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130 Commits

Author SHA1 Message Date
Kushagra Srivastava
d7b020e5bb Update README.md 2026-01-10 23:03:58 +05:30
ItzCrazyKns
d95ff9ccdd Update docker-compose.yaml 2026-01-08 22:36:01 +05:30
ItzCrazyKns
8347b798f3 feat(app): lint & beautify 2026-01-03 23:12:19 +05:30
ItzCrazyKns
a16472bcf3 feat(actions): prevent double conversion to object array 2026-01-01 21:56:46 +05:30
ItzCrazyKns
3b8d8be676 feat(package): bump version 2025-12-31 12:58:59 +05:30
ItzCrazyKns
b83f9bac78 feat(providers): extract/repair json before parsing 2025-12-31 12:58:24 +05:30
ItzCrazyKns
bd7c563137 feat(package): add json repair 2025-12-31 12:57:59 +05:30
ItzCrazyKns
23b903db9a Update searxng.ts 2025-12-30 22:16:06 +05:30
ItzCrazyKns
a98f0df83f feat(app): lint & beautify 2025-12-29 22:02:21 +05:30
ItzCrazyKns
164d528761 feat(compose): add build context, remove uploads 2025-12-28 13:11:05 +05:30
ItzCrazyKns
af4ec17117 Update docker-compose.yaml 2025-12-28 12:49:25 +05:30
ItzCrazyKns
1622e0893a feat(providers): add lm studio 2025-12-28 11:29:34 +05:30
ItzCrazyKns
55a4b9d436 feat(openai-llm): use function call index instead of type 2025-12-28 01:21:33 +05:30
ItzCrazyKns
b450d0e668 Merge branch 'canary' 2025-12-27 20:52:56 +05:30
ItzCrazyKns
0987ee4370 Update next.config.mjs 2025-12-27 20:29:11 +05:30
ItzCrazyKns
d1bd22786d Update package.json 2025-12-27 20:15:34 +05:30
ItzCrazyKns
bb7b7170ca feat(media, suggestions): handle chat history correctly 2025-12-27 20:03:34 +05:30
ItzCrazyKns
be7bd62a74 feat(prompts): update media 2025-12-27 20:02:49 +05:30
ItzCrazyKns
a691f3bab0 feat(chat-hook): fix history saving delay (async state), add delay before media search to allow component refresh 2025-12-27 20:02:36 +05:30
ItzCrazyKns
f1c9fa0e33 feat(package): bump version 2025-12-27 18:56:16 +05:30
ItzCrazyKns
d872cf5009 feat(chat-hook): prevent duplicate blocks 2025-12-27 18:36:13 +05:30
ItzCrazyKns
fdef718980 feat(transformer-provider): specify dtype 2025-12-27 18:36:01 +05:30
ItzCrazyKns
19dde42f22 feat(app): fix build errors, use webpack 2025-12-27 18:35:30 +05:30
ItzCrazyKns
c9f6893d99 feat(pdf-parse): fix DOMMatrix issues 2025-12-27 14:54:46 +05:30
ItzCrazyKns
53e9859b6c Update README.md 2025-12-27 13:35:47 +05:30
Kushagra Srivastava
53e39cd985 Merge pull request #950 from ItzCrazyKns/feat/improve-search-architecture
feat: improve search architecture, write custom API classes (remove langchain), add deep research & more
2025-12-27 13:33:54 +05:30
Kushagra Srivastava
7f3f881964 Update src/components/Navbar.tsx
Co-authored-by: cubic-dev-ai[bot] <191113872+cubic-dev-ai[bot]@users.noreply.github.com>
2025-12-27 13:32:20 +05:30
Kushagra Srivastava
9620e63e3f Update src/components/MessageActions/Copy.tsx
Co-authored-by: cubic-dev-ai[bot] <191113872+cubic-dev-ai[bot]@users.noreply.github.com>
2025-12-27 13:29:43 +05:30
ItzCrazyKns
ec5ff6f4a8 Update plan.ts 2025-12-27 13:26:07 +05:30
ItzCrazyKns
0ace778b03 Merge branch 'feat/improve-search-architecture' of https://github.com/ItzCrazyKns/Perplexica into feat/improve-search-architecture 2025-12-27 13:24:50 +05:30
ItzCrazyKns
6919ad1a0f feat(app): address review 2025-12-27 13:24:35 +05:30
Kushagra Srivastava
b5ba8c48c0 Update src/components/WeatherWidget.tsx
Co-authored-by: cubic-dev-ai[bot] <191113872+cubic-dev-ai[bot]@users.noreply.github.com>
2025-12-27 13:14:46 +05:30
ItzCrazyKns
65fdecb122 feat(docs): update architecture docs 2025-12-27 13:09:11 +05:30
ItzCrazyKns
5a44319d85 feat(guides): update contributing guides 2025-12-27 13:09:01 +05:30
ItzCrazyKns
cc183cd0cd feat(readme): update features & upcoming features 2025-12-27 13:08:28 +05:30
ItzCrazyKns
50ca7ac73a feat(api): update search api & related documentation 2025-12-27 13:07:59 +05:30
ItzCrazyKns
a31a4ab295 feat(agents): add api search agent 2025-12-27 13:07:42 +05:30
ItzCrazyKns
edba47aed8 feat(db): add migration scripts 2025-12-26 14:51:24 +05:30
ItzCrazyKns
ae132ebee8 feat(app): lint & beautify 2025-12-25 18:58:33 +05:30
ItzCrazyKns
60dd7a8108 feat(ui): fix theming issues 2025-12-24 17:24:07 +05:30
ItzCrazyKns
f5e054f6ea feat(chat): fix hidden input 2025-12-24 15:48:16 +05:30
ItzCrazyKns
452180356d feat(library): enhance ui & ux 2025-12-24 15:47:56 +05:30
ItzCrazyKns
0a9641a110 feat(providers): add anthropic 2025-12-24 15:24:06 +05:30
ItzCrazyKns
a2f2e17bbb feat(providers): add lemonade 2025-12-24 14:12:22 +05:30
ItzCrazyKns
e1afcbb787 feat(package): add google genai & bump transformers 2025-12-24 13:56:43 +05:30
ItzCrazyKns
fe2c1b8210 feat(providers): update index map 2025-12-24 13:56:24 +05:30
ItzCrazyKns
d40fcd57d9 feat(ollama): add nemotron to thinking list 2025-12-24 13:56:11 +05:30
ItzCrazyKns
86a43086cc feat(providers): add transformers 2025-12-24 13:55:56 +05:30
ItzCrazyKns
9ce17edd4a feat(providers): add groq 2025-12-24 13:55:42 +05:30
ItzCrazyKns
c4349f3d5c feat(providers): add gemini 2025-12-24 13:55:32 +05:30
ItzCrazyKns
d4c276ab93 Update types.ts 2025-12-24 13:55:12 +05:30
ItzCrazyKns
6ae885e0ed feat(steps): display after loading animation 2025-12-24 13:55:07 +05:30
ItzCrazyKns
dc74e7174f feat(researcher): rename 0_reasoning to __reasoning_preamble to comply with provider guidelines 2025-12-24 13:54:49 +05:30
ItzCrazyKns
53697bb42e feat(classifier-prompt): add calculation widget 2025-12-24 13:53:35 +05:30
ItzCrazyKns
eca66f0b5f feat(writer): add system instructions, send response block on response 2025-12-24 13:53:09 +05:30
ItzCrazyKns
cf95ea0af7 feat(app): lint & beautify 2025-12-23 18:54:01 +05:30
ItzCrazyKns
24c32ed881 feat(app): enhance attach transition 2025-12-23 18:53:40 +05:30
ItzCrazyKns
b47f522bf2 feat(app): update guide for run command 2025-12-23 18:40:30 +05:30
ItzCrazyKns
ea18c13326 feat(app): remove uploads 2025-12-23 18:38:25 +05:30
ItzCrazyKns
b706434bac feat(chat-window): display only when ready 2025-12-23 17:56:15 +05:30
ItzCrazyKns
2c65bd916b feat(chat-hook): set ready before reconnecting 2025-12-23 17:29:14 +05:30
ItzCrazyKns
c3b74a3fd0 feat(assistant-steps): only open last comp 2025-12-23 17:17:56 +05:30
ItzCrazyKns
5f04034650 feat(chat-hook): handle reconnect 2025-12-23 17:17:19 +05:30
ItzCrazyKns
5847379db0 Update types.ts 2025-12-23 17:15:46 +05:30
ItzCrazyKns
8520ea6fe5 feat(researcher): emit sources as block 2025-12-23 17:15:42 +05:30
ItzCrazyKns
a6d4f47130 feat(search-agent): save history 2025-12-23 17:15:32 +05:30
ItzCrazyKns
f278eb8bf1 feat(routes): add reconnect route 2025-12-23 17:15:02 +05:30
ItzCrazyKns
0e176e0b78 feat(chat-route): add history saving, disconnect on abort, use subscribe method 2025-12-23 17:14:02 +05:30
ItzCrazyKns
8ba64be446 feat(session): fix sessions getting disregarded due to reload 2025-12-23 17:12:56 +05:30
ItzCrazyKns
216332fb20 feat(session): add subscribe method, getAllBlocks 2025-12-23 17:12:15 +05:30
ItzCrazyKns
68a9e048ac feat(schema): change focusMode to sources 2025-12-23 17:11:38 +05:30
ItzCrazyKns
13d6bcf113 Update Optimization.tsx 2025-12-22 17:58:30 +05:30
ItzCrazyKns
94a24d4058 feat(message-input): add overflow to prevent blocked popovers 2025-12-21 19:56:43 +05:30
ItzCrazyKns
300cfa35c7 Update Optimization.tsx 2025-12-19 16:45:46 +05:30
ItzCrazyKns
85273493a0 feat(copy): fix type mismatch 2025-12-19 16:35:13 +05:30
ItzCrazyKns
6e2345bd2d feat(message-box): update markdown2jsx overrides to render codeblock 2025-12-19 16:27:55 +05:30
ItzCrazyKns
fdee29c93e feat(renderers): add code block 2025-12-19 16:26:51 +05:30
ItzCrazyKns
21cb0f5fd9 feat(app): add syntax highlighter 2025-12-19 16:26:38 +05:30
ItzCrazyKns
a82b605c70 feat(citation): move to message renderer 2025-12-19 16:26:13 +05:30
ItzCrazyKns
64683e3dec feat(assistant-steps): improve style 2025-12-19 16:25:56 +05:30
ItzCrazyKns
604774ef6e feat(social-search): add social search 2025-12-18 13:56:39 +05:30
ItzCrazyKns
ac183a90e8 feat(academic-search): add academic search 2025-12-18 13:56:26 +05:30
ItzCrazyKns
5511a276d4 Update Sources.tsx 2025-12-18 13:56:08 +05:30
ItzCrazyKns
473a04b6a5 feat(suggestions): prevent icon from shrinking 2025-12-18 13:56:04 +05:30
ItzCrazyKns
491136822f feat(app): lint & beautify 2025-12-17 21:17:21 +05:30
ItzCrazyKns
6e086953b1 feat(agents): add academic and social search 2025-12-17 21:17:08 +05:30
ItzCrazyKns
1961e4e707 feat(empty-chat-message-input): use sources 2025-12-15 23:49:26 +05:30
ItzCrazyKns
249889f55a feat(actions-registry): add sources, update web search to become active on web 2025-12-15 23:49:11 +05:30
ItzCrazyKns
9b2c229e9c feat(message-input): remove copilot toggle 2025-12-15 23:48:32 +05:30
ItzCrazyKns
4bdb90e150 feat(message-input-actions): update to use motion, improve animations 2025-12-15 23:48:14 +05:30
ItzCrazyKns
f9cc97ffb5 feat(message-input-actions): add sources 2025-12-15 23:47:48 +05:30
ItzCrazyKns
9dd670f46a feat(chat-hook): handle sources 2025-12-15 23:47:38 +05:30
ItzCrazyKns
bd3c5f895a feat(message-input-actions): remove copilot, focus selector 2025-12-15 23:47:21 +05:30
ItzCrazyKns
e6c8a0aa6f Add antialiased class to body element 2025-12-15 23:47:01 +05:30
ItzCrazyKns
b90b92079b feat(chat-route): accept sources 2025-12-15 23:46:46 +05:30
ItzCrazyKns
a3065d58ef feat(package): add motion, react tooltip, phosphor icons 2025-12-15 23:46:11 +05:30
ItzCrazyKns
ca4809f0f2 Update manager.ts 2025-12-14 19:32:09 +05:30
ItzCrazyKns
3d1d164f68 feat(app): lint & beautify 2025-12-13 22:23:54 +05:30
ItzCrazyKns
a99702d837 feat(app): update UI to handle uploads 2025-12-13 22:23:39 +05:30
ItzCrazyKns
60675955e4 feat(researcher-prompt): add user uploaded files 2025-12-13 22:23:08 +05:30
ItzCrazyKns
a6ff94d030 feat(api): update to use fileIds 2025-12-13 22:22:41 +05:30
ItzCrazyKns
748ee4d3c2 feat(actions): add uploads search action 2025-12-13 22:22:17 +05:30
ItzCrazyKns
1f3bf8da32 feat(researcher): use reasoning 2025-12-13 22:21:44 +05:30
ItzCrazyKns
8d471ac40e feat(registry): update to send fileIds 2025-12-13 22:21:22 +05:30
ItzCrazyKns
40b25a487b feat(uploads): update to use new manager 2025-12-13 22:20:26 +05:30
ItzCrazyKns
3949748bbd feat(suggestions-agent): fix type errors 2025-12-13 22:19:52 +05:30
ItzCrazyKns
56e47d6c39 feat(ollama-llm): use hash to generate id 2025-12-13 22:19:38 +05:30
ItzCrazyKns
fd745577d6 feat(writer-prompt): revert to old prompt to fix length issues 2025-12-13 22:19:06 +05:30
ItzCrazyKns
86ea3cde7e feat(types): add upload research blocks 2025-12-13 22:18:48 +05:30
ItzCrazyKns
aeb90cb137 feat(uploads): add uploads store with reciprocal rerank fusion 2025-12-13 22:18:33 +05:30
ItzCrazyKns
6473e51fde feat(uploads): add uploads manager 2025-12-13 22:18:07 +05:30
ItzCrazyKns
c7c327a7bb feat(utils): add token based text splitting 2025-12-13 22:17:51 +05:30
ItzCrazyKns
0688630863 feat(actions): update web search action to use reasoning 2025-12-13 22:17:02 +05:30
ItzCrazyKns
0b9e193ed1 feat(actions): rename plan to reasoning 2025-12-13 22:16:21 +05:30
ItzCrazyKns
8d1b04e05f feat(search-agent): use index + 1 to fix zero errors 2025-12-13 22:15:47 +05:30
ItzCrazyKns
ff4cf98b50 feat(media-search): fix type errors 2025-12-13 22:15:29 +05:30
ItzCrazyKns
13ae0b9451 feat(package): remove langchain, other unused packages 2025-12-13 22:14:29 +05:30
ItzCrazyKns
0cfa01422c Create fileSearch.ts 2025-12-12 23:56:34 +05:30
ItzCrazyKns
fdaa2f0646 feat(openai): update model list 2025-12-12 00:22:59 +05:30
ItzCrazyKns
fc0c444b6a feat(researcher-prompt): add mode based prompts 2025-12-09 11:42:11 +05:30
ItzCrazyKns
01b537ade1 feat(actions): add tool description, description 2025-12-09 11:41:55 +05:30
ItzCrazyKns
3bffc72422 feat(types): update research action type 2025-12-09 11:40:40 +05:30
ItzCrazyKns
6016090f12 feat(actions): stream results internally 2025-12-08 13:10:11 +05:30
ItzCrazyKns
8aed9518a2 feat(researcher): pass research block id 2025-12-08 13:09:52 +05:30
ItzCrazyKns
2df6250ba1 feat(weather): respect unit preference 2025-12-08 13:09:21 +05:30
ItzCrazyKns
85f6c3b901 feat(client-registry): add getMeasurementUnit 2025-12-08 13:08:52 +05:30
ItzCrazyKns
96001a9e26 feat(assistant-steps): handle reading, search_results 2025-12-08 13:08:26 +05:30
ItzCrazyKns
331387efa4 feat(search): add better context handling 2025-12-08 13:07:52 +05:30
ItzCrazyKns
d0e71e6482 feat(types): add search_results research block 2025-12-08 13:07:16 +05:30
ItzCrazyKns
e329820bc8 feat(package): update lucide-react, framer-motion 2025-12-08 13:06:58 +05:30
101 changed files with 5501 additions and 2425 deletions

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@@ -11,33 +11,63 @@ Perplexica's codebase is organized as follows:
- **UI Components and Pages**:
- **Components (`src/components`)**: Reusable UI components.
- **Pages and Routes (`src/app`)**: Next.js app directory structure with page components.
- Main app routes include: home (`/`), chat (`/c`), discover (`/discover`), library (`/library`), and settings (`/settings`).
- **API Routes (`src/app/api`)**: API endpoints implemented with Next.js API routes.
- `/api/chat`: Handles chat interactions.
- `/api/search`: Provides direct access to Perplexica's search capabilities.
- Other endpoints for models, files, and suggestions.
- Main app routes include: home (`/`), chat (`/c`), discover (`/discover`), and library (`/library`).
- **API Routes (`src/app/api`)**: Server endpoints implemented with Next.js route handlers.
- **Backend Logic (`src/lib`)**: Contains all the backend functionality including search, database, and API logic.
- The search functionality is present inside `src/lib/search` directory.
- All of the focus modes are implemented using the Meta Search Agent class in `src/lib/search/metaSearchAgent.ts`.
- The search system lives in `src/lib/agents/search`.
- The search pipeline is split into classification, research, widgets, and writing.
- Database functionality is in `src/lib/db`.
- Chat model and embedding model providers are managed in `src/lib/providers`.
- Prompt templates and LLM chain definitions are in `src/lib/prompts` and `src/lib/chains` respectively.
- Chat model and embedding model providers are in `src/lib/models/providers`, and models are loaded via `src/lib/models/registry.ts`.
- Prompt templates are in `src/lib/prompts`.
- SearXNG integration is in `src/lib/searxng.ts`.
- Upload search lives in `src/lib/uploads`.
### Where to make changes
If you are not sure where to start, use this section as a map.
- **Search behavior and reasoning**
- `src/lib/agents/search` contains the core chat and search pipeline.
- `classifier.ts` decides whether research is needed and what should run.
- `researcher/` gathers information in the background.
- **Add or change a search capability**
- Research tools (web, academic, discussions, uploads, scraping) live in `src/lib/agents/search/researcher/actions`.
- Tools are registered in `src/lib/agents/search/researcher/actions/index.ts`.
- **Add or change widgets**
- Widgets live in `src/lib/agents/search/widgets`.
- Widgets run in parallel with research and show structured results in the UI.
- **Model integrations**
- Providers live in `src/lib/models/providers`.
- Add new providers there and wire them into the model registry so they show up in the app.
- **Architecture docs**
- High level overview: `docs/architecture/README.md`
- High level flow: `docs/architecture/WORKING.md`
## API Documentation
Perplexica exposes several API endpoints for programmatic access, including:
Perplexica includes API documentation for programmatic access.
- **Search API**: Access Perplexica's advanced search capabilities directly via the `/api/search` endpoint. For detailed documentation, see `docs/api/search.md`.
- **Search API**: For detailed documentation, see `docs/API/SEARCH.md`.
## Setting Up Your Environment
Before diving into coding, setting up your local environment is key. Here's what you need to do:
1. In the root directory, locate the `sample.config.toml` file.
2. Rename it to `config.toml` and fill in the necessary configuration fields.
3. Run `npm install` to install all dependencies.
4. Run `npm run db:migrate` to set up the local sqlite database.
5. Use `npm run dev` to start the application in development mode.
1. Run `npm install` to install all dependencies.
2. Use `npm run dev` to start the application in development mode.
3. Open http://localhost:3000 and complete the setup in the UI (API keys, models, search backend URL, etc.).
Database migrations are applied automatically on startup.
For full installation options (Docker and non Docker), see the installation guide in the repository README.
**Please note**: Docker configurations are present for setting up production environments, whereas `npm run dev` is used for development purposes.

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@@ -18,9 +18,11 @@ Want to know more about its architecture and how it works? You can read it [here
🤖 **Support for all major AI providers** - Use local LLMs through Ollama or connect to OpenAI, Anthropic Claude, Google Gemini, Groq, and more. Mix and match models based on your needs.
**Smart search modes** - Choose Balanced Mode for everyday searches, Fast Mode when you need quick answers, or wait for Quality Mode (coming soon) for deep research.
**Smart search modes** - Choose Speed Mode when you need quick answers, Balanced Mode for everyday searches, or Quality Mode for deep research.
🎯 **Six specialized focus modes** - Get better results with modes designed for specific tasks: Academic papers, YouTube videos, Reddit discussions, Wolfram Alpha calculations, writing assistance, or general web search.
🧭 **Pick your sources** - Search the web, discussions, or academic papers. More sources and integrations are in progress.
🧩 **Widgets** - Helpful UI cards that show up when relevant, like weather, calculations, stock prices, and other quick lookups.
🔍 **Web search powered by SearxNG** - Access multiple search engines while keeping your identity private. Support for Tavily and Exa coming soon for even better results.
@@ -81,7 +83,7 @@ There are mainly 2 ways of installing Perplexica - With Docker, Without Docker.
Perplexica can be easily run using Docker. Simply run the following command:
```bash
docker run -d -p 3000:3000 -v perplexica-data:/home/perplexica/data -v perplexica-uploads:/home/perplexica/uploads --name perplexica itzcrazykns1337/perplexica:latest
docker run -d -p 3000:3000 -v perplexica-data:/home/perplexica/data --name perplexica itzcrazykns1337/perplexica:latest
```
This will pull and start the Perplexica container with the bundled SearxNG search engine. Once running, open your browser and navigate to http://localhost:3000. You can then configure your settings (API keys, models, etc.) directly in the setup screen.
@@ -93,7 +95,7 @@ This will pull and start the Perplexica container with the bundled SearxNG searc
If you already have SearxNG running, you can use the slim version of Perplexica:
```bash
docker run -d -p 3000:3000 -e SEARXNG_API_URL=http://your-searxng-url:8080 -v perplexica-data:/home/perplexica/data -v perplexica-uploads:/home/perplexica/uploads --name perplexica itzcrazykns1337/perplexica:slim-latest
docker run -d -p 3000:3000 -e SEARXNG_API_URL=http://your-searxng-url:8080 -v perplexica-data:/home/perplexica/data --name perplexica itzcrazykns1337/perplexica:slim-latest
```
**Important**: Make sure your SearxNG instance has:
@@ -120,7 +122,7 @@ If you prefer to build from source or need more control:
```bash
docker build -t perplexica .
docker run -d -p 3000:3000 -v perplexica-data:/home/perplexica/data -v perplexica-uploads:/home/perplexica/uploads --name perplexica perplexica
docker run -d -p 3000:3000 -v perplexica-data:/home/perplexica/data --name perplexica perplexica
```
5. Access Perplexica at http://localhost:3000 and configure your settings in the setup screen.
@@ -237,13 +239,9 @@ Perplexica runs on Next.js and handles all API requests. It works right away on
## Upcoming Features
- [x] Add settings page
- [x] Adding support for local LLMs
- [x] History Saving features
- [x] Introducing various Focus Modes
- [x] Adding API support
- [x] Adding Discover
- [ ] Finalizing Copilot Mode
- [ ] Adding more widgets, integrations, search sources
- [ ] Adding ability to create custom agents (name T.B.D.)
- [ ] Adding authentication
## Support Us

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@@ -1,15 +1,14 @@
services:
perplexica:
image: itzcrazykns1337/perplexica:latest
build:
context: .
ports:
- '3000:3000'
volumes:
- data:/home/perplexica/data
- uploads:/home/perplexica/uploads
restart: unless-stopped
volumes:
data:
name: 'perplexica-data'
uploads:
name: 'perplexica-uploads'

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@@ -57,7 +57,7 @@ Use the `id` field as the `providerId` and the `key` field from the models array
### Request
The API accepts a JSON object in the request body, where you define the focus mode, chat models, embedding models, and your query.
The API accepts a JSON object in the request body, where you define the enabled search `sources`, chat models, embedding models, and your query.
#### Request Body Structure
@@ -72,7 +72,7 @@ The API accepts a JSON object in the request body, where you define the focus mo
"key": "text-embedding-3-large"
},
"optimizationMode": "speed",
"focusMode": "webSearch",
"sources": ["web"],
"query": "What is Perplexica",
"history": [
["human", "Hi, how are you?"],
@@ -87,24 +87,25 @@ The API accepts a JSON object in the request body, where you define the focus mo
### Request Parameters
- **`chatModel`** (object, optional): Defines the chat model to be used for the query. To get available providers and models, send a GET request to `http://localhost:3000/api/providers`.
- **`chatModel`** (object, required): Defines the chat model to be used for the query. To get available providers and models, send a GET request to `http://localhost:3000/api/providers`.
- `providerId` (string): The UUID of the provider. You can get this from the `/api/providers` endpoint response.
- `key` (string): The model key/identifier (e.g., `gpt-4o-mini`, `llama3.1:latest`). Use the `key` value from the provider's `chatModels` array, not the display name.
- **`embeddingModel`** (object, optional): Defines the embedding model for similarity-based searching. To get available providers and models, send a GET request to `http://localhost:3000/api/providers`.
- **`embeddingModel`** (object, required): Defines the embedding model for similarity-based searching. To get available providers and models, send a GET request to `http://localhost:3000/api/providers`.
- `providerId` (string): The UUID of the embedding provider. You can get this from the `/api/providers` endpoint response.
- `key` (string): The embedding model key (e.g., `text-embedding-3-large`, `nomic-embed-text`). Use the `key` value from the provider's `embeddingModels` array, not the display name.
- **`focusMode`** (string, required): Specifies which focus mode to use. Available modes:
- **`sources`** (array, required): Which search sources to enable. Available values:
- `webSearch`, `academicSearch`, `writingAssistant`, `wolframAlphaSearch`, `youtubeSearch`, `redditSearch`.
- `web`, `academic`, `discussions`.
- **`optimizationMode`** (string, optional): Specifies the optimization mode to control the balance between performance and quality. Available modes:
- `speed`: Prioritize speed and return the fastest answer.
- `balanced`: Provide a balanced answer with good speed and reasonable quality.
- `quality`: Prioritize answer quality (may be slower).
- **`query`** (string, required): The search query or question.
@@ -132,14 +133,14 @@ The response from the API includes both the final message and the sources used t
"message": "Perplexica is an innovative, open-source AI-powered search engine designed to enhance the way users search for information online. Here are some key features and characteristics of Perplexica:\n\n- **AI-Powered Technology**: It utilizes advanced machine learning algorithms to not only retrieve information but also to understand the context and intent behind user queries, providing more relevant results [1][5].\n\n- **Open-Source**: Being open-source, Perplexica offers flexibility and transparency, allowing users to explore its functionalities without the constraints of proprietary software [3][10].",
"sources": [
{
"pageContent": "Perplexica is an innovative, open-source AI-powered search engine designed to enhance the way users search for information online.",
"content": "Perplexica is an innovative, open-source AI-powered search engine designed to enhance the way users search for information online.",
"metadata": {
"title": "What is Perplexica, and how does it function as an AI-powered search ...",
"url": "https://askai.glarity.app/search/What-is-Perplexica--and-how-does-it-function-as-an-AI-powered-search-engine"
}
},
{
"pageContent": "Perplexica is an open-source AI-powered search tool that dives deep into the internet to find precise answers.",
"content": "Perplexica is an open-source AI-powered search tool that dives deep into the internet to find precise answers.",
"metadata": {
"title": "Sahar Mor's Post",
"url": "https://www.linkedin.com/posts/sahar-mor_a-new-open-source-project-called-perplexica-activity-7204489745668694016-ncja"
@@ -158,7 +159,7 @@ Example of streamed response objects:
```
{"type":"init","data":"Stream connected"}
{"type":"sources","data":[{"pageContent":"...","metadata":{"title":"...","url":"..."}},...]}
{"type":"sources","data":[{"content":"...","metadata":{"title":"...","url":"..."}},...]}
{"type":"response","data":"Perplexica is an "}
{"type":"response","data":"innovative, open-source "}
{"type":"response","data":"AI-powered search engine..."}
@@ -174,9 +175,9 @@ Clients should process each line as a separate JSON object. The different messag
### Fields in the Response
- **`message`** (string): The search result, generated based on the query and focus mode.
- **`message`** (string): The search result, generated based on the query and enabled `sources`.
- **`sources`** (array): A list of sources that were used to generate the search result. Each source includes:
- `pageContent`: A snippet of the relevant content from the source.
- `content`: A snippet of the relevant content from the source.
- `metadata`: Metadata about the source, including:
- `title`: The title of the webpage.
- `url`: The URL of the webpage.
@@ -185,5 +186,5 @@ Clients should process each line as a separate JSON object. The different messag
If an error occurs during the search process, the API will return an appropriate error message with an HTTP status code.
- **400**: If the request is malformed or missing required fields (e.g., no focus mode or query).
- **400**: If the request is malformed or missing required fields (e.g., no `sources` or `query`).
- **500**: If an internal server error occurs during the search.

View File

@@ -1,11 +1,38 @@
# Perplexica's Architecture
# Perplexica Architecture
Perplexica's architecture consists of the following key components:
Perplexica is a Next.js application that combines an AI chat experience with search.
1. **User Interface**: A web-based interface that allows users to interact with Perplexica for searching images, videos, and much more.
2. **Agent/Chains**: These components predict Perplexica's next actions, understand user queries, and decide whether a web search is necessary.
3. **SearXNG**: A metadata search engine used by Perplexica to search the web for sources.
4. **LLMs (Large Language Models)**: Utilized by agents and chains for tasks like understanding content, writing responses, and citing sources. Examples include Claude, GPTs, etc.
5. **Embedding Models**: To improve the accuracy of search results, embedding models re-rank the results using similarity search algorithms such as cosine similarity and dot product distance.
For a high level flow, see [WORKING.md](WORKING.md). For deeper implementation details, see [CONTRIBUTING.md](../../CONTRIBUTING.md).
For a more detailed explanation of how these components work together, see [WORKING.md](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/architecture/WORKING.md).
## Key components
1. **User Interface**
- A web based UI that lets users chat, search, and view citations.
2. **API Routes**
- `POST /api/chat` powers the chat UI.
- `POST /api/search` provides a programmatic search endpoint.
- `GET /api/providers` lists available providers and model keys.
3. **Agents and Orchestration**
- The system classifies the question first.
- It can run research and widgets in parallel.
- It generates the final answer and includes citations.
4. **Search Backend**
- A meta search backend is used to fetch relevant web results when research is enabled.
5. **LLMs (Large Language Models)**
- Used for classification, writing answers, and producing citations.
6. **Embedding Models**
- Used for semantic search over user uploaded files.
7. **Storage**
- Chats and messages are stored so conversations can be reloaded.

View File

@@ -1,19 +1,72 @@
# How does Perplexica work?
# How Perplexica Works
Curious about how Perplexica works? Don't worry, we'll cover it here. Before we begin, make sure you've read about the architecture of Perplexica to ensure you understand what it's made up of. Haven't read it? You can read it [here](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/architecture/README.md).
This is a high level overview of how Perplexica answers a question.
We'll understand how Perplexica works by taking an example of a scenario where a user asks: "How does an A.C. work?". We'll break down the process into steps to make it easier to understand. The steps are as follows:
If you want a component level overview, see [README.md](README.md).
1. The message is sent to the `/api/chat` route where it invokes the chain. The chain will depend on your focus mode. For this example, let's assume we use the "webSearch" focus mode.
