LLM_Web_search

An extension for oobabooga/text-generation-webui that enables the LLM to search the web

Stars: 267

Visit

LLM_Web_search project gives local LLMs the ability to search the web by outputting a specific command. It uses regular expressions to extract search queries from model output and then utilizes duckduckgo-search to search the web. LangChain's Contextual compression and Okapi BM25 or SPLADE are used to extract relevant parts of web pages in search results. The extracted results are appended to the model's output.

README:

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, a web search is issued, returning a number of result pages. Finally, an ensemble of a dense embedding model 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.

Table of Contents

Installation

Go to the "Session" tab of the web UI and use "Install or update an extension" to download the latest code for this extension.
Run the appropriate update_wizard script inside the text-generation-webui folder and choose Install/update extensions requirements, then choose the name of this extension.
Launch the Web UI by running the appropriate start script and enable the extension under the session tab.
Alternatively, you can also start the server directly using the following command (assuming you have activated your conda/virtual environment):
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).

An example workflow of using this extension could be:

Load a model
Head over to the "LLM Web search" tab
Load a custom system message/prompt
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)
Pick a generation parameter preset that works well for you. You can read more about generation parameters here
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

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

Download_webpage\("(.*)"\)

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

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 (be sure to include http:// or https://). 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.

Search types

Simple search

Quickly finds answers using just the highlighted snippets from websites returned by the search engine. If you simply want results fast, choose this search type.
Note: Some advanced options in the UI will be hidden when simple search is enabled, as they have no effect in this case.
Note2: The snippets returned by SearXNG are often much more useful than those returned by DuckDuckGo, so consider using SearXNG as the search backend if you use simple search.

Full search

Scans entire websites in the results for a more comprehensive search. Ideally, this search type should be able to find "needle in the haystack" information hidden somewhere in the website text. Hence, choose this option if you want to trade a more resource intensive search process for generally more relevant search results.
For the best possible search results, also enable semantic chunking and use SPLADE as the keyword retriever.

Keyword retrievers

Okapi BM25

This extension comes out of the box with Okapi BM25 enabled, which is widely used and very popular 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 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.

Chunking Methods

Character-based Chunking

Naively partitions a website's text into fixed sized chunks without any regard for the text content. This is the default, since it is fast and requires no GPU.

Semantic Chunking

Tries to partition a website's text into chunks based on semantics. If two consecutive sentences have very different embeddings (based on the cosine distance between their embeddings), a new chunk will be started. How different two consecutive sentences have to be for them to end up in different chunks can be tuned using the sentence split threshold parameter in the UI.
For natural language, this method generally produces much better results than character-based chunking. However, it is noticeably slower, even when using the GPU.

Token Classification based Chunking

This chunking method employs a fine-tune of the DistilBERT transformer model, which has been trained to classify tokens (see chonky). If a token is classified as the positive class, a new paragraph (or a new chunk) is meant to be started after the token.

While semantic chunking only compares pairs of consecutive sentences when deciding on where to start a new chunk, the token classification model can utilize a much longer context. However, the need to process this context means that this chunking method is slower than semantic chunking.

Recommended models

If you (like me) have ≤ 12 GB VRAM, I recommend using one of:

Llama-3.1-8B-instruct
Gemma-2-9b-it
Mistral-Nemo-Instruct-2407
Gemma-3-it
Qwen3
Since the Qwen3 family consists of reasoning models, some unique problems arise:
1. It seems that Qwen3 models are harder to prompt to use the search command. I have uploaded the system prompt that has worked most reliably under the name "reasoning_enforce_search".
2. By ticking the checkbox "Enable thinking after searching" in the extension's settings, the model will resume thinking after each search. However, the main webUI only expects the model to think once at the start of the message, and so only the first thinking output will be put into a collapsible UI block. You can download a patch here that fixes this. Download and extract it, then navigate to your text-generation-webui directory, put the patch file there and finally run git apply ooba_multi_thinking.patch

For Tasks:

Click tags to check more tools for each tasks

search web pages extract relevant parts prompt model load model chat with model

For Jobs:

data scientist research analyst information retrieval specialist ai engineer machine learning engineer

Alternative AI tools for LLM_Web_search

Similar Open Source Tools

LLM_Web_search

github

: 267

proxyless-llm-websearch

Proxyless-LLM-WebSearch is a tool that enables users to perform large language model-based web search without the need for proxies. It leverages state-of-the-art language models to provide accurate and efficient web search results. The tool is designed to be user-friendly and accessible for individuals looking to conduct web searches at scale. With Proxyless-LLM-WebSearch, users can easily search the web using natural language queries and receive relevant results in a timely manner. This tool is particularly useful for researchers, data analysts, content creators, and anyone interested in leveraging advanced language models for web search tasks.

