Give your local LLM the ability to search the web!

This project gives local LLMs the ability to search the web by outputting a specific command. Once the command has been found in the model output using a regular expression, duckduckgo-search is used to search the web and return a number of result pages. Finally, an ensemble of LangChain's Contextual compression and Okapi BM25 is used to extract the relevant parts (if any) of each web page in the search results and the results are appended to the model's output.

Installation

1. Go to the "Session" tab of the web UI and use "Install or update an extension" to download the latest code for this extension.
2. Open the subfolder text-generation-webui/extensions/LLM_Web_search in a terminal or conda shell.
3. Update the conda environment in which you installed the dependencies of oobabooga's text-generation-webui. If you used the one-click install method, run the command conda env update -p <path_to_your_environment> --file environment.yml, where you need to replace <path_to_your_environment> with the path to the /installer_files/env subfolder within the text-generation-webui folder. Otherwise, if you made your own environment, use conda env update -n <name_of_your_environment> --file environment.yml
   (NB: Solving the environment will take a while)
4. Launch the Web UI with:
   python server.py --extension LLM_Web_search

If the installation was successful and the extension was loaded, a new tab with the title "LLM Web Search" should be visible in the web UI.

See https://github.com/oobabooga/text-generation-webui/wiki/07-%E2%80%90-Extensions for more information about extensions.

Usage

Search queries are extracted from the model's output using a regular expression. This is made easier by prompting the model to use a fixed search command (see system_prompts/ for example prompts).
Currently, only a single search query per model chat message is supported.

An example workflow of using this extension could be:

1. Load a model
2. Load a matching instruction template
3. Head over to the "LLM Web search" tab
4. Load a custom system message/prompt
5. Ensure that the query part of the command mentioned in the system message can be matched using the current "Search command regex string" (see "Using a custom regular expression" below)
6. Pick a hyperparameter generation preset that works well for you.
7. Choose "chat-instruct" or "instruct" mode and start chatting

Using a custom regular expression

The default regular expression is:

Search_web\("(.*)"\)

Where Search_web is the search command and everything between the quotation marks inside the parentheses will be used as the search query. Every custom regular expression must use a capture group to extract the search query. I recommend https://www.debuggex.com/ to try out custom regular expressions. If a regex fulfills the requirement above, the search query should be matched by "Group 1" in Debuggex.

Here is an example of a more flexible, but more complex, regex that works for several different models:

[Ss]earch_web\((?:["'])(.*)(?:["'])\)

Reading web pages

Experimental support exists for extracting the full text content from a webpage. The default regex to use this functionality is:

Open_url\("(.*)"\)

Note: The full content of a web page is likely to exceed the maximum context length of your average local LLM.

Search backends

DuckDuckGo

This is the default web search backend.

SearXNG

Rudimentary support exists for SearXNG. To use a local or remote SearXNG instance instead of DuckDuckGo, simply paste the URL into the "SearXNG URL" text field of the "LLM Web Search" settings tab. The instance must support returning results in JSON format.

Search parameters

To modify the categories, engines, languages etc. that should be used for a specific query, it must follow the SearXNG search syntax. Currently, automatic redirect and Special Queries are not supported.