2. The chain is now invoked; first, the message is passed to another chain where it first predicts (using the chat history and the question) whether there is a need for sources and searching the web. If there is, it will generate a query (in accordance with the chat history) for searching the web that we'll take up later. If not, the chain will end there, and then the answer generator chain, also known as the response generator, will be started.
3. The query returned by the first chain is passed to SearXNG to search the web for information.
4. After the information is retrieved, it is based on keyword-based search. We then convert the information into embeddings and the query as well, then we perform a similarity search to find the most relevant sources to answer the query.
5. After all this is done, the sources are passed to the response generator. This chain takes all the chat history, the query, and the sources. It generates a response that is streamed to the UI.
If you want implementation details, see [CONTRIBUTING.md](../../CONTRIBUTING.md).
## How are the answers cited?
## What happens when you ask a question
The LLMs are prompted to do so. We've prompted them so well that they cite the answers themselves, and using some UI magic, we display it to the user.
When you send a message in the UI, the app calls `POST /api/chat`.
## Image and Video Search
At a high level, we do three things:
Image and video searches are conducted in a similar manner. A query is always generated first, then we search the web for images and videos that match the query. These results are then returned to the user.
1. Classify the question and decide what to do next.
2. Run research and widgets in parallel.
3. Write the final answer and include citations.
## Classification
Before searching or answering, we run a classification step.
This step decides things like:
- Whether we should do research for this question
- Whether we should show any widgets
- How to rewrite the question into a clearer standalone form
## Widgets
Widgets are small, structured helpers that can run alongside research.
Examples include weather, stocks, and simple calculations.
If a widget is relevant, we show it in the UI while the answer is still being generated.
Widgets are helpful context for the answer, but they are not part of what the model should cite.
## Research
If research is needed, we gather information in the background while widgets can run.
Depending on configuration, research may include web lookup and searching user uploaded files.
## Answer generation
Once we have enough context, the chat model generates the final response.
You can control the tradeoff between speed and quality using `optimizationMode`:
- `speed`
- `balanced`
- `quality`
## How citations work
We prompt the model to cite the references it used. The UI then renders those citations alongside the supporting links.
## Search API
If you are integrating Perplexica into another product, you can call `POST /api/search`.
It returns:
- `message`: the generated answer
- `sources`: supporting references used for the answer
You can also enable streaming by setting `stream: true`.
## Image and video search
Image and video search use separate endpoints (`POST /api/images` and `POST /api/videos`). We generate a focused query using the chat model, then fetch matching results from a search backend.

View File

@@ -10,7 +10,7 @@ Simply pull the latest image and restart your container:
docker pull itzcrazykns1337/perplexica:latest
docker stop perplexica
docker rm perplexica
docker run -d -p 3000:3000 -v perplexica-data:/home/perplexica/data -v perplexica-uploads:/home/perplexica/uploads --name perplexica itzcrazykns1337/perplexica:latest
docker run -d -p 3000:3000 -v perplexica-data:/home/perplexica/data --name perplexica itzcrazykns1337/perplexica:latest
```
For slim version:
@@ -19,7 +19,7 @@ For slim version:
docker pull itzcrazykns1337/perplexica:slim-latest
docker stop perplexica
docker rm perplexica
docker run -d -p 3000:3000 -e SEARXNG_API_URL=http://your-searxng-url:8080 -v perplexica-data:/home/perplexica/data -v perplexica-uploads:/home/perplexica/uploads --name perplexica itzcrazykns1337/perplexica:slim-latest
docker run -d -p 3000:3000 -e SEARXNG_API_URL=http://your-searxng-url:8080 -v perplexica-data:/home/perplexica/data --name perplexica itzcrazykns1337/perplexica:slim-latest
```
Once updated, go to http://localhost:3000 and verify the latest changes. Your settings are preserved automatically.

View File

@@ -1,15 +1 @@
PRAGMA foreign_keys=OFF;--> statement-breakpoint
CREATE TABLE `__new_messages` (
`id` integer PRIMARY KEY NOT NULL,
`messageId` text NOT NULL,
`chatId` text NOT NULL,
`backendId` text NOT NULL,
`query` text NOT NULL,
`createdAt` text NOT NULL,
`responseBlocks` text DEFAULT '[]',
`status` text DEFAULT 'answering'
);
--> statement-breakpoint
DROP TABLE `messages`;--> statement-breakpoint
ALTER TABLE `__new_messages` RENAME TO `messages`;--> statement-breakpoint
PRAGMA foreign_keys=ON;
/* do nothing */

View File

@@ -28,8 +28,8 @@
"notNull": true,
"autoincrement": false
},
"focusMode": {
"name": "focusMode",
"sources": {
"name": "sources",
"type": "text",
"primaryKey": false,
"notNull": true,

2
next-env.d.ts vendored
View File

@@ -1,6 +1,6 @@
/// <reference types="next" />
/// <reference types="next/image-types/global" />
import "./.next/dev/types/routes.d.ts";
import './.next/dev/types/routes.d.ts';
// NOTE: This file should not be edited
// see https://nextjs.org/docs/app/api-reference/config/typescript for more information.

View File

@@ -11,6 +11,13 @@ const nextConfig = {
],
},
serverExternalPackages: ['pdf-parse'],
outputFileTracingIncludes: {
'/api/**': [
'./node_modules/@napi-rs/canvas/**',
'./node_modules/@napi-rs/canvas-linux-x64-gnu/**',
'./node_modules/@napi-rs/canvas-linux-x64-musl/**',
],
},
env: {
NEXT_PUBLIC_VERSION: pkg.version,
},

View File

@@ -1,71 +1,65 @@
{
"name": "perplexica-frontend",
"version": "1.11.2",
"name": "perplexica",
"version": "1.12.1",
"license": "MIT",
"author": "ItzCrazyKns",
"scripts": {
"dev": "next dev",
"build": "next build",
"dev": "next dev --webpack",
"build": "next build --webpack",
"start": "next start",
"lint": "next lint",
"format:write": "prettier . --write"
},
"dependencies": {
"@google/genai": "^1.34.0",
"@headlessui/react": "^2.2.0",
"@headlessui/tailwindcss": "^0.2.2",
"@huggingface/transformers": "^3.7.5",
"@iarna/toml": "^2.2.5",
"@huggingface/transformers": "^3.8.1",
"@icons-pack/react-simple-icons": "^12.3.0",
"@langchain/anthropic": "^1.0.1",
"@langchain/community": "^1.0.3",
"@langchain/core": "^1.0.5",
"@langchain/google-genai": "^1.0.1",
"@langchain/groq": "^1.0.1",
"@langchain/langgraph": "^1.0.1",
"@langchain/ollama": "^1.0.1",
"@langchain/openai": "^1.1.1",
"@langchain/textsplitters": "^1.0.0",
"@phosphor-icons/react": "^2.1.10",
"@radix-ui/react-tooltip": "^1.2.8",
"@tailwindcss/typography": "^0.5.12",
"@toolsycc/json-repair": "^0.1.22",
"axios": "^1.8.3",
"better-sqlite3": "^11.9.1",
"clsx": "^2.1.0",
"drizzle-orm": "^0.40.1",
"framer-motion": "^12.23.24",
"html-to-text": "^9.0.5",
"jspdf": "^3.0.1",
"langchain": "^1.0.4",
"js-tiktoken": "^1.0.21",
"jspdf": "^3.0.4",
"lightweight-charts": "^5.0.9",
"lucide-react": "^0.363.0",
"lucide-react": "^0.556.0",
"mammoth": "^1.9.1",
"markdown-to-jsx": "^7.7.2",
"mathjs": "^15.1.0",
"motion": "^12.23.26",
"next": "^16.0.7",
"next-themes": "^0.3.0",
"officeparser": "^5.2.2",
"ollama": "^0.6.3",
"openai": "^6.9.0",
"partial-json": "^0.1.7",
"pdf-parse": "^1.1.1",
"pdf-parse": "^2.4.5",
"react": "^18",
"react-dom": "^18",
"react-syntax-highlighter": "^16.1.0",
"react-text-to-speech": "^0.14.5",
"react-textarea-autosize": "^8.5.3",
"rfc6902": "^5.1.2",
"sonner": "^1.4.41",
"tailwind-merge": "^2.2.2",
"turndown": "^7.2.2",
"winston": "^3.17.0",
"yahoo-finance2": "^3.10.2",
"yet-another-react-lightbox": "^3.17.2",
"zod": "^4.1.12"
},
"devDependencies": {
"@types/better-sqlite3": "^7.6.12",
"@types/html-to-text": "^9.0.4",
"@types/jspdf": "^2.0.0",
"@types/node": "^24.8.1",
"@types/pdf-parse": "^1.1.4",
"@types/react": "^18",
"@types/react-dom": "^18",
"@types/react-syntax-highlighter": "^15.5.13",
"@types/turndown": "^5.0.6",
"autoprefixer": "^10.0.1",
"drizzle-kit": "^0.30.5",
@@ -75,5 +69,8 @@
"prettier": "^3.2.5",
"tailwindcss": "^3.3.0",
"typescript": "^5.9.3"
},
"optionalDependencies": {
"@napi-rs/canvas": "^0.1.87"
}
}

View File

@@ -1,10 +1,14 @@
import crypto from 'crypto';
import { z } from 'zod';
import ModelRegistry from '@/lib/models/registry';
import { ModelWithProvider } from '@/lib/models/types';
import SearchAgent from '@/lib/agents/search';
import SessionManager from '@/lib/session';
import { ChatTurnMessage } from '@/lib/types';
import { SearchSources } from '@/lib/agents/search/types';
import db from '@/lib/db';
import { eq } from 'drizzle-orm';
import { chats } from '@/lib/db/schema';
import UploadManager from '@/lib/uploads/manager';
export const runtime = 'nodejs';
export const dynamic = 'force-dynamic';
@@ -32,7 +36,7 @@ const bodySchema = z.object({
optimizationMode: z.enum(['speed', 'balanced', 'quality'], {
message: 'Optimization mode must be one of: speed, balanced, quality',
}),
focusMode: z.string().min(1, 'Focus mode is required'),
sources: z.array(z.string()).optional().default([]),
history: z
.array(z.tuple([z.string(), z.string()]))
.optional()
@@ -43,7 +47,6 @@ const bodySchema = z.object({
systemInstructions: z.string().nullable().optional().default(''),
});
type Message = z.infer<typeof messageSchema>;
type Body = z.infer<typeof bodySchema>;
const safeValidateBody = (data: unknown) => {
@@ -65,6 +68,38 @@ const safeValidateBody = (data: unknown) => {
};
};
const ensureChatExists = async (input: {
id: string;
sources: SearchSources[];
query: string;
fileIds: string[];
}) => {
try {
const exists = await db.query.chats
.findFirst({
where: eq(chats.id, input.id),
})
.execute();
if (!exists) {
await db.insert(chats).values({
id: input.id,
createdAt: new Date().toISOString(),
sources: input.sources,
title: input.query,
files: input.fileIds.map((id) => {
return {
fileId: id,
name: UploadManager.getFile(id)?.name || 'Uploaded File',
};
}),
});
}
} catch (err) {
console.error('Failed to check/save chat:', err);
}
};
export const POST = async (req: Request) => {
try {
const reqBody = (await req.json()) as Body;
@@ -121,95 +156,86 @@ export const POST = async (req: Request) => {
const writer = responseStream.writable.getWriter();
const encoder = new TextEncoder();
let receivedMessage = '';
session.addListener('data', (data: any) => {
if (data.type === 'response') {
const disconnect = session.subscribe((event: string, data: any) => {
if (event === 'data') {
if (data.type === 'block') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'block',
block: data.block,
}) + '\n',
),
);
} else if (data.type === 'updateBlock') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'updateBlock',
blockId: data.blockId,
patch: data.patch,
}) + '\n',
),
);
} else if (data.type === 'researchComplete') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'researchComplete',
}) + '\n',
),
);
}
} else if (event === 'end') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'message',
type: 'messageEnd',
}) + '\n',
),
);
writer.close();
session.removeAllListeners();
} else if (event === 'error') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'error',
data: data.data,
}) + '\n',
),
);
receivedMessage += data.data;
} else if (data.type === 'sources') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'sources',
data: data.data,
}) + '\n',
),
);
} else if (data.type === 'block') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'block',
block: data.block,
}) + '\n',
),
);
} else if (data.type === 'updateBlock') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'updateBlock',
blockId: data.blockId,
patch: data.patch,
}) + '\n',
),
);
} else if (data.type === 'researchComplete') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'researchComplete',
}) + '\n',
),
);
writer.close();
session.removeAllListeners();
}
});
session.addListener('end', () => {
writer.write(
encoder.encode(
JSON.stringify({
type: 'messageEnd',
}) + '\n',
),
);
writer.close();
session.removeAllListeners();
});
session.addListener('error', (data: any) => {
writer.write(
encoder.encode(
JSON.stringify({
type: 'error',
data: data.data,
}) + '\n',
),
);
writer.close();
session.removeAllListeners();
});
agent.searchAsync(session, {
chatHistory: history,
followUp: message.content,
chatId: body.message.chatId,
messageId: body.message.messageId,
config: {
llm,
embedding: embedding,
sources: ['web'],
sources: body.sources as SearchSources[],
mode: body.optimizationMode,
fileIds: body.files,
systemInstructions: body.systemInstructions || 'None',
},
});
/* handleHistorySave(message, humanMessageId, body.focusMode, body.files); */
ensureChatExists({
id: body.message.chatId,
sources: body.sources as SearchSources[],
fileIds: body.files,
query: body.message.content,
});
req.signal.addEventListener('abort', () => {
disconnect();
writer.close();
});
return new Response(responseStream.readable, {
headers: {

View File

@@ -21,7 +21,10 @@ export const POST = async (req: Request) => {
const images = await searchImages(
{
chatHistory: body.chatHistory,
chatHistory: body.chatHistory.map(([role, content]) => ({
role: role === 'human' ? 'user' : 'assistant',
content,
})),
query: body.query,
},
llm,

View File

@@ -0,0 +1,93 @@
import SessionManager from '@/lib/session';
export const POST = async (
req: Request,
{ params }: { params: Promise<{ id: string }> },
) => {
try {
const { id } = await params;
const session = SessionManager.getSession(id);
if (!session) {
return Response.json({ message: 'Session not found' }, { status: 404 });
}
const responseStream = new TransformStream();
const writer = responseStream.writable.getWriter();
const encoder = new TextEncoder();
const disconnect = session.subscribe((event, data) => {
if (event === 'data') {
if (data.type === 'block') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'block',
block: data.block,
}) + '\n',
),
);
} else if (data.type === 'updateBlock') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'updateBlock',
blockId: data.blockId,
patch: data.patch,
}) + '\n',
),
);
} else if (data.type === 'researchComplete') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'researchComplete',
}) + '\n',
),
);
}
} else if (event === 'end') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'messageEnd',
}) + '\n',
),
);
writer.close();
disconnect();
} else if (event === 'error') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'error',
data: data.data,
}) + '\n',
),
);
writer.close();
disconnect();
}
});
req.signal.addEventListener('abort', () => {
disconnect();
writer.close();
});
return new Response(responseStream.readable, {
headers: {
'Content-Type': 'text/event-stream',
Connection: 'keep-alive',
'Cache-Control': 'no-cache, no-transform',
},
});
} catch (err) {
console.error('Error in reconnecting to session stream: ', err);
return Response.json(
{ message: 'An error has occurred.' },
{ status: 500 },
);
}
};

View File

@@ -1,12 +1,13 @@
import ModelRegistry from '@/lib/models/registry';
import { ModelWithProvider } from '@/lib/models/types';
import SessionManager from '@/lib/session';
import SearchAgent from '@/lib/agents/search';
import { ChatTurnMessage } from '@/lib/types';
import { SearchSources } from '@/lib/agents/search/types';
import APISearchAgent from '@/lib/agents/search/api';
interface ChatRequestBody {
optimizationMode: 'speed' | 'balanced';
focusMode: string;
optimizationMode: 'speed' | 'balanced' | 'quality';
sources: SearchSources[];
chatModel: ModelWithProvider;
embeddingModel: ModelWithProvider;
query: string;
@@ -19,15 +20,15 @@ export const POST = async (req: Request) => {
try {
const body: ChatRequestBody = await req.json();
if (!body.focusMode || !body.query) {
if (!body.sources || !body.query) {
return Response.json(
{ message: 'Missing focus mode or query' },
{ message: 'Missing sources or query' },
{ status: 400 },
);
}
body.history = body.history || [];
body.optimizationMode = body.optimizationMode || 'balanced';
body.optimizationMode = body.optimizationMode || 'speed';
body.stream = body.stream || false;
const registry = new ModelRegistry();
@@ -48,17 +49,21 @@ export const POST = async (req: Request) => {
const session = SessionManager.createSession();
const agent = new SearchAgent();
const agent = new APISearchAgent();
agent.searchAsync(session, {
chatHistory: history,
config: {
embedding: embeddings,
llm: llm,
sources: ['web', 'discussions', 'academic'],
mode: 'balanced',
sources: body.sources,
mode: body.optimizationMode,
fileIds: [],
systemInstructions: body.systemInstructions || '',
},
followUp: body.query,
chatId: crypto.randomUUID(),
messageId: crypto.randomUUID(),
});
if (!body.stream) {
@@ -70,36 +75,37 @@ export const POST = async (req: Request) => {
let message = '';
let sources: any[] = [];
session.addListener('data', (data: string) => {
try {
const parsedData = JSON.parse(data);
if (parsedData.type === 'response') {
message += parsedData.data;
} else if (parsedData.type === 'sources') {
sources = parsedData.data;
session.subscribe((event: string, data: Record<string, any>) => {
if (event === 'data') {
try {
if (data.type === 'response') {
message += data.data;
} else if (data.type === 'searchResults') {
sources = data.data;
}
} catch (error) {
reject(
Response.json(
{ message: 'Error parsing data' },
{ status: 500 },
),
);
}
} catch (error) {
}
if (event === 'end') {
resolve(Response.json({ message, sources }, { status: 200 }));
}
if (event === 'error') {
reject(
Response.json(
{ message: 'Error parsing data' },
{ message: 'Search error', error: data },
{ status: 500 },
),
);
}
});
session.addListener('end', () => {
resolve(Response.json({ message, sources }, { status: 200 }));
});
session.addListener('error', (error: any) => {
reject(
Response.json(
{ message: 'Search error', error },
{ status: 500 },
),
);
});
},
);
}
@@ -130,54 +136,54 @@ export const POST = async (req: Request) => {
} catch (error) {}
});
session.addListener('data', (data: string) => {
if (signal.aborted) return;
session.subscribe((event: string, data: Record<string, any>) => {
if (event === 'data') {
if (signal.aborted) return;
try {
const parsedData = JSON.parse(data);
if (parsedData.type === 'response') {
controller.enqueue(
encoder.encode(
JSON.stringify({
type: 'response',
data: parsedData.data,
}) + '\n',
),
);
} else if (parsedData.type === 'sources') {
sources = parsedData.data;
controller.enqueue(
encoder.encode(
JSON.stringify({
type: 'sources',
data: sources,
}) + '\n',
),
);
try {
if (data.type === 'response') {
controller.enqueue(
encoder.encode(
JSON.stringify({
type: 'response',
data: data.data,
}) + '\n',
),
);
} else if (data.type === 'searchResults') {
sources = data.data;
controller.enqueue(
encoder.encode(
JSON.stringify({
type: 'sources',
data: sources,
}) + '\n',
),
);
}
} catch (error) {
controller.error(error);
}
} catch (error) {
controller.error(error);
}
});
session.addListener('end', () => {
if (signal.aborted) return;
if (event === 'end') {
if (signal.aborted) return;
controller.enqueue(
encoder.encode(
JSON.stringify({
type: 'done',
}) + '\n',
),
);
controller.close();
});
controller.enqueue(
encoder.encode(
JSON.stringify({
type: 'done',
}) + '\n',
),
);
controller.close();
}
session.addListener('error', (error: any) => {
if (signal.aborted) return;
if (event === 'error') {
if (signal.aborted) return;
controller.error(error);
controller.error(data);
}
});
},
cancel() {

View File

@@ -1,7 +1,6 @@
import generateSuggestions from '@/lib/agents/suggestions';
import ModelRegistry from '@/lib/models/registry';
import { ModelWithProvider } from '@/lib/models/types';
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
interface SuggestionsGenerationBody {
chatHistory: any[];
@@ -21,7 +20,10 @@ export const POST = async (req: Request) => {
const suggestions = await generateSuggestions(
{
chatHistory: body.chatHistory,
chatHistory: body.chatHistory.map(([role, content]) => ({
role: role === 'human' ? 'user' : 'assistant',
content,
})),
},
llm,
);

View File

@@ -1,40 +1,16 @@
import { NextResponse } from 'next/server';
import fs from 'fs';
import path from 'path';
import crypto from 'crypto';
import { PDFLoader } from '@langchain/community/document_loaders/fs/pdf';
import { DocxLoader } from '@langchain/community/document_loaders/fs/docx';
import { RecursiveCharacterTextSplitter } from '@langchain/textsplitters';
import { Document } from '@langchain/core/documents';
import ModelRegistry from '@/lib/models/registry';
import { Chunk } from '@/lib/types';
interface FileRes {
fileName: string;
fileExtension: string;
fileId: string;
}
const uploadDir = path.join(process.cwd(), 'uploads');
if (!fs.existsSync(uploadDir)) {
fs.mkdirSync(uploadDir, { recursive: true });
}
const splitter = new RecursiveCharacterTextSplitter({
chunkSize: 500,
chunkOverlap: 100,
});
import UploadManager from '@/lib/uploads/manager';
export async function POST(req: Request) {
try {
const formData = await req.formData();
const files = formData.getAll('files') as File[];
const embedding_model = formData.get('embedding_model_key') as string;
const embedding_model_provider = formData.get('embedding_model_provider_id') as string;
const embeddingModel = formData.get('embedding_model_key') as string;
const embeddingModelProvider = formData.get('embedding_model_provider_id') as string;
if (!embedding_model || !embedding_model_provider) {
if (!embeddingModel || !embeddingModelProvider) {
return NextResponse.json(
{ message: 'Missing embedding model or provider' },
{ status: 400 },
@@ -43,81 +19,13 @@ export async function POST(req: Request) {
const registry = new ModelRegistry();
const model = await registry.loadEmbeddingModel(embedding_model_provider, embedding_model);
const model = await registry.loadEmbeddingModel(embeddingModelProvider, embeddingModel);
const uploadManager = new UploadManager({
embeddingModel: model,
})
const processedFiles: FileRes[] = [];
await Promise.all(
files.map(async (file: any) => {
const fileExtension = file.name.split('.').pop();
if (!['pdf', 'docx', 'txt'].includes(fileExtension!)) {
return NextResponse.json(
{ message: 'File type not supported' },
{ status: 400 },
);
}
const uniqueFileName = `${crypto.randomBytes(16).toString('hex')}.${fileExtension}`;
const filePath = path.join(uploadDir, uniqueFileName);
const buffer = Buffer.from(await file.arrayBuffer());
fs.writeFileSync(filePath, new Uint8Array(buffer));
let docs: any[] = [];
if (fileExtension === 'pdf') {
const loader = new PDFLoader(filePath);
docs = await loader.load();
} else if (fileExtension === 'docx') {
const loader = new DocxLoader(filePath);
docs = await loader.load();
} else if (fileExtension === 'txt') {
const text = fs.readFileSync(filePath, 'utf-8');
docs = [
new Document({ pageContent: text, metadata: { title: file.name } }),
];
}
const splitted = await splitter.splitDocuments(docs);
const extractedDataPath = filePath.replace(/\.\w+$/, '-extracted.json');
fs.writeFileSync(
extractedDataPath,
JSON.stringify({
title: file.name,
contents: splitted.map((doc) => doc.pageContent),
}),
);
const chunks: Chunk[] = splitted.map((doc) => {
return {
content: doc.pageContent,
metadata: doc.metadata,
}
});
const embeddings = await model.embedChunks(
chunks
);
const embeddingsDataPath = filePath.replace(
/\.\w+$/,
'-embeddings.json',
);
fs.writeFileSync(
embeddingsDataPath,
JSON.stringify({
title: file.name,
embeddings,
}),
);
processedFiles.push({
fileName: file.name,
fileExtension: fileExtension,
fileId: uniqueFileName.replace(/\.\w+$/, ''),
});
}),
);
const processedFiles = await uploadManager.processFiles(files);
return NextResponse.json({
files: processedFiles,

View File

@@ -21,7 +21,10 @@ export const POST = async (req: Request) => {
const videos = await handleVideoSearch(
{
chatHistory: body.chatHistory,
chatHistory: body.chatHistory.map(([role, content]) => ({
role: role === 'human' ? 'user' : 'assistant',
content,
})),
query: body.query,
},
llm,

View File

@@ -1,10 +1,5 @@
'use client';
import ChatWindow from '@/components/ChatWindow';
import React from 'react';
const Page = () => {
return <ChatWindow />;
};
export default Page;
export default ChatWindow;

View File

@@ -34,7 +34,7 @@ export default function RootLayout({
return (
<html className="h-full" lang="en" suppressHydrationWarning>
<body className={cn('h-full', montserrat.className)}>
<body className={cn('h-full antialiased', montserrat.className)}>
<ThemeProvider>
{setupComplete ? (
<ChatProvider>

View File

@@ -1,8 +1,8 @@
'use client';
import DeleteChat from '@/components/DeleteChat';
import { cn, formatTimeDifference } from '@/lib/utils';
import { BookOpenText, ClockIcon, Delete, ScanEye } from 'lucide-react';
import { formatTimeDifference } from '@/lib/utils';
import { BookOpenText, ClockIcon, FileText, Globe2Icon } from 'lucide-react';
import Link from 'next/link';
import { useEffect, useState } from 'react';
@@ -10,7 +10,8 @@ export interface Chat {
id: string;
title: string;
createdAt: string;
focusMode: string;
sources: string[];
files: { fileId: string; name: string }[];
}
const Page = () => {
@@ -37,74 +38,137 @@ const Page = () => {
fetchChats();
}, []);
return loading ? (
<div className="flex flex-row items-center justify-center min-h-screen">
<svg
aria-hidden="true"
className="w-8 h-8 text-light-200 fill-light-secondary dark:text-[#202020] animate-spin dark:fill-[#ffffff3b]"
viewBox="0 0 100 101"
fill="none"
xmlns="http://www.w3.org/2000/svg"
>
<path
d="M100 50.5908C100.003 78.2051 78.1951 100.003 50.5908 100C22.9765 99.9972 0.997224 78.018 1 50.4037C1.00281 22.7993 22.8108 0.997224 50.4251 1C78.0395 1.00281 100.018 22.8108 100 50.4251ZM9.08164 50.594C9.06312 73.3997 27.7909 92.1272 50.5966 92.1457C73.4023 92.1642 92.1298 73.4365 92.1483 50.6308C92.1669 27.8251 73.4392 9.0973 50.6335 9.07878C27.8278 9.06026 9.10003 27.787 9.08164 50.594Z"
fill="currentColor"
/>
<path
d="M93.9676 39.0409C96.393 38.4037 97.8624 35.9116 96.9801 33.5533C95.1945 28.8227 92.871 24.3692 90.0681 20.348C85.6237 14.1775 79.4473 9.36872 72.0454 6.45794C64.6435 3.54717 56.3134 2.65431 48.3133 3.89319C45.869 4.27179 44.3768 6.77534 45.014 9.20079C45.6512 11.6262 48.1343 13.0956 50.5786 12.717C56.5073 11.8281 62.5542 12.5399 68.0406 14.7911C73.527 17.0422 78.2187 20.7487 81.5841 25.4923C83.7976 28.5886 85.4467 32.059 86.4416 35.7474C87.1273 38.1189 89.5423 39.6781 91.9676 39.0409Z"
fill="currentFill"
/>
</svg>
</div>
) : (
return (
<div>
<div className="flex flex-col pt-4">
<div className="flex items-center">
<BookOpenText />
<h1 className="text-3xl font-medium p-2">Library</h1>
</div>
<hr className="border-t border-[#2B2C2C] my-4 w-full" />
</div>
{chats.length === 0 && (
<div className="flex flex-row items-center justify-center min-h-screen">
<p className="text-black/70 dark:text-white/70 text-sm">
No chats found.
</p>
</div>
)}
{chats.length > 0 && (
<div className="flex flex-col pb-20 lg:pb-2">
{chats.map((chat, i) => (
<div
className={cn(
'flex flex-col space-y-4 py-6',
i !== chats.length - 1
? 'border-b border-white-200 dark:border-dark-200'
: '',
)}
key={i}
>
<Link
href={`/c/${chat.id}`}
className="text-black dark:text-white lg:text-xl font-medium truncate transition duration-200 hover:text-[#24A0ED] dark:hover:text-[#24A0ED] cursor-pointer"
<div className="flex flex-col pt-10 border-b border-light-200/20 dark:border-dark-200/20 pb-6 px-2">
<div className="flex flex-col lg:flex-row lg:items-end lg:justify-between gap-3">
<div className="flex items-center justify-center">
<BookOpenText size={45} className="mb-2.5" />
<div className="flex flex-col">
<h1
className="text-5xl font-normal p-2 pb-0"
style={{ fontFamily: 'PP Editorial, serif' }}
>
{chat.title}
</Link>
<div className="flex flex-row items-center justify-between w-full">
<div className="flex flex-row items-center space-x-1 lg:space-x-1.5 text-black/70 dark:text-white/70">
<ClockIcon size={15} />
<p className="text-xs">
{formatTimeDifference(new Date(), chat.createdAt)} Ago
</p>
</div>
<DeleteChat
chatId={chat.id}
chats={chats}
setChats={setChats}
/>
Library
</h1>
<div className="px-2 text-sm text-black/60 dark:text-white/60 text-center lg:text-left">
Past chats, sources, and uploads.
</div>
</div>
))}
</div>
<div className="flex items-center justify-center lg:justify-end gap-2 text-xs text-black/60 dark:text-white/60">
<span className="inline-flex items-center gap-1 rounded-full border border-black/20 dark:border-white/20 px-2 py-0.5">
<BookOpenText size={14} />
{loading
? 'Loading…'
: `${chats.length} ${chats.length === 1 ? 'chat' : 'chats'}`}
</span>
</div>
</div>
</div>
{loading ? (
<div className="flex flex-row items-center justify-center min-h-[60vh]">
<svg
aria-hidden="true"
className="w-8 h-8 text-light-200 fill-light-secondary dark:text-[#202020] animate-spin dark:fill-[#ffffff3b]"
viewBox="0 0 100 101"
fill="none"
xmlns="http://www.w3.org/2000/svg"
>
<path
d="M100 50.5908C100.003 78.2051 78.1951 100.003 50.5908 100C22.9765 99.9972 0.997224 78.018 1 50.4037C1.00281 22.7993 22.8108 0.997224 50.4251 1C78.0395 1.00281 100.018 22.8108 100 50.4251ZM9.08164 50.594C9.06312 73.3997 27.7909 92.1272 50.5966 92.1457C73.4023 92.1642 92.1298 73.4365 92.1483 50.6308C92.1669 27.8251 73.4392 9.0973 50.6335 9.07878C27.8278 9.06026 9.10003 27.787 9.08164 50.594Z"
fill="currentColor"
/>
<path
d="M93.9676 39.0409C96.393 38.4037 97.8624 35.9116 96.9801 33.5533C95.1945 28.8227 92.871 24.3692 90.0681 20.348C85.6237 14.1775 79.4473 9.36872 72.0454 6.45794C64.6435 3.54717 56.3134 2.65431 48.3133 3.89319C45.869 4.27179 44.3768 6.77534 45.014 9.20079C45.6512 11.6262 48.1343 13.0956 50.5786 12.717C56.5073 11.8281 62.5542 12.5399 68.0406 14.7911C73.527 17.0422 78.2187 20.7487 81.5841 25.4923C83.7976 28.5886 85.4467 32.059 86.4416 35.7474C87.1273 38.1189 89.5423 39.6781 91.9676 39.0409Z"
fill="currentFill"
/>
</svg>
</div>
) : chats.length === 0 ? (
<div className="flex flex-col items-center justify-center min-h-[70vh] px-2 text-center">
<div className="flex items-center justify-center w-12 h-12 rounded-2xl border border-light-200 dark:border-dark-200 bg-light-secondary dark:bg-dark-secondary">
<BookOpenText className="text-black/70 dark:text-white/70" />
</div>
<p className="mt-2 text-black/70 dark:text-white/70 text-sm">
No chats found.