github

: 122

ai-powered-search

AI-Powered Search provides code examples for the book 'AI-Powered Search' by Trey Grainger, Doug Turnbull, and Max Irwin. The book teaches modern machine learning techniques for building search engines that continuously learn from users and content to deliver more intelligent and domain-aware search experiences. It covers semantic search, retrieval augmented generation, question answering, summarization, fine-tuning transformer-based models, personalized search, machine-learned ranking, click models, and more. The code examples are in Python, leveraging PySpark for data processing and Apache Solr as the default search engine. The repository is open source under the Apache License, Version 2.0.

github

: 172

web-bench

Web-bench is a simple tool for benchmarking web servers. It is designed to generate a large number of requests to a web server and measure the performance of the server under load. The tool allows users to specify the number of requests, concurrency level, and other parameters to simulate different traffic scenarios. Web-bench provides detailed statistics on response times, throughput, and errors encountered during the benchmarking process. It is a useful tool for web developers, system administrators, and anyone interested in evaluating the performance of web servers.

github

: 205

dbt-mcp

The dbt MCP Server is a Model Context Protocol server that provides tools to interact with dbt. It allows users to provide AI agents with context of their project in dbt Core, dbt Fusion, and dbt Platform. The server architecture enables agents to connect to various tools, and users can refer to the documentation for more details on its capabilities. Users can also contribute to the project by following the instructions in the CONTRIBUTING.md file.

github

: 375

opencode.nvim

Opencode.nvim is a Neovim plugin that provides a simple and efficient way to browse, search, and open files in a project. It enhances the file navigation experience by offering features like fuzzy finding, file preview, and quick access to frequently used files. With Opencode.nvim, users can easily navigate through their project files, jump to specific locations, and manage their workflow more effectively. The plugin is designed to improve productivity and streamline the development process by simplifying file handling tasks within Neovim.

github

: 516

obsidian-NotEMD

Obsidian-NotEMD is a plugin for the Obsidian note-taking app that allows users to export notes in various formats without converting them to EMD. It simplifies the process of sharing and collaborating on notes by providing seamless export options. With Obsidian-NotEMD, users can easily export their notes to PDF, HTML, Markdown, and other formats directly from Obsidian, saving time and effort. This plugin enhances the functionality of Obsidian by streamlining the export process and making it more convenient for users to work with their notes across different platforms and applications.

github

: 60

waidrin

Waidrin is a powerful web scraping tool that allows users to easily extract data from websites. It provides a user-friendly interface for creating custom web scraping scripts and supports various data formats for exporting the extracted data. With Waidrin, users can automate the process of collecting information from multiple websites, saving time and effort. The tool is designed to be flexible and scalable, making it suitable for both beginners and advanced users in the field of web scraping.

github

: 229

mcp-fundamentals

The mcp-fundamentals repository is a collection of fundamental concepts and examples related to microservices, cloud computing, and DevOps. It covers topics such as containerization, orchestration, CI/CD pipelines, and infrastructure as code. The repository provides hands-on exercises and code samples to help users understand and apply these concepts in real-world scenarios. Whether you are a beginner looking to learn the basics or an experienced professional seeking to refresh your knowledge, mcp-fundamentals has something for everyone.

github

: 85

udm14

udm14 is a basic website designed to facilitate easy searches on Google with the &udm=14 parameter, ensuring AI-free results without knowledge panels. The tool simplifies access to these specific search results buried within Google's interface, providing a straightforward solution for users seeking this functionality.

github

: 84

Generative-AI-Indepth-Basic-to-Advance

Generative AI Indepth Basic to Advance is a repository focused on providing tutorials and resources related to generative artificial intelligence. The repository covers a wide range of topics from basic concepts to advanced techniques in the field of generative AI. Users can find detailed explanations, code examples, and practical demonstrations to help them understand and implement generative AI algorithms. The goal of this repository is to help beginners get started with generative AI and to provide valuable insights for more experienced practitioners.

github

: 114

trubrics-sdk

Trubrics-sdk is a software development kit designed to facilitate the integration of analytics features into applications. It provides a set of tools and functionalities that enable developers to easily incorporate analytics capabilities, such as data collection, analysis, and reporting, into their software products. The SDK streamlines the process of implementing analytics solutions, allowing developers to focus on building and enhancing their applications' functionality and user experience. By leveraging trubrics-sdk, developers can quickly and efficiently integrate robust analytics features, gaining valuable insights into user behavior and application performance.

github

: 126

LightLLM

LightLLM is a lightweight library for linear and logistic regression models. It provides a simple and efficient way to train and deploy machine learning models for regression tasks. The library is designed to be easy to use and integrate into existing projects, making it suitable for both beginners and experienced data scientists. With LightLLM, users can quickly build and evaluate regression models using a variety of algorithms and hyperparameters. The library also supports feature engineering and model interpretation, allowing users to gain insights from their data and make informed decisions based on the model predictions.

github

: 3.6k

RecAI

RecAI is a project that explores the integration of Large Language Models (LLMs) into recommender systems, addressing the challenges of interactivity, explainability, and controllability. It aims to bridge the gap between general-purpose LLMs and domain-specific recommender systems, providing a holistic perspective on the practical requirements of LLM4Rec. The project investigates various techniques, including Recommender AI agents, selective knowledge injection, fine-tuning language models, evaluation, and LLMs as model explainers, to create more sophisticated, interactive, and user-centric recommender systems.

