Compare commits

..

11 Commits

Author SHA1 Message Date
Eli Grinfeld, MBA
7f287c45a6 Merge 9f4ae1baac into 46541e6c0c 2025-02-03 09:31:06 +01:00
eligrinfeld
9f4ae1baac feat: update backend services and routes
- Add business routes and middleware\n- Update search and database services\n- Improve health check implementation\n- Update CI workflow configuration
2025-01-06 21:25:15 -07:00
eligrinfeld
79f26fce25 feat: add frontend setup with Tailwind CSS 2025-01-06 21:25:03 -07:00
eligrinfeld
7fa0e9dd9d feat: update database schema and migrations 2025-01-06 21:24:54 -07:00
eligrinfeld
765c8e549c chore: update dependencies and lock files 2025-01-06 21:24:45 -07:00
eligrinfeld
2ac1cb3943 refactor: improve server initialization and port handling
- Separate server setup from initialization\n- Add port availability check utility\n- Fix double server start issue\n- Improve error handling for port conflicts
2025-01-06 21:24:30 -07:00
eligrinfeld
ce97671da3 test: add CI/CD workflow 2025-01-05 14:16:31 -07:00
eligrinfeld
66d44c0774 feat(cleanup): Enhanced business data validation and cleaning
- Added confidence scoring system (0-1) for data quality
- Implemented strict validation for contact info
- Added batch processing and timeout protection
- Improved error handling with fallbacks
- Added smart caching based on confidence scores

Technical changes:
- Added regex validation for emails, phones, addresses
- Implemented business type detection
- Enhanced post-processing for consistent formatting
- Added JSDoc comments for maintainability

Testing:
- Verified with restaurant and plumber searches
- Confirmed improved data quality
- Validated timeout handling
2025-01-04 21:00:55 -07:00
eligrinfeld
6bcee39e63 feat(cleanup): Enhanced business data validation and cleaning
- Added confidence scoring system for data quality
- Implemented strict validation for emails, phones, and addresses
- Added batch processing to prevent LLM overload
- Improved error handling and fallback mechanisms
- Added caching based on confidence scores

Technical changes:
- Added regex validation for contact info
- Implemented scoring system (0-1 scale)
- Added timeout protection for LLM calls
- Enhanced post-processing for consistent formatting
- Added business type detection for context

Breaking changes: None
Dependencies: No new dependencies required
2025-01-04 20:59:00 -07:00
eligrinfeld
fde5b5e318 Add project files:
- Add database initialization scripts
- Add configuration files
- Add documentation
- Add public assets
- Add source code structure
- Update README
2025-01-04 17:22:46 -07:00
eligrinfeld
372943801d Refactor business search functionality:
- Add utility functions for business ID generation
- Improve database service with proper types
- Fix type safety issues in search implementation
- Add caching layer for search results
2025-01-04 17:16:22 -07:00
230 changed files with 38045 additions and 7953 deletions

11
.env.example Normal file
View File

@@ -0,0 +1,11 @@
PORT=3000
NODE_ENV=development
SUPABASE_URL=your_supabase_url
SUPABASE_KEY=your_supabase_key
OLLAMA_URL=http://localhost:11434
OLLAMA_MODEL=llama2
SEARXNG_URL=http://localhost:4000
SEARXNG_INSTANCES=["http://localhost:4000"]
MAX_RESULTS_PER_QUERY=50
CACHE_DURATION_HOURS=24
CACHE_DURATION_DAYS=7

29
.github/workflows/ci.yml vendored Normal file
View File

@@ -0,0 +1,29 @@
---
name: CI
on:
push:
branches: [ main ]
pull_request:
branches: [ main ]
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Setup Node.js
uses: actions/setup-node@v2
with:
node-version: '18'
- name: Install dependencies
run: npm ci
- name: Run tests
run: npm test
- name: Run type check
run: npm run build