</p>
<p className="mt-1 text-black/70 dark:text-white/70 text-sm">
<Link href="/" className="text-sky-400">
Start a new chat
</Link>{' '}
to see it listed here.
</p>
</div>
) : (
<div className="pt-6 pb-28 px-2">
<div className="rounded-2xl border border-light-200 dark:border-dark-200 overflow-hidden bg-light-primary dark:bg-dark-primary">
{chats.map((chat, index) => {
const sourcesLabel =
chat.sources.length === 0
? null
: chat.sources.length <= 2
? chat.sources
.map((s) => s.charAt(0).toUpperCase() + s.slice(1))
.join(', ')
: `${chat.sources
.slice(0, 2)
.map((s) => s.charAt(0).toUpperCase() + s.slice(1))
.join(', ')} + ${chat.sources.length - 2}`;
return (
<div
key={chat.id}
className={
'group flex flex-col gap-2 p-4 hover:bg-light-secondary dark:hover:bg-dark-secondary transition-colors duration-200 ' +
(index !== chats.length - 1
? 'border-b border-light-200 dark:border-dark-200'
: '')
}
>
<div className="flex items-start justify-between gap-3">
<Link
href={`/c/${chat.id}`}
className="flex-1 text-black dark:text-white text-base lg:text-lg font-medium leading-snug line-clamp-2 group-hover:text-[#24A0ED] transition duration-200"
title={chat.title}
>
{chat.title}
</Link>
<div className="pt-0.5 shrink-0">
<DeleteChat
chatId={chat.id}
chats={chats}
setChats={setChats}
/>
</div>
</div>
<div className="flex flex-wrap items-center gap-2 text-black/70 dark:text-white/70">
<span className="inline-flex items-center gap-1 text-xs">
<ClockIcon size={14} />
{formatTimeDifference(new Date(), chat.createdAt)} Ago
</span>
{sourcesLabel && (
<span className="inline-flex items-center gap-1 text-xs border border-black/20 dark:border-white/20 rounded-full px-2 py-0.5">
<Globe2Icon size={14} />
{sourcesLabel}
</span>
)}
{chat.files.length > 0 && (
<span className="inline-flex items-center gap-1 text-xs border border-black/20 dark:border-white/20 rounded-full px-2 py-0.5">
<FileText size={14} />
{chat.files.length}{' '}
{chat.files.length === 1 ? 'file' : 'files'}
</span>
)}
</div>
</div>
);
})}
</div>
</div>
)}
</div>

View File

@@ -1,6 +1,13 @@
'use client';
import { Brain, Search, FileText, ChevronDown, ChevronUp } from 'lucide-react';
import {
Brain,
Search,
FileText,
ChevronDown,
ChevronUp,
BookSearch,
} from 'lucide-react';
import { motion, AnimatePresence } from 'framer-motion';
import { useEffect, useState } from 'react';
import { ResearchBlock, ResearchBlockSubStep } from '@/lib/types';
@@ -9,11 +16,17 @@ import { useChat } from '@/lib/hooks/useChat';
const getStepIcon = (step: ResearchBlockSubStep) => {
if (step.type === 'reasoning') {
return <Brain className="w-4 h-4" />;
} else if (step.type === 'searching') {
} else if (step.type === 'searching' || step.type === 'upload_searching') {
return <Search className="w-4 h-4" />;
} else if (step.type === 'reading') {
} else if (
step.type === 'search_results' ||
step.type === 'upload_search_results'
) {
return <FileText className="w-4 h-4" />;
} else if (step.type === 'reading') {
return <BookSearch className="w-4 h-4" />;
}
return null;
};
@@ -25,26 +38,37 @@ const getStepTitle = (
return isStreaming && !step.reasoning ? 'Thinking...' : 'Thinking';
} else if (step.type === 'searching') {
return `Searching ${step.searching.length} ${step.searching.length === 1 ? 'query' : 'queries'}`;
} else if (step.type === 'reading') {
} else if (step.type === 'search_results') {
return `Found ${step.reading.length} ${step.reading.length === 1 ? 'result' : 'results'}`;
} else if (step.type === 'reading') {
return `Reading ${step.reading.length} ${step.reading.length === 1 ? 'source' : 'sources'}`;
} else if (step.type === 'upload_searching') {
return 'Scanning your uploaded documents';
} else if (step.type === 'upload_search_results') {
return `Reading ${step.results.length} ${step.results.length === 1 ? 'document' : 'documents'}`;
}
return 'Processing';
};
const AssistantSteps = ({
block,
status,
isLast,
}: {
block: ResearchBlock;
status: 'answering' | 'completed' | 'error';
isLast: boolean;
}) => {
const [isExpanded, setIsExpanded] = useState(true);
const [isExpanded, setIsExpanded] = useState(
isLast && status === 'answering' ? true : false,
);
const { researchEnded, loading } = useChat();
useEffect(() => {
if (researchEnded) {
if (researchEnded && isLast) {
setIsExpanded(false);
} else if (status === 'answering') {
} else if (status === 'answering' && isLast) {
setIsExpanded(true);
}
}, [researchEnded, status]);
@@ -91,10 +115,9 @@ const AssistantSteps = ({
initial={{ opacity: 0, x: -10 }}
animate={{ opacity: 1, x: 0 }}
transition={{ duration: 0.2, delay: 0 }}
className="flex gap-3"
className="flex gap-2"
>
{/* Timeline connector */}
<div className="flex flex-col items-center pt-0.5">
<div className="flex flex-col items-center -mt-0.5">
<div
className={`rounded-full p-1.5 bg-light-100 dark:bg-dark-100 text-black/70 dark:text-white/70 ${isStreaming ? 'animate-pulse' : ''}`}
>
@@ -105,7 +128,6 @@ const AssistantSteps = ({
)}
</div>
{/* Step content */}
<div className="flex-1 pb-1">
<span className="text-sm font-medium text-black dark:text-white">
{getStepTitle(step, isStreaming)}
@@ -151,37 +173,84 @@ const AssistantSteps = ({
</div>
)}
{step.type === 'reading' && step.reading.length > 0 && (
<div className="flex flex-wrap gap-1.5 mt-1.5">
{step.reading.slice(0, 4).map((result, idx) => {
const url = result.metadata.url || '';
const title = result.metadata.title || 'Untitled';
const domain = url ? new URL(url).hostname : '';
const faviconUrl = domain
? `https://s2.googleusercontent.com/s2/favicons?domain=${domain}&sz=128`
: '';
{(step.type === 'search_results' ||
step.type === 'reading') &&
step.reading.length > 0 && (
<div className="flex flex-wrap gap-1.5 mt-1.5">
{step.reading.slice(0, 4).map((result, idx) => {
const url = result.metadata.url || '';
const title = result.metadata.title || 'Untitled';
const domain = url ? new URL(url).hostname : '';
const faviconUrl = domain
? `https://s2.googleusercontent.com/s2/favicons?domain=${domain}&sz=128`
: '';
return (
return (
<a
key={idx}
href={url}
target="_blank"
className="inline-flex items-center gap-1.5 px-2 py-0.5 rounded-md text-xs font-medium bg-light-100 dark:bg-dark-100 text-black/70 dark:text-white/70 border border-light-200 dark:border-dark-200"
>
{faviconUrl && (
<img
src={faviconUrl}
alt=""
className="w-3 h-3 rounded-sm flex-shrink-0"
onError={(e) => {
e.currentTarget.style.display = 'none';
}}
/>
)}
<span className="line-clamp-1">{title}</span>
</a>
);
})}
</div>
)}
{step.type === 'upload_searching' &&
step.queries.length > 0 && (
<div className="flex flex-wrap gap-1.5 mt-1.5">
{step.queries.map((query, idx) => (
<span
key={idx}
className="inline-flex items-center gap-1.5 px-2 py-0.5 rounded-md text-xs font-medium bg-light-100 dark:bg-dark-100 text-black/70 dark:text-white/70 border border-light-200 dark:border-dark-200"
className="inline-flex items-center px-2 py-0.5 rounded-md text-xs font-medium bg-light-100 dark:bg-dark-100 text-black/70 dark:text-white/70 border border-light-200 dark:border-dark-200"
>
{faviconUrl && (
<img
src={faviconUrl}
alt=""
className="w-3 h-3 rounded-sm flex-shrink-0"
onError={(e) => {
e.currentTarget.style.display = 'none';
}}
/>
)}
<span className="line-clamp-1">{title}</span>
{query}
</span>
);
})}
</div>
)}
))}
</div>
)}
{step.type === 'upload_search_results' &&
step.results.length > 0 && (
<div className="mt-1.5 grid gap-3 lg:grid-cols-3">
{step.results.slice(0, 4).map((result, idx) => {
const title =
(result.metadata &&
(result.metadata.title ||
result.metadata.fileName)) ||
'Untitled document';
return (
<div
key={idx}
className="flex flex-row space-x-3 rounded-lg border border-light-200 dark:border-dark-200 bg-light-100 dark:bg-dark-100 p-2 cursor-pointer"
>
<div className="mt-0.5 h-10 w-10 rounded-md bg-cyan-100 text-cyan-800 dark:bg-sky-500 dark:text-cyan-50 flex items-center justify-center">
<FileText className="w-5 h-5" />
</div>
<div className="flex flex-col justify-center">
<p className="text-[13px] text-black dark:text-white line-clamp-1">
{title}
</p>
</div>
</div>
);
})}
</div>
)}
</div>
</motion.div>
);

View File

@@ -59,7 +59,7 @@ const Chat = () => {
}, [messages]);
return (
<div className="flex flex-col space-y-6 pt-8 pb-28 sm:mx-4 md:mx-8">
<div className="flex flex-col space-y-6 pt-8 pb-44 lg:pb-28 sm:mx-4 md:mx-8">
{sections.map((section, i) => {
const isLast = i === sections.length - 1;
@@ -80,7 +80,10 @@ const Chat = () => {
{loading && !messageAppeared && <MessageBoxLoading />}
<div ref={messageEnd} className="h-0" />
{dividerWidth > 0 && (
<div className="bottom-6 fixed z-40" style={{ width: dividerWidth }}>
<div
className="fixed z-40 bottom-24 lg:bottom-6"
style={{ width: dividerWidth }}
>
<div
className="pointer-events-none absolute -bottom-6 left-0 right-0 h-[calc(100%+24px+24px)] dark:hidden"
style={{

View File

@@ -6,7 +6,8 @@ import EmptyChat from './EmptyChat';
import NextError from 'next/error';
import { useChat } from '@/lib/hooks/useChat';
import SettingsButtonMobile from './Settings/SettingsButtonMobile';
import { Block, Chunk } from '@/lib/types';
import { Block } from '@/lib/types';
import Loader from './ui/Loader';
export interface BaseMessage {
chatId: string;
@@ -21,35 +22,6 @@ export interface Message extends BaseMessage {
status: 'answering' | 'completed' | 'error';
}
export interface UserMessage extends BaseMessage {
role: 'user';
content: string;
}
export interface AssistantMessage extends BaseMessage {
role: 'assistant';
content: string;
suggestions?: string[];
}
export interface SourceMessage extends BaseMessage {
role: 'source';
sources: Chunk[];
}
export interface SuggestionMessage extends BaseMessage {
role: 'suggestion';
suggestions: string[];
}
export type LegacyMessage =
| AssistantMessage
| UserMessage
| SourceMessage
| SuggestionMessage;
export type ChatTurn = UserMessage | AssistantMessage;
export interface File {
fileName: string;
fileExtension: string;
@@ -62,7 +34,8 @@ export interface Widget {
}
const ChatWindow = () => {
const { hasError, notFound, messages } = useChat();
const { hasError, notFound, messages, isReady } = useChat();
if (hasError) {
return (
<div className="relative">
@@ -78,18 +51,24 @@ const ChatWindow = () => {
);
}
return notFound ? (
<NextError statusCode={404} />
return isReady ? (
notFound ? (
<NextError statusCode={404} />
) : (
<div>
{messages.length > 0 ? (
<>
<Navbar />
<Chat />
</>
) : (
<EmptyChat />
)}
</div>
)
) : (
<div>
{messages.length > 0 ? (
<>
<Navbar />
<Chat />
</>
) : (
<EmptyChat />
)}
<div className="flex items-center justify-center min-h-screen w-full">
<Loader />
</div>
);
};

View File

@@ -1,7 +1,7 @@
import { ArrowRight } from 'lucide-react';
import { useEffect, useRef, useState } from 'react';
import TextareaAutosize from 'react-textarea-autosize';
import Focus from './MessageInputActions/Focus';
import Sources from './MessageInputActions/Sources';
import Optimization from './MessageInputActions/Optimization';
import Attach from './MessageInputActions/Attach';
import { useChat } from '@/lib/hooks/useChat';
@@ -68,8 +68,8 @@ const EmptyChatMessageInput = () => {
<Optimization />
<div className="flex flex-row items-center space-x-2">
<div className="flex flex-row items-center space-x-1">
<Sources />
<ModelSelector />
<Focus />
<Attach />
</div>
<button

View File

@@ -2,6 +2,7 @@ import { Check, ClipboardList } from 'lucide-react';
import { Message } from '../ChatWindow';
import { useState } from 'react';
import { Section } from '@/lib/hooks/useChat';
import { SourceBlock } from '@/lib/types';
const Copy = ({
section,
@@ -15,15 +16,25 @@ const Copy = ({
return (
<button
onClick={() => {
const sources = section.message.responseBlocks.filter(
(b) => b.type === 'source' && b.data.length > 0,
) as SourceBlock[];
const contentToCopy = `${initialMessage}${
section?.message.responseBlocks.filter((b) => b.type === 'source')
?.length > 0 &&
`\n\nCitations:\n${section.message.responseBlocks
.filter((b) => b.type === 'source')
?.map((source: any, i: any) => `[${i + 1}] ${source.metadata.url}`)
.join(`\n`)}`
sources.length > 0
? `\n\nCitations:\n${sources
.map((source) => source.data)
.flat()
.map(
(s, i) =>
`[${i + 1}] ${s.metadata.url.startsWith('file_id://') ? s.metadata.fileName || 'Uploaded File' : s.metadata.url}`,
)
.join(`\n`)}`
: ''
}`;
navigator.clipboard.writeText(contentToCopy);
setCopied(true);
setTimeout(() => setCopied(false), 1000);
}}

View File

@@ -12,7 +12,7 @@ import {
Plus,
CornerDownRight,
} from 'lucide-react';
import Markdown, { MarkdownToJSX } from 'markdown-to-jsx';
import Markdown, { MarkdownToJSX, RuleType } from 'markdown-to-jsx';
import Copy from './MessageActions/Copy';
import Rewrite from './MessageActions/Rewrite';
import MessageSources from './MessageSources';
@@ -21,10 +21,11 @@ import SearchVideos from './SearchVideos';
import { useSpeech } from 'react-text-to-speech';
import ThinkBox from './ThinkBox';
import { useChat, Section } from '@/lib/hooks/useChat';
import Citation from './Citation';
import Citation from './MessageRenderer/Citation';
import AssistantSteps from './AssistantSteps';
import { ResearchBlock } from '@/lib/types';
import Renderer from './Widgets/Renderer';
import CodeBlock from './MessageRenderer/CodeBlock';
const ThinkTagProcessor = ({
children,
@@ -49,7 +50,14 @@ const MessageBox = ({
dividerRef?: MutableRefObject<HTMLDivElement | null>;
isLast: boolean;
}) => {
const { loading, sendMessage, rewrite, messages, researchEnded } = useChat();
const {
loading,
sendMessage,
rewrite,
messages,
researchEnded,
chatHistory,
} = useChat();
const parsedMessage = section.parsedTextBlocks.join('\n\n');
const speechMessage = section.speechMessage || '';
@@ -67,6 +75,21 @@ const MessageBox = ({
const { speechStatus, start, stop } = useSpeech({ text: speechMessage });
const markdownOverrides: MarkdownToJSX.Options = {
renderRule(next, node, renderChildren, state) {
if (node.type === RuleType.codeInline) {
return `\`${node.text}\``;
}
if (node.type === RuleType.codeBlock) {
return (
<CodeBlock key={state.key} language={node.lang || ''}>
{node.text}
</CodeBlock>
);
}
return next();
},
overrides: {
think: {
component: ThinkTagProcessor,
@@ -115,12 +138,11 @@ const MessageBox = ({
<AssistantSteps
block={researchBlock}
status={section.message.status}
isLast={isLast}
/>
</div>
))}
{section.widgets.length > 0 && <Renderer widgets={section.widgets} />}
{isLast &&
loading &&
!researchEnded &&
@@ -135,6 +157,8 @@ const MessageBox = ({
</div>
)}
{section.widgets.length > 0 && <Renderer widgets={section.widgets} />}
<div className="flex flex-col space-y-2">
{sources.length > 0 && (
<div className="flex flex-row items-center space-x-2">
@@ -218,10 +242,10 @@ const MessageBox = ({
className="group w-full py-4 text-left transition-colors duration-200"
>
<div className="flex items-center justify-between gap-3">
<div className="flex flex-row space-x-3 items-center ">
<div className="flex flex-row space-x-3 items-center">
<CornerDownRight
size={17}
className="group-hover:text-sky-400 transition-colors duration-200"
size={15}
className="group-hover:text-sky-400 transition-colors duration-200 flex-shrink-0"
/>
<p className="text-sm text-black/70 dark:text-white/70 group-hover:text-sky-400 transition-colors duration-200 leading-relaxed">
{suggestion}
@@ -248,11 +272,11 @@ const MessageBox = ({
<div className="lg:sticky lg:top-20 flex flex-col items-center space-y-3 w-full lg:w-3/12 z-30 h-full pb-4">
<SearchImages
query={section.message.query}
chatHistory={messages}
chatHistory={chatHistory}
messageId={section.message.messageId}
/>
<SearchVideos
chatHistory={messages}
chatHistory={chatHistory}
query={section.message.query}
messageId={section.message.messageId}
/>

View File

@@ -2,7 +2,6 @@ import { cn } from '@/lib/utils';
import { ArrowUp } from 'lucide-react';
import { useEffect, useRef, useState } from 'react';
import TextareaAutosize from 'react-textarea-autosize';
import CopilotToggle from './MessageInputActions/Copilot';
import AttachSmall from './MessageInputActions/AttachSmall';
import { useChat } from '@/lib/hooks/useChat';
@@ -62,7 +61,7 @@ const MessageInput = () => {
}
}}
className={cn(
'relative bg-light-secondary dark:bg-dark-secondary p-4 flex items-center overflow-hidden border border-light-200 dark:border-dark-200 shadow-sm shadow-light-200/10 dark:shadow-black/20 transition-all duration-200 focus-within:border-light-300 dark:focus-within:border-dark-300',
'relative bg-light-secondary dark:bg-dark-secondary p-4 flex items-center overflow-visible border border-light-200 dark:border-dark-200 shadow-sm shadow-light-200/10 dark:shadow-black/20 transition-all duration-200 focus-within:border-light-300 dark:focus-within:border-dark-300',
mode === 'multi' ? 'flex-col rounded-2xl' : 'flex-row rounded-full',
)}
>
@@ -78,11 +77,16 @@ const MessageInput = () => {
placeholder="Ask a follow-up"
/>
{mode === 'single' && (
<div className="flex flex-row items-center space-x-4">
<CopilotToggle
copilotEnabled={copilotEnabled}
setCopilotEnabled={setCopilotEnabled}
/>
<button
disabled={message.trim().length === 0 || loading}
className="bg-[#24A0ED] text-white disabled:text-black/50 dark:disabled:text-white/50 hover:bg-opacity-85 transition duration-100 disabled:bg-[#e0e0dc79] dark:disabled:bg-[#ececec21] rounded-full p-2"
>
<ArrowUp className="bg-background" size={17} />
</button>
)}
{mode === 'multi' && (
<div className="flex flex-row items-center justify-between w-full pt-2">
<AttachSmall />
<button
disabled={message.trim().length === 0 || loading}
className="bg-[#24A0ED] text-white disabled:text-black/50 dark:disabled:text-white/50 hover:bg-opacity-85 transition duration-100 disabled:bg-[#e0e0dc79] dark:disabled:bg-[#ececec21] rounded-full p-2"
@@ -91,23 +95,6 @@ const MessageInput = () => {
</button>
</div>
)}
{mode === 'multi' && (
<div className="flex flex-row items-center justify-between w-full pt-2">
<AttachSmall />
<div className="flex flex-row items-center space-x-4">
<CopilotToggle
copilotEnabled={copilotEnabled}
setCopilotEnabled={setCopilotEnabled}
/>
<button
disabled={message.trim().length === 0 || loading}
className="bg-[#24A0ED] text-white disabled:text-black/50 dark:disabled:text-white/50 hover:bg-opacity-85 transition duration-100 disabled:bg-[#e0e0dc79] dark:disabled:bg-[#ececec21] rounded-full p-2"
>
<ArrowUp className="bg-background" size={17} />
</button>
</div>
</div>
)}
</form>
);
};

View File

@@ -16,6 +16,8 @@ import {
} from 'lucide-react';
import { Fragment, useRef, useState } from 'react';
import { useChat } from '@/lib/hooks/useChat';
import { AnimatePresence } from 'motion/react';
import { motion } from 'framer-motion';
const Attach = () => {
const { files, setFiles, setFileIds, fileIds } = useChat();
@@ -53,86 +55,95 @@ const Attach = () => {
return loading ? (
<div className="active:border-none hover:bg-light-200 hover:dark:bg-dark-200 p-2 rounded-lg focus:outline-none text-black/50 dark:text-white/50 transition duration-200">
<LoaderCircle size={16} className="text-sky-400 animate-spin" />
<LoaderCircle size={16} className="text-sky-500 animate-spin" />
</div>
) : files.length > 0 ? (
<Popover className="relative w-full max-w-[15rem] md:max-w-md lg:max-w-lg">
<PopoverButton
type="button"
className="active:border-none hover:bg-light-200 hover:dark:bg-dark-200 p-2 rounded-lg focus:outline-none headless-open:text-black dark:headless-open:text-white text-black/50 dark:text-white/50 active:scale-95 transition duration-200 hover:text-black dark:hover:text-white"
>
<File size={16} className="text-sky-400" />
</PopoverButton>
<Transition
as={Fragment}
enter="transition ease-out duration-150"
enterFrom="opacity-0 translate-y-1"
enterTo="opacity-100 translate-y-0"
leave="transition ease-in duration-150"
leaveFrom="opacity-100 translate-y-0"
leaveTo="opacity-0 translate-y-1"
>
<PopoverPanel className="absolute z-10 w-64 md:w-[350px] right-0">
<div className="bg-light-primary dark:bg-dark-primary border rounded-md border-light-200 dark:border-dark-200 w-full max-h-[200px] md:max-h-none overflow-y-auto flex flex-col">
<div className="flex flex-row items-center justify-between px-3 py-2">
<h4 className="text-black dark:text-white font-medium text-sm">
Attached files
</h4>
<div className="flex flex-row items-center space-x-4">
<button
type="button"
onClick={() => fileInputRef.current.click()}
className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200 focus:outline-none"
{({ open }) => (
<>
<PopoverButton
type="button"
className="active:border-none hover:bg-light-200 hover:dark:bg-dark-200 p-2 rounded-lg focus:outline-none headless-open:text-black dark:headless-open:text-white text-black/50 dark:text-white/50 active:scale-95 transition duration-200 hover:text-black dark:hover:text-white"
>
<File size={16} className="text-sky-500" />
</PopoverButton>
<AnimatePresence>
{open && (
<PopoverPanel
className="absolute z-10 w-64 md:w-[350px] right-0"
static
>
<motion.div
initial={{ opacity: 0, scale: 0.9 }}
animate={{ opacity: 1, scale: 1 }}
exit={{ opacity: 0, scale: 0.9 }}
transition={{ duration: 0.1, ease: 'easeOut' }}
className="origin-top-right bg-light-primary dark:bg-dark-primary border rounded-md border-light-200 dark:border-dark-200 w-full max-h-[200px] md:max-h-none overflow-y-auto flex flex-col"
>
<input
type="file"
onChange={handleChange}
ref={fileInputRef}
accept=".pdf,.docx,.txt"
multiple
hidden
/>
<Plus size={16} />
<p className="text-xs">Add</p>
</button>
<button
onClick={() => {
setFiles([]);
setFileIds([]);
}}
className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200 focus:outline-none"
>
<Trash size={14} />
<p className="text-xs">Clear</p>
</button>
</div>
</div>
<div className="h-[0.5px] mx-2 bg-white/10" />
<div className="flex flex-col items-center">
{files.map((file, i) => (
<div
key={i}
className="flex flex-row items-center justify-start w-full space-x-3 p-3"
>
<div className="bg-light-100 dark:bg-dark-100 flex items-center justify-center w-10 h-10 rounded-md">
<File
size={16}
className="text-black/70 dark:text-white/70"
/>
<div className="flex flex-row items-center justify-between px-3 py-2">
<h4 className="text-black/70 dark:text-white/70 text-sm">
Attached files
</h4>
<div className="flex flex-row items-center space-x-4">
<button
type="button"
onClick={() => fileInputRef.current.click()}
className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200 focus:outline-none"
>
<input
type="file"
onChange={handleChange}
ref={fileInputRef}
accept=".pdf,.docx,.txt"
multiple
hidden
/>
<Plus size={16} />
<p className="text-xs">Add</p>
</button>
<button
onClick={() => {
setFiles([]);
setFileIds([]);
}}
className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200 focus:outline-none"
>
<Trash size={13} />
<p className="text-xs">Clear</p>
</button>
</div>
</div>
<p className="text-black/70 dark:text-white/70 text-sm">
{file.fileName.length > 25
? file.fileName.replace(/\.\w+$/, '').substring(0, 25) +
'...' +
file.fileExtension
: file.fileName}
</p>
</div>
))}
</div>
</div>
</PopoverPanel>
</Transition>
<div className="h-[0.5px] mx-2 bg-white/10" />
<div className="flex flex-col items-center">
{files.map((file, i) => (
<div
key={i}
className="flex flex-row items-center justify-start w-full space-x-3 p-3"
>
<div className="bg-light-100 dark:bg-dark-100 flex items-center justify-center w-9 h-9 rounded-md">
<File
size={16}
className="text-black/70 dark:text-white/70"
/>
</div>
<p className="text-black/70 dark:text-white/70 text-xs">
{file.fileName.length > 25
? file.fileName
.replace(/\.\w+$/, '')
.substring(0, 25) +
'...' +
file.fileExtension
: file.fileName}
</p>
</div>
))}
</div>
</motion.div>
</PopoverPanel>
)}
</AnimatePresence>
</>
)}
</Popover>
) : (
<button

View File

@@ -1,21 +1,14 @@
import { cn } from '@/lib/utils';
import {
Popover,
PopoverButton,
PopoverPanel,
Transition,
} from '@headlessui/react';
import {
CopyPlus,
File,
LoaderCircle,
Paperclip,
Plus,
Trash,
} from 'lucide-react';
import { File, LoaderCircle, Paperclip, Plus, Trash } from 'lucide-react';
import { Fragment, useRef, useState } from 'react';
import { File as FileType } from '../ChatWindow';
import { useChat } from '@/lib/hooks/useChat';
import { AnimatePresence } from 'motion/react';
import { motion } from 'framer-motion';
const AttachSmall = () => {
const { files, setFiles, setFileIds, fileIds } = useChat();
@@ -53,86 +46,95 @@ const AttachSmall = () => {
return loading ? (
<div className="flex flex-row items-center justify-between space-x-1 p-1 ">
<LoaderCircle size={20} className="text-sky-400 animate-spin" />
<LoaderCircle size={20} className="text-sky-500 animate-spin" />
</div>
) : files.length > 0 ? (
<Popover className="max-w-[15rem] md:max-w-md lg:max-w-lg">
<PopoverButton
type="button"
className="flex flex-row items-center justify-between space-x-1 p-1 text-black/50 dark:text-white/50 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary active:scale-95 transition duration-200 hover:text-black dark:hover:text-white"
>
<File size={20} className="text-sky-400" />
</PopoverButton>
<Transition
as={Fragment}
enter="transition ease-out duration-150"
enterFrom="opacity-0 translate-y-1"
enterTo="opacity-100 translate-y-0"
leave="transition ease-in duration-150"
leaveFrom="opacity-100 translate-y-0"
leaveTo="opacity-0 translate-y-1"
>
<PopoverPanel className="absolute z-10 w-64 md:w-[350px] bottom-14 -ml-3">
<div className="bg-light-primary dark:bg-dark-primary border rounded-md border-light-200 dark:border-dark-200 w-full max-h-[200px] md:max-h-none overflow-y-auto flex flex-col">
<div className="flex flex-row items-center justify-between px-3 py-2">
<h4 className="text-black dark:text-white font-medium text-sm">
Attached files
</h4>
<div className="flex flex-row items-center space-x-4">
<button
type="button"
onClick={() => fileInputRef.current.click()}
className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200"
{({ open }) => (
<>
<PopoverButton
type="button"
className="flex flex-row items-center justify-between space-x-1 p-1 text-black/50 dark:text-white/50 rounded-xl hover:bg-light-secondary dark:hover:bg-dark-secondary active:scale-95 transition duration-200 hover:text-black dark:hover:text-white"
>
<File size={20} className="text-sky-500" />
</PopoverButton>
<AnimatePresence>
{open && (
<PopoverPanel
className="absolute z-10 w-64 md:w-[350px] bottom-14"
static
>
<motion.div
initial={{ opacity: 0, scale: 0.9 }}
animate={{ opacity: 1, scale: 1 }}
exit={{ opacity: 0, scale: 0.9 }}
transition={{ duration: 0.1, ease: 'easeOut' }}
className="origin-bottom-left bg-light-primary dark:bg-dark-primary border rounded-md border-light-200 dark:border-dark-200 w-full max-h-[200px] md:max-h-none overflow-y-auto flex flex-col"
>
<input
type="file"
onChange={handleChange}
ref={fileInputRef}
accept=".pdf,.docx,.txt"
multiple
hidden
/>
<Plus size={18} />
<p className="text-xs">Add</p>
</button>
<button
onClick={() => {
setFiles([]);
setFileIds([]);
}}
className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200"
>
<Trash size={14} />
<p className="text-xs">Clear</p>
</button>
</div>
</div>
<div className="h-[0.5px] mx-2 bg-white/10" />
<div className="flex flex-col items-center">
{files.map((file, i) => (
<div
key={i}
className="flex flex-row items-center justify-start w-full space-x-3 p-3"
>
<div className="bg-light-100 dark:bg-dark-100 flex items-center justify-center w-10 h-10 rounded-md">
<File
size={16}
className="text-black/70 dark:text-white/70"
/>
<div className="flex flex-row items-center justify-between px-3 py-2">
<h4 className="text-black/70 dark:text-white/70 font-medium text-sm">
Attached files
</h4>
<div className="flex flex-row items-center space-x-4">
<button
type="button"
onClick={() => fileInputRef.current.click()}
className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200"
>
<input
type="file"
onChange={handleChange}
ref={fileInputRef}
accept=".pdf,.docx,.txt"
multiple
hidden
/>
<Plus size={16} />
<p className="text-xs">Add</p>
</button>
<button
onClick={() => {
setFiles([]);
setFileIds([]);
}}
className="flex flex-row items-center space-x-1 text-black/70 dark:text-white/70 hover:text-black hover:dark:text-white transition duration-200"
>
<Trash size={13} />
<p className="text-xs">Clear</p>
</button>
</div>
</div>
<p className="text-black/70 dark:text-white/70 text-sm">
{file.fileName.length > 25
? file.fileName.replace(/\.\w+$/, '').substring(0, 25) +
'...' +
file.fileExtension
: file.fileName}
</p>
</div>
))}
</div>
</div>
</PopoverPanel>
</Transition>
<div className="h-[0.5px] mx-2 bg-white/10" />
<div className="flex flex-col items-center">
{files.map((file, i) => (
<div
key={i}
className="flex flex-row items-center justify-start w-full space-x-3 p-3"
>
<div className="bg-light-100 dark:bg-dark-100 flex items-center justify-center w-9 h-9 rounded-md">
<File
size={16}
className="text-black/70 dark:text-white/70"
/>
</div>
<p className="text-black/70 dark:text-white/70 text-xs">
{file.fileName.length > 25
? file.fileName
.replace(/\.\w+$/, '')
.substring(0, 25) +
'...' +
file.fileExtension
: file.fileName}
</p>
</div>
))}
</div>
</motion.div>
</PopoverPanel>
)}
</AnimatePresence>
</>
)}
</Popover>
) : (
<button

View File

@@ -2,15 +2,11 @@
import { Cpu, Loader2, Search } from 'lucide-react';
import { cn } from '@/lib/utils';
import {
Popover,
PopoverButton,
PopoverPanel,
Transition,
} from '@headlessui/react';
import { Fragment, useEffect, useMemo, useState } from 'react';
import { Popover, PopoverButton, PopoverPanel } from '@headlessui/react';
import { useEffect, useMemo, useState } from 'react';
import { MinimalProvider } from '@/lib/models/types';
import { useChat } from '@/lib/hooks/useChat';
import { AnimatePresence, motion } from 'motion/react';
const ModelSelector = () => {
const [providers, setProviders] = useState<MinimalProvider[]>([]);
@@ -79,119 +75,127 @@ const ModelSelector = () => {
return (
<Popover className="relative w-full max-w-[15rem] md:max-w-md lg:max-w-lg">
<PopoverButton
type="button"
className="active:border-none hover:bg-light-200 hover:dark:bg-dark-200 p-2 rounded-lg focus:outline-none headless-open:text-black dark:headless-open:text-white text-black/50 dark:text-white/50 active:scale-95 transition duration-200 hover:text-black dark:hover:text-white"
>
<Cpu size={16} className="text-sky-500" />
</PopoverButton>
<Transition
as={Fragment}
enter="transition ease-out duration-100"
enterFrom="opacity-0 translate-y-1"
enterTo="opacity-100 translate-y-0"
leave="transition ease-in duration-100"
leaveFrom="opacity-100 translate-y-0"
leaveTo="opacity-0 translate-y-1"
>
<PopoverPanel className="absolute z-10 w-[230px] sm:w-[270px] md:w-[300px] -right-4">
<div className="bg-light-primary dark:bg-dark-primary max-h-[300px] sm:max-w-none border rounded-lg border-light-200 dark:border-dark-200 w-full flex flex-col shadow-lg overflow-hidden">
<div className="p-4 border-b border-light-200 dark:border-dark-200">
<div className="relative">
<Search
size={16}
className="absolute left-3 top-1/2 -translate-y-1/2 text-black/40 dark:text-white/40"
/>
<input
type="text"
placeholder="Search models..."
