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https://github.com/shreyaskarnik/DistiLlama

Chrome Extension to Summarize or Chat with Web Pages/Local Documents Using locally running LLMs. Keep all of your data and conversations private. 🔐
https://github.com/shreyaskarnik/DistiLlama

chrome-extension langchain llama2 llms local-llm mistral-7b ollama privacy-preserving-computing private readability retrieval-augmented-generation summarization zephyr

Last synced: about 2 months ago
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Chrome Extension to Summarize or Chat with Web Pages/Local Documents Using locally running LLMs. Keep all of your data and conversations private. 🔐

Host: GitHub
URL: https://github.com/shreyaskarnik/DistiLlama
Owner: shreyaskarnik
License: mit
Created: 2023-10-26T04:34:12.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2024-09-02T23:50:12.000Z (4 months ago)
Last Synced: 2024-11-05T06:42:30.432Z (about 2 months ago)
Topics: chrome-extension, langchain, llama2, llms, local-llm, mistral-7b, ollama, privacy-preserving-computing, private, readability, retrieval-augmented-generation, summarization, zephyr
Language: TypeScript
Homepage:
Size: 9.68 MB
Stars: 278
Watchers: 6
Forks: 28
Open Issues: 9
Metadata Files:
- Readme: README.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS

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README

        # DistiLlama

![image](public/icon-128.png)

## What is DistiLlama?

DistiLlama is a Chrome extension that leverages locally running LLM perform following  tasks.

![Overview](./Overview.png)

One of the things that I was experimenting with is how to use a locally running LLM instance for various tasks and summarization (tl;dr) was on the top of my list. It was key to have all calls to LLM be local and all the data to stay private.

This project utilizes [Ollama](https://ollama.ai/) as the locally running LLM instance. Ollama is a great project that is easy to setup and use. I highly recommend checking it out.

To generate the summary I am using the following approach:

- Grab the current active tab id

- Use [Readability](https://github.com/mozilla/readability) to extract the text content from the page. In my experiments it was clear that the quality of the summary was much better when using Readability as it removed a lot of un-necessary content from the page.

- Use [LangChain (LangChain.js)](https://js.langchain.com/docs/get_started/introduction/) to summarize the text content.

- Display the summary in a popup window.

## How to use DistiLlama?

- Prerequisites:

  - Install [Ollama](https://ollama.ai/download) you can also choose to run Ollama in a [Docker container](https://ollama.ai/blog/ollama-is-now-available-as-an-official-docker-image).

  - Start Ollama using the following command: `OLLAMA_ORIGINS=* OLLAMA_HOST=127.0.0.1:11435 ollama serve`

  - In another terminal you can run `ollama pull llama2:latest` or `ollama pull mistral:latest`

  - Choice of model depends on your use case. Here are the models supported by Ollama 

  - Make sure you set OLLAMA_ORIGINS=* for the Ollama environment by following instructions [here](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-do-i-configure-ollama-server)

- Clone this repo

  - Install pnpm `npm install -g pnpm`

  - run `pnpm install`

  - run `pnpm dev`

  - Open Chrome and navigate to `chrome://extensions/`

    - Enable developer mode (if not already enabled)

    - Click on `Load unpacked` and select the `dist` folder from the base of the cloned project.

    - You should see the DistiLlama added to your Chrome extensions.

    - You may want to pin the extension to your Chrome toolbar for easy access.

## Demo

### Chat with LLM

![Chat](./Chat.gif)

### Chat with Documents (PDF)

![ChatWithDocs](./ChatWithDocs.gif)

### Chat with Web Page

![ChatWithPage](./ChatWithPage.gif)

### Summarization

![Summary](./Summary.gif)

## TODOS

- [ ] Make the summarization chain configurable

- [x] Make LLM model configurable

- [ ] Save summary in local storage

- [ ] Improve the UI (not an expert in this area but will try to learn)

- [ ] Add TTS support

- [ ] Check out performance with different tuned prompts

- [x] Extend to chat with the page (use embeddings and LLMs for RAG)

- [x] Use [transformers.js](https://github.com/xenova/transformers.js) for local in browser embeddings and [Voy](https://github.com/tantaraio/voy) for the storage similar to this [Building LLM-Powered Web Apps with Client-Side Technology](https://ollama.ai/blog/building-llm-powered-web-apps)

- [ ] Focus on improving the quality of the summarization and chat

- [ ] Multimodal support

## References and Inspiration

- [LangChain](https://github.com/langchain-ai/langchainjs)

- [Ollama](https://ollama.ai/)

- [Building LLM-Powered Web Apps with Client-Side Technology](https://ollama.ai/blog/building-llm-powered-web-apps)

- [Chrome Extension Template](https://github.com/Jonghakseo/chrome-extension-boilerplate-react-vite)

- Artwork generated using [DALL·E 3](https://openai.com/dall-e-3)