.io, ensure that the server hosting Ollama has a publicly accessible IP address. Note that 127.0.0.1 is not a publicly accessible IP address.
- If you deploy RAGFlow locally, ensure that Ollama and RAGFlow are in the same LAN and can communicate with each other.

See [Deploy a local LLM](./guides/models/deploy_local_llm.mdx) for more information.

---

### Do you offer examples of using DeepDoc to parse PDF or other files?

Yes, we do. See the Python files under the **rag/app** folder.

---

### `FileNotFoundError: [Errno 2] No such file or directory`

1. Check the status of the MinIO Docker container:

   ```bash
   $ docker ps
   ```

   *The status of a healthy Elasticsearch component should look as follows:*

   ```bash
   cd29bcb254bc   quay.io/minio/minio:RELEASE.2023-12-20T01-00-02Z       "/usr/bin/docker-ent…"   2 weeks ago    Up 11 hours      0.0.0.0:9001->9001/tcp, :::9001->9001/tcp, 0.0.0.0:9000->9000/tcp, :::9000->9000/tcp     ragflow-minio
   ```

2. Follow [this document](./guides/run_health_check.md) to check the health status of the Elasticsearch service.

:::danger IMPORTANT
The status of a Docker container status does not necessarily reflect the status of the service. You may find that your services are unhealthy even when the corresponding Docker containers are up running. Possible reasons for this include network failures, incorrect port numbers, or DNS issues.
:::

---

## Usage

---

### How to run RAGFlow with a locally deployed LLM?

You can use Ollama or Xinference to deploy local LLM. See [here](./guides/models/deploy_local_llm.mdx) for more information.

---

### How to add an LLM that is not supported?

If your model is not currently supported but has APIs compatible with those of OpenAI, click **OpenAI-API-Compatible** on the **Model providers** page to configure your model:

![openai-api-compatible](https://github.com/user-attachments/assets/b1e964f2-b86e-41af-8528-fd8a96dc5f6f)

---

### How to integrate RAGFlow with Ollama?

- If RAGFlow is locally deployed, ensure that your RAGFlow and Ollama are in the same LAN.
- If you are using our online demo, ensure that the IP address of your Ollama server is public and accessible.

See [here](./guides/models/deploy_local_llm.mdx) for more information.

---

### How to change the file size limit?

For a locally deployed RAGFlow: the total file size limit per upload is 1GB, with a batch upload limit of 32 files. There is no cap on the total number of files per account. To update this 1GB file size limit:

- In **docker/.env**, uncomment `# MAX_CONTENT_LENGTH=1073741824`, adjust the value as needed, and note that `1073741824` represents 1GB in bytes.
- If you update the value of `MAX_CONTENT_LENGTH` in **docker/.env**, ensure that you update `client_max_body_size` in **nginx/nginx.conf** accordingly.

:::tip NOTE
It is not recommended to manually change the 32-file batch upload limit. However, if you use RAGFlow's HTTP API or Python SDK to upload files, the 32-file batch upload limit is automatically removed.
:::

---

### `Error: Range of input length should be [1, 30000]`

This error occurs because there are too many chunks matching your search criteria. Try reducing the **TopN** and increasing **Similarity threshold** to fix this issue:

1. Click **Chat** in the middle top of the page.
2. Right-click the desired conversation > **Edit** > **Prompt engine**
3. Reduce the **TopN** and/or raise **Similarity threshold**.
4. Click **OK** to confirm your changes.

![topn](https://github.com/infiniflow/ragflow/assets/93570324/7ec72ab3-0dd2-4cff-af44-e2663b67b2fc)

---

### How to get an API key for integration with third-party applications?

See [Acquire a RAGFlow API key](./develop/acquire_ragflow_api_key.md).

---

### How to upgrade RAGFlow?

See [Upgrade RAGFlow](./administrator/upgrade_ragflow.mdx) for more information.