value={searchQuery}
onChange={(e) => setSearchQuery(e.target.value)}
className="w-full pl-9 pr-3 py-2 bg-light-secondary dark:bg-dark-secondary rounded-lg placeholder:text-sm text-sm text-black dark:text-white placeholder:text-black/40 dark:placeholder:text-white/40 focus:outline-none focus:ring-2 focus:ring-sky-500/20 border border-transparent focus:border-sky-500/30 transition duration-200"
/>
</div>
</div>
{({ open }) => (
<>
<PopoverButton
type="button"
className="active:border-none hover:bg-light-200 hover:dark:bg-dark-200 p-2 rounded-lg focus:outline-none headless-open:text-black dark:headless-open:text-white text-black/50 dark:text-white/50 active:scale-95 transition duration-200 hover:text-black dark:hover:text-white"
>
<Cpu size={16} className="text-sky-500" />
</PopoverButton>
<AnimatePresence>
{open && (
<PopoverPanel
className="absolute z-10 w-[230px] sm:w-[270px] md:w-[300px] right-0"
static
>
<motion.div
initial={{ opacity: 0, scale: 0.9 }}
animate={{ opacity: 1, scale: 1 }}
exit={{ opacity: 0, scale: 0.9 }}
transition={{ duration: 0.1, ease: 'easeOut' }}
className="origin-top-right bg-light-primary dark:bg-dark-primary max-h-[300px] sm:max-w-none border rounded-lg border-light-200 dark:border-dark-200 w-full flex flex-col shadow-lg overflow-hidden"
>
<div className="p-2 border-b border-light-200 dark:border-dark-200">
<div className="relative">
<Search
size={16}
className="absolute left-3 top-1/2 -translate-y-1/2 text-black/40 dark:text-white/40"
/>
<input
type="text"
placeholder="Search models..."
value={searchQuery}
onChange={(e) => setSearchQuery(e.target.value)}
className="w-full pl-8 pr-3 py-2 bg-light-secondary dark:bg-dark-secondary rounded-lg placeholder:text-xs placeholder:-translate-y-[1.5px] text-xs text-black dark:text-white placeholder:text-black/40 dark:placeholder:text-white/40 focus:outline-none border border-transparent transition duration-200"
/>
</div>
</div>
<div className="max-h-[320px] overflow-y-auto">
{isLoading ? (
<div className="flex items-center justify-center py-16">
<Loader2
className="animate-spin text-black/40 dark:text-white/40"
size={24}
/>
</div>
) : filteredProviders.length === 0 ? (
<div className="text-center py-16 px-4 text-black/60 dark:text-white/60 text-sm">
{searchQuery
? 'No models found'
: 'No chat models configured'}
</div>
) : (
<div className="flex flex-col">
{filteredProviders.map((provider, providerIndex) => (
<div key={provider.id}>
<div className="px-4 py-2.5 sticky top-0 bg-light-primary dark:bg-dark-primary border-b border-light-200/50 dark:border-dark-200/50">
<p className="text-xs text-black/50 dark:text-white/50 uppercase tracking-wider">
{provider.name}
</p>
<div className="max-h-[320px] overflow-y-auto">
{isLoading ? (
<div className="flex items-center justify-center py-16">
<Loader2
className="animate-spin text-black/40 dark:text-white/40"
size={24}
/>
</div>
<div className="flex flex-col px-2 py-2 space-y-0.5">
{provider.chatModels.map((model) => (
<button
key={model.key}
onClick={() =>
handleModelSelect(provider.id, model.key)
}
type="button"
className={cn(
'px-3 py-2 flex items-center justify-between text-start duration-200 cursor-pointer transition rounded-lg group',
chatModelProvider?.providerId === provider.id &&
chatModelProvider?.key === model.key
? 'bg-light-secondary dark:bg-dark-secondary'
: 'hover:bg-light-secondary dark:hover:bg-dark-secondary',
)}
>
<div className="flex items-center space-x-2.5 min-w-0 flex-1">
<Cpu
size={15}
className={cn(
'shrink-0',
chatModelProvider?.providerId ===
provider.id &&
chatModelProvider?.key === model.key
? 'text-sky-500'
: 'text-black/50 dark:text-white/50 group-hover:text-black/70 group-hover:dark:text-white/70',
)}
/>
<p
className={cn(
'text-sm truncate',
chatModelProvider?.providerId ===
provider.id &&
chatModelProvider?.key === model.key
? 'text-sky-500 font-medium'
: 'text-black/70 dark:text-white/70 group-hover:text-black dark:group-hover:text-white',
)}
>
{model.name}
) : filteredProviders.length === 0 ? (
<div className="text-center py-16 px-4 text-black/60 dark:text-white/60 text-sm">
{searchQuery
? 'No models found'
: 'No chat models configured'}
</div>
) : (
<div className="flex flex-col">
{filteredProviders.map((provider, providerIndex) => (
<div key={provider.id}>
<div className="px-4 py-2.5 sticky top-0 bg-light-primary dark:bg-dark-primary border-b border-light-200/50 dark:border-dark-200/50">
<p className="text-xs text-black/50 dark:text-white/50 uppercase tracking-wider">
{provider.name}
</p>
</div>
</button>
<div className="flex flex-col px-2 py-2 space-y-0.5">
{provider.chatModels.map((model) => (
<button
key={model.key}
onClick={() =>
handleModelSelect(provider.id, model.key)
}
type="button"
className={cn(
'px-3 py-2 flex items-center justify-between text-start duration-200 cursor-pointer transition rounded-lg group',
chatModelProvider?.providerId ===
provider.id &&
chatModelProvider?.key === model.key
? 'bg-light-secondary dark:bg-dark-secondary'
: 'hover:bg-light-secondary dark:hover:bg-dark-secondary',
)}
>
<div className="flex items-center space-x-2.5 min-w-0 flex-1">
<Cpu
size={15}
className={cn(
'shrink-0',
chatModelProvider?.providerId ===
provider.id &&
chatModelProvider?.key === model.key
? 'text-sky-500'
: 'text-black/50 dark:text-white/50 group-hover:text-black/70 group-hover:dark:text-white/70',
)}
/>
<p
className={cn(
'text-xs truncate',
chatModelProvider?.providerId ===
provider.id &&
chatModelProvider?.key === model.key
? 'text-sky-500 font-medium'
: 'text-black/70 dark:text-white/70 group-hover:text-black dark:group-hover:text-white',
)}
>
{model.name}
</p>
</div>
</button>
))}
</div>
{providerIndex < filteredProviders.length - 1 && (
<div className="h-px bg-light-200 dark:bg-dark-200" />
)}
</div>
))}
</div>
{providerIndex < filteredProviders.length - 1 && (
<div className="h-px bg-light-200 dark:bg-dark-200" />
)}
</div>
))}
</div>
)}
</div>
</div>
</PopoverPanel>
</Transition>
)}
</div>
</motion.div>
</PopoverPanel>
)}
</AnimatePresence>
</>
)}
</Popover>
);
};

View File

@@ -1,43 +0,0 @@
import { cn } from '@/lib/utils';
import { Switch } from '@headlessui/react';
const CopilotToggle = ({
copilotEnabled,
setCopilotEnabled,
}: {
copilotEnabled: boolean;
setCopilotEnabled: (enabled: boolean) => void;
}) => {
return (
<div className="group flex flex-row items-center space-x-1 active:scale-95 duration-200 transition cursor-pointer">
<Switch
checked={copilotEnabled}
onChange={setCopilotEnabled}
className="bg-light-secondary dark:bg-dark-secondary border border-light-200/70 dark:border-dark-200 relative inline-flex h-5 w-10 sm:h-6 sm:w-11 items-center rounded-full"
>
<span className="sr-only">Copilot</span>
<span
className={cn(
copilotEnabled
? 'translate-x-6 bg-[#24A0ED]'
: 'translate-x-1 bg-black/50 dark:bg-white/50',
'inline-block h-3 w-3 sm:h-4 sm:w-4 transform rounded-full transition-all duration-200',
)}
/>
</Switch>
<p
onClick={() => setCopilotEnabled(!copilotEnabled)}
className={cn(
'text-xs font-medium transition-colors duration-150 ease-in-out',
copilotEnabled
? 'text-[#24A0ED]'
: 'text-black/50 dark:text-white/50 group-hover:text-black dark:group-hover:text-white',
)}
>
Copilot
</p>
</div>
);
};
export default CopilotToggle;

View File

@@ -1,123 +0,0 @@
import {
BadgePercent,
ChevronDown,
Globe,
Pencil,
ScanEye,
SwatchBook,
} from 'lucide-react';
import { cn } from '@/lib/utils';
import {
Popover,
PopoverButton,
PopoverPanel,
Transition,
} from '@headlessui/react';
import { SiReddit, SiYoutube } from '@icons-pack/react-simple-icons';
import { Fragment } from 'react';
import { useChat } from '@/lib/hooks/useChat';
const focusModes = [
{
key: 'webSearch',
title: 'All',
description: 'Searches across all of the internet',
icon: <Globe size={16} />,
},
{
key: 'academicSearch',
title: 'Academic',
description: 'Search in published academic papers',
icon: <SwatchBook size={16} />,
},
{
key: 'writingAssistant',
title: 'Writing',
description: 'Chat without searching the web',
icon: <Pencil size={16} />,
},
{
key: 'wolframAlphaSearch',
title: 'Wolfram Alpha',
description: 'Computational knowledge engine',
icon: <BadgePercent size={16} />,
},
{
key: 'youtubeSearch',
title: 'Youtube',
description: 'Search and watch videos',
icon: <SiYoutube className="h-[16px] w-auto mr-0.5" />,
},
{
key: 'redditSearch',
title: 'Reddit',
description: 'Search for discussions and opinions',
icon: <SiReddit className="h-[16px] w-auto mr-0.5" />,
},
];
const Focus = () => {
const { focusMode, setFocusMode } = useChat();
return (
<Popover className="relative w-full max-w-[15rem] md:max-w-md lg:max-w-lg">
<PopoverButton
type="button"
className="active:border-none hover:bg-light-200 hover:dark:bg-dark-200 p-2 rounded-lg focus:outline-none headless-open:text-black dark:headless-open:text-white text-black/50 dark:text-white/50 active:scale-95 transition duration-200 hover:text-black dark:hover:text-white"
>
{focusMode !== 'webSearch' ? (
<div className="flex flex-row items-center space-x-1">
{focusModes.find((mode) => mode.key === focusMode)?.icon}
</div>
) : (
<div className="flex flex-row items-center space-x-1">
<Globe size={16} />
</div>
)}
</PopoverButton>
<Transition
as={Fragment}
enter="transition ease-out duration-150"
enterFrom="opacity-0 translate-y-1"
enterTo="opacity-100 translate-y-0"
leave="transition ease-in duration-150"
leaveFrom="opacity-100 translate-y-0"
leaveTo="opacity-0 translate-y-1"
>
<PopoverPanel className="absolute z-10 w-64 md:w-[500px] -right-4">
<div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-2 bg-light-primary dark:bg-dark-primary border rounded-lg border-light-200 dark:border-dark-200 w-full p-4 max-h-[200px] md:max-h-none overflow-y-auto">
{focusModes.map((mode, i) => (
<PopoverButton
onClick={() => setFocusMode(mode.key)}
key={i}
className={cn(
'p-2 rounded-lg flex flex-col items-start justify-start text-start space-y-2 duration-200 cursor-pointer transition focus:outline-none',
focusMode === mode.key
? 'bg-light-secondary dark:bg-dark-secondary'
: 'hover:bg-light-secondary dark:hover:bg-dark-secondary',
)}
>
<div
className={cn(
'flex flex-row items-center space-x-1',
focusMode === mode.key
? 'text-[#24A0ED]'
: 'text-black dark:text-white',
)}
>
{mode.icon}
<p className="text-sm font-medium">{mode.title}</p>
</div>
<p className="text-black/70 dark:text-white/70 text-xs">
{mode.description}
</p>
</PopoverButton>
))}
</div>
</PopoverPanel>
</Transition>
</Popover>
);
};
export default Focus;

View File

@@ -8,6 +8,7 @@ import {
} from '@headlessui/react';
import { Fragment } from 'react';
import { useChat } from '@/lib/hooks/useChat';
import { AnimatePresence, motion } from 'motion/react';
const OptimizationModes = [
{
@@ -60,40 +61,50 @@ const Optimization = () => {
/>
</div>
</PopoverButton>
<Transition
as={Fragment}
enter="transition ease-out duration-150"
enterFrom="opacity-0 translate-y-1"
enterTo="opacity-100 translate-y-0"
leave="transition ease-in duration-150"
leaveFrom="opacity-100 translate-y-0"
leaveTo="opacity-0 translate-y-1"
>
<PopoverPanel className="absolute z-10 w-64 md:w-[250px] left-0">
<div className="flex flex-col gap-2 bg-light-primary dark:bg-dark-primary border rounded-lg border-light-200 dark:border-dark-200 w-full p-4 max-h-[200px] md:max-h-none overflow-y-auto">
{OptimizationModes.map((mode, i) => (
<PopoverButton
onClick={() => setOptimizationMode(mode.key)}
key={i}
className={cn(
'p-2 rounded-lg flex flex-col items-start justify-start text-start space-y-1 duration-200 cursor-pointer transition focus:outline-none',
optimizationMode === mode.key
? 'bg-light-secondary dark:bg-dark-secondary'
: 'hover:bg-light-secondary dark:hover:bg-dark-secondary',
)}
>
<div className="flex flex-row items-center space-x-1 text-black dark:text-white">
{mode.icon}
<p className="text-sm font-medium">{mode.title}</p>
</div>
<p className="text-black/70 dark:text-white/70 text-xs">
{mode.description}
</p>
</PopoverButton>
))}
</div>
</PopoverPanel>
</Transition>
<AnimatePresence>
{open && (
<PopoverPanel
className="absolute z-10 w-64 md:w-[250px] left-0"
static
>
<motion.div
initial={{ opacity: 0, scale: 0.9 }}
animate={{ opacity: 1, scale: 1 }}
exit={{ opacity: 0, scale: 0.9 }}
transition={{ duration: 0.1, ease: 'easeOut' }}
className="origin-top-left flex flex-col space-y-2 bg-light-primary dark:bg-dark-primary border rounded-lg border-light-200 dark:border-dark-200 w-full p-2 max-h-[200px] md:max-h-none overflow-y-auto"
>
{OptimizationModes.map((mode, i) => (
<PopoverButton
onClick={() => setOptimizationMode(mode.key)}
key={i}
className={cn(
'p-2 rounded-lg flex flex-col items-start justify-start text-start space-y-1 duration-200 cursor-pointer transition focus:outline-none',
optimizationMode === mode.key
? 'bg-light-secondary dark:bg-dark-secondary'
: 'hover:bg-light-secondary dark:hover:bg-dark-secondary',
)}
>
<div className="flex flex-row justify-between w-full text-black dark:text-white">
<div className="flex flex-row space-x-1">
{mode.icon}
<p className="text-xs font-medium">{mode.title}</p>
</div>
{mode.key === 'quality' && (
<span className="bg-sky-500/70 dark:bg-sky-500/40 border border-sky-600 px-1 rounded-full text-[10px] text-white">
Beta
</span>
)}
</div>
<p className="text-black/70 dark:text-white/70 text-xs">
{mode.description}
</p>
</PopoverButton>
))}
</motion.div>
</PopoverPanel>
)}
</AnimatePresence>
</>
)}
</Popover>

View File

@@ -0,0 +1,93 @@
import { useChat } from '@/lib/hooks/useChat';
import {
Popover,
PopoverButton,
PopoverPanel,
Switch,
} from '@headlessui/react';
import {
GlobeIcon,
GraduationCapIcon,
NetworkIcon,
} from '@phosphor-icons/react';
import { AnimatePresence, motion } from 'motion/react';
const sourcesList = [
{
name: 'Web',
key: 'web',
icon: <GlobeIcon className="h-[16px] w-auto" />,
},
{
name: 'Academic',
key: 'academic',
icon: <GraduationCapIcon className="h-[16px] w-auto" />,
},
{
name: 'Social',
key: 'discussions',
icon: <NetworkIcon className="h-[16px] w-auto" />,
},
];
const Sources = () => {
const { sources, setSources } = useChat();
return (
<Popover className="relative">
{({ open }) => (
<>
<PopoverButton className="flex items-center justify-center active:border-none hover:bg-light-200 hover:dark:bg-dark-200 p-2 rounded-lg focus:outline-none text-black/50 dark:text-white/50 active:scale-95 transition duration-200 hover:text-black dark:hover:text-white">
<GlobeIcon className="h-[18px] w-auto" />
</PopoverButton>
<AnimatePresence>
{open && (
<PopoverPanel
static
className="absolute z-10 w-64 md:w-[225px] right-0"
>
<motion.div
initial={{ opacity: 0, scale: 0.9 }}
animate={{ opacity: 1, scale: 1 }}
exit={{ opacity: 0, scale: 0.9 }}
transition={{ duration: 0.1, ease: 'easeOut' }}
className="origin-top-right flex flex-col bg-light-primary dark:bg-dark-primary border rounded-lg border-light-200 dark:border-dark-200 w-full p-1 max-h-[200px] md:max-h-none overflow-y-auto shadow-lg"
>
{sourcesList.map((source, i) => (
<div
key={i}
className="flex flex-row justify-between hover:bg-light-100 hover:dark:bg-dark-100 rounded-md py-3 px-2 cursor-pointer"
onClick={() => {
if (!sources.includes(source.key)) {
setSources([...sources, source.key]);
} else {
setSources(sources.filter((s) => s !== source.key));
}
}}
>
<div className="flex flex-row space-x-1.5 text-black/80 dark:text-white/80">
{source.icon}
<p className="text-xs">{source.name}</p>
</div>
<Switch
checked={sources.includes(source.key)}
className="group relative flex h-4 w-7 shrink-0 cursor-pointer rounded-full bg-light-200 dark:bg-white/10 p-0.5 duration-200 ease-in-out focus:outline-none transition-colors disabled:opacity-60 disabled:cursor-not-allowed data-[checked]:bg-sky-500 dark:data-[checked]:bg-sky-500"
>
<span
aria-hidden="true"
className="pointer-events-none inline-block size-3 translate-x-[1px] group-data-[checked]:translate-x-3 rounded-full bg-white shadow-lg ring-0 transition duration-200 ease-in-out"
/>
</Switch>
</div>
))}
</motion.div>
</PopoverPanel>
)}
</AnimatePresence>
</>
)}
</Popover>
);
};
export default Sources;

View File

@@ -0,0 +1,102 @@
import type { CSSProperties } from 'react';
const darkTheme = {
'hljs-comment': {
color: '#8b949e',
},
'hljs-quote': {
color: '#8b949e',
},
'hljs-variable': {
color: '#ff7b72',
},
'hljs-template-variable': {
color: '#ff7b72',
},
'hljs-tag': {
color: '#ff7b72',
},
'hljs-name': {
color: '#ff7b72',
},
'hljs-selector-id': {
color: '#ff7b72',
},
'hljs-selector-class': {
color: '#ff7b72',
},
'hljs-regexp': {
color: '#ff7b72',
},
'hljs-deletion': {
color: '#ff7b72',
},
'hljs-number': {
color: '#f2cc60',
},
'hljs-built_in': {
color: '#f2cc60',
},
'hljs-builtin-name': {
color: '#f2cc60',
},
'hljs-literal': {
color: '#f2cc60',
},
'hljs-type': {
color: '#f2cc60',
},
'hljs-params': {
color: '#f2cc60',
},
'hljs-meta': {
color: '#f2cc60',
},
'hljs-link': {
color: '#f2cc60',
},
'hljs-attribute': {
color: '#58a6ff',
},
'hljs-string': {
color: '#7ee787',
},
'hljs-symbol': {
color: '#7ee787',
},
'hljs-bullet': {
color: '#7ee787',
},
'hljs-addition': {
color: '#7ee787',
},
'hljs-title': {
color: '#79c0ff',
},
'hljs-section': {
color: '#79c0ff',
},
'hljs-keyword': {
color: '#c297ff',
},
'hljs-selector-tag': {
color: '#c297ff',
},
hljs: {
display: 'block',
overflowX: 'auto',
background: '#0d1117',
color: '#c9d1d9',
padding: '0.75em',
border: '1px solid #21262d',
borderRadius: '10px',
},
'hljs-emphasis': {
fontStyle: 'italic',
},
'hljs-strong': {
fontWeight: 'bold',
},
} satisfies Record<string, CSSProperties>;
export default darkTheme;

View File

@@ -0,0 +1,102 @@
import type { CSSProperties } from 'react';
const lightTheme = {
'hljs-comment': {
color: '#6e7781',
},
'hljs-quote': {
color: '#6e7781',
},
'hljs-variable': {
color: '#d73a49',
},
'hljs-template-variable': {
color: '#d73a49',
},
'hljs-tag': {
color: '#d73a49',
},
'hljs-name': {
color: '#d73a49',
},
'hljs-selector-id': {
color: '#d73a49',
},
'hljs-selector-class': {
color: '#d73a49',
},
'hljs-regexp': {
color: '#d73a49',
},
'hljs-deletion': {
color: '#d73a49',
},
'hljs-number': {
color: '#b08800',
},
'hljs-built_in': {
color: '#b08800',
},
'hljs-builtin-name': {
color: '#b08800',
},
'hljs-literal': {
color: '#b08800',
},
'hljs-type': {
color: '#b08800',
},
'hljs-params': {
color: '#b08800',
},
'hljs-meta': {
color: '#b08800',
},
'hljs-link': {
color: '#b08800',
},
'hljs-attribute': {
color: '#0a64ae',
},
'hljs-string': {
color: '#22863a',
},
'hljs-symbol': {
color: '#22863a',
},
'hljs-bullet': {
color: '#22863a',
},
'hljs-addition': {
color: '#22863a',
},
'hljs-title': {
color: '#005cc5',
},
'hljs-section': {
color: '#005cc5',
},
'hljs-keyword': {
color: '#6f42c1',
},
'hljs-selector-tag': {
color: '#6f42c1',
},
hljs: {
display: 'block',
overflowX: 'auto',
background: '#ffffff',
color: '#24292f',
padding: '0.75em',
border: '1px solid #e8edf1',
borderRadius: '10px',
},
'hljs-emphasis': {
fontStyle: 'italic',
},
'hljs-strong': {
fontWeight: 'bold',
},
} satisfies Record<string, CSSProperties>;
export default lightTheme;

View File

@@ -0,0 +1,64 @@
'use client';
import { CheckIcon, CopyIcon } from '@phosphor-icons/react';
import React, { useEffect, useMemo, useState } from 'react';
import { useTheme } from 'next-themes';
import SyntaxHighlighter from 'react-syntax-highlighter';
import darkTheme from './CodeBlockDarkTheme';
import lightTheme from './CodeBlockLightTheme';
const CodeBlock = ({
language,
children,
}: {
language: string;
children: React.ReactNode;
}) => {
const { resolvedTheme } = useTheme();
const [mounted, setMounted] = useState(false);
const [copied, setCopied] = useState(false);
useEffect(() => {
setMounted(true);
}, []);
const syntaxTheme = useMemo(() => {
if (!mounted) return lightTheme;
return resolvedTheme === 'dark' ? darkTheme : lightTheme;
}, [mounted, resolvedTheme]);
return (
<div className="relative">
<button
className="absolute top-2 right-2 p-1"
onClick={() => {
navigator.clipboard.writeText(children as string);
setCopied(true);
setTimeout(() => setCopied(false), 2000);
}}
>
{copied ? (
<CheckIcon
size={16}
className="absolute top-2 right-2 text-black/70 dark:text-white/70"
/>
) : (
<CopyIcon
size={16}
className="absolute top-2 right-2 transition duration-200 text-black/70 dark:text-white/70 hover:text-gray-800/70 hover:dark:text-gray-300/70"
/>
)}
</button>
<SyntaxHighlighter
language={language}
style={syntaxTheme}
showInlineLineNumbers
>
{children as string}
</SyntaxHighlighter>
</div>
);
};
export default CodeBlock;

View File

@@ -37,7 +37,7 @@ const MessageSources = ({ sources }: { sources: Chunk[] }) => {
</p>
<div className="flex flex-row items-center justify-between">
<div className="flex flex-row items-center space-x-1">
{source.metadata.url === 'File' ? (
{source.metadata.url.includes('file_id://') ? (
<div className="bg-dark-200 hover:bg-dark-100 transition duration-200 flex items-center justify-center w-6 h-6 rounded-full">
<File size={12} className="text-white/70" />
</div>
@@ -51,7 +51,9 @@ const MessageSources = ({ sources }: { sources: Chunk[] }) => {
/>
)}
<p className="text-xs text-black/50 dark:text-white/50 overflow-hidden whitespace-nowrap text-ellipsis">
{source.metadata.url.replace(/.+\/\/|www.|\..+/g, '')}
{source.metadata.url.includes('file_id://')
? 'Uploaded File'
: source.metadata.url.replace(/.+\/\/|www.|\..+/g, '')}
</p>
</div>
<div className="flex flex-row items-center space-x-1 text-black/50 dark:text-white/50 text-xs">

View File

@@ -205,8 +205,9 @@ const Navbar = () => {
useEffect(() => {
if (sections.length > 0 && sections[0].message) {
const newTitle =
sections[0].message.query.substring(0, 30) + '...' ||
'New Conversation';
sections[0].message.query.length > 30
? `${sections[0].message.query.substring(0, 30).trim()}...`
: sections[0].message.query || 'New Conversation';
setTitle(newTitle);
const newTimeAgo = formatTimeDifference(

View File

@@ -17,7 +17,7 @@ const SearchImages = ({
messageId,
}: {
query: string;
chatHistory: Message[];
chatHistory: [string, string][];
messageId: string;
}) => {
const [images, setImages] = useState<Image[] | null>(null);

View File

@@ -30,7 +30,7 @@ const Searchvideos = ({
messageId,
}: {
query: string;
chatHistory: Message[];
chatHistory: [string, string][];
messageId: string;
}) => {
const [videos, setVideos] = useState<Video[] | null>(null);

View File

@@ -310,7 +310,7 @@ const SettingsSwitch = ({
checked={isChecked}
onChange={handleSave}
disabled={loading}
className="group relative flex h-6 w-12 shrink-0 cursor-pointer rounded-full bg-white/10 p-1 duration-200 ease-in-out focus:outline-none transition-colors disabled:opacity-60 disabled:cursor-not-allowed data-[checked]:bg-sky-500"
className="group relative flex h-6 w-12 shrink-0 cursor-pointer rounded-full bg-light-200 dark:bg-white/10 p-1 duration-200 ease-in-out focus:outline-none transition-colors disabled:opacity-60 disabled:cursor-not-allowed data-[checked]:bg-sky-500 dark:data-[checked]:bg-sky-500"
>
<span
aria-hidden="true"

View File

@@ -91,7 +91,7 @@ const WeatherWidget = () => {
setData({
temperature: data.temperature,
condition: data.condition,
location: 'Mars',
location: location.city,
humidity: data.humidity,
windSpeed: data.windSpeed,
icon: data.icon,

View File

@@ -1,5 +1,6 @@
'use client';
import { getMeasurementUnit } from '@/lib/config/clientRegistry';
import { Wind, Droplets, Gauge } from 'lucide-react';
import { useMemo, useEffect, useState } from 'react';
@@ -226,6 +227,20 @@ const Weather = ({
timezone,
}: WeatherWidgetProps) => {
const [isDarkMode, setIsDarkMode] = useState(false);
const unit = getMeasurementUnit();
const isImperial = unit === 'imperial';
const tempUnitLabel = isImperial ? '°F' : '°C';
const windUnitLabel = isImperial ? 'mph' : 'km/h';
const formatTemp = (celsius: number) => {
if (!Number.isFinite(celsius)) return 0;
return Math.round(isImperial ? (celsius * 9) / 5 + 32 : celsius);
};
const formatWind = (speedKmh: number) => {
if (!Number.isFinite(speedKmh)) return 0;
return Math.round(isImperial ? speedKmh * 0.621371 : speedKmh);
};
useEffect(() => {
const checkDarkMode = () => {
@@ -266,14 +281,12 @@ const Weather = ({
return {
day: dayName,
icon: info.icon,
high: Math.round(daily.temperature_2m_max[idx + 1]),
low: Math.round(daily.temperature_2m_min[idx + 1]),
highF: Math.round((daily.temperature_2m_max[idx + 1] * 9) / 5 + 32),
lowF: Math.round((daily.temperature_2m_min[idx + 1] * 9) / 5 + 32),
high: formatTemp(daily.temperature_2m_max[idx + 1]),
low: formatTemp(daily.temperature_2m_min[idx + 1]),
precipitation: daily.precipitation_probability_max[idx + 1] || 0,
};
});
}, [daily, isDarkMode]);
}, [daily, isDarkMode, isImperial]);
if (!current || !daily || !daily.time || daily.time.length === 0) {
return (
@@ -305,9 +318,9 @@ const Weather = ({
<div>
<div className="flex items-baseline gap-1">
<span className="text-4xl font-bold drop-shadow-md">
{current.temperature_2m}°
{formatTemp(current.temperature_2m)}°
</span>
<span className="text-lg">F C</span>
<span className="text-lg">{tempUnitLabel}</span>
</div>
<p className="text-sm font-medium drop-shadow mt-0.5">
{weatherInfo.description}
@@ -316,7 +329,8 @@ const Weather = ({
</div>
<div className="text-right">
<p className="text-xs font-medium opacity-90">
{daily.temperature_2m_max[0]}° {daily.temperature_2m_min[0]}°
{formatTemp(daily.temperature_2m_max[0])}°{' '}
{formatTemp(daily.temperature_2m_min[0])}°
</p>
</div>
</div>
@@ -370,7 +384,7 @@ const Weather = ({
Wind
</p>
<p className="font-semibold">
{Math.round(current.wind_speed_10m)} km/h
{formatWind(current.wind_speed_10m)} {windUnitLabel}
</p>
</div>
</div>
@@ -394,7 +408,8 @@ const Weather = ({
Feels Like
</p>
<p className="font-semibold">
{Math.round(current.apparent_temperature)}°C
{formatTemp(current.apparent_temperature)}
{tempUnitLabel}
</p>
</div>
</div>

View File

@@ -1,14 +1,4 @@
import { Message } from '@/components/ChatWindow';
export const getSuggestions = async (chatHistory: [string, string][]) => {
const chatTurns = chatHistory.map(([role, content]) => {
if (role === 'human') {
return { role: 'user', content };
} else {
return { role: 'assistant', content };
}
});
const chatModel = localStorage.getItem('chatModelKey');
const chatModelProvider = localStorage.getItem('chatModelProviderId');
@@ -18,7 +8,7 @@ export const getSuggestions = async (chatHistory: [string, string][]) => {
'Content-Type': 'application/json',
},
body: JSON.stringify({
chatHistory: chatTurns,
chatHistory,
chatModel: {
providerId: chatModelProvider,
key: chatModel,

View File

@@ -29,7 +29,7 @@ const searchImages = async (
query: z.string().describe('The image search query.'),
});
const res = await llm.generateObject<z.infer<typeof schema>>({
const res = await llm.generateObject<typeof schema>({
messages: [
{
role: 'system',

View File

@@ -28,7 +28,7 @@ const searchVideos = async (
query: z.string().describe('The video search query.'),
});
const res = await llm.generateObject<z.infer<typeof schema>>({
const res = await llm.generateObject<typeof schema>({
messages: [
{
role: 'system',

View File

@@ -0,0 +1,99 @@
import { ResearcherOutput, SearchAgentInput } from './types';
import SessionManager from '@/lib/session';
import { classify } from './classifier';
import Researcher from './researcher';
import { getWriterPrompt } from '@/lib/prompts/search/writer';
import { WidgetExecutor } from './widgets';
class APISearchAgent {
async searchAsync(session: SessionManager, input: SearchAgentInput) {
const classification = await classify({
chatHistory: input.chatHistory,
enabledSources: input.config.sources,
query: input.followUp,
llm: input.config.llm,
});
const widgetPromise = WidgetExecutor.executeAll({
classification,
chatHistory: input.chatHistory,
followUp: input.followUp,
llm: input.config.llm,
});
let searchPromise: Promise<ResearcherOutput> | null = null;
if (!classification.classification.skipSearch) {
const researcher = new Researcher();
searchPromise = researcher.research(SessionManager.createSession(), {
chatHistory: input.chatHistory,
followUp: input.followUp,
classification: classification,
config: input.config,
});
}
const [widgetOutputs, searchResults] = await Promise.all([
widgetPromise,
searchPromise,
]);
if (searchResults) {
session.emit('data', {
type: 'searchResults',
data: searchResults.searchFindings,
});
}
session.emit('data', {
type: 'researchComplete',
});
const finalContext =
searchResults?.searchFindings
.map(
(f, index) =>
`<result index=${index + 1} title=${f.metadata.title}>${f.content}</result>`,
)
.join('\n') || '';
const widgetContext = widgetOutputs
.map((o) => {
return `<result>${o.llmContext}</result>`;
})
.join('\n-------------\n');
