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.
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.
The corresponding APIs are now available. See the [RAGFlow HTTP API Reference](./http_api_reference.md) or the [RAGFlow Python API Reference](./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'
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**.
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:
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**.
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 ragflow-server
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.
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.
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.
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.
- 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.
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.
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:
