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Added some debugging FAQs (#413)
### What problem does this PR solve? ### Type of change - [x] Documentation Update
This commit is contained in:
43
docs/faq.md
43
docs/faq.md
@ -96,6 +96,8 @@ Parsing requests have to wait in queue due to limited server resources. We are c
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### Why does my document parsing stall at under one percent?
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If your RAGFlow is deployed *locally*, try the following:
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1. Check the log of your RAGFlow server to see if it is running properly:
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@ -105,6 +107,16 @@ docker logs -f ragflow-server
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2. Check if the **tast_executor.py** process exist.
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3. Check if your RAGFlow server can access hf-mirror.com or huggingface.com.
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### `MaxRetryError: HTTPSConnectionPool(host='hf-mirror.com', port=443)`
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This error suggests that you do not have Internet access or are unable to connect to hf-mirror.com. Try the following:
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1. Manually download the resource files from [huggingface.co/InfiniFlow/deepdoc](https://huggingface.co/InfiniFlow/deepdoc) to your local folder **~/deepdoc**.
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2. Add a volumes to **docker-compose.yml**, for example:
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```
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- ~/deepdoc:/ragflow/rag/res/deepdoc
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```
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### `Index failure`
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An index failure usually indicates an unavailable Elasticsearch service.
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@ -165,7 +177,7 @@ Your IP address or port number may be incorrect. If you are using the default co
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A correct Ollama IP address and port is crucial to adding models to Ollama:
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- 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.
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- 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.
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- If you deploy RAGFlow locally, ensure that Ollama and RAGFlow are in the same LAN and can comunicate with each other.
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### Do you offer examples of using deepdoc to parse PDF or other files?
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@ -191,3 +203,32 @@ docker compose up ragflow -d
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```
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*Now you should be able to upload files of sizes less than 100MB.*
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### `Table 'rag_flow.document' doesn't exist`
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This exception occurs when starting up the RAGFlow server. Try the following:
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1. Prolong the sleep time: Go to **docker/entrypoint.sh**, locate line 26, and replace `sleep 60` with `sleep 280`.
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2. Go to **docker/docker-compose.yml**, add the following after line 109:
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```
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./entrypoint.sh:/ragflow/entrypoint.sh
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```
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3. Change directory:
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```bash
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cd docker
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```
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4. Stop the RAGFlow server:
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```bash
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docker compose stop
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```
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5. Restart up the RAGFlow server:
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```bash
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docker compose up
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```
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### `hint : 102 Fail to access model Connection error`
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1. Ensure that the RAGFlow server can access the base URL.
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2. Do not forget to append **/v1/** to **http://IP:port**:
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**http://IP:port/v1/**
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