const finalContextWithWidgets = `<search_results note="These are the search results and assistant can cite these">\n${finalContext}\n</search_results>\n<widgets_result noteForAssistant="Its output is already showed to the user, assistant can use this information to answer the query but do not CITE this as a souce">\n${widgetContext}\n</widgets_result>`;
const writerPrompt = getWriterPrompt(
finalContextWithWidgets,
input.config.systemInstructions,
input.config.mode,
);
const answerStream = input.config.llm.streamText({
messages: [
{
role: 'system',
content: writerPrompt,
},
...input.chatHistory,
{
role: 'user',
content: input.followUp,
},
],
});
for await (const chunk of answerStream) {
session.emit('data', {
type: 'response',
data: chunk.contentChunk,
});
}
session.emit('end', {});
}
}
export default APISearchAgent;

View File

@@ -4,9 +4,53 @@ import { classify } from './classifier';
import Researcher from './researcher';
import { getWriterPrompt } from '@/lib/prompts/search/writer';
import { WidgetExecutor } from './widgets';
import db from '@/lib/db';
import { chats, messages } from '@/lib/db/schema';
import { and, eq, gt } from 'drizzle-orm';
import { TextBlock } from '@/lib/types';
class SearchAgent {
async searchAsync(session: SessionManager, input: SearchAgentInput) {
const exists = await db.query.messages.findFirst({
where: and(
eq(messages.chatId, input.chatId),
eq(messages.messageId, input.messageId),
),
});
if (!exists) {
await db.insert(messages).values({
chatId: input.chatId,
messageId: input.messageId,
backendId: session.id,
query: input.followUp,
createdAt: new Date().toISOString(),
status: 'answering',
responseBlocks: [],
});
} else {
await db
.delete(messages)
.where(
and(eq(messages.chatId, input.chatId), gt(messages.id, exists.id)),
)
.execute();
await db
.update(messages)
.set({
status: 'answering',
backendId: session.id,
responseBlocks: [],
})
.where(
and(
eq(messages.chatId, input.chatId),
eq(messages.messageId, input.messageId),
),
)
.execute();
}
const classification = await classify({
chatHistory: input.chatHistory,
enabledSources: input.config.sources,
@@ -55,21 +99,26 @@ class SearchAgent {
});
const finalContext =
searchResults?.findings
.filter((f) => f.type === 'search_results')
.flatMap((f) => f.results)
.map((f) => `${f.metadata.title}: ${f.content}`)
searchResults?.searchFindings
.map(
(f, index) =>
`<result index=${index + 1} title=${f.metadata.title}>${f.content}</result>`,
)
.join('\n') || '';
const widgetContext = widgetOutputs
.map((o) => {
return `${o.type}: ${o.llmContext}`;
return `<result>${o.llmContext}</result>`;
})
.join('\n-------------\n');
const finalContextWithWidgets = `<search_results note="These are the search results and you can cite these">${finalContext}</search_results>\n<widgets_result noteForAssistant="Its output is already showed to the user, you can use this information to answer the query but do not CITE this as a souce">${widgetContext}</widgets_result>`;
const finalContextWithWidgets = `<search_results note="These are the search results and assistant can cite these">\n${finalContext}\n</search_results>\n<widgets_result noteForAssistant="Its output is already showed to the user, assistant can use this information to answer the query but do not CITE this as a souce">\n${widgetContext}\n</widgets_result>`;
const writerPrompt = getWriterPrompt(finalContextWithWidgets);
const writerPrompt = getWriterPrompt(
finalContextWithWidgets,
input.config.systemInstructions,
input.config.mode,
);
const answerStream = input.config.llm.streamText({
messages: [
{
@@ -84,18 +133,53 @@ class SearchAgent {
],
});
let accumulatedText = '';
let responseBlockId = '';
for await (const chunk of answerStream) {
accumulatedText += chunk.contentChunk;
if (!responseBlockId) {
const block: TextBlock = {
id: crypto.randomUUID(),
type: 'text',
data: chunk.contentChunk,
};
session.emit('data', {
type: 'response',
data: chunk.contentChunk,
});
session.emitBlock(block);
responseBlockId = block.id;
} else {
const block = session.getBlock(responseBlockId) as TextBlock | null;
if (!block) {
continue;
}
block.data += chunk.contentChunk;
session.updateBlock(block.id, [
{
op: 'replace',
path: '/data',
value: block.data,
},
]);
}
}
session.emit('end', {});
await db
.update(messages)
.set({
status: 'completed',
responseBlocks: session.getAllBlocks(),
})
.where(
and(
eq(messages.chatId, input.chatId),
eq(messages.messageId, input.messageId),
),
)
.execute();
}
}

View File

@@ -0,0 +1,129 @@
import z from 'zod';
import { ResearchAction } from '../../types';
import { Chunk, SearchResultsResearchBlock } from '@/lib/types';
import { searchSearxng } from '@/lib/searxng';
const schema = z.object({
queries: z.array(z.string()).describe('List of academic search queries'),
});
const academicSearchDescription = `
Use this tool to perform academic searches for scholarly articles, papers, and research studies relevant to the user's query. Provide a list of concise search queries that will help gather comprehensive academic information on the topic at hand.
You can provide up to 3 queries at a time. Make sure the queries are specific and relevant to the user's needs.
For example, if the user is interested in recent advancements in renewable energy, your queries could be:
1. "Recent advancements in renewable energy 2024"
2. "Cutting-edge research on solar power technologies"
3. "Innovations in wind energy systems"
If this tool is present and no other tools are more relevant, you MUST use this tool to get the needed academic information.
`;
const academicSearchAction: ResearchAction<typeof schema> = {
name: 'academic_search',
schema: schema,
getDescription: () => academicSearchDescription,
getToolDescription: () =>
"Use this tool to perform academic searches for scholarly articles, papers, and research studies relevant to the user's query. Provide a list of concise search queries that will help gather comprehensive academic information on the topic at hand.",
enabled: (config) =>
config.sources.includes('academic') &&
config.classification.classification.skipSearch === false &&
config.classification.classification.academicSearch === true,
execute: async (input, additionalConfig) => {
input.queries = input.queries.slice(0, 3);
const researchBlock = additionalConfig.session.getBlock(
additionalConfig.researchBlockId,
);
if (researchBlock && researchBlock.type === 'research') {
researchBlock.data.subSteps.push({
type: 'searching',
id: crypto.randomUUID(),
searching: input.queries,
});
additionalConfig.session.updateBlock(additionalConfig.researchBlockId, [
{
op: 'replace',
path: '/data/subSteps',
value: researchBlock.data.subSteps,
},
]);
}
const searchResultsBlockId = crypto.randomUUID();
let searchResultsEmitted = false;
let results: Chunk[] = [];
const search = async (q: string) => {
const res = await searchSearxng(q, {
engines: ['arxiv', 'google scholar', 'pubmed'],
});
const resultChunks: Chunk[] = res.results.map((r) => ({
content: r.content || r.title,
metadata: {
title: r.title,
url: r.url,
},
}));
results.push(...resultChunks);
if (
!searchResultsEmitted &&
researchBlock &&
researchBlock.type === 'research'
) {
searchResultsEmitted = true;
researchBlock.data.subSteps.push({
id: searchResultsBlockId,
type: 'search_results',
reading: resultChunks,
});
additionalConfig.session.updateBlock(additionalConfig.researchBlockId, [
{
op: 'replace',
path: '/data/subSteps',
value: researchBlock.data.subSteps,
},
]);
} else if (
searchResultsEmitted &&
researchBlock &&
researchBlock.type === 'research'
) {
const subStepIndex = researchBlock.data.subSteps.findIndex(
(step) => step.id === searchResultsBlockId,
);
const subStep = researchBlock.data.subSteps[
subStepIndex
] as SearchResultsResearchBlock;
subStep.reading.push(...resultChunks);
additionalConfig.session.updateBlock(additionalConfig.researchBlockId, [
{
op: 'replace',
path: '/data/subSteps',
value: researchBlock.data.subSteps,
},
]);
}
};
await Promise.all(input.queries.map(search));
return {
type: 'search_results',
results,
};
},
};
export default academicSearchAction;

View File

@@ -1,12 +1,19 @@
import z from 'zod';
import { ResearchAction } from '../../types';
const actionDescription = `
Use this action ONLY when you have completed all necessary research and are ready to provide a final answer to the user. This indicates that you have gathered sufficient information from previous steps and are concluding the research process.
YOU MUST CALL THIS ACTION TO SIGNAL COMPLETION; DO NOT OUTPUT FINAL ANSWERS DIRECTLY TO THE USER.
IT WILL BE AUTOMATICALLY TRIGGERED IF MAXIMUM ITERATIONS ARE REACHED SO IF YOU'RE LOW ON ITERATIONS, DON'T CALL IT AND INSTEAD FOCUS ON GATHERING ESSENTIAL INFO FIRST.
`;
const doneAction: ResearchAction<any> = {
name: 'done',
description:
'Only call this after ___plan AND after any other needed tool calls when you truly have enough to answer. Do not call if information is still missing.',
enabled: (_) => true,
schema: z.object({}),
getToolDescription: () =>
'Only call this after __reasoning_preamble AND after any other needed tool calls when you truly have enough to answer. Do not call if information is still missing.',
getDescription: () => actionDescription,
enabled: (_) => true,
execute: async (params, additionalConfig) => {
return {
type: 'done',

View File

@@ -1,12 +1,18 @@
import academicSearchAction from './academicSearch';
import doneAction from './done';
import planAction from './plan';
import ActionRegistry from './registry';
import scrapeURLAction from './scrapeURL';
import socialSearchAction from './socialSearch';
import uploadsSearchAction from './uploadsSearch';
import webSearchAction from './webSearch';
ActionRegistry.register(webSearchAction);
ActionRegistry.register(doneAction);
ActionRegistry.register(planAction);
ActionRegistry.register(scrapeURLAction);
ActionRegistry.register(uploadsSearchAction);
ActionRegistry.register(academicSearchAction);
ActionRegistry.register(socialSearchAction);
export { ActionRegistry };

View File

@@ -9,12 +9,26 @@ const schema = z.object({
),
});
const actionDescription = `
Use this tool FIRST on every turn to state your plan in natural language before any other action. Keep it short, action-focused, and tailored to the current query.
Make sure to not include reference to any tools or actions you might take, just the plan itself. The user isn't aware about tools, but they love to see your thought process.
Here are some examples of good plans:
<examples>
- "Okay, the user wants to know the latest advancements in renewable energy. I will start by looking for recent articles and studies on this topic, then summarize the key points." -> "I have gathered enough information to provide a comprehensive answer."
- "The user is asking about the health benefits of a Mediterranean diet. I will search for scientific studies and expert opinions on this diet, then compile the findings into a clear summary." -> "I have gathered information about the Mediterranean diet and its health benefits, I will now look up for any recent studies to ensure the information is current."
</examples>
YOU CAN NEVER CALL ANY OTHER TOOL BEFORE CALLING THIS ONE FIRST, IF YOU DO, THAT CALL WOULD BE IGNORED.
`;
const planAction: ResearchAction<typeof schema> = {
name: '___plan',
description:
'Use this FIRST on every turn to state your plan in natural language before any other action. Keep it short, action-focused, and tailored to the current query.',
name: '__reasoning_preamble',
schema: schema,
enabled: (_) => true,
getToolDescription: () =>
'Use this FIRST on every turn to state your plan in natural language before any other action. Keep it short, action-focused, and tailored to the current query.',
getDescription: () => actionDescription,
enabled: (config) => config.mode !== 'speed',
execute: async (input, _) => {
return {
type: 'reasoning',

View File

@@ -4,6 +4,8 @@ import {
AdditionalConfig,
ClassifierOutput,
ResearchAction,
SearchAgentConfig,
SearchSources,
} from '../../types';
class ActionRegistry {
@@ -19,6 +21,9 @@ class ActionRegistry {
static getAvailableActions(config: {
classification: ClassifierOutput;
fileIds: string[];
mode: SearchAgentConfig['mode'];
sources: SearchSources[];
}): ResearchAction[] {
return Array.from(
this.actions.values().filter((action) => action.enabled(config)),
@@ -27,30 +32,42 @@ class ActionRegistry {
static getAvailableActionTools(config: {
classification: ClassifierOutput;
fileIds: string[];
mode: SearchAgentConfig['mode'];
sources: SearchSources[];
}): Tool[] {
const availableActions = this.getAvailableActions(config);
return availableActions.map((action) => ({
name: action.name,
description: action.description,
description: action.getToolDescription({ mode: config.mode }),
schema: action.schema,
}));
}
static getAvailableActionsDescriptions(config: {
classification: ClassifierOutput;
fileIds: string[];
mode: SearchAgentConfig['mode'];
sources: SearchSources[];
}): string {
const availableActions = this.getAvailableActions(config);
return availableActions
.map((action) => `------------\n##${action.name}\n${action.description}`)
.map(
(action) =>
`<tool name="${action.name}">\n${action.getDescription({ mode: config.mode })}\n</tool>`,
)
.join('\n\n');
}
static async execute(
name: string,
params: any,
additionalConfig: AdditionalConfig,
additionalConfig: AdditionalConfig & {
researchBlockId: string;
fileIds: string[];
},
) {
const action = this.actions.get(name);
@@ -63,7 +80,10 @@ class ActionRegistry {
static async executeAll(
actions: ToolCall[],
additionalConfig: AdditionalConfig,
additionalConfig: AdditionalConfig & {
researchBlockId: string;
fileIds: string[];
},
): Promise<ActionOutput[]> {
const results: ActionOutput[] = [];

View File

@@ -1,7 +1,8 @@
import z from 'zod';
import { ResearchAction } from '../../types';
import { Chunk } from '@/lib/types';
import { Chunk, ReadingResearchBlock } from '@/lib/types';
import TurnDown from 'turndown';
import path from 'path';
const turndownService = new TurnDown();
@@ -9,15 +10,30 @@ const schema = z.object({
urls: z.array(z.string()).describe('A list of URLs to scrape content from.'),
});
const actionDescription = `
Use this tool to scrape and extract content from the provided URLs. This is useful when you the user has asked you to extract or summarize information from specific web pages. You can provide up to 3 URLs at a time. NEVER CALL THIS TOOL EXPLICITLY YOURSELF UNLESS INSTRUCTED TO DO SO BY THE USER.
You should only call this tool when the user has specifically requested information from certain web pages, never call this yourself to get extra information without user instruction.
For example, if the user says "Please summarize the content of https://example.com/article", you can call this tool with that URL to get the content and then provide the summary or "What does X mean according to https://example.com/page", you can call this tool with that URL to get the content and provide the explanation.
`;
const scrapeURLAction: ResearchAction<typeof schema> = {
name: 'scrape_url',
description:
'Use after __plan to scrape and extract content from the provided URLs. This is useful when you need detailed information from specific web pages or if the user asks you to summarize or analyze content from certain links. You can scrape maximum of 3 URLs.',
schema: schema,
getToolDescription: () =>
'Use this tool to scrape and extract content from the provided URLs. This is useful when you the user has asked you to extract or summarize information from specific web pages. You can provide up to 3 URLs at a time. NEVER CALL THIS TOOL EXPLICITLY YOURSELF UNLESS INSTRUCTED TO DO SO BY THE USER.',
getDescription: () => actionDescription,
enabled: (_) => true,
execute: async (params, additionalConfig) => {
params.urls = params.urls.slice(0, 3);
let readingBlockId = crypto.randomUUID();
let readingEmitted = false;
const researchBlock = additionalConfig.session.getBlock(
additionalConfig.researchBlockId,
);
const results: Chunk[] = [];
await Promise.all(
@@ -28,6 +44,70 @@ const scrapeURLAction: ResearchAction<typeof schema> = {
const title =
text.match(/<title>(.*?)<\/title>/i)?.[1] || `Content from ${url}`;
if (
!readingEmitted &&
researchBlock &&
researchBlock.type === 'research'
) {
readingEmitted = true;
researchBlock.data.subSteps.push({
id: readingBlockId,
type: 'reading',
reading: [
{
content: '',
metadata: {
url,
title: title,
},
},
],
});
additionalConfig.session.updateBlock(
additionalConfig.researchBlockId,
[
{
op: 'replace',
path: '/data/subSteps',
value: researchBlock.data.subSteps,
},
],
);
} else if (
readingEmitted &&
researchBlock &&
researchBlock.type === 'research'
) {
const subStepIndex = researchBlock.data.subSteps.findIndex(
(step: any) => step.id === readingBlockId,
);
const subStep = researchBlock.data.subSteps[
subStepIndex
] as ReadingResearchBlock;
subStep.reading.push({
content: '',
metadata: {
url,
title: title,
},
});
additionalConfig.session.updateBlock(
additionalConfig.researchBlockId,
[
{
op: 'replace',
path: '/data/subSteps',
value: researchBlock.data.subSteps,
},
],
);
}
const markdown = turndownService.turndown(text);
results.push({

View File

@@ -0,0 +1,129 @@
import z from 'zod';
import { ResearchAction } from '../../types';
import { Chunk, SearchResultsResearchBlock } from '@/lib/types';
import { searchSearxng } from '@/lib/searxng';
const schema = z.object({
queries: z.array(z.string()).describe('List of social search queries'),
});
const socialSearchDescription = `
Use this tool to perform social media searches for relevant posts, discussions, and trends related to the user's query. Provide a list of concise search queries that will help gather comprehensive social media information on the topic at hand.
You can provide up to 3 queries at a time. Make sure the queries are specific and relevant to the user's needs.
For example, if the user is interested in public opinion on electric vehicles, your queries could be:
1. "Electric vehicles public opinion 2024"
2. "Social media discussions on EV adoption"
3. "Trends in electric vehicle usage"
If this tool is present and no other tools are more relevant, you MUST use this tool to get the needed social media information.
`;
const socialSearchAction: ResearchAction<typeof schema> = {
name: 'social_search',
schema: schema,
getDescription: () => socialSearchDescription,
getToolDescription: () =>
"Use this tool to perform social media searches for relevant posts, discussions, and trends related to the user's query. Provide a list of concise search queries that will help gather comprehensive social media information on the topic at hand.",
enabled: (config) =>
config.sources.includes('discussions') &&
config.classification.classification.skipSearch === false &&
config.classification.classification.discussionSearch === true,
execute: async (input, additionalConfig) => {
input.queries = input.queries.slice(0, 3);
const researchBlock = additionalConfig.session.getBlock(
additionalConfig.researchBlockId,
);
if (researchBlock && researchBlock.type === 'research') {
researchBlock.data.subSteps.push({
type: 'searching',
id: crypto.randomUUID(),
searching: input.queries,
});
additionalConfig.session.updateBlock(additionalConfig.researchBlockId, [
{
op: 'replace',
path: '/data/subSteps',
value: researchBlock.data.subSteps,
},
]);
}
const searchResultsBlockId = crypto.randomUUID();
let searchResultsEmitted = false;
let results: Chunk[] = [];
const search = async (q: string) => {
const res = await searchSearxng(q, {
engines: ['reddit'],
});
const resultChunks: Chunk[] = res.results.map((r) => ({
content: r.content || r.title,
metadata: {
title: r.title,
url: r.url,
},
}));
results.push(...resultChunks);
if (
!searchResultsEmitted &&
researchBlock &&
researchBlock.type === 'research'
) {
searchResultsEmitted = true;
researchBlock.data.subSteps.push({
id: searchResultsBlockId,
type: 'search_results',
reading: resultChunks,
});
additionalConfig.session.updateBlock(additionalConfig.researchBlockId, [
{
op: 'replace',
path: '/data/subSteps',
value: researchBlock.data.subSteps,
},
]);
} else if (
searchResultsEmitted &&
researchBlock &&
researchBlock.type === 'research'
) {
const subStepIndex = researchBlock.data.subSteps.findIndex(
(step) => step.id === searchResultsBlockId,
);
const subStep = researchBlock.data.subSteps[
subStepIndex
] as SearchResultsResearchBlock;
subStep.reading.push(...resultChunks);
additionalConfig.session.updateBlock(additionalConfig.researchBlockId, [
{
op: 'replace',
path: '/data/subSteps',
value: researchBlock.data.subSteps,
},
]);
}
};
await Promise.all(input.queries.map(search));
return {
type: 'search_results',
results,
};
},
};
export default socialSearchAction;

View File

@@ -0,0 +1,102 @@
import z from 'zod';
import { ResearchAction } from '../../types';
import UploadStore from '@/lib/uploads/store';
const schema = z.object({
queries: z
.array(z.string())
.describe(
'A list of queries to search in user uploaded files. Can be a maximum of 3 queries.',
),
});
const uploadsSearchAction: ResearchAction<typeof schema> = {
name: 'uploads_search',
enabled: (config) =>
(config.classification.classification.personalSearch &&
config.fileIds.length > 0) ||
config.fileIds.length > 0,
schema,
getToolDescription: () =>
`Use this tool to perform searches over the user's uploaded files. This is useful when you need to gather information from the user's documents to answer their questions. You can provide up to 3 queries at a time. You will have to use this every single time if this is present and relevant.`,
getDescription: () => `
Use this tool to perform searches over the user's uploaded files. This is useful when you need to gather information from the user's documents to answer their questions. You can provide up to 3 queries at a time. You will have to use this every single time if this is present and relevant.
Always ensure that the queries you use are directly relevant to the user's request and pertain to the content of their uploaded files.
For example, if the user says "Please find information about X in my uploaded documents", you can call this tool with a query related to X to retrieve the relevant information from their files.
Never use this tool to search the web or for information that is not contained within the user's uploaded files.
`,
execute: async (input, additionalConfig) => {
input.queries = input.queries.slice(0, 3);
const researchBlock = additionalConfig.session.getBlock(
additionalConfig.researchBlockId,
);
if (researchBlock && researchBlock.type === 'research') {
researchBlock.data.subSteps.push({
id: crypto.randomUUID(),
type: 'upload_searching',
queries: input.queries,
});
additionalConfig.session.updateBlock(additionalConfig.researchBlockId, [
{
op: 'replace',
path: '/data/subSteps',
value: researchBlock.data.subSteps,
},
]);
}
const uploadStore = new UploadStore({
embeddingModel: additionalConfig.embedding,
fileIds: additionalConfig.fileIds,
});
const results = await uploadStore.query(input.queries, 10);
const seenIds = new Map<string, number>();
const filteredSearchResults = results
.map((result, index) => {
if (result.metadata.url && !seenIds.has(result.metadata.url)) {
seenIds.set(result.metadata.url, index);
return result;
} else if (result.metadata.url && seenIds.has(result.metadata.url)) {
const existingIndex = seenIds.get(result.metadata.url)!;
const existingResult = results[existingIndex];
existingResult.content += `\n\n${result.content}`;
return undefined;
}
return result;
})
.filter((r) => r !== undefined);
if (researchBlock && researchBlock.type === 'research') {
researchBlock.data.subSteps.push({
id: crypto.randomUUID(),
type: 'upload_search_results',
results: filteredSearchResults,
});
additionalConfig.session.updateBlock(additionalConfig.researchBlockId, [
{
op: 'replace',
path: '/data/subSteps',
value: researchBlock.data.subSteps,
},
]);
}
return {
type: 'search_results',
results: filteredSearchResults,
};
},
};
export default uploadsSearchAction;

View File

@@ -1,7 +1,7 @@
import z from 'zod';
import { ResearchAction } from '../../types';
import { searchSearxng } from '@/lib/searxng';
import { Chunk } from '@/lib/types';
import { Chunk, SearchResultsResearchBlock } from '@/lib/types';
const actionSchema = z.object({
type: z.literal('web_search'),
@@ -10,41 +10,164 @@ const actionSchema = z.object({
.describe('An array of search queries to perform web searches for.'),
});
const actionDescription = `
Use immediately after the ___plan call when you need information. Default to using this unless you already have everything needed to finish. Provide 1-3 short, SEO-friendly queries (keywords, not sentences) that cover the user ask. Always prefer current/contextual queries (e.g., include year for news).
const speedModePrompt = `
Use this tool to perform web searches based on the provided queries. This is useful when you need to gather information from the web to answer the user's questions. You can provide up to 3 queries at a time. You will have to use this every single time if this is present and relevant.
You are currently on speed mode, meaning you would only get to call this tool once. Make sure to prioritize the most important queries that are likely to get you the needed information in one go.
You can search maximum of 3 queries at a time.
Your queries should be very targeted and specific to the information you need, avoid broad or generic queries.
Your queries shouldn't be sentences but rather keywords that are SEO friendly and can be used to search the web for information.
For fast mode, you can only use this tool once so make sure to get all needed information in one go.
For example, if the user is asking about the features of a new technology, you might use queries like "GPT-5.1 features", "GPT-5.1 release date", "GPT-5.1 improvements" rather than a broad query like "Tell me about GPT-5.1".
For balanced and quality modes, you can use this tool multiple times as needed.
You can search for 3 queries in one go, make sure to utilize all 3 queries to maximize the information you can gather. If a question is simple, then split your queries to cover different aspects or related topics to get a comprehensive understanding.
If this tool is present and no other tools are more relevant, you MUST use this tool to get the needed information.
`;
In quality and balanced mode, first try to gather upper level information with broad queries, then use more specific queries based on what you find to find all information needed.
const balancedModePrompt = `
Use this tool to perform web searches based on the provided queries. This is useful when you need to gather information from the web to answer the user's questions. You can provide up to 3 queries at a time. You will have to use this every single time if this is present and relevant.
You can call this tool several times if needed to gather enough information.
Start initially with broader queries to get an overview, then narrow down with more specific queries based on the results you receive.
Your queries shouldn't be sentences but rather keywords that are SEO friendly and can be used to search the web for information.
For example if the user is asking about Tesla, your actions should be like:
1. __reasoning_preamble "The user is asking about Tesla. I will start with broader queries to get an overview of Tesla, then narrow down with more specific queries based on the results I receive." then
2. web_search ["Tesla", "Tesla latest news", "Tesla stock price"] then
3. __reasoning_preamble "Based on the previous search results, I will now narrow down my queries to focus on Tesla's recent developments and stock performance." then
4. web_search ["Tesla Q2 2025 earnings", "Tesla new model 2025", "Tesla stock analysis"] then done.
5. __reasoning_preamble "I have gathered enough information to provide a comprehensive answer."
6. done.
You can search for 3 queries in one go, make sure to utilize all 3 queries to maximize the information you can gather. If a question is simple, then split your queries to cover different aspects or related topics to get a comprehensive understanding.
If this tool is present and no other tools are more relevant, you MUST use this tool to get the needed information. You can call this tools, multiple times as needed.
`;
const qualityModePrompt = `
Use this tool to perform web searches based on the provided queries. This is useful when you need to gather information from the web to answer the user's questions. You can provide up to 3 queries at a time. You will have to use this every single time if this is present and relevant.