View File

@@ -8,12 +8,18 @@ on:
types: [published]
jobs:
build-amd64:
build-and-push:
runs-on: ubuntu-latest
strategy:
matrix:
service: [backend, app]
steps:
- name: Checkout code
uses: actions/checkout@v3
- name: Set up QEMU
uses: docker/setup-qemu-action@v2
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v2
with:
@@ -30,109 +36,38 @@ jobs:
id: version
run: echo "RELEASE_VERSION=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV
- name: Build and push AMD64 Docker image
- name: Build and push Docker image for ${{ matrix.service }}
if: github.ref == 'refs/heads/master' && github.event_name == 'push'
run: |
DOCKERFILE=app.dockerfile
IMAGE_NAME=perplexica
docker buildx build --platform linux/amd64 \
--cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:amd64 \
docker buildx create --use
if [[ "${{ matrix.service }}" == "backend" ]]; then \
DOCKERFILE=backend.dockerfile; \
IMAGE_NAME=perplexica-backend; \
else \
DOCKERFILE=app.dockerfile; \
IMAGE_NAME=perplexica-frontend; \
fi
docker buildx build --platform linux/amd64,linux/arm64 \
--cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:main \
--cache-to=type=inline \
--provenance false \
-f $DOCKERFILE \
-t itzcrazykns1337/${IMAGE_NAME}:amd64 \
-t itzcrazykns1337/${IMAGE_NAME}:main \
--push .
- name: Build and push AMD64 release Docker image
- name: Build and push release Docker image for ${{ matrix.service }}
if: github.event_name == 'release'
run: |
DOCKERFILE=app.dockerfile
IMAGE_NAME=perplexica
docker buildx build --platform linux/amd64 \
--cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-amd64 \
docker buildx create --use
if [[ "${{ matrix.service }}" == "backend" ]]; then \
DOCKERFILE=backend.dockerfile; \
IMAGE_NAME=perplexica-backend; \
else \
DOCKERFILE=app.dockerfile; \
IMAGE_NAME=perplexica-frontend; \
fi
docker buildx build --platform linux/amd64,linux/arm64 \
--cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }} \
--cache-to=type=inline \
--provenance false \
-f $DOCKERFILE \
-t itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-amd64 \
-t itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }} \
--push .
build-arm64:
runs-on: ubuntu-24.04-arm
steps:
- name: Checkout code
uses: actions/checkout@v3
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v2
with:
install: true
- name: Log in to DockerHub
uses: docker/login-action@v2
with:
username: ${{ secrets.DOCKER_USERNAME }}
password: ${{ secrets.DOCKER_PASSWORD }}
- name: Extract version from release tag
if: github.event_name == 'release'
id: version
run: echo "RELEASE_VERSION=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV
- name: Build and push ARM64 Docker image
if: github.ref == 'refs/heads/master' && github.event_name == 'push'
run: |
DOCKERFILE=app.dockerfile
IMAGE_NAME=perplexica
docker buildx build --platform linux/arm64 \
--cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:arm64 \
--cache-to=type=inline \
--provenance false \
-f $DOCKERFILE \
-t itzcrazykns1337/${IMAGE_NAME}:arm64 \
--push .
- name: Build and push ARM64 release Docker image
if: github.event_name == 'release'
run: |
DOCKERFILE=app.dockerfile
IMAGE_NAME=perplexica
docker buildx build --platform linux/arm64 \
--cache-from=type=registry,ref=itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-arm64 \
--cache-to=type=inline \
--provenance false \
-f $DOCKERFILE \
-t itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-arm64 \
--push .
manifest:
needs: [build-amd64, build-arm64]
runs-on: ubuntu-latest
steps:
- name: Log in to DockerHub
uses: docker/login-action@v2
with:
username: ${{ secrets.DOCKER_USERNAME }}
password: ${{ secrets.DOCKER_PASSWORD }}
- name: Extract version from release tag
if: github.event_name == 'release'
id: version
run: echo "RELEASE_VERSION=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV
- name: Create and push multi-arch manifest for main
if: github.ref == 'refs/heads/master' && github.event_name == 'push'
run: |
IMAGE_NAME=perplexica
docker manifest create itzcrazykns1337/${IMAGE_NAME}:main \
--amend itzcrazykns1337/${IMAGE_NAME}:amd64 \
--amend itzcrazykns1337/${IMAGE_NAME}:arm64
docker manifest push itzcrazykns1337/${IMAGE_NAME}:main
- name: Create and push multi-arch manifest for releases
if: github.event_name == 'release'
run: |
IMAGE_NAME=perplexica
docker manifest create itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }} \
--amend itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-amd64 \
--amend itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}-arm64
docker manifest push itzcrazykns1337/${IMAGE_NAME}:${{ env.RELEASE_VERSION }}