github

: 494

DashAI

DashAI is a powerful tool for building interactive web applications with Python. It allows users to create data visualization dashboards and deploy machine learning models with ease. The tool provides a simple and intuitive way to design and customize web apps without the need for extensive front-end development knowledge. With DashAI, users can easily showcase their data analysis results and predictive models in a user-friendly and interactive manner, making it ideal for data scientists, developers, and business professionals looking to share insights and predictions with stakeholders.

github

: 65

Build-Modern-AI-Apps

This repository serves as a hub for Microsoft Official Build & Modernize AI Applications reference solutions and content. It provides access to projects demonstrating how to build Generative AI applications using Azure services like Azure OpenAI, Azure Container Apps, Azure Kubernetes, and Azure Cosmos DB. The solutions include Vector Search & AI Assistant, Real-Time Payment and Transaction Processing, and Medical Claims Processing. Additionally, there are workshops like the Intelligent App Workshop for Microsoft Copilot Stack, focusing on infusing intelligence into traditional software systems using foundation models and design thinking.

github

: 142

For similar tasks

LLM_Web_search

github

: 267

node-llama-cpp

node-llama-cpp is a tool that allows users to run AI models locally on their machines. It provides pre-built bindings with the option to build from source using cmake. Users can interact with text generation models, chat with models using a chat wrapper, and force models to generate output in a parseable format like JSON. The tool supports Metal and CUDA, offers CLI functionality for chatting with models without coding, and ensures up-to-date compatibility with the latest version of llama.cpp. Installation includes pre-built binaries for macOS, Linux, and Windows, with the option to build from source if binaries are not available for the platform.

github

: 853

Jlama

Jlama is a modern Java inference engine designed for large language models. It supports various model types such as Gemma, Llama, Mistral, GPT-2, BERT, and more. The tool implements features like Flash Attention, Mixture of Experts, and supports different model quantization formats. Built with Java 21 and utilizing the new Vector API for faster inference, Jlama allows users to add LLM inference directly to their Java applications. The tool includes a CLI for running models, a simple UI for chatting with LLMs, and examples for different model types.

github

: 987

torchchat

torchchat is a codebase showcasing the ability to run large language models (LLMs) seamlessly. It allows running LLMs using Python in various environments such as desktop, server, iOS, and Android. The tool supports running models via PyTorch, chatting, generating text, running chat in the browser, and running models on desktop/server without Python. It also provides features like AOT Inductor for faster execution, running in C++ using the runner, and deploying and running on iOS and Android. The tool supports popular hardware and OS including Linux, Mac OS, Android, and iOS, with various data types and execution modes available.

github

: 3.5k

chatgpt-cli

ChatGPT CLI provides a powerful command-line interface for seamless interaction with ChatGPT models via OpenAI and Azure. It features streaming capabilities, extensive configuration options, and supports various modes like streaming, query, and interactive mode. Users can manage thread-based context, sliding window history, and provide custom context from any source. The CLI also offers model and thread listing, advanced configuration options, and supports GPT-4, GPT-3.5-turbo, and Perplexity's models. Installation is available via Homebrew or direct download, and users can configure settings through default values, a config.yaml file, or environment variables.

github

: 788

elmer

Elmer is a user-friendly wrapper over common APIs for calling llm’s, with support for streaming and easy registration and calling of R functions. Users can interact with Elmer in various ways, such as interactive chat console, interactive method call, programmatic chat, and streaming results. Elmer also supports async usage for running multiple chat sessions concurrently, useful for Shiny applications. The tool calling feature allows users to define external tools that Elmer can request to execute, enhancing the capabilities of the chat model.

github

: 71

mlx-lm

MLX LM is a Python package designed for generating text and fine-tuning large language models on Apple silicon using MLX. It offers integration with the Hugging Face Hub for easy access to thousands of LLMs, support for quantizing and uploading models to the Hub, low-rank and full model fine-tuning capabilities, and distributed inference and fine-tuning with `mx.distributed`. Users can interact with the package through command line options or the Python API, enabling tasks such as text generation, chatting with language models, model conversion, streaming generation, and sampling. MLX LM supports various Hugging Face models and provides tools for efficient scaling to long prompts and generations, including a rotating key-value cache and prompt caching. It requires macOS 15.0 or higher for optimal performance.

github

: 2.2k

keras-llm-robot

The Keras-llm-robot Web UI project is an open-source tool designed for offline deployment and testing of various open-source models from the Hugging Face website. It allows users to combine multiple models through configuration to achieve functionalities like multimodal, RAG, Agent, and more. The project consists of three main interfaces: chat interface for language models, configuration interface for loading models, and tools & agent interface for auxiliary models. Users can interact with the language model through text, voice, and image inputs, and the tool supports features like model loading, quantization, fine-tuning, role-playing, code interpretation, speech recognition, image recognition, network search engine, and function calling.

github

: 204

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 502

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 669

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 953

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 1.3k

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.2k