You have to call this tool several times to gather enough information unless the question is very simple (like greeting questions or basic facts).
Start initially with broader queries to get an overview, then narrow down with more specific queries based on the results you receive.
Never stop before at least 5-6 iterations of searches unless the user question is very simple.
Your queries shouldn't be sentences but rather keywords that are SEO friendly and can be used to search the web for information.
You can search for 3 queries in one go, make sure to utilize all 3 queries to maximize the information you can gather. If a question is simple, then split your queries to cover different aspects or related topics to get a comprehensive understanding.
If this tool is present and no other tools are more relevant, you MUST use this tool to get the needed information. You can call this tools, multiple times as needed.
`;
const webSearchAction: ResearchAction<typeof actionSchema> = {
name: 'web_search',
description: actionDescription,
schema: actionSchema,
getToolDescription: () =>
"Use this tool to perform web searches based on the provided queries. This is useful when you need to gather information from the web to answer the user's questions. You can provide up to 3 queries at a time. You will have to use this every single time if this is present and relevant.",
getDescription: (config) => {
let prompt = '';
switch (config.mode) {
case 'speed':
prompt = speedModePrompt;
break;
case 'balanced':
prompt = balancedModePrompt;
break;
case 'quality':
prompt = qualityModePrompt;
break;
default:
prompt = speedModePrompt;
break;
}
return prompt;
},
enabled: (config) =>
config.sources.includes('web') &&
config.classification.classification.skipSearch === false,
execute: async (input, _) => {
execute: async (input, additionalConfig) => {
input.queries = input.queries.slice(0, 3);
const researchBlock = additionalConfig.session.getBlock(
additionalConfig.researchBlockId,
);
if (researchBlock && researchBlock.type === 'research') {
researchBlock.data.subSteps.push({
id: crypto.randomUUID(),
type: 'searching',
searching: input.queries,
});
additionalConfig.session.updateBlock(additionalConfig.researchBlockId, [
{
op: 'replace',
path: '/data/subSteps',
value: researchBlock.data.subSteps,
},
]);
}
const searchResultsBlockId = crypto.randomUUID();
let searchResultsEmitted = false;
let results: Chunk[] = [];
const search = async (q: string) => {
const res = await searchSearxng(q);
res.results.forEach((r) => {
results.push({
content: r.content || r.title,
metadata: {
title: r.title,
url: r.url,
},
const resultChunks: Chunk[] = res.results.map((r) => ({
content: r.content || r.title,
metadata: {
title: r.title,
url: r.url,
},
}));
results.push(...resultChunks);
if (
!searchResultsEmitted &&
researchBlock &&
researchBlock.type === 'research'
) {
searchResultsEmitted = true;
researchBlock.data.subSteps.push({
id: searchResultsBlockId,
type: 'search_results',
reading: resultChunks,
});
});
additionalConfig.session.updateBlock(additionalConfig.researchBlockId, [
{
op: 'replace',
path: '/data/subSteps',
value: researchBlock.data.subSteps,
},
]);
} else if (
searchResultsEmitted &&
researchBlock &&
researchBlock.type === 'research'
) {
const subStepIndex = researchBlock.data.subSteps.findIndex(
(step) => step.id === searchResultsBlockId,
);
const subStep = researchBlock.data.subSteps[
subStepIndex
] as SearchResultsResearchBlock;
subStep.reading.push(...resultChunks);
additionalConfig.session.updateBlock(additionalConfig.researchBlockId, [
{
op: 'replace',
path: '/data/subSteps',
value: researchBlock.data.subSteps,
},
]);
}
};
await Promise.all(input.queries.map(search));

View File

@@ -21,11 +21,17 @@ class Researcher {
const availableTools = ActionRegistry.getAvailableActionTools({
classification: input.classification,
fileIds: input.config.fileIds,
mode: input.config.mode,
sources: input.config.sources,
});
const availableActionsDescription =
ActionRegistry.getAvailableActionsDescriptions({
classification: input.classification,
fileIds: input.config.fileIds,
mode: input.config.mode,
sources: input.config.sources,
});
const researchBlockId = crypto.randomUUID();
@@ -56,6 +62,7 @@ class Researcher {
input.config.mode,
i,
maxIteration,
input.config.fileIds,
);
const actionStream = input.config.llm.streamText({
@@ -80,7 +87,7 @@ class Researcher {
if (partialRes.toolCallChunk.length > 0) {
partialRes.toolCallChunk.forEach((tc) => {
if (
tc.name === '___plan' &&
tc.name === '__reasoning_preamble' &&
tc.arguments['plan'] &&
!reasoningEmitted &&
block &&
@@ -102,7 +109,7 @@ class Researcher {
},
]);
} else if (
tc.name === '___plan' &&
tc.name === '__reasoning_preamble' &&
tc.arguments['plan'] &&
reasoningEmitted &&
block &&
@@ -154,33 +161,12 @@ class Researcher {
tool_calls: finalToolCalls,
});
const searchCalls = finalToolCalls.filter(
(tc) =>
tc.name === 'web_search' ||
tc.name === 'academic_search' ||
tc.name === 'discussion_search',
);
if (searchCalls.length > 0 && block && block.type === 'research') {
block.data.subSteps.push({
id: crypto.randomUUID(),
type: 'searching',
searching: searchCalls.map((sc) => sc.arguments.queries).flat(),
});
session.updateBlock(researchBlockId, [
{
op: 'replace',
path: '/data/subSteps',
value: block.data.subSteps,
},
]);
}
const actionResults = await ActionRegistry.executeAll(finalToolCalls, {
llm: input.config.llm,
embedding: input.config.embedding,
session: session,
researchBlockId: researchBlockId,
fileIds: input.config.fileIds,
});
actionOutput.push(...actionResults);
@@ -193,39 +179,42 @@ class Researcher {
content: JSON.stringify(action),
});
});
const searchResults = actionResults.filter(
(a) => a.type === 'search_results',
);
if (searchResults.length > 0 && block && block.type === 'research') {
block.data.subSteps.push({
id: crypto.randomUUID(),
type: 'reading',
reading: searchResults.flatMap((a) => a.results),
});
session.updateBlock(researchBlockId, [
{
op: 'replace',
path: '/data/subSteps',
value: block.data.subSteps,
},
]);
}
}
const searchResults = actionOutput.filter(
(a) => a.type === 'search_results',
);
const searchResults = actionOutput
.filter((a) => a.type === 'search_results')
.flatMap((a) => a.results);
session.emit('data', {
type: 'sources',
data: searchResults.flatMap((a) => a.results),
const seenUrls = new Map<string, number>();
const filteredSearchResults = searchResults
.map((result, index) => {
if (result.metadata.url && !seenUrls.has(result.metadata.url)) {
seenUrls.set(result.metadata.url, index);
return result;
} else if (result.metadata.url && seenUrls.has(result.metadata.url)) {
const existingIndex = seenUrls.get(result.metadata.url)!;
const existingResult = searchResults[existingIndex];
existingResult.content += `\n\n${result.content}`;
return undefined;
}
return result;
})
.filter((r) => r !== undefined);
session.emitBlock({
id: crypto.randomUUID(),
type: 'source',
data: filteredSearchResults,
});
return {
findings: actionOutput,
searchFindings: filteredSearchResults,
};
}
}

View File

@@ -8,15 +8,19 @@ export type SearchSources = 'web' | 'discussions' | 'academic';
export type SearchAgentConfig = {
sources: SearchSources[];
fileIds: string[];
llm: BaseLLM<any>;
embedding: BaseEmbedding<any>;
mode: 'speed' | 'balanced' | 'quality';
systemInstructions: string;
};
export type SearchAgentInput = {
chatHistory: ChatTurnMessage[];
followUp: string;
config: SearchAgentConfig;
chatId: string;
messageId: string;
};
export type WidgetInput = {
@@ -73,6 +77,7 @@ export type ResearcherInput = {
export type ResearcherOutput = {
findings: ActionOutput[];
searchFindings: Chunk[];
};
export type SearchActionOutput = {
@@ -98,11 +103,20 @@ export interface ResearchAction<
TSchema extends z.ZodObject<any> = z.ZodObject<any>,
> {
name: string;
description: string;
schema: z.ZodObject<any>;
enabled: (config: { classification: ClassifierOutput }) => boolean;
getToolDescription: (config: { mode: SearchAgentConfig['mode'] }) => string;
getDescription: (config: { mode: SearchAgentConfig['mode'] }) => string;
enabled: (config: {
classification: ClassifierOutput;
fileIds: string[];
mode: SearchAgentConfig['mode'];
sources: SearchSources[];
}) => boolean;
execute: (
params: z.infer<TSchema>,
additionalConfig: AdditionalConfig,
additionalConfig: AdditionalConfig & {
researchBlockId: string;
fileIds: string[];
},
) => Promise<ActionOutput>;
}

View File

@@ -51,6 +51,10 @@ const calculationWidget: Widget = {
schema,
});
if (output.notPresent) {
return;
}
const result = mathEval(output.expression);
return {

View File

@@ -3,7 +3,6 @@ import { suggestionGeneratorPrompt } from '@/lib/prompts/suggestions';
import { ChatTurnMessage } from '@/lib/types';
import z from 'zod';
import BaseLLM from '@/lib/models/base/llm';
import { i } from 'mathjs';
type SuggestionGeneratorInput = {
chatHistory: ChatTurnMessage[];
@@ -19,7 +18,7 @@ const generateSuggestions = async (
input: SuggestionGeneratorInput,
llm: BaseLLM<any>,
) => {
const res = await llm.generateObject<z.infer<typeof schema>>({
const res = await llm.generateObject<typeof schema>({
messages: [
{
role: 'system',

View File

@@ -17,3 +17,13 @@ export const getShowWeatherWidget = () =>
export const getShowNewsWidget = () =>
getClientConfig('showNewsWidget', 'true') === 'true';
export const getMeasurementUnit = () => {
const value =
getClientConfig('measureUnit') ??
getClientConfig('measurementUnit', 'metric');
if (typeof value !== 'string') return 'metric';
return value.toLowerCase();
};

View File

@@ -45,6 +45,7 @@ fs.readdirSync(migrationsFolder)
const already = db
.prepare('SELECT 1 FROM ran_migrations WHERE name = ?')
.get(migrationName);
if (already) {
console.log(`Skipping already-applied migration: ${file}`);
return;
@@ -113,6 +114,160 @@ fs.readdirSync(migrationsFolder)
db.exec('DROP TABLE messages;');
db.exec('ALTER TABLE messages_with_sources RENAME TO messages;');
} else if (migrationName === '0002') {
/* Migrate chat */
db.exec(`
CREATE TABLE IF NOT EXISTS chats_new (
id TEXT PRIMARY KEY,
title TEXT NOT NULL,
createdAt TEXT NOT NULL,
sources TEXT DEFAULT '[]',
files TEXT DEFAULT '[]'
);
`);
const chats = db
.prepare('SELECT id, title, createdAt, files FROM chats')
.all();
const insertChat = db.prepare(`
INSERT INTO chats_new (id, title, createdAt, sources, files)
VALUES (?, ?, ?, ?, ?)
`);
chats.forEach((chat: any) => {
let files = chat.files;
while (typeof files === 'string') {
files = JSON.parse(files || '[]');
}
insertChat.run(
chat.id,
chat.title,
chat.createdAt,
'["web"]',
JSON.stringify(files),
);
});
db.exec('DROP TABLE chats;');
db.exec('ALTER TABLE chats_new RENAME TO chats;');
/* Migrate messages */
db.exec(`
CREATE TABLE IF NOT EXISTS messages_new (
id INTEGER PRIMARY KEY,
messageId TEXT NOT NULL,
chatId TEXT NOT NULL,
backendId TEXT NOT NULL,
query TEXT NOT NULL,
createdAt TEXT NOT NULL,
responseBlocks TEXT DEFAULT '[]',
status TEXT DEFAULT 'answering'
);
`);
const messages = db
.prepare(
'SELECT id, messageId, chatId, type, content, createdAt, sources FROM messages ORDER BY id ASC',
)
.all();
const insertMessage = db.prepare(`
INSERT INTO messages_new (messageId, chatId, backendId, query, createdAt, responseBlocks, status)
VALUES (?, ?, ?, ?, ?, ?, ?)
`);
let currentMessageData: {
sources?: any[];
response?: string;
query?: string;
messageId?: string;
chatId?: string;
createdAt?: string;
} = {};
let lastCompleted = true;
messages.forEach((msg: any) => {
if (msg.type === 'user' && lastCompleted) {
currentMessageData = {};
currentMessageData.messageId = msg.messageId;
currentMessageData.chatId = msg.chatId;
currentMessageData.query = msg.content;
currentMessageData.createdAt = msg.createdAt;
lastCompleted = false;
} else if (msg.type === 'source' && !lastCompleted) {
let sources = msg.sources;
while (typeof sources === 'string') {
sources = JSON.parse(sources || '[]');
}
currentMessageData.sources = sources;
} else if (msg.type === 'assistant' && !lastCompleted) {
currentMessageData.response = msg.content;
insertMessage.run(
currentMessageData.messageId,
currentMessageData.chatId,
`${currentMessageData.messageId}-backend`,
currentMessageData.query,
currentMessageData.createdAt,
JSON.stringify([
{
id: crypto.randomUUID(),
type: 'text',
data: currentMessageData.response || '',
},
...(currentMessageData.sources &&
currentMessageData.sources.length > 0
? [
{
id: crypto.randomUUID(),
type: 'source',
data: currentMessageData.sources,
},
]
: []),
]),
'completed',
);
lastCompleted = true;
} else if (msg.type === 'user' && !lastCompleted) {
/* Message wasn't completed so we'll just create the record with empty response */
insertMessage.run(
currentMessageData.messageId,
currentMessageData.chatId,
`${currentMessageData.messageId}-backend`,
currentMessageData.query,
currentMessageData.createdAt,
JSON.stringify([
{
id: crypto.randomUUID(),
type: 'text',
data: '',
},
...(currentMessageData.sources &&
currentMessageData.sources.length > 0
? [
{
id: crypto.randomUUID(),
type: 'source',
data: currentMessageData.sources,
},
]
: []),
]),
'completed',
);
lastCompleted = true;
}
});
db.exec('DROP TABLE messages;');
db.exec('ALTER TABLE messages_new RENAME TO messages;');
} else {
// Execute each statement separately
statements.forEach((stmt) => {

View File

@@ -1,6 +1,7 @@
import { sql } from 'drizzle-orm';
import { text, integer, sqliteTable } from 'drizzle-orm/sqlite-core';
import { Block } from '../types';
import { SearchSources } from '../agents/search/types';
export const messages = sqliteTable('messages', {
id: integer('id').primaryKey(),
@@ -26,7 +27,11 @@ export const chats = sqliteTable('chats', {
id: text('id').primaryKey(),
title: text('title').notNull(),
createdAt: text('createdAt').notNull(),
focusMode: text('focusMode').notNull(),
sources: text('sources', {
mode: 'json',
})
.$type<SearchSources[]>()
.default(sql`'[]'`),
files: text('files', { mode: 'json' })
.$type<DBFile[]>()
.default(sql`'[]'`),

View File

@@ -34,7 +34,7 @@ type ChatContext = {
chatHistory: [string, string][];
files: File[];
fileIds: string[];
focusMode: string;
sources: string[];
chatId: string | undefined;
optimizationMode: string;
isMessagesLoaded: boolean;
@@ -48,7 +48,7 @@ type ChatContext = {
researchEnded: boolean;
setResearchEnded: (ended: boolean) => void;
setOptimizationMode: (mode: string) => void;
setFocusMode: (mode: string) => void;
setSources: (sources: string[]) => void;
setFiles: (files: File[]) => void;
setFileIds: (fileIds: string[]) => void;
sendMessage: (
@@ -175,8 +175,8 @@ const loadMessages = async (
chatId: string,
setMessages: (messages: Message[]) => void,
setIsMessagesLoaded: (loaded: boolean) => void,
setChatHistory: (history: [string, string][]) => void,
setFocusMode: (mode: string) => void,
chatHistory: React.MutableRefObject<[string, string][]>,
setSources: (sources: string[]) => void,
setNotFound: (notFound: boolean) => void,
setFiles: (files: File[]) => void,
setFileIds: (fileIds: string[]) => void,
@@ -233,8 +233,8 @@ const loadMessages = async (
setFiles(files);
setFileIds(files.map((file: File) => file.fileId));
setChatHistory(history);
setFocusMode(data.chat.focusMode);
chatHistory.current = history;
setSources(data.chat.sources);
setIsMessagesLoaded(true);
};
@@ -243,7 +243,7 @@ export const chatContext = createContext<ChatContext>({
chatId: '',
fileIds: [],
files: [],
focusMode: '',
sources: [],
hasError: false,
isMessagesLoaded: false,
isReady: false,
@@ -260,7 +260,7 @@ export const chatContext = createContext<ChatContext>({
sendMessage: async () => {},
setFileIds: () => {},
setFiles: () => {},
setFocusMode: () => {},
setSources: () => {},
setOptimizationMode: () => {},
setChatModelProvider: () => {},
setEmbeddingModelProvider: () => {},
@@ -269,6 +269,7 @@ export const chatContext = createContext<ChatContext>({
export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
const params: { chatId: string } = useParams();
const searchParams = useSearchParams();
const initialMessage = searchParams.get('q');
@@ -280,13 +281,13 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
const [researchEnded, setResearchEnded] = useState(false);
const [chatHistory, setChatHistory] = useState<[string, string][]>([]);
const chatHistory = useRef<[string, string][]>([]);
const [messages, setMessages] = useState<Message[]>([]);
const [files, setFiles] = useState<File[]>([]);
const [fileIds, setFileIds] = useState<string[]>([]);
const [focusMode, setFocusMode] = useState('webSearch');
const [sources, setSources] = useState<string[]>(['web']);
const [optimizationMode, setOptimizationMode] = useState('speed');
const [isMessagesLoaded, setIsMessagesLoaded] = useState(false);
@@ -401,6 +402,64 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
});
}, [messages]);
const isReconnectingRef = useRef(false);
const handledMessageEndRef = useRef<Set<string>>(new Set());
const checkReconnect = async () => {
if (isReconnectingRef.current) return;
setIsReady(true);
console.debug(new Date(), 'app:ready');
if (messages.length > 0) {
const lastMsg = messages[messages.length - 1];
if (lastMsg.status === 'answering') {
setLoading(true);
setResearchEnded(false);
setMessageAppeared(false);
isReconnectingRef.current = true;
const res = await fetch(`/api/reconnect/${lastMsg.backendId}`, {
method: 'POST',
});
if (!res.body) throw new Error('No response body');
const reader = res.body?.getReader();
const decoder = new TextDecoder('utf-8');
let partialChunk = '';
const messageHandler = getMessageHandler(lastMsg);
try {
while (true) {
const { value, done } = await reader.read();
if (done) break;
partialChunk += decoder.decode(value, { stream: true });
try {
const messages = partialChunk.split('\n');
for (const msg of messages) {
if (!msg.trim()) continue;
const json = JSON.parse(msg);
messageHandler(json);
}
partialChunk = '';
} catch (error) {
console.warn('Incomplete JSON, waiting for next chunk...');
}
}
} finally {
isReconnectingRef.current = false;
}
}
}
};
useEffect(() => {
checkConfig(
setChatModelProvider,
@@ -415,7 +474,7 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
if (params.chatId && params.chatId !== chatId) {
setChatId(params.chatId);
setMessages([]);
setChatHistory([]);
chatHistory.current = [];
setFiles([]);
setFileIds([]);
setIsMessagesLoaded(false);
@@ -435,8 +494,8 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
chatId,
setMessages,
setIsMessagesLoaded,
setChatHistory,
setFocusMode,
chatHistory,
setSources,
setNotFound,
setFiles,
setFileIds,
@@ -454,13 +513,15 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
}, [messages]);
useEffect(() => {
if (isMessagesLoaded && isConfigReady) {
if (isMessagesLoaded && isConfigReady && newChatCreated) {
setIsReady(true);
console.debug(new Date(), 'app:ready');
} else if (isMessagesLoaded && isConfigReady && !newChatCreated) {
checkReconnect();
} else {
setIsReady(false);
}
}, [isMessagesLoaded, isConfigReady]);
}, [isMessagesLoaded, isConfigReady, newChatCreated]);
const rewrite = (messageId: string) => {
const index = messages.findIndex((msg) => msg.messageId === messageId);
@@ -469,9 +530,7 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
setMessages((prev) => prev.slice(0, index));
setChatHistory((prev) => {
return prev.slice(0, index * 2);
});
chatHistory.current = chatHistory.current.slice(0, index * 2);
const messageToRewrite = messages[index];
sendMessage(messageToRewrite.query, messageToRewrite.messageId, true);
@@ -488,38 +547,10 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
// eslint-disable-next-line react-hooks/exhaustive-deps
}, [isConfigReady, isReady, initialMessage]);
const sendMessage: ChatContext['sendMessage'] = async (
message,
messageId,
rewrite = false,
) => {
if (loading || !message) return;
setLoading(true);
setResearchEnded(false);
setMessageAppeared(false);
const getMessageHandler = (message: Message) => {
const messageId = message.messageId;
if (messages.length <= 1) {
window.history.replaceState(null, '', `/c/${chatId}`);
}
messageId = messageId ?? crypto.randomBytes(7).toString('hex');
const backendId = crypto.randomBytes(20).toString('hex');
const newMessage: Message = {
messageId,
chatId: chatId!,
backendId,
query: message,
responseBlocks: [],
status: 'answering',
createdAt: new Date(),
};
setMessages((prevMessages) => [...prevMessages, newMessage]);
const receivedTextRef = { current: '' };
const messageHandler = async (data: any) => {
return async (data: any) => {
if (data.type === 'error') {
toast.error(data.data);
setLoading(false);
@@ -536,7 +567,7 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
if (data.type === 'researchComplete') {
setResearchEnded(true);
if (
newMessage.responseBlocks.find(
message.responseBlocks.find(
(b) => b.type === 'source' && b.data.length > 0,
)
) {
@@ -548,6 +579,20 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
setMessages((prev) =>
prev.map((msg) => {
if (msg.messageId === messageId) {
const exists = msg.responseBlocks.findIndex(
(b) => b.id === data.block.id,
);
if (exists !== -1) {
const existingBlocks = [...msg.responseBlocks];
existingBlocks[exists] = data.block;
return {
...msg,
responseBlocks: existingBlocks,
};
}
return {
...msg,
responseBlocks: [...msg.responseBlocks, data.block],
@@ -556,6 +601,13 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
return msg;
}),
);
if (
(data.block.type === 'source' && data.block.data.length > 0) ||
data.block.type === 'text'
) {
setMessageAppeared(true);
}
}
if (data.type === 'updateBlock') {
@@ -577,75 +629,28 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
);
}
if (data.type === 'sources') {
const sourceBlock: Block = {
id: crypto.randomBytes(7).toString('hex'),
type: 'source',
data: data.data,
};
setMessages((prev) =>
prev.map((msg) => {
if (msg.messageId === messageId) {
return {
...msg,
responseBlocks: [...msg.responseBlocks, sourceBlock],
};
}
return msg;
}),
);
if (data.data.length > 0) {
setMessageAppeared(true);
}
}
if (data.type === 'message') {
receivedTextRef.current += data.data;
setMessages((prev) =>
prev.map((msg) => {
if (msg.messageId === messageId) {
const existingTextBlockIndex = msg.responseBlocks.findIndex(
(b) => b.type === 'text',
);
if (existingTextBlockIndex >= 0) {
const updatedBlocks = [...msg.responseBlocks];
const existingBlock = updatedBlocks[
existingTextBlockIndex
] as Block & { type: 'text' };
updatedBlocks[existingTextBlockIndex] = {
...existingBlock,
data: existingBlock.data + data.data,
};
return { ...msg, responseBlocks: updatedBlocks };
} else {
const textBlock: Block = {
id: crypto.randomBytes(7).toString('hex'),
type: 'text',
data: data.data,
};
return {
...msg,
responseBlocks: [...msg.responseBlocks, textBlock],
};
}
}
return msg;
}),
);
setMessageAppeared(true);
}
if (data.type === 'messageEnd') {
if (handledMessageEndRef.current.has(messageId)) {
return;
}
handledMessageEndRef.current.add(messageId);
const currentMsg = messagesRef.current.find(
(msg) => msg.messageId === messageId,
);
const newHistory: [string, string][] = [
...chatHistory,
['human', message],
['assistant', receivedTextRef.current],
...chatHistory.current,
['human', message.query],
[
'assistant',
currentMsg?.responseBlocks.find((b) => b.type === 'text')?.data ||
'',
],
];
setChatHistory(newHistory);
chatHistory.current = newHistory;
setMessages((prev) =>
prev.map((msg) =>
@@ -662,19 +667,18 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
const autoMediaSearch = getAutoMediaSearch();
if (autoMediaSearch) {
document
.getElementById(`search-images-${lastMsg.messageId}`)
?.click();
setTimeout(() => {
document
.getElementById(`search-images-${lastMsg.messageId}`)
?.click();
document
.getElementById(`search-videos-${lastMsg.messageId}`)
?.click();
document
.getElementById(`search-videos-${lastMsg.messageId}`)
?.click();
}, 200);
}
// Check if there are sources and no suggestions
const currentMsg = messagesRef.current.find(
(msg) => msg.messageId === messageId,
);
const hasSourceBlocks = currentMsg?.responseBlocks.some(
(block) => block.type === 'source' && block.data.length > 0,
@@ -705,6 +709,36 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
}
}
};
};
const sendMessage: ChatContext['sendMessage'] = async (
message,
messageId,
rewrite = false,
) => {
if (loading || !message) return;
setLoading(true);
setResearchEnded(false);
setMessageAppeared(false);
if (messages.length <= 1) {
window.history.replaceState(null, '', `/c/${chatId}`);
}
messageId = messageId ?? crypto.randomBytes(7).toString('hex');
const backendId = crypto.randomBytes(20).toString('hex');
const newMessage: Message = {
messageId,
chatId: chatId!,
backendId,
query: message,
responseBlocks: [],
status: 'answering',
createdAt: new Date(),
};
setMessages((prevMessages) => [...prevMessages, newMessage]);
const messageIndex = messages.findIndex((m) => m.messageId === messageId);
@@ -722,11 +756,14 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
},
chatId: chatId!,
files: fileIds,
focusMode: focusMode,
sources: sources,
optimizationMode: optimizationMode,
history: rewrite
? chatHistory.slice(0, messageIndex === -1 ? undefined : messageIndex)
: chatHistory,
? chatHistory.current.slice(
0,
messageIndex === -1 ? undefined : messageIndex,
)
: chatHistory.current,
chatModel: {
key: chatModelProvider.key,
providerId: chatModelProvider.providerId,
@@ -746,6 +783,8 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
let partialChunk = '';
const messageHandler = getMessageHandler(newMessage);
while (true) {
const { value, done } = await reader.read();
if (done) break;
@@ -771,10 +810,10 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
value={{
messages,
sections,
chatHistory,
chatHistory: chatHistory.current,
files,
fileIds,
focusMode,
sources,
chatId,
hasError,
isMessagesLoaded,
@@ -785,7 +824,7 @@ export const ChatProvider = ({ children }: { children: React.ReactNode }) => {
optimizationMode,
setFileIds,
setFiles,
setFocusMode,
setSources,
setOptimizationMode,
rewrite,
sendMessage,

View File

@@ -0,0 +1,5 @@
import OpenAILLM from '../openai/openaiLLM';
class AnthropicLLM extends OpenAILLM {}
export default AnthropicLLM;

View File

@@ -0,0 +1,115 @@
import { UIConfigField } from '@/lib/config/types';
import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
import { Model, ModelList, ProviderMetadata } from '../../types';
import BaseEmbedding from '../../base/embedding';
import BaseModelProvider from '../../base/provider';
import BaseLLM from '../../base/llm';
import AnthropicLLM from './anthropicLLM';
interface AnthropicConfig {
apiKey: string;
}
const providerConfigFields: UIConfigField[] = [
{
type: 'password',
name: 'API Key',
key: 'apiKey',
description: 'Your Anthropic API key',
required: true,
placeholder: 'Anthropic API Key',
env: 'ANTHROPIC_API_KEY',
scope: 'server',
},
];
class AnthropicProvider extends BaseModelProvider<AnthropicConfig> {
constructor(id: string, name: string, config: AnthropicConfig) {
super(id, name, config);
}
async getDefaultModels(): Promise<ModelList> {
const res = await fetch('https://api.anthropic.com/v1/models?limit=999', {
method: 'GET',
headers: {
'x-api-key': this.config.apiKey,
'anthropic-version': '2023-06-01',
'Content-type': 'application/json',
},
});
if (!res.ok) {
throw new Error(`Failed to fetch Anthropic models: ${res.statusText}`);
}
const data = (await res.json()).data;
const models: Model[] = data.map((m: any) => {
return {
key: m.id,
name: m.display_name,
};
});
return {
embedding: [],
chat: models,
};
}
async getModelList(): Promise<ModelList> {
const defaultModels = await this.getDefaultModels();
const configProvider = getConfiguredModelProviderById(this.id)!;
return {
embedding: [],
chat: [...defaultModels.chat, ...configProvider.chatModels],
};
}
async loadChatModel(key: string): Promise<BaseLLM<any>> {
const modelList = await this.getModelList();
const exists = modelList.chat.find((m) => m.key === key);
if (!exists) {
throw new Error(
'Error Loading Anthropic Chat Model. Invalid Model Selected',
);
}
return new AnthropicLLM({
apiKey: this.config.apiKey,
model: key,
baseURL: 'https://api.anthropic.com/v1',
});
}
async loadEmbeddingModel(key: string): Promise<BaseEmbedding<any>> {
throw new Error('Anthropic provider does not support embedding models.');
}
static parseAndValidate(raw: any): AnthropicConfig {
if (!raw || typeof raw !== 'object')
throw new Error('Invalid config provided. Expected object');
if (!raw.apiKey)
throw new Error('Invalid config provided. API key must be provided');
return {
apiKey: String(raw.apiKey),
};
}
static getProviderConfigFields(): UIConfigField[] {
return providerConfigFields;
}
static getProviderMetadata(): ProviderMetadata {
return {
key: 'anthropic',
name: 'Anthropic',
};
}
}
export default AnthropicProvider;

View File

@@ -0,0 +1,5 @@
import OpenAIEmbedding from '../openai/openaiEmbedding';
class GeminiEmbedding extends OpenAIEmbedding {}
export default GeminiEmbedding;

View File

@@ -0,0 +1,5 @@
import OpenAILLM from '../openai/openaiLLM';
class GeminiLLM extends OpenAILLM {}
export default GeminiLLM;

View File

@@ -0,0 +1,144 @@
import { UIConfigField } from '@/lib/config/types';
import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
import { Model, ModelList, ProviderMetadata } from '../../types';
import GeminiEmbedding from './geminiEmbedding';
import BaseEmbedding from '../../base/embedding';
import BaseModelProvider from '../../base/provider';
import BaseLLM from '../../base/llm';
import GeminiLLM from './geminiLLM';
interface GeminiConfig {
apiKey: string;
}
const providerConfigFields: UIConfigField[] = [
{
type: 'password',
name: 'API Key',
key: 'apiKey',
description: 'Your Gemini API key',
required: true,
placeholder: 'Gemini API Key',
env: 'GEMINI_API_KEY',
scope: 'server',
},
];
class GeminiProvider extends BaseModelProvider<GeminiConfig> {
constructor(id: string, name: string, config: GeminiConfig) {
super(id, name, config);
}
async getDefaultModels(): Promise<ModelList> {
const res = await fetch(
`https://generativelanguage.googleapis.com/v1beta/models?key=${this.config.apiKey}`,
{
method: 'GET',
headers: {
'Content-Type': 'application/json',
},
},
);
const data = await res.json();
let defaultEmbeddingModels: Model[] = [];
let defaultChatModels: Model[] = [];
data.models.forEach((m: any) => {
if (
m.supportedGenerationMethods.some(
(genMethod: string) =>
genMethod === 'embedText' || genMethod === 'embedContent',
)
) {
defaultEmbeddingModels.push({
key: m.name,
name: m.displayName,
});
} else if (m.supportedGenerationMethods.includes('generateContent')) {
defaultChatModels.push({
key: m.name,
name: m.displayName,
});
}
});
return {
embedding: defaultEmbeddingModels,
chat: defaultChatModels,
};
}
async getModelList(): Promise<ModelList> {
const defaultModels = await this.getDefaultModels();
const configProvider = getConfiguredModelProviderById(this.id)!;
return {
embedding: [
...defaultModels.embedding,
...configProvider.embeddingModels,
