Added some debugging FAQs (#413)

### What problem does this PR solve?

### Type of change

- [x] Documentation Update
This commit is contained in:
writinwaters
2024-04-17 16:32:36 +08:00
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parent 800b5c7aaa
commit 3719ff7299
2 changed files with 44 additions and 3 deletions

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## 📌 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.
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```
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.