### 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.
### Which languages does RAGFlow support?
English, simplified Chinese, traditional Chinese for now.
## Performance
### 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 model. This contributes to the additional time required.
## Feature
### Which architectures or devices does RAGFlow support?
ARM64 and Ascend GPU are not supported.
### Do you offer an API for integration with third-party applications?
These APIs are still in development. Contributions are welcome.
### Do you support stream output?
No, this feature is still in development. Contributions are welcome.
### Is it possible to share dialogue through URL?
This feature and the related APIs are still in development. Contributions are welcome.
### Do you support multiple rounds of dialogues, i.e., referencing previous dialogues as context for the current dialogue?
This feature and the related APIs are still in development. Contributions are welcome.
## Configurations
### How to increase the length of RAGFlow responses?
You limit what the system responds to what you specify in **Empty response** if nothing is retrieved from your knowledge base. If you do not specify anything in **Empty response**, you let your LLM improvise, giving it a chance to hallucinate.
### `dependency failed to start: container ragflow-mysql is unhealthy`
`dependency failed to start: container ragflow-mysql is unhealthy` means that your MySQL container failed to start. If you are using a Mac with an M1/M2 chip, replace `mysql:5.7.18` with `mariadb:10.5.8` in **docker-compose-base.yml**.
### `Realtime synonym is disabled, since no redis connection`
1. Check the status of your Elasticsearch component:
```bash
$ docker ps
```
*The status of a 'healthy' Elasticsearch component in your RAGFlow 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. 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).
3. If your issue persists, ensure that the ES host setting is correct:
- If you are running RAGFlow with Docker, it is in **docker/service_conf.yml**. Set it as follows:
Your IP address or port number may be incorrect. If you are using the default configurations, enter http://<IP_OF_YOUR_MACHINE> (**NOT `localhost`, 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. 127.0.0.1 is not an accessible IP address.
- If you deploy RAGFlow locally, ensure that Ollama and RAGFlow are in the same LAN and can comunicate with each other.
### Do you offer examples of using deepdoc to parse PDF or other files?