---

### How to switch the document engine to Infinity?

To switch your document engine from Elasticsearch to [Infinity](https://github.com/infiniflow/infinity):

1. Stop all running containers:

   ```bash
   $ docker compose -f docker/docker-compose.yml down -v
   ```
   :::caution WARNING
   `-v` will delete all Docker container volumes, and the existing data will be cleared.
   :::

2. In **docker/.env**, set `DOC_ENGINE=${DOC_ENGINE:-infinity}`
3. Restart your Docker image:

   ```bash
   $ docker compose -f docker-compose.yml up -d
   ```

---

### Where are my uploaded files stored in RAGFlow's image?

All uploaded files are stored in Minio, RAGFlow's object storage solution. For instance, if you upload your file directly to a dataset, it is located at `<knowledgebase_id>/filename`.

---

### How to tune batch size for document parsing and embedding?

You can control the batch size for document parsing and embedding by setting the environment variables `DOC_BULK_SIZE` and `EMBEDDING_BATCH_SIZE`. Increasing these values may improve throughput for large-scale data processing, but will also increase memory usage. Adjust them according to your hardware resources.

---

### How to accelerate the question-answering speed of my chat assistant?

See [here](./guides/chat/best_practices/accelerate_question_answering.mdx).

---

### How to accelerate the question-answering speed of my Agent?

See [here](./guides/agent/best_practices/accelerate_agent_question_answering.md).

### How to use MinerU to parse PDF documents?

From v0.22.0 onwards, RAGFlow includes MinerU (&ge; 2.6.3) as an optional PDF parser of multiple backends. Please note that RAGFlow acts only as a *remote client* for MinerU, calling the MinerU API to parse PDFs and reading the returned files. To use this feature:

1. Prepare a reachable MinerU API service (FastAPI server).
2. In the **.env** file or from the **Model providers** page in the UI, configure RAGFlow as a remote client to MinerU:
   - `MINERU_APISERVER`: The MinerU API endpoint (e.g., `http://mineru-host:8886`).
   - `MINERU_BACKEND`: The MinerU backend:
      - `"pipeline"` (default)
      - `"vlm-http-client"`
      - `"vlm-transformers"`
      - `"vlm-vllm-engine"`
      - `"vlm-mlx-engine"`
      - `"vlm-vllm-async-engine"`
      - `"vlm-lmdeploy-engine"`.
   - `MINERU_SERVER_URL`: (optional) The downstream vLLM HTTP server (e.g., `http://vllm-host:30000`). Applicable when `MINERU_BACKEND` is set to `"vlm-http-client"`.
   - `MINERU_OUTPUT_DIR`: (optional) The local directory for holding the outputs of the MinerU API service (zip/JSON) before ingestion.
   - `MINERU_DELETE_OUTPUT`: Whether to delete temporary output when a temporary directory is used:
     - `1`: Delete.
     - `0`: Retain.
3. In the web UI, navigate to your dataset's **Configuration** page and find the **Ingestion pipeline** section:
   - If you decide to use a chunking method from the **Built-in** dropdown, ensure it supports PDF parsing, then select **MinerU** from the **PDF parser** dropdown.
   - If you use a custom ingestion pipeline instead, select **MinerU** in the **PDF parser** section of the **Parser** component.

:::note
All MinerU environment variables are optional. When set, these values are used to auto-provision a MinerU OCR model for the tenant on first use. To avoid auto-provisioning, skip the environment variable settings and only configure MinerU from the **Model providers** page in the UI.
:::

:::caution WARNING
Third-party visual models are marked **Experimental**, because we have not fully tested these models for the aforementioned data extraction tasks.
:::
---

### How to configure MinerU-specific settings?

The table below summarizes the most frequently used MinerU environment variables for remote MinerU:

