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Fixed a Docusaurus display issue. (#5969)

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### What problem does this PR solve?



### Type of change


- [x] Documentation Update
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writinwaters 2025-03-12 16:07:22 +08:00 committed by GitHub
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2
docker/.env
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@ -122,7 +122,7 @@ TIMEZONE='Asia/Shanghai'
# HF_ENDPOINT=https://hf-mirror.com
# Optimizations for MacOS
# Uncomment the following line if your OS is MacOS:
# Uncomment the following line if your operating system is MacOS:
# MACOS=1
# The maximum file size for each uploaded file, in bytes.

10
docs/faq.md
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@ -318,7 +318,7 @@ The status of a Docker container status does not necessarily reflect the status
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.
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.
@ -347,7 +347,7 @@ 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.
See [Deploy a local LLM](./guides/deploy_local_llm.mdx) for more information.
See [Deploy a local LLM](./guides/models/deploy_local_llm.mdx) for more information.
---
@ -395,7 +395,7 @@ Ensure that you update the **MAX_CONTENT_LENGTH** environment variable:
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.
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.
@ -417,7 +417,7 @@ The status of a Docker container status does not necessarily reflect the status
### How to run RAGFlow with a locally deployed LLM?
You can use Ollama or Xinference to deploy local LLM. See [here](../guides/deploy_local_llm.mdx) for more information.
You can use Ollama or Xinference to deploy local LLM. See [here](./guides/models/deploy_local_llm.mdx) for more information.
---
@ -434,7 +434,7 @@ If your model is not currently supported but has APIs compatible with those of O
- 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/deploy_local_llm.mdx) for more information.
See [here](./guides/models/deploy_local_llm.mdx) for more information.
---

2
docs/guides/agent/general_purpose_chatbot.md
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@ -11,7 +11,7 @@ Create a general-purpose chatbot.
Chatbot is one of the most common AI scenarios. However, effectively understanding user queries and responding appropriately remains a challenge. RAGFlow's general-purpose chatbot agent is our attempt to tackle this longstanding issue.
This chatbot closely resembles the chatbot introduced in [Start an AI chat](../start_chat.md), but with a key difference - it introduces a reflective mechanism that allows it to improve the retrieval from the target knowledge bases by rewriting the user's query.
This chatbot closely resembles the chatbot introduced in [Start an AI chat](../chat/start_chat.md), but with a key difference - it introduces a reflective mechanism that allows it to improve the retrieval from the target knowledge bases by rewriting the user's query.
This document provides guides on creating such a chatbot using our chatbot template.