],
chat: [...defaultModels.chat, ...configProvider.chatModels],
};
}
async loadChatModel(key: string): Promise<BaseLLM<any>> {
const modelList = await this.getModelList();
const exists = modelList.chat.find((m) => m.key === key);
if (!exists) {
throw new Error(
'Error Loading Gemini Chat Model. Invalid Model Selected',
);
}
return new GeminiLLM({
apiKey: this.config.apiKey,
model: key,
baseURL: 'https://generativelanguage.googleapis.com/v1beta/openai',
});
}
async loadEmbeddingModel(key: string): Promise<BaseEmbedding<any>> {
const modelList = await this.getModelList();
const exists = modelList.embedding.find((m) => m.key === key);
if (!exists) {
throw new Error(
'Error Loading Gemini Embedding Model. Invalid Model Selected.',
);
}
return new GeminiEmbedding({
apiKey: this.config.apiKey,
model: key,
baseURL: 'https://generativelanguage.googleapis.com/v1beta/openai',
});
}
static parseAndValidate(raw: any): GeminiConfig {
if (!raw || typeof raw !== 'object')
throw new Error('Invalid config provided. Expected object');
if (!raw.apiKey)
throw new Error('Invalid config provided. API key must be provided');
return {
apiKey: String(raw.apiKey),
};
}
static getProviderConfigFields(): UIConfigField[] {
return providerConfigFields;
}
static getProviderMetadata(): ProviderMetadata {
return {
key: 'gemini',
name: 'Gemini',
};
}
}
export default GeminiProvider;

View File

@@ -0,0 +1,5 @@
import OpenAILLM from '../openai/openaiLLM';
class GroqLLM extends OpenAILLM {}
export default GroqLLM;

View File

@@ -0,0 +1,113 @@
import { UIConfigField } from '@/lib/config/types';
import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
import { Model, ModelList, ProviderMetadata } from '../../types';
import BaseEmbedding from '../../base/embedding';
import BaseModelProvider from '../../base/provider';
import BaseLLM from '../../base/llm';
import GroqLLM from './groqLLM';
interface GroqConfig {
apiKey: string;
}
const providerConfigFields: UIConfigField[] = [
{
type: 'password',
name: 'API Key',
key: 'apiKey',
description: 'Your Groq API key',
required: true,
placeholder: 'Groq API Key',
env: 'GROQ_API_KEY',
scope: 'server',
},
];
class GroqProvider extends BaseModelProvider<GroqConfig> {
constructor(id: string, name: string, config: GroqConfig) {
super(id, name, config);
}
async getDefaultModels(): Promise<ModelList> {
const res = await fetch(`https://api.groq.com/openai/v1/models`, {
method: 'GET',
headers: {
'Content-Type': 'application/json',
Authorization: `Bearer ${this.config.apiKey}`,
},
});
const data = await res.json();
const defaultChatModels: Model[] = [];
data.data.forEach((m: any) => {
defaultChatModels.push({
key: m.id,
name: m.id,
});
});
return {
embedding: [],
chat: defaultChatModels,
};
}
async getModelList(): Promise<ModelList> {
const defaultModels = await this.getDefaultModels();
const configProvider = getConfiguredModelProviderById(this.id)!;
return {
embedding: [
...defaultModels.embedding,
...configProvider.embeddingModels,
],
chat: [...defaultModels.chat, ...configProvider.chatModels],
};
}
async loadChatModel(key: string): Promise<BaseLLM<any>> {
const modelList = await this.getModelList();
const exists = modelList.chat.find((m) => m.key === key);
if (!exists) {
throw new Error('Error Loading Groq Chat Model. Invalid Model Selected');
}
return new GroqLLM({
apiKey: this.config.apiKey,
model: key,
baseURL: 'https://api.groq.com/openai/v1',
});
}
async loadEmbeddingModel(key: string): Promise<BaseEmbedding<any>> {
throw new Error('Groq Provider does not support embedding models.');
}
static parseAndValidate(raw: any): GroqConfig {
if (!raw || typeof raw !== 'object')
throw new Error('Invalid config provided. Expected object');
if (!raw.apiKey)
throw new Error('Invalid config provided. API key must be provided');
return {
apiKey: String(raw.apiKey),
};
}
static getProviderConfigFields(): UIConfigField[] {
return providerConfigFields;
}
static getProviderMetadata(): ProviderMetadata {
return {
key: 'groq',
name: 'Groq',
};
}
}
export default GroqProvider;

View File

@@ -2,10 +2,22 @@ import { ModelProviderUISection } from '@/lib/config/types';
import { ProviderConstructor } from '../base/provider';
import OpenAIProvider from './openai';
import OllamaProvider from './ollama';
import GeminiProvider from './gemini';
import TransformersProvider from './transformers';
import GroqProvider from './groq';
import LemonadeProvider from './lemonade';
import AnthropicProvider from './anthropic';
import LMStudioProvider from './lmstudio';
export const providers: Record<string, ProviderConstructor<any>> = {
openai: OpenAIProvider,
ollama: OllamaProvider,
gemini: GeminiProvider,
transformers: TransformersProvider,
groq: GroqProvider,
lemonade: LemonadeProvider,
anthropic: AnthropicProvider,
lmstudio: LMStudioProvider,
};
export const getModelProvidersUIConfigSection =

View File

@@ -0,0 +1,153 @@
import { UIConfigField } from '@/lib/config/types';
import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
import BaseModelProvider from '../../base/provider';
import { Model, ModelList, ProviderMetadata } from '../../types';
import BaseLLM from '../../base/llm';
import LemonadeLLM from './lemonadeLLM';
import BaseEmbedding from '../../base/embedding';
import LemonadeEmbedding from './lemonadeEmbedding';
interface LemonadeConfig {
baseURL: string;
apiKey?: string;
}
const providerConfigFields: UIConfigField[] = [
{
type: 'string',
name: 'Base URL',
key: 'baseURL',
description: 'The base URL for Lemonade API',
required: true,
placeholder: 'https://api.lemonade.ai/v1',
env: 'LEMONADE_BASE_URL',
scope: 'server',
},
{
type: 'password',
name: 'API Key',
key: 'apiKey',
description: 'Your Lemonade API key (optional)',
required: false,
placeholder: 'Lemonade API Key',
env: 'LEMONADE_API_KEY',
scope: 'server',
},
];
class LemonadeProvider extends BaseModelProvider<LemonadeConfig> {
constructor(id: string, name: string, config: LemonadeConfig) {
super(id, name, config);
}
async getDefaultModels(): Promise<ModelList> {
try {
const res = await fetch(`${this.config.baseURL}/models`, {
method: 'GET',
headers: {
'Content-Type': 'application/json',
...(this.config.apiKey
? { Authorization: `Bearer ${this.config.apiKey}` }
: {}),
},
});
const data = await res.json();
const models: Model[] = data.data
.filter((m: any) => m.recipe === 'llamacpp')
.map((m: any) => {
return {
name: m.id,
key: m.id,
};
});
return {
embedding: models,
chat: models,
};
} catch (err) {
if (err instanceof TypeError) {
throw new Error(
'Error connecting to Lemonade API. Please ensure the base URL is correct and the service is available.',
);
}
throw err;
}
}
async getModelList(): Promise<ModelList> {
const defaultModels = await this.getDefaultModels();
const configProvider = getConfiguredModelProviderById(this.id)!;
return {
embedding: [
...defaultModels.embedding,
...configProvider.embeddingModels,
],
chat: [...defaultModels.chat, ...configProvider.chatModels],
};
}
async loadChatModel(key: string): Promise<BaseLLM<any>> {
const modelList = await this.getModelList();
const exists = modelList.chat.find((m) => m.key === key);
if (!exists) {
throw new Error(
'Error Loading Lemonade Chat Model. Invalid Model Selected',
);
}
return new LemonadeLLM({
apiKey: this.config.apiKey || 'not-needed',
model: key,
baseURL: this.config.baseURL,
});
}
async loadEmbeddingModel(key: string): Promise<BaseEmbedding<any>> {
const modelList = await this.getModelList();
const exists = modelList.embedding.find((m) => m.key === key);
if (!exists) {
throw new Error(
'Error Loading Lemonade Embedding Model. Invalid Model Selected.',
);
}
return new LemonadeEmbedding({
apiKey: this.config.apiKey || 'not-needed',
model: key,
baseURL: this.config.baseURL,
});
}
static parseAndValidate(raw: any): LemonadeConfig {
if (!raw || typeof raw !== 'object')
throw new Error('Invalid config provided. Expected object');
if (!raw.baseURL)
throw new Error('Invalid config provided. Base URL must be provided');
return {
baseURL: String(raw.baseURL),
apiKey: raw.apiKey ? String(raw.apiKey) : undefined,
};
}
static getProviderConfigFields(): UIConfigField[] {
return providerConfigFields;
}
static getProviderMetadata(): ProviderMetadata {
return {
key: 'lemonade',
name: 'Lemonade',
};
}
}
export default LemonadeProvider;

View File

@@ -0,0 +1,5 @@
import OpenAIEmbedding from '../openai/openaiEmbedding';
class LemonadeEmbedding extends OpenAIEmbedding {}
export default LemonadeEmbedding;

View File

@@ -0,0 +1,5 @@
import OpenAILLM from '../openai/openaiLLM';
class LemonadeLLM extends OpenAILLM {}
export default LemonadeLLM;

View File

@@ -0,0 +1,143 @@
import { UIConfigField } from '@/lib/config/types';
import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
import BaseModelProvider from '../../base/provider';
import { Model, ModelList, ProviderMetadata } from '../../types';
import LMStudioLLM from './lmstudioLLM';
import BaseLLM from '../../base/llm';
import BaseEmbedding from '../../base/embedding';
import LMStudioEmbedding from './lmstudioEmbedding';
interface LMStudioConfig {
baseURL: string;
}
const providerConfigFields: UIConfigField[] = [
{
type: 'string',
name: 'Base URL',
key: 'baseURL',
description: 'The base URL for LM Studio server',
required: true,
placeholder: 'http://localhost:1234',
env: 'LM_STUDIO_BASE_URL',
scope: 'server',
},
];
class LMStudioProvider extends BaseModelProvider<LMStudioConfig> {
constructor(id: string, name: string, config: LMStudioConfig) {
super(id, name, config);
}
private normalizeBaseURL(url: string): string {
const trimmed = url.trim().replace(/\/+$/, '');
return trimmed.endsWith('/v1') ? trimmed : `${trimmed}/v1`;
}
async getDefaultModels(): Promise<ModelList> {
try {
const baseURL = this.normalizeBaseURL(this.config.baseURL);
const res = await fetch(`${baseURL}/models`, {
method: 'GET',
headers: {
'Content-Type': 'application/json',
},
});
const data = await res.json();
const models: Model[] = data.data.map((m: any) => {
return {
name: m.id,
key: m.id,
};
});
return {
embedding: models,
chat: models,
};
} catch (err) {
if (err instanceof TypeError) {
throw new Error(
'Error connecting to LM Studio. Please ensure the base URL is correct and the LM Studio server is running.',
);
}
throw err;
}
}
async getModelList(): Promise<ModelList> {
const defaultModels = await this.getDefaultModels();
const configProvider = getConfiguredModelProviderById(this.id)!;
return {
embedding: [
...defaultModels.embedding,
...configProvider.embeddingModels,
],
chat: [...defaultModels.chat, ...configProvider.chatModels],
};
}
async loadChatModel(key: string): Promise<BaseLLM<any>> {
const modelList = await this.getModelList();
const exists = modelList.chat.find((m) => m.key === key);
if (!exists) {
throw new Error(
'Error Loading LM Studio Chat Model. Invalid Model Selected',
);
}
return new LMStudioLLM({
apiKey: 'lm-studio',
model: key,
baseURL: this.normalizeBaseURL(this.config.baseURL),
});
}
async loadEmbeddingModel(key: string): Promise<BaseEmbedding<any>> {
const modelList = await this.getModelList();
const exists = modelList.embedding.find((m) => m.key === key);
if (!exists) {
throw new Error(
'Error Loading LM Studio Embedding Model. Invalid Model Selected.',
);
}
return new LMStudioEmbedding({
apiKey: 'lm-studio',
model: key,
baseURL: this.normalizeBaseURL(this.config.baseURL),
});
}
static parseAndValidate(raw: any): LMStudioConfig {
if (!raw || typeof raw !== 'object')
throw new Error('Invalid config provided. Expected object');
if (!raw.baseURL)
throw new Error('Invalid config provided. Base URL must be provided');
return {
baseURL: String(raw.baseURL),
};
}
static getProviderConfigFields(): UIConfigField[] {
return providerConfigFields;
}
static getProviderMetadata(): ProviderMetadata {
return {
key: 'lmstudio',
name: 'LM Studio',
};
}
}
export default LMStudioProvider;

View File

@@ -0,0 +1,5 @@
import OpenAIEmbedding from '../openai/openaiEmbedding';
class LMStudioEmbedding extends OpenAIEmbedding {}
export default LMStudioEmbedding;

View File

@@ -0,0 +1,5 @@
import OpenAILLM from '../openai/openaiLLM';
class LMStudioLLM extends OpenAILLM {}
export default LMStudioLLM;

View File

@@ -11,6 +11,7 @@ import { Ollama, Tool as OllamaTool, Message as OllamaMessage } from 'ollama';
import { parse } from 'partial-json';
import crypto from 'crypto';
import { Message } from '@/lib/types';
import { repairJson } from '@toolsycc/json-repair';
type OllamaConfig = {
baseURL: string;
@@ -24,6 +25,7 @@ const reasoningModels = [
'qwen3',
'deepseek-v3.1',
'magistral',
'nemotron-3-nano',
];
class OllamaLLM extends BaseLLM<OllamaConfig> {
@@ -161,8 +163,13 @@ class OllamaLLM extends BaseLLM<OllamaConfig> {
yield {
contentChunk: chunk.message.content,
toolCallChunk:
chunk.message.tool_calls?.map((tc) => ({
id: crypto.randomUUID(),
chunk.message.tool_calls?.map((tc, i) => ({
id: crypto
.createHash('sha256')
.update(
`${i}-${tc.function.name}`,
) /* Ollama currently doesn't return a tool call ID so we're creating one based on the index and tool call name */
.digest('hex'),
name: tc.function.name,
arguments: tc.function.arguments,
})) || [],
@@ -199,7 +206,13 @@ class OllamaLLM extends BaseLLM<OllamaConfig> {
});
try {
return input.schema.parse(JSON.parse(response.message.content)) as T;
return input.schema.parse(
JSON.parse(
repairJson(response.message.content, {
extractJson: true,
}) as string,
),
) as T;
} catch (err) {
throw new Error(`Error parsing response from Ollama: ${err}`);
}

View File

@@ -61,6 +61,22 @@ const defaultChatModels: Model[] = [
name: 'GPT 5 Mini',
key: 'gpt-5-mini',
},
{
name: 'GPT 5 Pro',
key: 'gpt-5-pro',
},
{
name: 'GPT 5.1',
key: 'gpt-5.1',
},
{
name: 'GPT 5.2',
key: 'gpt-5.2',
},
{
name: 'GPT 5.2 Pro',
key: 'gpt-5.2-pro',
},
{
name: 'o1',
key: 'o1',

View File

@@ -18,6 +18,7 @@ import {
ChatCompletionToolMessageParam,
} from 'openai/resources/index.mjs';
import { Message } from '@/lib/types';
import { repairJson } from '@toolsycc/json-repair';
type OpenAIConfig = {
apiKey: string;
@@ -167,7 +168,7 @@ class OpenAILLM extends BaseLLM<OpenAIConfig> {
contentChunk: chunk.choices[0].delta.content || '',
toolCallChunk:
toolCalls?.map((tc) => {
if (tc.type === 'function') {
if (!recievedToolCalls[tc.index]) {
const call = {
name: tc.function?.name!,
id: tc.id!,
@@ -213,7 +214,13 @@ class OpenAILLM extends BaseLLM<OpenAIConfig> {
if (response.choices && response.choices.length > 0) {
try {
return input.schema.parse(response.choices[0].message.parsed) as T;
return input.schema.parse(
JSON.parse(
repairJson(response.choices[0].message.content!, {
extractJson: true,
}) as string,
),
) as T;
} catch (err) {
throw new Error(`Error parsing response from OpenAI: ${err}`);
}

View File

@@ -0,0 +1,88 @@
import { UIConfigField } from '@/lib/config/types';
import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
import { Model, ModelList, ProviderMetadata } from '../../types';
import BaseModelProvider from '../../base/provider';
import BaseLLM from '../../base/llm';
import BaseEmbedding from '../../base/embedding';
import TransformerEmbedding from './transformerEmbedding';
interface TransformersConfig {}
const defaultEmbeddingModels: Model[] = [
{
name: 'all-MiniLM-L6-v2',
key: 'Xenova/all-MiniLM-L6-v2',
},
{
name: 'mxbai-embed-large-v1',
key: 'mixedbread-ai/mxbai-embed-large-v1',
},
{
name: 'nomic-embed-text-v1',
key: 'Xenova/nomic-embed-text-v1',
},
];
const providerConfigFields: UIConfigField[] = [];
class TransformersProvider extends BaseModelProvider<TransformersConfig> {
constructor(id: string, name: string, config: TransformersConfig) {
super(id, name, config);
}
async getDefaultModels(): Promise<ModelList> {
return {
embedding: [...defaultEmbeddingModels],
chat: [],
};
}
async getModelList(): Promise<ModelList> {
const defaultModels = await this.getDefaultModels();
const configProvider = getConfiguredModelProviderById(this.id)!;
return {
embedding: [
...defaultModels.embedding,
...configProvider.embeddingModels,
],
chat: [],
};
}
async loadChatModel(key: string): Promise<BaseLLM<any>> {
throw new Error('Transformers Provider does not support chat models.');
}
async loadEmbeddingModel(key: string): Promise<BaseEmbedding<any>> {
const modelList = await this.getModelList();
const exists = modelList.embedding.find((m) => m.key === key);
if (!exists) {
throw new Error(
'Error Loading OpenAI Embedding Model. Invalid Model Selected.',
);
}
return new TransformerEmbedding({
model: key,
});
}
static parseAndValidate(raw: any): TransformersConfig {
return {};
}
static getProviderConfigFields(): UIConfigField[] {
return providerConfigFields;
}
static getProviderMetadata(): ProviderMetadata {
return {
key: 'transformers',
name: 'Transformers',
};
}
}
export default TransformersProvider;

View File

@@ -0,0 +1,41 @@
import { Chunk } from '@/lib/types';
import BaseEmbedding from '../../base/embedding';
import { FeatureExtractionPipeline } from '@huggingface/transformers';
type TransformerConfig = {
model: string;
};
class TransformerEmbedding extends BaseEmbedding<TransformerConfig> {
private pipelinePromise: Promise<FeatureExtractionPipeline> | null = null;
constructor(protected config: TransformerConfig) {
super(config);
}
async embedText(texts: string[]): Promise<number[][]> {
return this.embed(texts);
}
async embedChunks(chunks: Chunk[]): Promise<number[][]> {
return this.embed(chunks.map((c) => c.content));
}
private async embed(texts: string[]) {
if (!this.pipelinePromise) {
this.pipelinePromise = (async () => {
const { pipeline } = await import('@huggingface/transformers');
const result = await pipeline('feature-extraction', this.config.model, {
dtype: 'fp32',
});
return result as FeatureExtractionPipeline;
})();
}
const pipe = await this.pipelinePromise;
const output = await pipe(texts, { pooling: 'mean', normalize: true });
return output.tolist() as number[][];
}
}
export default TransformerEmbedding;

View File

@@ -3,6 +3,7 @@ import { ChatTurnMessage } from '@/lib/types';
export const imageSearchPrompt = `
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question so it is a standalone question that can be used by the LLM to search the web for images.
You need to make sure the rephrased question agrees with the conversation and is relevant to the conversation.
Make sure to make the querey standalone and not something very broad, use context from the answers in the conversation to make it specific so user can get best image search results.
Output only the rephrased query in query key JSON format. Do not include any explanation or additional text.
`;

View File

@@ -3,6 +3,7 @@ import { ChatTurnMessage } from '@/lib/types';
export const videoSearchPrompt = `
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question so it is a standalone question that can be used by the LLM to search Youtube for videos.
You need to make sure the rephrased question agrees with the conversation and is relevant to the conversation.
Make sure to make the querey standalone and not something very broad, use context from the answers in the conversation to make it specific so user can get best video search results.
Output only the rephrased query in query key JSON format. Do not include any explanation or additional text.
`;

View File

@@ -55,7 +55,8 @@ You must respond in the following JSON format without any extra text, explanatio
"academicSearch": boolean,
"discussionSearch": boolean,
"showWeatherWidget": boolean,
"showStockWidget": boolean
"showStockWidget": boolean,
"showCalculationWidget": boolean,
},
"standaloneFollowUp": string
}

View File

@@ -1,8 +1,11 @@
export const getResearcherPrompt = (
import BaseEmbedding from '@/lib/models/base/embedding';
import UploadStore from '@/lib/uploads/store';
const getSpeedPrompt = (
actionDesc: string,
mode: 'speed' | 'balanced' | 'quality',
i: number,
maxIteration: number,
fileDesc: string,
) => {
const today = new Date().toLocaleDateString('en-US', {
year: 'numeric',
@@ -11,235 +14,341 @@ export const getResearcherPrompt = (
});
return `
You are an action orchestrator. Your job is to fulfill user requests by selecting and executing appropriate actions - whether that's searching for information, creating calendar events, sending emails, or any other available action.
You will be shared with the conversation history between user and AI, along with the user's latest follow-up question and your previous actions' results (if any. Note that they're per conversation so if they contain any previous actions it was executed for the last follow up (the one you're currently handling)). Based on this, you must decide the best next action(s) to take to fulfill the user's request.
Assistant is an action orchestrator. Your job is to fulfill user requests by selecting and executing the available tools—no free-form replies.
You will be shared with the conversation history between user and an AI, along with the user's latest follow-up question. Based on this, you must use the available tools to fulfill the user's request.
Today's date: ${today}
Today's date: ${today}
You are operating in "${mode}" mode. ${
mode === 'speed'
? 'Prioritize speed - use as few actions as possible to get the needed information quickly.'
: mode === 'balanced'
? 'Balance speed and depth - use a moderate number of actions to get good information efficiently. Never stop at the first action unless there is no action available or the query is simple.'
: 'Conduct deep research - use multiple actions to gather comprehensive information, even if it takes longer.'
}
You are currently on iteration ${i + 1} of your research process and have ${maxIteration} total iterations so act efficiently.
When you are finished, you must call the \`done\` tool. Never output text directly.
You are currently on iteration ${i + 1} of your research process and have ${maxIteration} total iterations so please take action accordingly. After max iterations, the done action would get called automatically so you don't have to worry about that unless you want to end the research early.
<goal>
Fulfill the user's request as quickly as possible using the available tools.
Call tools to gather information or perform tasks as needed.
</goal>
<available_actions>
${actionDesc}
</available_actions>
<core_principle>
Your knowledge is outdated; if you have web search, use it to ground answers even for seemingly basic facts.
</core_principle>
<core_principle>
<examples>
NEVER ASSUME - your knowledge may be outdated. When a user asks about something you're not certain about, go find out. Don't assume it exists or doesn't exist - just look it up directly.
## Example 1: Unknown Subject
User: "What is Kimi K2?"
Action: web_search ["Kimi K2", "Kimi K2 AI"] then done.
</core_principle>
## Example 2: Subject You're Uncertain About
User: "What are the features of GPT-5.1?"
Action: web_search ["GPT-5.1", "GPT-5.1 features", "GPT-5.1 release"] then done.
<reasoning_approach>
You never speak your reasoning to the user. You MUST call the ___plan tool first on every turn and put your reasoning there.
The plan must be 2-4 concise sentences, starting with "Okay, the user wants to..." and outlining the steps you will take next.
</reasoning_approach>
## Example 3: After Tool calls Return Results
User: "What are the features of GPT-5.1?"
[Previous tool calls returned the needed info]
Action: done.
<examples>
</examples>
## Example 1: Unknown Subject
<available_tools>
${actionDesc}
</available_tools>
User: "What is Kimi K2?"
Good reasoning:
"I'm not sure what Kimi K2 is - could be an AI model, a product, or something else. Let me look it up to find out what it actually is and get the relevant details."
Actions: web_search ["Kimi K2", "Kimi K2 AI"]
## Example 2: Subject You're Uncertain About
User: "What are the features of GPT-5.1?"
Good reasoning:
"I don't have current information on GPT-5.1 - my knowledge might be outdated. Let me look up GPT-5.1 to see what's available and what features it has."
Actions: web_search ["GPT-5.1", "GPT-5.1 features", "GPT-5.1 release"]
Bad reasoning (wastes time on verification):
"GPT-5.1 might not exist based on my knowledge. I need to verify if it exists first before looking for features."
## Example 3: After Actions Return Results
User: "What are the features of GPT-5.1?"
[Previous actions returned information about GPT-5.1]
Good reasoning:
"Got the information I needed about GPT-5.1. The results cover its features and capabilities - I can now provide a complete answer."
Action: done
## Example 4: Ambiguous Query
User: "Tell me about Mercury"
Good reasoning:
"Mercury could refer to several things - the planet, the element, or something else. I'll look up both main interpretations to give a useful answer."
Actions: web_search ["Mercury planet facts", "Mercury element"]
## Example 5: Current Events
User: "What's happening with AI regulation?"
Good reasoning:
"I need current news on AI regulation developments. Let me find the latest updates on this topic."
Actions: web_search ["AI regulation news 2024", "AI regulation bill latest"]
## Example 6: Technical Query
User: "How do I set up authentication in Next.js 14?"
Good reasoning:
"This is a technical implementation question. I'll find the current best practices and documentation for Next.js 14 authentication."
Actions: web_search ["Next.js 14 authentication guide", "NextAuth.js App Router"]
## Example 7: Comparison Query
User: "Prisma vs Drizzle - which should I use?"
Good reasoning:
"Need to find factual comparisons between these ORMs - performance, features, trade-offs. Let me gather objective information."
Actions: web_search ["Prisma vs Drizzle comparison 2024", "Drizzle ORM performance"]
## Example 8: Fact-Check
User: "Is it true you only use 10% of your brain?"
Good reasoning:
"This is a common claim that needs scientific verification. Let me find what the actual research says about this."
Actions: web_search ["10 percent brain myth science", "brain usage neuroscience"]
## Example 9: Recent Product
User: "What are the specs of MacBook Pro M4?"
Good reasoning:
"I need current information on the MacBook Pro M4. Let me look up the latest specs and details."
Actions: web_search ["MacBook Pro M4 specs", "MacBook Pro M4 specifications Apple"]
## Example 10: Multi-Part Query
User: "Population of Tokyo vs New York?"
Good reasoning:
"Need current population stats for both cities. I'll look up the comparison data."
Actions: web_search ["Tokyo population 2024", "Tokyo vs New York population"]
## Example 11: Calendar Task
User: "Add a meeting with John tomorrow at 3pm"
Good reasoning:
"This is a calendar task. I have all the details - meeting with John, tomorrow, 3pm. I'll create the event."
Action: create_calendar_event with the provided details
## Example 12: Email Task
User: "Send an email to sarah@company.com about the project update"
Good reasoning:
"Need to send an email. I have the recipient but need to compose appropriate content about the project update."
Action: send_email to sarah@company.com with project update content
## Example 13: Multi-Step Task
User: "What's the weather in Tokyo and add a reminder to pack an umbrella if it's rainy"
Good reasoning:
"Two things here - first I need to check Tokyo's weather, then based on that I might need to create a reminder. Let me start with the weather lookup."
Actions: web_search ["Tokyo weather today forecast"]
## Example 14: Research Then Act
User: "Find the best Italian restaurant near me and make a reservation for 7pm"
Good reasoning:
"I need to first find top Italian restaurants in the area, then make a reservation. Let me start by finding the options."
Actions: web_search ["best Italian restaurant near me", "top rated Italian restaurants"]
</examples>
<action_guidelines>
## For Information Queries:
- Just look it up - don't overthink whether something exists
- Use 1-3 targeted queries
- Done when you have useful information to answer with
## For Task Execution:
- Calendar, email, reminders: execute directly with the provided details
- If details are missing, note what you need
## For Multi-Step Requests:
- Break it down logically
- Complete one part before moving to the next
- Some tasks require information before you can act
## When to Select "done":
- You have the information needed to answer
- You've completed the requested task
- Further actions would be redundant
</action_guidelines>
<query_formulation>
**General subjects:**
- ["subject name", "subject name + context"]
**Current events:**
- Include year: "topic 2024", "topic latest news"
**Technical topics:**
- Include versions: "framework v14 guide"
- Add context: "documentation", "tutorial", "how to"
**Comparisons:**
- "X vs Y comparison", "X vs Y benchmarks"
**Keep it simple:**
- 1-3 actions per iteration
- Don't over-complicate queries
</query_formulation>
<mistakes_to_avoid>
<mistakes_to_avoid>
1. **Over-assuming**: Don't assume things exist or don't exist - just look them up
2. **Verification obsession**: Don't waste actions "verifying existence" - just search for the thing directly
2. **Verification obsession**: Don't waste tool calls "verifying existence" - just search for the thing directly
3. **Endless loops**: If 2-3 actions don't find something, it probably doesn't exist - report that and move on
3. **Endless loops**: If 2-3 tool calls don't find something, it probably doesn't exist - report that and move on
4. **Ignoring task context**: If user wants a calendar event, don't just search - create the event
5. **Overthinking**: Keep reasoning simple and action-focused
5. **Overthinking**: Keep reasoning simple and tool calls focused
</mistakes_to_avoid>
<response_protocol>
<response_protocol>
- NEVER output normal text to the user. ONLY call tools.
- Every turn MUST start with a call to the planning tool: name = "___plan", argument: { plan: "Okay, the user wants to ..." + concise 2-4 sentence plan }.
- Immediately after ___plan, if any information is missing, call \`web_search\` with up to 3 targeted queries. Default to searching unless you are certain you have enough.
- Call \`done\` only after planning AND any required searches when you have enough to answer.
- Do not invent tools. Do not return JSON. Do not echo the plan outside of the tool call.
- If nothing else is needed after planning, call \`done\` immediately after the plan.
</response_protocol>
`;
- Choose the appropriate tools based on the action descriptions provided above.
- Default to web_search when information is missing or stale; keep queries targeted (max 3 per call).
- Call done when you have gathered enough to answer or performed the required actions.
- Do not invent tools. Do not return JSON.
</response_protocol>
${
fileDesc.length > 0
? `<user_uploaded_files>
The user has uploaded the following files which may be relevant to their request:
${fileDesc}
You can use the uploaded files search tool to look for information within these documents if needed.
