LLM_Web_search_OWUI

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LLM_Web_search_OWUI

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LLM_Web_search ported to Open WebUI

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# LLM_Web_search_OWUI

This repo contains the source code of the LLM Web Search tool for Open WebUI. The code is [LLM_Web_search](https://github.com/mamei16/LLM_Web_search) ported to Open WebUI, retaining as much functionality as possible given the different environments. This means that the documentation on search types, keyword retrievers and chunking methods from the original project still apply to this  project.

* **[Table of Contents](#table-of-contents)**
  * [Initial Setup](#initial-setup)
  * [Settings (Valves)](#settings-valves)
    + [Ensemble Weighting](#ensemble-weighting)
  * [Possible Issue with ROCm on Linux](#possible-issue-with-rocm-on-linux)

## Initial Setup

### Configure Embedding Model Save Location

This tool processes search results locally on device. For this purpose, three embedding models need to be downloaded automatically when the first web search is executed. Before using this tool for the first time, you'll need to configure the "Embedding Model Save Path" setting (or "Valve"), which controls where the embedding models will be downloaded to.

### Update DuckDuckGo Search Python Package

The `duckduckgo-search` python package is now called `ddgs` and may now also retrieve results from other search engines, such as Bing. If you're fine with this, you can try to avoid the `202 Ratelimit` exceptions that regularly appear when using the old `duckduckgo-search` by manually installing `ddgs` before using this extension:
   
```
pip install ddgs
```

If you choose to use SearXNG as the search engine backend, you of course won't need to install `ddgs`.

## Settings (Valves)

Most settings are already explained briefly in the UI, and are expanded on in the readme of the [original project](https://github.com/mamei16/LLM_Web_search?tab=readme-ov-file#search-types). In the following subsection(s), some extra info that would be too verbose to put into the UI will be added.

### Ensemble Weighting

Set this value to 0 to use keyword retrieval only, or 1 to use purely dense retrieval. If you choose to run the tool on CPU only, I recommend to either use BM25 as the keyword retrieval method or use purely dense retrieval, as the SPLADE document encoder will run slowly on a CPU.

## Proxy Support
This tool adheres to the proxy settings of the main web UI, as described in the [open webui documentation](https://docs.openwebui.com/getting-started/env-configuration#proxy-settings). Optionally, both requests fetching search results from Duckduckgo and requests downloading result webpages can be routed through a proxy.

## Possible Issue with AMD ROCm on Linux

If you're running both the LLM server (such as Oobabooga's textgen webUI or Ollama) and Open WebUI on the same linux machine, have an AMD GPU and use ROCm, you may encounter freezes/hanging in either the web search itself (being stuck at either the "Downloading and chunking webpages..." or "Retrieving relevant results..." stage) or the LLM response(s) following the web search.

This is currently an open issue and has so far only been confirmed to occur when using Oobabooga's textgen webUI as the LLM server. You can avoid any issues by using the [llama.ccp server](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md) directly.

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