53
.gitignore vendored
View File

@@ -1,39 +1,32 @@
# Node.js
node_modules/
npm-debug.log
yarn-error.log
# Build output
.next/
out/
dist/
# IDE/Editor specific
.vscode/
.idea/
*.iml
# Environment variables
.env
.env.local
.env.development.local
.env.test.local
.env.production.local
.env.*
!.env.example
# Config files
config.toml
# Dependencies
node_modules/
yarn-error.log
npm-debug.log
# Log files
logs/
*.log
# Build outputs
dist/
build/
.next/
# Testing
/coverage/
# IDE/Editor
.vscode/
.idea/
*.swp
*.swo
# Miscellaneous
# OS
.DS_Store
Thumbs.db
# Db
db.sqlite
/searxng
# Logs
logs/
*.log
# Cache
.cache/
.npm/

View File

@@ -6,6 +6,7 @@ const config = {
endOfLine: 'auto',
singleQuote: true,
tabWidth: 2,
semi: true,
};
module.exports = config;

View File

@@ -1,43 +1,32 @@
# How to Contribute to Perplexica
Thanks for your interest in contributing to Perplexica! Your help makes this project better. This guide explains how to contribute effectively.
Perplexica is a modern AI chat application with advanced search capabilities.
Hey there, thanks for deciding to contribute to Perplexica. Anything you help with will support the development of Perplexica and will make it better. Let's walk you through the key aspects to ensure your contributions are effective and in harmony with the project's setup.
## Project Structure
Perplexica's codebase is organized as follows:
Perplexica's design consists of two main domains:
- **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.
- **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`.
- 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.
## API Documentation
Perplexica exposes several API endpoints for programmatic access, including:
- **Search API**: Access Perplexica's advanced search capabilities directly via the `/api/search` endpoint. For detailed documentation, see `docs/api/search.md`.
- **Frontend (`ui` directory)**: This is a Next.js application holding all user interface components. It's a self-contained environment that manages everything the user interacts with.
- **Backend (root and `src` directory)**: The backend logic is situated in the `src` folder, but the root directory holds the main `package.json` for backend dependency management.
- All of the focus modes are created using the Meta Search Agent class present in `src/search/metaSearchAgent.ts`. The main logic behind Perplexica lies there.
## Setting Up Your Environment
Before diving into coding, setting up your local environment is key. Here's what you need to do:
### Backend
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:push` to set up the local sqlite database.
5. Use `npm run dev` to start the application in development mode.
2. Rename it to `config.toml` and fill in the necessary configuration fields specific to the backend.
3. Run `npm install` to install dependencies.
4. Run `npm run db:push` to set up the local sqlite.
5. Use `npm run dev` to start the backend in development mode.
### Frontend
1. Navigate to the `ui` folder and repeat the process of renaming `.env.example` to `.env`, making sure to provide the frontend-specific variables.
2. Execute `npm install` within the `ui` directory to get the frontend dependencies ready.
3. Launch the frontend development server with `npm run dev`.
**Please note**: Docker configurations are present for setting up production environments, whereas `npm run dev` is used for development purposes.

