pengjunjie/ragflow
1
0
Fork 1
mirror of https://github.com/infiniflow/ragflow.git synced 2025-12-08 20:42:30 +08:00
Code Issues Packages Projects Releases Wiki Activity

Docs: Removed /v1 from Ollama base URLs (#10067)

Browse Source 
### What problem does this PR solve?


### Type of change

- [x] Documentation Update
This commit is contained in:
writinwaters
2025-09-17 13:48:29 +08:00
committed by GitHub
parent 04ef5b2783
commit 5a8bc88147
2 changed files with 2 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
docs/guides/chat/start_chat.md
View File

@ -106,7 +106,7 @@ RAGFlow offers HTTP and Python APIs for you to integrate RAGFlow's capabilities
You can use iframe to embed the created chat assistant into a third-party webpage: You can use iframe to embed the created chat assistant into a third-party webpage:
1. Before proceeding, you must [acquire an API key](../models/llm_api_key_setup.md); otherwise, an error message would appear. 1. Before proceeding, you must [acquire an API key](../../develop/acquire_ragflow_api_key.md); otherwise, an error message would appear.
2. Hover over an intended chat assistant **>** **Edit** to show the **iframe** window: 2. Hover over an intended chat assistant **>** **Edit** to show the **iframe** window:
![chat-embed](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/embed_chat_into_webpage.jpg) ![chat-embed](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/embed_chat_into_webpage.jpg)

2
docs/guides/models/deploy_local_llm.mdx
View File

@ -91,7 +91,7 @@ In RAGFlow, click on your logo on the top right of the page **>** **Model provid
In the popup window, complete basic settings for Ollama: In the popup window, complete basic settings for Ollama:
1. Ensure that your model name and type match those been pulled at step 1 (Deploy Ollama using Docker). For example, (`llama3.2` and `chat`) or (`bge-m3` and `embedding`). 1. Ensure that your model name and type match those been pulled at step 1 (Deploy Ollama using Docker). For example, (`llama3.2` and `chat`) or (`bge-m3` and `embedding`).
2. In Ollama base URL, put the URL you found in step 2 followed by `/v1`, i.e. `http://host.docker.internal:11434/v1`, `http://localhost:11434/v1` or `http://${IP_OF_OLLAMA_MACHINE}:11434/v1`. 2. Put in the Ollama base URL, i.e. `http://host.docker.internal:11434`, `http://localhost:11434` or `http://${IP_OF_OLLAMA_MACHINE}:11434`.
3. OPTIONAL: Switch on the toggle under **Does it support Vision?** if your model includes an image-to-text model. 3. OPTIONAL: Switch on the toggle under **Does it support Vision?** if your model includes an image-to-text model.