| Environment variable   | Description                        | Default                             | Example                                                                                         |
| ---------------------- | ---------------------------------- | ----------------------------------- | ----------------------------------------------------------------------------------------------- |
| `MINERU_APISERVER`     | URL of the MinerU API service      | _unset_                             | `MINERU_APISERVER=http://your-mineru-server:8886`                                              |
| `MINERU_BACKEND`       | MinerU parsing backend             | `pipeline`                          | `MINERU_BACKEND=pipeline\|vlm-transformers\|vlm-vllm-engine\|vlm-mlx-engine\|vlm-vllm-async-engine\|vlm-http-client` |
| `MINERU_SERVER_URL`    | URL of remote vLLM server (for `vlm-http-client`) | _unset_ | `MINERU_SERVER_URL=http://your-vllm-server-ip:30000`                                           |
| `MINERU_OUTPUT_DIR`    | Directory for MinerU output files  | System-defined temporary directory  | `MINERU_OUTPUT_DIR=/home/ragflow/mineru/output`                                                |
| `MINERU_DELETE_OUTPUT` | Whether to delete MinerU output directory when a temp dir is used | `1` (delete temp output) | `MINERU_DELETE_OUTPUT=0`                                                                       |

1. Set `MINERU_APISERVER` to point RAGFlow to your MinerU API server.
2. Set `MINERU_BACKEND` to specify a parsing backend.
3. If using the `"vlm-http-client"` backend, set `MINERU_SERVER_URL` to your vLLM server's URL. MinerU API expects `backend=vlm-http-client` and `server_url=http://<server>:30000` in the request body.
4. Set `MINERU_OUTPUT_DIR` to specify where RAGFlow stores MinerU API output; otherwise, a system temp directory is used.
5. Set `MINERU_DELETE_OUTPUT` to `0` to keep MinerU's temp output (useful for debugging).

:::tip NOTE
For information about other environment variables natively supported by MinerU, see [here](https://opendatalab.github.io/MinerU/usage/cli_tools/#environment-variables-description).
:::

---

### How to use MinerU with a vLLM server for document parsing?

RAGFlow supports MinerU's `vlm-http-client` backend, enabling you to delegate document parsing tasks to a remote vLLM server while calling MinerU via HTTP. To configure:

1. Ensure a MinerU API service is reachable (for example `http://mineru-host:8886`).
2. Set up or point to a vLLM HTTP server (for example `http://vllm-host:30000`).
3. Configure the following in your **docker/.env** file (or your shell if running from source):
   - `MINERU_APISERVER=http://mineru-host:8886`
   - `MINERU_BACKEND="vlm-http-client"`
   - `MINERU_SERVER_URL="http://vllm-host:30000"`
   MinerU API calls expect `backend=vlm-http-client` and `server_url=http://<server>:30000` in the request body.
4. Configure `MINERU_OUTPUT_DIR` / `MINERU_DELETE_OUTPUT` as desired to manage the returned zip/JSON before ingestion.

:::tip NOTE
When using the `vlm-http-client` backend, the RAGFlow server requires no GPU, only network connectivity. This enables cost-effective distributed deployment with multiple RAGFlow instances sharing one remote vLLM server.
:::

### How to use an external Docling Serve server for document parsing?

RAGFlow supports Docling in two modes:

1. **Local Docling** (existing mode): install Docling in the RAGFlow runtime (`USE_DOCLING=true`) and parse in-process.
2. **External Docling Serve** (remote mode): point RAGFlow to a Docling Serve endpoint.

To enable remote mode, set:

```bash
DOCLING_SERVER_URL=http://your-docling-serve-host:5001
```

Behavior:

- When `DOCLING_SERVER_URL` is set, RAGFlow sends PDFs to Docling Serve using `/v1/convert/source` (and falls back to `/v1alpha/convert/source` for older servers).
- When `DOCLING_SERVER_URL` is not set, RAGFlow uses local in-process Docling.

### How to use PaddleOCR for document parsing?

From v0.24.0 onwards, RAGFlow includes PaddleOCR as an optional PDF parser. Please note that RAGFlow acts only as a *remote client* for PaddleOCR, calling the PaddleOCR API to parse PDFs and reading the returned files.

There are two main ways to configure and use PaddleOCR in RAGFlow:

#### 1. Using PaddleOCR Official API

This method uses PaddleOCR's official API service with an access token.