250
README.md
View File

@@ -1,194 +1,120 @@
# 🚀 Perplexica - An AI-powered search engine 🔎 <!-- omit in toc -->
# BizSearch
<div align="center" markdown="1">
<sup>Special thanks to:</sup>
<br>
<br>
<a href="https://www.warp.dev/perplexica">
<img alt="Warp sponsorship" width="400" src="https://github.com/user-attachments/assets/775dd593-9b5f-40f1-bf48-479faff4c27b">
</a>
A tool for finding and analyzing local businesses using AI-powered data extraction.
### [Warp, the AI Devtool that lives in your terminal](https://www.warp.dev/perplexica)
## Prerequisites
[Available for MacOS, Linux, & Windows](https://www.warp.dev/perplexica)
</div>
<hr/>
[![Discord](https://dcbadge.vercel.app/api/server/26aArMy8tT?style=flat&compact=true)](https://discord.gg/26aArMy8tT)
![preview](.assets/perplexica-screenshot.png?)
## Table of Contents <!-- omit in toc -->
- [Overview](#overview)
- [Preview](#preview)
- [Features](#features)
- [Installation](#installation)
- [Getting Started with Docker (Recommended)](#getting-started-with-docker-recommended)
- [Non-Docker Installation](#non-docker-installation)
- [Ollama Connection Errors](#ollama-connection-errors)
- [Using as a Search Engine](#using-as-a-search-engine)
- [Using Perplexica's API](#using-perplexicas-api)
- [Expose Perplexica to a network](#expose-perplexica-to-network)
- [One-Click Deployment](#one-click-deployment)
- [Upcoming Features](#upcoming-features)
- [Support Us](#support-us)
- [Donations](#donations)
- [Contribution](#contribution)
- [Help and Support](#help-and-support)
## Overview
Perplexica is an open-source AI-powered searching tool or an AI-powered search engine that goes deep into the internet to find answers. Inspired by Perplexity AI, it's an open-source option that not just searches the web but understands your questions. It uses advanced machine learning algorithms like similarity searching and embeddings to refine results and provides clear answers with sources cited.
Using SearxNG to stay current and fully open source, Perplexica ensures you always get the most up-to-date information without compromising your privacy.
Want to know more about its architecture and how it works? You can read it [here](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/architecture/README.md).
## Preview
![video-preview](.assets/perplexica-preview.gif)
## Features
- **Local LLMs**: You can make use local LLMs such as Llama3 and Mixtral using Ollama.
- **Two Main Modes:**
- **Copilot Mode:** (In development) Boosts search by generating different queries to find more relevant internet sources. Like normal search instead of just using the context by SearxNG, it visits the top matches and tries to find relevant sources to the user's query directly from the page.
- **Normal Mode:** Processes your query and performs a web search.
- **Focus Modes:** Special modes to better answer specific types of questions. Perplexica currently has 6 focus modes:
- **All Mode:** Searches the entire web to find the best results.
- **Writing Assistant Mode:** Helpful for writing tasks that do not require searching the web.
- **Academic Search Mode:** Finds articles and papers, ideal for academic research.
- **YouTube Search Mode:** Finds YouTube videos based on the search query.
- **Wolfram Alpha Search Mode:** Answers queries that need calculations or data analysis using Wolfram Alpha.
- **Reddit Search Mode:** Searches Reddit for discussions and opinions related to the query.
- **Current Information:** Some search tools might give you outdated info because they use data from crawling bots and convert them into embeddings and store them in a index. Unlike them, Perplexica uses SearxNG, a metasearch engine to get the results and rerank and get the most relevant source out of it, ensuring you always get the latest information without the overhead of daily data updates.
- **API**: Integrate Perplexica into your existing applications and make use of its capibilities.
It has many more features like image and video search. Some of the planned features are mentioned in [upcoming features](#upcoming-features).
- Node.js 16+
- Ollama (for local LLM)
- SearxNG instance
## Installation
There are mainly 2 ways of installing Perplexica - With Docker, Without Docker. Using Docker is highly recommended.
1. Install Ollama:
```bash
# On macOS
brew install ollama
```
### Getting Started with Docker (Recommended)
2. Start Ollama:
```bash
# Start and enable on login
brew services start ollama
1. Ensure Docker is installed and running on your system.
2. Clone the Perplexica repository:
# Or run without auto-start
/usr/local/opt/ollama/bin/ollama serve
```
```bash
git clone https://github.com/ItzCrazyKns/Perplexica.git
```
3. Pull the required model:
```bash
ollama pull mistral
```
3. After cloning, navigate to the directory containing the project files.
4. Clone and set up the project:
```bash
git clone https://github.com/yourusername/bizsearch.git
cd bizsearch
npm install
```
4. Rename the `sample.config.toml` file to `config.toml`. For Docker setups, you need only fill in the following fields:
5. Configure environment:
```bash
cp .env.example .env
# Edit .env with your settings
```
- `OPENAI`: Your OpenAI API key. **You only need to fill this if you wish to use OpenAI's models**.
- `OLLAMA`: Your Ollama API URL. You should enter it as `http://host.docker.internal:PORT_NUMBER`. If you installed Ollama on port 11434, use `http://host.docker.internal:11434`. For other ports, adjust accordingly. **You need to fill this if you wish to use Ollama's models instead of OpenAI's**.
- `GROQ`: Your Groq API key. **You only need to fill this if you wish to use Groq's hosted models**.
- `ANTHROPIC`: Your Anthropic API key. **You only need to fill this if you wish to use Anthropic models**.
6. Start the application:
```bash
npm run dev
```
**Note**: You can change these after starting Perplexica from the settings dialog.
7. Open http://localhost:3000 in your browser
- `SIMILARITY_MEASURE`: The similarity measure to use (This is filled by default; you can leave it as is if you are unsure about it.)
## Troubleshooting
5. Ensure you are in the directory containing the `docker-compose.yaml` file and execute:
If Ollama fails to start:
```bash
# Stop any existing instance
brew services stop ollama
# Wait a few seconds
sleep 5
# Start again
brew services start ollama
```
```bash
docker compose up -d
```
To verify Ollama is running:
```bash
