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Added some debugging FAQs (#413)

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### What problem does this PR solve?

### Type of change

- [x] Documentation Update
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writinwaters 2024-04-17 16:32:36 +08:00 committed by GitHub
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4
README.md
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@ -56,7 +56,7 @@
## 📌 Latest Features
- 2024-04-16 Add an embedding model 'bce-embedding-base_v1' from [BCEmbedding](https://github.com/netease-youdao/BCEmbedding).
- 2024-04-16 Add [FastEmbed](https://github.com/qdrant/fastembed) is designed for light and speeding embedding.
- 2024-04-16 Add [FastEmbed](https://github.com/qdrant/fastembed), which is designed specifically for light and speedy embedding.
- 2024-04-11 Support [Xinference](./docs/xinference.md) for local LLM deployment.
- 2024-04-10 Add a new layout recognization model for analyzing Laws documentation.
- 2024-04-08 Support [Ollama](./docs/ollama.md) for local LLM deployment.
@ -139,7 +139,7 @@
```
5. In your web browser, enter the IP address of your server and log in to RAGFlow.
> In the given scenario, you only need to enter `http://IP_OF_YOUR_MACHINE` (sans port number) as the default HTTP serving port `80` can be omitted when using the default configurations.
> In the given scenario, you only need to enter `http://IP_OF_YOUR_MACHINE` (**sans** port number) as the default HTTP serving port `80` can be omitted when using the default configurations.
6. In [service_conf.yaml](./docker/service_conf.yaml), select the desired LLM factory in `user_default_llm` and update the `API_KEY` field with the corresponding API key.
> See [./docs/llm_api_key_setup.md](./docs/llm_api_key_setup.md) for more information.

43
docs/faq.md
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@ -96,6 +96,8 @@ Parsing requests have to wait in queue due to limited server resources. We are c
### Why does my document parsing stall at under one percent?
![stall](https://github.com/infiniflow/ragflow/assets/93570324/3589cc25-c733-47d5-bbfc-fedb74a3da50)
If your RAGFlow is deployed *locally*, try the following:
1. Check the log of your RAGFlow server to see if it is running properly:
@ -105,6 +107,16 @@ docker logs -f ragflow-server
2. Check if the **tast_executor.py** process exist.
3. Check if your RAGFlow server can access hf-mirror.com or huggingface.com.
### `MaxRetryError: HTTPSConnectionPool(host='hf-mirror.com', port=443)`
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:
```
- ~/deepdoc:/ragflow/rag/res/deepdoc
```
### `Index failure`
An index failure usually indicates an unavailable Elasticsearch service.
@ -165,7 +177,7 @@ Your IP address or port number may be incorrect. If you are using the default co
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 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 comunicate with each other.
### Do you offer examples of using deepdoc to parse PDF or other files?
@ -191,3 +203,32 @@ docker compose up ragflow -d
```
*Now you should be able to upload files of sizes less than 100MB.*
### `Table 'rag_flow.document' doesn't exist`
This exception occurs when starting up the RAGFlow server. Try the following:
1. Prolong the sleep time: Go to **docker/entrypoint.sh**, locate line 26, and replace `sleep 60` with `sleep 280`.
2. Go to **docker/docker-compose.yml**, add the following after line 109:
```
./entrypoint.sh:/ragflow/entrypoint.sh
```
3. Change directory:
```bash
cd docker
```
4. Stop the RAGFlow server:
```bash
docker compose stop
```
5. Restart up the RAGFlow server:
```bash
docker compose up
```
### `hint : 102 Fail to access model Connection error`
![hint102](https://github.com/infiniflow/ragflow/assets/93570324/6633d892-b4f8-49b5-9a0a-37a0a8fba3d2)
1. Ensure that the RAGFlow server can access the base URL.
2. Do not forget to append **/v1/** to **http://IP:port**:
**http://IP:port/v1/**