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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 (Or alternatively, SPLADE) 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. llm_websearch

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. To install the extension's depencies you have two options:
    1. The easy way: Run the appropriate update_wizard script inside the text-generation-webui folder and choose Install/update extensions requirements. This installs everything using pip, which means using the unofficial faiss-cpu package. Therefore, it is not guaranteed to work with your system (see the official disclaimer).
    2. The safe way: Manually update the conda environment in which you installed the dependencies of oobabooga's text-generation-webui. Open the subfolder text-generation-webui/extensions/LLM_Web_search in a terminal or conda shell. 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 can take a while)
  3. 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.

Keyword retrievers

Okapi BM25

This extension comes out of the box with Okapi BM25 enabled, which is widely used and very popuplar for keyword based document retrieval. It runs on the CPU and, for the purpose of this extension, it is fast.

SPLADE

If you don't run the extension in "CPU only" mode and have some VRAM to spare, you can also select SPLADE in the "Advanced settings" section as an alternative. It has been shown to outperform BM25 in multiple benchmarks and uses a technique called "query expansion" to add additional contextually relevant words to the original query. However, it is slower than BM25. You can read more about it here.
To use SPLADE, you have to install the additional dependency qdrant-client. Simply activate the conda environment of the main web UI and run pip install qdrant-client.
To improve performance, documents are embedded in batches and in parallel. Increasing the "SPLADE batch size" parameter setting improves performance up to a certain point, but VRAM usage ramps up quickly with increasing batch size. A batch size of 8 appears to be a good trade-off, but the default value is 2 to avoid running out of memory on smaller GPUs.

Recommended models

If you (like me) have ≤ 12 GB VRAM, I recommend using Llama-3-8B-instruct. You can find a matching instruction template in the extension's instruction_templates folder. Simply copy it to the main web UI's instruction-templates folder. Note: Several existing GGUF versions have a stop token issue, which can be solved by editing the file's metadata. A GGUF version where this issue has already been fixed can be found here.