curl http://localhost:11434/api/version
```
6. Wait a few minutes for the setup to complete. You can access Perplexica at http://localhost:3000 in your web browser.
## Features
**Note**: After the containers are built, you can start Perplexica directly from Docker without having to open a terminal.
- Business search with location filtering
- Contact information extraction
- AI-powered data validation
- Clean, user-friendly interface
- Service health monitoring
### Non-Docker Installation
## Configuration
1. Install SearXNG and allow `JSON` format in the SearXNG settings.
2. Clone the repository and rename the `sample.config.toml` file to `config.toml` in the root directory. Ensure you complete all required fields in this file.
3. After populating the configuration run `npm i`.
4. Install the dependencies and then execute `npm run build`.
5. Finally, start the app by running `npm rum start`
Key environment variables:
- `SEARXNG_URL`: Your SearxNG instance URL
- `OLLAMA_URL`: Ollama API endpoint (default: http://localhost:11434)
- `SUPABASE_URL`: Your Supabase project URL
- `SUPABASE_ANON_KEY`: Your Supabase anonymous key
- `CACHE_DURATION_DAYS`: How long to cache results (default: 7)
**Note**: Using Docker is recommended as it simplifies the setup process, especially for managing environment variables and dependencies.
## Supabase Setup
See the [installation documentation](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/installation) for more information like updating, etc.
1. Create a new Supabase project
2. Run the SQL commands in `db/init.sql` to create the cache table
3. Copy your project URL and anon key to `.env`
### Ollama Connection Errors
## License
If you're encountering an Ollama connection error, it is likely due to the backend being unable to connect to Ollama's API. To fix this issue you can:
MIT
1. **Check your Ollama API URL:** Ensure that the API URL is correctly set in the settings menu.
2. **Update API URL Based on OS:**
## Cache Management
- **Windows:** Use `http://host.docker.internal:11434`
- **Mac:** Use `http://host.docker.internal:11434`
- **Linux:** Use `http://<private_ip_of_host>:11434`
The application uses Supabase for caching search results. Cache entries expire after 7 days.
Adjust the port number if you're using a different one.
### Manual Cache Cleanup
3. **Linux Users - Expose Ollama to Network:**
If automatic cleanup is not available, you can manually clean up expired entries:
- Inside `/etc/systemd/system/ollama.service`, you need to add `Environment="OLLAMA_HOST=0.0.0.0"`. Then restart Ollama by `systemctl restart ollama`. For more information see [Ollama docs](https://github.com/ollama/ollama/blob/main/docs/faq.md#setting-environment-variables-on-linux)
1. Using the API:
```bash
curl -X POST http://localhost:3000/api/cleanup
```
- Ensure that the port (default is 11434) is not blocked by your firewall.
2. Using SQL:
```sql
select manual_cleanup();
```
## Using as a Search Engine
### Cache Statistics
If you wish to use Perplexica as an alternative to traditional search engines like Google or Bing, or if you want to add a shortcut for quick access from your browser's search bar, follow these steps:
1. Open your browser's settings.
2. Navigate to the 'Search Engines' section.
3. Add a new site search with the following URL: `http://localhost:3000/?q=%s`. Replace `localhost` with your IP address or domain name, and `3000` with the port number if Perplexica is not hosted locally.
4. Click the add button. Now, you can use Perplexica directly from your browser's search bar.
## Using Perplexica's API
Perplexica also provides an API for developers looking to integrate its powerful search engine into their own applications. You can run searches, use multiple models and get answers to your queries.
For more details, check out the full documentation [here](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/API/SEARCH.md).
## Expose Perplexica to network
Perplexica runs on Next.js and handles all API requests. It works right away on the same network and stays accessible even with port forwarding.
## One-Click Deployment
[![Deploy to Sealos](https://raw.githubusercontent.com/labring-actions/templates/main/Deploy-on-Sealos.svg)](https://usw.sealos.io/?openapp=system-template%3FtemplateName%3Dperplexica)
[![Deploy to RepoCloud](https://d16t0pc4846x52.cloudfront.net/deploylobe.svg)](https://repocloud.io/details/?app_id=267)
[![Run on ClawCloud](https://raw.githubusercontent.com/ClawCloud/Run-Template/refs/heads/main/Run-on-ClawCloud.svg)](https://template.run.claw.cloud/?referralCode=U11MRQ8U9RM4&openapp=system-fastdeploy%3FtemplateName%3Dperplexica)
## 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
## Support Us
If you find Perplexica useful, consider giving us a star on GitHub. This helps more people discover Perplexica and supports the development of new features. Your support is greatly appreciated.
### Donations
We also accept donations to help sustain our project. If you would like to contribute, you can use the following options to donate. Thank you for your support!
| Ethereum |
| ----------------------------------------------------- |
| Address: `0xB025a84b2F269570Eb8D4b05DEdaA41D8525B6DD` |
## Contribution
Perplexica is built on the idea that AI and large language models should be easy for everyone to use. If you find bugs or have ideas, please share them in via GitHub Issues. For more information on contributing to Perplexica you can read the [CONTRIBUTING.md](CONTRIBUTING.md) file to learn more about Perplexica and how you can contribute to it.
## Help and Support
If you have any questions or feedback, please feel free to reach out to us. You can create an issue on GitHub or join our Discord server. There, you can connect with other users, share your experiences and reviews, and receive more personalized help. [Click here](https://discord.gg/EFwsmQDgAu) to join the Discord server. To discuss matters outside of regular support, feel free to contact me on Discord at `itzcrazykns`.
Thank you for exploring Perplexica, the AI-powered search engine designed to enhance your search experience. We are constantly working to improve Perplexica and expand its capabilities. We value your feedback and contributions which help us make Perplexica even better. Don't forget to check back for updates and new features!
View cache statistics using:
```sql
select * from cache_stats;
```