**Step 1: Configure RAGFlow**
- **Via Environment Variables:**
   ```bash
   # In your docker/.env file:
   PADDLEOCR_API_URL=https://your-paddleocr-api-endpoint
   PADDLEOCR_ALGORITHM=PaddleOCR-VL
   PADDLEOCR_ACCESS_TOKEN=your-access-token-here
   ```

- **Via UI:**
   - Navigate to **Model providers** page
   - Add a new OCR model with factory type "PaddleOCR"
   - Configure the following fields:
      - **PaddleOCR API URL**: Your PaddleOCR API endpoint
      - **PaddleOCR Algorithm**: Select the algorithm corresponding to the API endpoint
      - **AI Studio Access Token**: Your access token for the PaddleOCR API

**Step 2: Usage in Dataset Configuration**
- In your dataset's **Configuration** page, find the **Ingestion pipeline** section
- If using built-in chunking methods that support PDF parsing, select **PaddleOCR** from the **PDF parser** dropdown
- If using custom ingestion pipeline, select **PaddleOCR** in the **Parser** component

**Notes:**
- To obtain the API URL, visit the [PaddleOCR official website](https://aistudio.baidu.com/paddleocr), click the **API** button, choose the example code for the specific algorithm you want to use (e.g., PaddleOCR-VL), and copy the `API_URL`.
- Access tokens can be obtained from the [AI Studio platform](https://aistudio.baidu.com/account/accessToken).
- This method requires internet connectivity to reach the official PaddleOCR API.

#### 2. Using Self-Hosted PaddleOCR Service

This method allows you to deploy your own PaddleOCR service and use it without an access token.

**Step 1: Deploy PaddleOCR Service**
Follow the [PaddleOCR serving documentation](https://www.paddleocr.ai/latest/en/version3.x/deployment/serving.html) to deploy your own service. For layout parsing, you can use an endpoint like:

```bash
http://localhost:8080/layout-parsing
```

**Step 2: Configure RAGFlow**
- **Via Environment Variables:**
  ```bash
  PADDLEOCR_API_URL=http://localhost:8080/layout-parsing
  PADDLEOCR_ALGORITHM=PaddleOCR-VL
  # No access token required for self-hosted service
  ```

- **Via UI:**
   - Navigate to **Model providers** page
   - Add a new OCR model with factory type "PaddleOCR"
   - Configure the following fields:
      - **PaddleOCR API URL**: The endpoint of your deployed service
      - **PaddleOCR Algorithm**: Select the algorithm corresponding to the deployed service
      - **AI Studio Access Token**: Leave empty

**Step 3: Usage in Dataset Configuration**
- In your dataset's **Configuration** page, find the **Ingestion pipeline** section
- If using built-in chunking methods that support PDF parsing, select **PaddleOCR** from the **PDF parser** dropdown
- If using custom ingestion pipeline, select **PaddleOCR** in the **Parser** component

#### Environment Variables Summary

| Environment Variable | Description | Default | Required |
|---------------------|-------------|---------|----------|
| `PADDLEOCR_API_URL` | PaddleOCR API endpoint URL | `""` | Yes, when using environment variables |
| `PADDLEOCR_ALGORITHM` | Algorithm to use for parsing | `"PaddleOCR-VL"` | No |
| `PADDLEOCR_ACCESS_TOKEN` | Access token for official API | `None` | Only when using official API |

Environment variables can be used for auto-provisioning, but are not required if configuring via UI. When environment variables are set, these values are used to auto-provision a PaddleOCR model for the tenant on first use.


### How do I use Ollama with RAGFlow for local LLM inference?

RAGFlow supports Ollama as a local model provider for private, offline inference.

**Step 1: Start Ollama and pull a model**

```bash
export OLLAMA_HOST=0.0.0.0
ollama serve
ollama pull llama3
```

**Step 2: Add Ollama in RAGFlow**

1. Go to **Settings** > **Model providers** > **Ollama**.
2. Set the Base URL to `http://host.docker.internal:11434` (Docker) or `http://localhost:11434` (bare-metal).
3. Enter the model name (e.g., `llama3`) and click **Save**.

**Step 3: Use Ollama in your assistant**

- Open an assistant's **Configuration** page and select the Ollama model under **Chat model**.
