### 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 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.
---
### 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:
-`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.
-`full`/`slim`: The RAGFlow edition.
-`full`: The full RAGFlow edition.
-`slim`: The RAGFlow edition without embedding models and Python packages.
---
### 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.
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.
A locally deployed RAGflow downloads OCR and embedding modules 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**:
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:
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 ragflow-server
```
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!
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
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 demo.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.
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 increase the length of RAGFlow responses?
1. Right-click the desired dialog to display the **Chat Configuration** window.
2. Switch to the **Model Setting** tab and adjust the **Max Tokens** slider to get the desired length.
3. Click **OK** to confirm your change.
---
### How to run RAGFlow with a locally deployed LLM?
### Is it possible 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:
### `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**.