View File

@@ -1,35 +1,15 @@
FROM node:20.18.0-slim AS builder
FROM node:20.18.0-alpine
ARG NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
ARG NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
ENV NEXT_PUBLIC_WS_URL=${NEXT_PUBLIC_WS_URL}
ENV NEXT_PUBLIC_API_URL=${NEXT_PUBLIC_API_URL}
WORKDIR /home/perplexica
COPY package.json yarn.lock ./
RUN yarn install --frozen-lockfile --network-timeout 600000
COPY ui /home/perplexica/
COPY tsconfig.json next.config.mjs next-env.d.ts postcss.config.js drizzle.config.ts tailwind.config.ts ./
COPY src ./src
COPY public ./public
RUN mkdir -p /home/perplexica/data
RUN yarn install --frozen-lockfile
RUN yarn build
RUN yarn add --dev @vercel/ncc
RUN yarn ncc build ./src/lib/db/migrate.ts -o migrator
FROM node:20.18.0-slim
WORKDIR /home/perplexica
COPY --from=builder /home/perplexica/public ./public
COPY --from=builder /home/perplexica/.next/static ./public/_next/static
COPY --from=builder /home/perplexica/.next/standalone ./
COPY --from=builder /home/perplexica/data ./data
COPY drizzle ./drizzle
COPY --from=builder /home/perplexica/migrator/build ./build
COPY --from=builder /home/perplexica/migrator/index.js ./migrate.js
RUN mkdir /home/perplexica/uploads
COPY entrypoint.sh ./entrypoint.sh
RUN chmod +x ./entrypoint.sh
CMD ["./entrypoint.sh"]
CMD ["yarn", "start"]