</user_uploaded_files>`
: ''
}
`;
};
const getBalancedPrompt = (
actionDesc: string,
i: number,
maxIteration: number,
fileDesc: string,
) => {
const today = new Date().toLocaleDateString('en-US', {
year: 'numeric',
month: 'long',
day: 'numeric',
});
return `
Assistant is an action orchestrator. Your job is to fulfill user requests by reasoning briefly and executing the available tools—no free-form replies.
You will be shared with the conversation history between user and an AI, along with the user's latest follow-up question. Based on this, you must use the available tools to fulfill the user's request.
Today's date: ${today}
You are currently on iteration ${i + 1} of your research process and have ${maxIteration} total iterations so act efficiently.
When you are finished, you must call the \`done\` tool. Never output text directly.
<goal>
Fulfill the user's request with concise reasoning plus focused actions.
You must call the __reasoning_preamble tool before every tool call in this assistant turn. Alternate: __reasoning_preamble → tool → __reasoning_preamble → tool ... and finish with __reasoning_preamble → done. Open each __reasoning_preamble with a brief intent phrase (e.g., "Okay, the user wants to...", "Searching for...", "Looking into...") and lay out your reasoning for the next step. Keep it natural language, no tool names.
</goal>
<core_principle>
Your knowledge is outdated; if you have web search, use it to ground answers even for seemingly basic facts.
You can call at most 6 tools total per turn: up to 2 reasoning (__reasoning_preamble counts as reasoning), 2-3 information-gathering calls, and 1 done. If you hit the cap, stop after done.
Aim for at least two information-gathering calls when the answer is not already obvious; only skip the second if the question is trivial or you already have sufficient context.
Do not spam searches—pick the most targeted queries.
</core_principle>
<done_usage>
Call done only after the reasoning plus the necessary tool calls are completed and you have enough to answer. If you call done early, stop. If you reach the tool cap, call done to conclude.
</done_usage>
<examples>
## Example 1: Unknown Subject
User: "What is Kimi K2?"
Reason: "Okay, the user wants to know about Kimi K2. I will start by looking for what Kimi K2 is and its key details, then summarize the findings."
Action: web_search ["Kimi K2", "Kimi K2 AI"] then reasoning then done.
## Example 2: Subject You're Uncertain About
User: "What are the features of GPT-5.1?"
Reason: "The user is asking about GPT-5.1 features. I will search for current feature and release information, then compile a summary."
Action: web_search ["GPT-5.1", "GPT-5.1 features", "GPT-5.1 release"] then reasoning then done.
## Example 3: After Tool calls Return Results
User: "What are the features of GPT-5.1?"
[Previous tool calls returned the needed info]
Reason: "I have gathered enough information about GPT-5.1 features; I will now wrap up."
Action: done.
</examples>
<available_tools>
YOU MUST CALL __reasoning_preamble BEFORE EVERY TOOL CALL IN THIS ASSISTANT TURN. IF YOU DO NOT CALL IT, THE TOOL CALL WILL BE IGNORED.
${actionDesc}
</available_tools>
<mistakes_to_avoid>
1. **Over-assuming**: Don't assume things exist or don't exist - just look them up
2. **Verification obsession**: Don't waste tool calls "verifying existence" - just search for the thing directly
3. **Endless loops**: If 2-3 tool calls don't find something, it probably doesn't exist - report that and move on
4. **Ignoring task context**: If user wants a calendar event, don't just search - create the event
5. **Overthinking**: Keep reasoning simple and tool calls focused
6. **Skipping the reasoning step**: Always call __reasoning_preamble first to outline your approach before other actions
</mistakes_to_avoid>
<response_protocol>
- NEVER output normal text to the user. ONLY call tools.
- Start with __reasoning_preamble and call __reasoning_preamble before every tool call (including done): open with intent phrase ("Okay, the user wants to...", "Looking into...", etc.) and lay out your reasoning for the next step. No tool names.
- Choose tools based on the action descriptions provided above.
- Default to web_search when information is missing or stale; keep queries targeted (max 3 per call).
- Use at most 6 tool calls total (__reasoning_preamble + 2-3 info calls + __reasoning_preamble + done). If done is called early, stop.
- Do not stop after a single information-gathering call unless the task is trivial or prior results already cover the answer.
- Call done only after you have the needed info or actions completed; do not call it early.
- Do not invent tools. Do not return JSON.
</response_protocol>
${
fileDesc.length > 0
? `<user_uploaded_files>
The user has uploaded the following files which may be relevant to their request:
${fileDesc}
You can use the uploaded files search tool to look for information within these documents if needed.
</user_uploaded_files>`
: ''
}
`;
};
const getQualityPrompt = (
actionDesc: string,
i: number,
maxIteration: number,
fileDesc: string,
) => {
const today = new Date().toLocaleDateString('en-US', {
year: 'numeric',
month: 'long',
day: 'numeric',
});
return `
Assistant is a deep-research orchestrator. Your job is to fulfill user requests with the most thorough, comprehensive research possible—no free-form replies.
You will be shared with the conversation history between user and an AI, along with the user's latest follow-up question. Based on this, you must use the available tools to fulfill the user's request with depth and rigor.
Today's date: ${today}
You are currently on iteration ${i + 1} of your research process and have ${maxIteration} total iterations. Use every iteration wisely to gather comprehensive information.
When you are finished, you must call the \`done\` tool. Never output text directly.
<goal>
Conduct the deepest, most thorough research possible. Leave no stone unturned.
Follow an iterative reason-act loop: call __reasoning_preamble before every tool call to outline the next step, then call the tool, then __reasoning_preamble again to reflect and decide the next step. Repeat until you have exhaustive coverage.
Open each __reasoning_preamble with a brief intent phrase (e.g., "Okay, the user wants to know about...", "From the results, it looks like...", "Now I need to dig into...") and describe what you'll do next. Keep it natural language, no tool names.
Finish with done only when you have comprehensive, multi-angle information.
</goal>
<core_principle>
Your knowledge is outdated; always use the available tools to ground answers.
This is DEEP RESEARCH mode—be exhaustive. Explore multiple angles: definitions, features, comparisons, recent news, expert opinions, use cases, limitations, and alternatives.
You can call up to 10 tools total per turn. Use an iterative loop: __reasoning_preamble → tool call(s) → __reasoning_preamble → tool call(s) → ... → __reasoning_preamble → done.
Never settle for surface-level answers. If results hint at more depth, reason about your next step and follow up. Cross-reference information from multiple queries.
</core_principle>
<done_usage>
Call done only after you have gathered comprehensive, multi-angle information. Do not call done early—exhaust your research budget first. If you reach the tool cap, call done to conclude.
</done_usage>
<examples>
## Example 1: Unknown Subject - Deep Dive
User: "What is Kimi K2?"
Reason: "Okay, the user wants to know about Kimi K2. I'll start by finding out what it is and its key capabilities."
[calls info-gathering tool]
Reason: "From the results, Kimi K2 is an AI model by Moonshot. Now I need to dig into how it compares to competitors and any recent news."
[calls info-gathering tool]
Reason: "Got comparison info. Let me also check for limitations or critiques to give a balanced view."
[calls info-gathering tool]
Reason: "I now have comprehensive coverage—definition, capabilities, comparisons, and critiques. Wrapping up."
Action: done.
## Example 2: Feature Research - Comprehensive
User: "What are the features of GPT-5.1?"
Reason: "The user wants comprehensive GPT-5.1 feature information. I'll start with core features and specs."
[calls info-gathering tool]
Reason: "Got the basics. Now I should look into how it compares to GPT-4 and benchmark performance."
[calls info-gathering tool]
Reason: "Good comparison data. Let me also gather use cases and expert opinions for depth."
[calls info-gathering tool]
Reason: "I have exhaustive coverage across features, comparisons, benchmarks, and reviews. Done."
Action: done.
## Example 3: Iterative Refinement
User: "Tell me about quantum computing applications in healthcare."
Reason: "Okay, the user wants to know about quantum computing in healthcare. I'll start with an overview of current applications."
[calls info-gathering tool]
Reason: "Results mention drug discovery and diagnostics. Let me dive deeper into drug discovery use cases."
[calls info-gathering tool]
Reason: "Now I'll explore the diagnostics angle and any recent breakthroughs."
[calls info-gathering tool]
Reason: "Comprehensive coverage achieved. Wrapping up."
Action: done.
</examples>
<available_tools>
YOU MUST CALL __reasoning_preamble BEFORE EVERY TOOL CALL IN THIS ASSISTANT TURN. IF YOU DO NOT CALL IT, THE TOOL CALL WILL BE IGNORED.
${actionDesc}
</available_tools>
<research_strategy>
For any topic, consider searching:
1. **Core definition/overview** - What is it?
2. **Features/capabilities** - What can it do?
3. **Comparisons** - How does it compare to alternatives?
4. **Recent news/updates** - What's the latest?
5. **Reviews/opinions** - What do experts say?
6. **Use cases** - How is it being used?
7. **Limitations/critiques** - What are the downsides?
</research_strategy>
<mistakes_to_avoid>
1. **Shallow research**: Don't stop after one or two searches—dig deeper from multiple angles
2. **Over-assuming**: Don't assume things exist or don't exist - just look them up
3. **Missing perspectives**: Search for both positive and critical viewpoints
4. **Ignoring follow-ups**: If results hint at interesting sub-topics, explore them
5. **Premature done**: Don't call done until you've exhausted reasonable research avenues
6. **Skipping the reasoning step**: Always call __reasoning_preamble first to outline your research strategy
</mistakes_to_avoid>
<response_protocol>
- NEVER output normal text to the user. ONLY call tools.
- Follow an iterative loop: __reasoning_preamble → tool call → __reasoning_preamble → tool call → ... → __reasoning_preamble → done.
- Each __reasoning_preamble should reflect on previous results (if any) and state the next research step. No tool names in the reasoning.
- Choose tools based on the action descriptions provided above—use whatever tools are available to accomplish the task.
- Aim for 4-7 information-gathering calls covering different angles; cross-reference and follow up on interesting leads.
- Call done only after comprehensive, multi-angle research is complete.
- Do not invent tools. Do not return JSON.
</response_protocol>
${
fileDesc.length > 0
? `<user_uploaded_files>
The user has uploaded the following files which may be relevant to their request:
${fileDesc}
You can use the uploaded files search tool to look for information within these documents if needed.
</user_uploaded_files>`
: ''
}
`;
};
export const getResearcherPrompt = (
actionDesc: string,
mode: 'speed' | 'balanced' | 'quality',
i: number,
maxIteration: number,
fileIds: string[],
) => {
let prompt = '';
const filesData = UploadStore.getFileData(fileIds);
const fileDesc = filesData
.map(
(f) =>
`<file><name>${f.fileName}</name><initial_content>${f.initialContent}</initial_content></file>`,
)
.join('\n');
switch (mode) {
case 'speed':
prompt = getSpeedPrompt(actionDesc, i, maxIteration, fileDesc);
break;
case 'balanced':
prompt = getBalancedPrompt(actionDesc, i, maxIteration, fileDesc);
break;
case 'quality':
prompt = getQualityPrompt(actionDesc, i, maxIteration, fileDesc);
break;
default:
prompt = getSpeedPrompt(actionDesc, i, maxIteration, fileDesc);
break;
}
return prompt;
};

View File

@@ -1,87 +1,54 @@
export const getWriterPrompt = (context: string) => {
export const getWriterPrompt = (
context: string,
systemInstructions: string,
mode: 'speed' | 'balanced' | 'quality',
) => {
return `
You are Perplexica, an AI assistant that provides helpful, accurate, and engaging answers. You combine web search results with a warm, conversational tone to deliver responses that feel personal and genuinely useful.
You are Perplexica, an AI model skilled in web search and crafting detailed, engaging, and well-structured answers. You excel at summarizing web pages and extracting relevant information to create professional, blog-style responses.
## Core Principles
Your task is to provide answers that are:
- **Informative and relevant**: Thoroughly address the user's query using the given context.
- **Well-structured**: Include clear headings and subheadings, and use a professional tone to present information concisely and logically.
- **Engaging and detailed**: Write responses that read like a high-quality blog post, including extra details and relevant insights.
- **Cited and credible**: Use inline citations with [number] notation to refer to the context source(s) for each fact or detail included.
- **Explanatory and Comprehensive**: Strive to explain the topic in depth, offering detailed analysis, insights, and clarifications wherever applicable.
**Be warm and conversational**: Write like you're having a friendly conversation with someone curious about the topic. Show genuine interest in helping them understand. Avoid being robotic or overly formal.
### Formatting Instructions
- **Structure**: Use a well-organized format with proper headings (e.g., "## Example heading 1" or "## Example heading 2"). Present information in paragraphs or concise bullet points where appropriate.
- **Tone and Style**: Maintain a neutral, journalistic tone with engaging narrative flow. Write as though you're crafting an in-depth article for a professional audience.
- **Markdown Usage**: Format your response with Markdown for clarity. Use headings, subheadings, bold text, and italicized words as needed to enhance readability.
- **Length and Depth**: Provide comprehensive coverage of the topic. Avoid superficial responses and strive for depth without unnecessary repetition. Expand on technical or complex topics to make them easier to understand for a general audience.
- **No main heading/title**: Start your response directly with the introduction unless asked to provide a specific title.
- **Conclusion or Summary**: Include a concluding paragraph that synthesizes the provided information or suggests potential next steps, where appropriate.
**Be informative and thorough**: Address the user's query comprehensively using the provided context. Explain concepts clearly and anticipate follow-up questions they might have.
### Citation Requirements
- Cite every single fact, statement, or sentence using [number] notation corresponding to the source from the provided \`context\`.
- Integrate citations naturally at the end of sentences or clauses as appropriate. For example, "The Eiffel Tower is one of the most visited landmarks in the world[1]."
- Ensure that **every sentence in your response includes at least one citation**, even when information is inferred or connected to general knowledge available in the provided context.
- Use multiple sources for a single detail if applicable, such as, "Paris is a cultural hub, attracting millions of visitors annually[1][2]."
- Always prioritize credibility and accuracy by linking all statements back to their respective context sources.
- Avoid citing unsupported assumptions or personal interpretations; if no source supports a statement, clearly indicate the limitation.
**Be honest and credible**: Cite your sources using [number] notation. If information is uncertain or unavailable, say so transparently.
### Special Instructions
- If the query involves technical, historical, or complex topics, provide detailed background and explanatory sections to ensure clarity.
- If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
- If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
${mode === 'quality' ? "- YOU ARE CURRENTLY SET IN QUALITY MODE, GENERATE VERY DEEP, DETAILED AND COMPREHENSIVE RESPONSES USING THE FULL CONTEXT PROVIDED. ASSISTANT'S RESPONSES SHALL NOT BE LESS THAN AT LEAST 2000 WORDS, COVER EVERYTHING AND FRAME IT LIKE A RESEARCH REPORT." : ''}
### User instructions
These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
${systemInstructions}
**No emojis**: Keep responses clean and professional. Never use emojis unless the user explicitly requests them.
### Example Output
- Begin with a brief introduction summarizing the event or query topic.
- Follow with detailed sections under clear headings, covering all aspects of the query if possible.
- Provide explanations or historical context as needed to enhance understanding.
- End with a conclusion or overall perspective if relevant.
## Formatting Guidelines
<context>
${context}
</context>
**Use Markdown effectively**:
- Use headings (## and ###) to organize longer responses into logical sections
- Use **bold** for key terms and *italics* for emphasis
- Use bullet points and numbered lists to break down complex information
- Use tables when comparing data, features, or options
- Use code blocks for technical content when appropriate
**Adapt length to the query**:
- Simple questions (weather, calculations, quick facts): Brief, direct answers
- Complex topics: Structured responses with sections, context, and depth
- Always start with the direct answer before expanding into details
**No main title**: Jump straight into your response without a title heading.
**No references section**: Never include a "Sources" or "References" section at the end. Citations are handled inline only.
## Citation Rules
**Cite all factual claims** using [number] notation corresponding to sources in the context:
- Place citations at the end of the relevant sentence or clause
- Example: "The Great Wall of China stretches over 13,000 miles[1]."
- Use multiple citations when information comes from several sources[1][2]
**Never cite widget data**: Weather, stock prices, calculations, and other widget data should be stated directly without any citation notation.
**Never list citation mappings**: Only use [number] in the text. Do not provide a list showing which number corresponds to which source.
**CRITICAL - No references section**: NEVER include a "Sources", "References", footnotes, or any numbered list at the end of your response that maps citations to their sources. This is strictly forbidden. The system handles source display separately. Your response must end with your final paragraph of content, not a list of sources.
## Widget Data
Widget data (weather, stocks, calculations) is displayed to the user in interactive cards above your response.
**IMPORTANT**: When widget data is present, keep your response VERY brief (2-3 sentences max). The user already sees the detailed data in the widget card. Do NOT repeat all the widget data in your text response.
For example, for a weather query, just say:
"It's currently -8.7°C in New York with overcast skies. You can see the full details including hourly and daily forecasts in the weather card above."
**Do NOT**:
- List out all the weather metrics (temperature, humidity, wind, pressure, etc.)
- Provide forecasts unless explicitly asked
- Add citations to widget data
- Repeat information that's already visible in the widget
## Response Style
**Opening**: Start with a direct, engaging answer to the question. Get to the point quickly.
**Body**: Expand with relevant details, context, or explanations. Use formatting to make information scannable and easy to digest.
**Closing**: For longer responses, summarize key takeaways or suggest related topics they might find interesting. Keep it natural, not formulaic.
## When Information is Limited
If you cannot find relevant information, respond honestly:
"I wasn't able to find specific information about this topic. You might want to try rephrasing your question, or I can help you explore related areas."
Suggest alternative angles or related topics that might be helpful.
<context>
${context}
</context>
Current date & time in ISO format (UTC timezone) is: ${new Date().toISOString()}.
FINAL REMINDERS:
1. DO NOT add a references/sources section at the end. Your response ends with content, not citations.
2. For widget queries (weather, stocks, calculations): Keep it to 2-3 sentences. The widget shows the details.
3. No emojis.
Current date & time in ISO format (UTC timezone) is: ${new Date().toISOString()}.
`;
};

View File

@@ -1,4 +1,3 @@
import axios from 'axios';
import { getSearxngURL } from './config/serverRegistry';
interface SearxngSearchOptions {

View File

@@ -2,8 +2,14 @@ import { EventEmitter } from 'stream';
import { applyPatch } from 'rfc6902';
import { Block } from './types';
const sessions =
(global as any)._sessionManagerSessions || new Map<string, SessionManager>();
if (process.env.NODE_ENV !== 'production') {
(global as any)._sessionManagerSessions = sessions;
}
class SessionManager {
private static sessions = new Map<string, SessionManager>();
private static sessions: Map<string, SessionManager> = sessions;
readonly id: string;
private blocks = new Map<string, Block>();
private events: { event: string; data: any }[] = [];
@@ -67,15 +73,32 @@ class SessionManager {
}
}
addListener(event: string, listener: (data: any) => void) {
this.emitter.addListener(event, listener);
getAllBlocks() {
return Array.from(this.blocks.values());
}
replay() {
for (const { event, data } of this.events) {
/* Using emitter directly to avoid infinite loop */
this.emitter.emit(event, data);
subscribe(listener: (event: string, data: any) => void): () => void {
const currentEventsLength = this.events.length;
const handler = (event: string) => (data: any) => listener(event, data);
const dataHandler = handler('data');
const endHandler = handler('end');
const errorHandler = handler('error');
this.emitter.on('data', dataHandler);
this.emitter.on('end', endHandler);
this.emitter.on('error', errorHandler);
for (let i = 0; i < currentEventsLength; i++) {
const { event, data } = this.events[i];
listener(event, data);
}
return () => {
this.emitter.off('data', dataHandler);
this.emitter.off('end', endHandler);
this.emitter.off('error', errorHandler);
};
}
}

View File

@@ -75,16 +75,37 @@ export type SearchingResearchBlock = {
searching: string[];
};
export type SearchResultsResearchBlock = {
id: string;
type: 'search_results';
reading: Chunk[];
};
export type ReadingResearchBlock = {
id: string;
type: 'reading';
reading: Chunk[];
};
export type UploadSearchingResearchBlock = {
id: string;
type: 'upload_searching';
queries: string[];
};
export type UploadSearchResultsResearchBlock = {
id: string;
type: 'upload_search_results';
results: Chunk[];
};
export type ResearchBlockSubStep =
| ReasoningResearchBlock
| SearchingResearchBlock
| ReadingResearchBlock;
| SearchResultsResearchBlock
| ReadingResearchBlock
| UploadSearchingResearchBlock
| UploadSearchResultsResearchBlock;
export type ResearchBlock = {
id: string;

218
src/lib/uploads/manager.ts Normal file
View File

@@ -0,0 +1,218 @@
import path from "path";
import BaseEmbedding from "../models/base/embedding"
import crypto from "crypto"
import fs from 'fs';
import { splitText } from "../utils/splitText";
import { PDFParse } from 'pdf-parse';
import { CanvasFactory } from 'pdf-parse/worker';
import officeParser from 'officeparser'
const supportedMimeTypes = ['application/pdf', 'application/vnd.openxmlformats-officedocument.wordprocessingml.document', 'text/plain'] as const
type SupportedMimeType = typeof supportedMimeTypes[number];
type UploadManagerParams = {
embeddingModel: BaseEmbedding<any>;
}
type RecordedFile = {
id: string;
name: string;
filePath: string;
contentPath: string;
uploadedAt: string;
}
type FileRes = {
fileName: string;
fileExtension: string;
fileId: string;
}
class UploadManager {
private embeddingModel: BaseEmbedding<any>;
static uploadsDir = path.join(process.cwd(), 'data', 'uploads');
static uploadedFilesRecordPath = path.join(this.uploadsDir, 'uploaded_files.json');
constructor(private params: UploadManagerParams) {
this.embeddingModel = params.embeddingModel;
if (!fs.existsSync(UploadManager.uploadsDir)) {
fs.mkdirSync(UploadManager.uploadsDir, { recursive: true });
}
if (!fs.existsSync(UploadManager.uploadedFilesRecordPath)) {
const data = {
files: []
}
fs.writeFileSync(UploadManager.uploadedFilesRecordPath, JSON.stringify(data, null, 2));
}
}
private static getRecordedFiles(): RecordedFile[] {
const data = fs.readFileSync(UploadManager.uploadedFilesRecordPath, 'utf-8');
return JSON.parse(data).files;
}
private static addNewRecordedFile(fileRecord: RecordedFile) {
const currentData = this.getRecordedFiles()
currentData.push(fileRecord);
fs.writeFileSync(UploadManager.uploadedFilesRecordPath, JSON.stringify({ files: currentData }, null, 2));
}
static getFile(fileId: string): RecordedFile | null {
const recordedFiles = this.getRecordedFiles();
return recordedFiles.find(f => f.id === fileId) || null;
}
static getFileChunks(fileId: string): { content: string; embedding: number[] }[] {
try {
const recordedFile = this.getFile(fileId);
if (!recordedFile) {
throw new Error(`File with ID ${fileId} not found`);
}
const contentData = JSON.parse(fs.readFileSync(recordedFile.contentPath, 'utf-8'))
return contentData.chunks;
} catch (err) {
console.log('Error getting file chunks:', err);
return [];
}
}
private async extractContentAndEmbed(filePath: string, fileType: SupportedMimeType): Promise<string> {
switch (fileType) {
case 'text/plain':
const content = fs.readFileSync(filePath, 'utf-8');
const splittedText = splitText(content, 512, 128)
const embeddings = await this.embeddingModel.embedText(splittedText)
if (embeddings.length !== splittedText.length) {
throw new Error('Embeddings and text chunks length mismatch');
}
const contentPath = filePath.split('.').slice(0, -1).join('.') + '.content.json';
const data = {
chunks: splittedText.map((text, i) => {
return {
content: text,
embedding: embeddings[i],
}
})
}
fs.writeFileSync(contentPath, JSON.stringify(data, null, 2));
return contentPath;
case 'application/pdf':
const pdfBuffer = fs.readFileSync(filePath);
const parser = new PDFParse({
data: pdfBuffer,
CanvasFactory
})
const pdfText = await parser.getText().then(res => res.text)
const pdfSplittedText = splitText(pdfText, 512, 128)
const pdfEmbeddings = await this.embeddingModel.embedText(pdfSplittedText)
if (pdfEmbeddings.length !== pdfSplittedText.length) {
throw new Error('Embeddings and text chunks length mismatch');
}
const pdfContentPath = filePath.split('.').slice(0, -1).join('.') + '.content.json';
const pdfData = {
chunks: pdfSplittedText.map((text, i) => {
return {
content: text,
embedding: pdfEmbeddings[i],
}
})
}
fs.writeFileSync(pdfContentPath, JSON.stringify(pdfData, null, 2));
return pdfContentPath;
case 'application/vnd.openxmlformats-officedocument.wordprocessingml.document':
const docBuffer = fs.readFileSync(filePath);
const docText = await officeParser.parseOfficeAsync(docBuffer)
const docSplittedText = splitText(docText, 512, 128)
const docEmbeddings = await this.embeddingModel.embedText(docSplittedText)
if (docEmbeddings.length !== docSplittedText.length) {
throw new Error('Embeddings and text chunks length mismatch');
}
const docContentPath = filePath.split('.').slice(0, -1).join('.') + '.content.json';
const docData = {
chunks: docSplittedText.map((text, i) => {
return {
content: text,
embedding: docEmbeddings[i],
}
})
}
fs.writeFileSync(docContentPath, JSON.stringify(docData, null, 2));
return docContentPath;
default:
throw new Error(`Unsupported file type: ${fileType}`);
}
}
async processFiles(files: File[]): Promise<FileRes[]> {
const processedFiles: FileRes[] = [];
await Promise.all(files.map(async (file) => {
if (!(supportedMimeTypes as unknown as string[]).includes(file.type)) {
throw new Error(`File type ${file.type} not supported`);
}
const fileId = crypto.randomBytes(16).toString('hex');
const fileExtension = file.name.split('.').pop();
const fileName = `${crypto.randomBytes(16).toString('hex')}.${fileExtension}`;
const filePath = path.join(UploadManager.uploadsDir, fileName);
const buffer = Buffer.from(await file.arrayBuffer())
fs.writeFileSync(filePath, buffer);
const contentFilePath = await this.extractContentAndEmbed(filePath, file.type as SupportedMimeType);
const fileRecord: RecordedFile = {
id: fileId,
name: file.name,
filePath: filePath,
contentPath: contentFilePath,
uploadedAt: new Date().toISOString(),
}
UploadManager.addNewRecordedFile(fileRecord);
processedFiles.push({
fileExtension: fileExtension || '',
fileId,
fileName: file.name
});
}))
return processedFiles;
}
}
export default UploadManager;

122
src/lib/uploads/store.ts Normal file
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import BaseEmbedding from "../models/base/embedding";
import UploadManager from "./manager";
import computeSimilarity from "../utils/computeSimilarity";
import { Chunk } from "../types";
import { hashObj } from "../serverUtils";
import fs from 'fs';
type UploadStoreParams = {
embeddingModel: BaseEmbedding<any>;
fileIds: string[];
}
type StoreRecord = {
embedding: number[];
content: string;
fileId: string;
metadata: Record<string, any>
}
class UploadStore {
embeddingModel: BaseEmbedding<any>;
fileIds: string[];
records: StoreRecord[] = [];
constructor(private params: UploadStoreParams) {
this.embeddingModel = params.embeddingModel;
this.fileIds = params.fileIds;
this.initializeStore()
}
initializeStore() {
this.fileIds.forEach((fileId) => {
const file = UploadManager.getFile(fileId)
if (!file) {
throw new Error(`File with ID ${fileId} not found`);
}
const chunks = UploadManager.getFileChunks(fileId);
this.records.push(...chunks.map((chunk) => ({
embedding: chunk.embedding,
content: chunk.content,
fileId: fileId,
metadata: {
fileName: file.name,
title: file.name,
url: `file_id://${file.id}`,
}
})))
})
}
async query(queries: string[], topK: number): Promise<Chunk[]> {
const queryEmbeddings = await this.embeddingModel.embedText(queries)
const results: { chunk: Chunk; score: number; }[][] = [];
const hashResults: string[][] = []
await Promise.all(queryEmbeddings.map(async (query) => {
const similarities = this.records.map((record, idx) => {
return {
chunk: {
content: record.content,
metadata: {
...record.metadata,
fileId: record.fileId,
}
},
score: computeSimilarity(query, record.embedding)
} as { chunk: Chunk; score: number; };
}).sort((a, b) => b.score - a.score)
results.push(similarities)
hashResults.push(similarities.map(s => hashObj(s)))
}))
const chunkMap: Map<string, Chunk> = new Map();
const scoreMap: Map<string, number> = new Map();
const k = 60;
for (let i = 0; i < results.length; i++) {
for (let j = 0; j < results[i].length; j++) {
const chunkHash = hashResults[i][j]
chunkMap.set(chunkHash, results[i][j].chunk);
scoreMap.set(chunkHash, (scoreMap.get(chunkHash) || 0) + results[i][j].score / (j + 1 + k));
}
}
const finalResults = Array.from(scoreMap.entries())
.sort((a, b) => b[1] - a[1])
.map(([chunkHash, _score]) => {
return chunkMap.get(chunkHash)!;
})
return finalResults.slice(0, topK);
}
static getFileData(fileIds: string[]): { fileName: string; initialContent: string }[] {
const filesData: { fileName: string; initialContent: string }[] = [];
fileIds.forEach((fileId) => {
const file = UploadManager.getFile(fileId)
if (!file) {
throw new Error(`File with ID ${fileId} not found`);
}
const chunks = UploadManager.getFileChunks(fileId);
filesData.push({
fileName: file.name,
initialContent: chunks.slice(0, 3).map(c => c.content).join('\n---\n'),
})
})
return filesData
}
}
export default UploadStore

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import { getEncoding } from 'js-tiktoken';
const splitRegex = /(?<=\. |\n|! |\? |; |:\s|\d+\.\s|- |\* )/g;
const enc = getEncoding('cl100k_base');
const getTokenCount = (text: string): number => {
try {
return enc.encode(text).length;
} catch {
return Math.ceil(text.length / 4);
}
};
export const splitText = (
text: string,
maxTokens = 512,
overlapTokens = 64,
): string[] => {
const segments = text.split(splitRegex).filter(Boolean);
if (segments.length === 0) {
return [];
}
const segmentTokenCounts = segments.map(getTokenCount);
const result: string[] = [];
let chunkStart = 0;
while (chunkStart < segments.length) {
let chunkEnd = chunkStart;
let currentTokenCount = 0;
while (chunkEnd < segments.length && currentTokenCount < maxTokens) {
if (currentTokenCount + segmentTokenCounts[chunkEnd] > maxTokens) {
break;
}
currentTokenCount += segmentTokenCounts[chunkEnd];
chunkEnd++;
}
let overlapBeforeStart = Math.max(0, chunkStart - 1);
let overlapBeforeTokenCount = 0;
while (overlapBeforeStart >= 0 && overlapBeforeTokenCount < overlapTokens) {
if (
overlapBeforeTokenCount + segmentTokenCounts[overlapBeforeStart] >
overlapTokens
) {
break;
}
overlapBeforeTokenCount += segmentTokenCounts[overlapBeforeStart];
overlapBeforeStart--;
}
const overlapStartIndex = Math.max(0, overlapBeforeStart + 1);
const overlapBeforeContent = segments
.slice(overlapStartIndex, chunkStart)
.join('');
const chunkContent = segments.slice(chunkStart, chunkEnd).join('');
result.push(overlapBeforeContent + chunkContent);
chunkStart = chunkEnd;
}
return result;
};

2
uploads/.gitignore vendored
View File

@@ -1,2 +0,0 @@
*
!.gitignore

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