17
backend.dockerfile Normal file
View File

@@ -0,0 +1,17 @@
FROM node:18-slim
WORKDIR /home/perplexica
COPY src /home/perplexica/src
COPY tsconfig.json /home/perplexica/
COPY drizzle.config.ts /home/perplexica/
COPY package.json /home/perplexica/
COPY yarn.lock /home/perplexica/
RUN mkdir /home/perplexica/data
RUN mkdir /home/perplexica/uploads
RUN yarn install --frozen-lockfile --network-timeout 600000
RUN yarn build
CMD ["yarn", "start"]

14
config.toml Normal file
View File

@@ -0,0 +1,14 @@
[GENERAL]
PORT = 3001 # Port to run the server on
SIMILARITY_MEASURE = "cosine" # "cosine" or "dot"
KEEP_ALIVE = "5m" # How long to keep Ollama models loaded into memory. (Instead of using -1 use "-1m")
[API_KEYS]
OPENAI = "" # OpenAI API key - sk-1234567890abcdef1234567890abcdef
GROQ = "" # Groq API key - gsk_1234567890abcdef1234567890abcdef
ANTHROPIC = "" # Anthropic API key - sk-ant-1234567890abcdef1234567890abcdef
GEMINI = "" # Gemini API key - sk-1234567890abcdef1234567890abcdef
[API_ENDPOINTS]
SEARXNG = "http://localhost:32768" # SearxNG API URL
OLLAMA = "" # Ollama API URL - http://host.docker.internal:11434

189
db/init.sql Normal file
View File

@@ -0,0 +1,189 @@
-- Enable required extensions
create extension if not exists "uuid-ossp"; -- For UUID generation
create extension if not exists pg_cron; -- For scheduled jobs
-- Create the search_cache table
create table public.search_cache (
id uuid default uuid_generate_v4() primary key,
query text not null,
results jsonb not null,
location text not null,
category text not null,
created_at timestamp with time zone default timezone('utc'::text, now()) not null,
updated_at timestamp with time zone default timezone('utc'::text, now()) not null,
expires_at timestamp with time zone default timezone('utc'::text, now() + interval '7 days') not null
);
-- Create indexes
create index search_cache_query_idx on public.search_cache (query);
create index search_cache_location_category_idx on public.search_cache (location, category);
create index search_cache_expires_at_idx on public.search_cache (expires_at);
-- Enable RLS
alter table public.search_cache enable row level security;
-- Create policies
create policy "Allow public read access"
on public.search_cache for select
using (true);
create policy "Allow service write access"
on public.search_cache for insert
with check (true);
create policy "Allow service update access"
on public.search_cache for update
using (true)
with check (true);
create policy "Allow delete expired records"
on public.search_cache for delete
using (expires_at < now());
-- Create function to clean up expired records
create or replace function cleanup_expired_cache()
returns void
language plpgsql
security definer
as $$
begin
delete from public.search_cache
where expires_at < now();
end;
$$;
-- Create a manual cleanup function since pg_cron might not be available
create or replace function manual_cleanup()
returns void
language plpgsql
security definer
as $$
begin
delete from public.search_cache
where expires_at < now();
end;
$$;
-- Create a view for cache statistics
create or replace view cache_stats as
select
count(*) as total_entries,
count(*) filter (where expires_at < now()) as expired_entries,
count(*) filter (where expires_at >= now()) as valid_entries,
min(created_at) as oldest_entry,
max(created_at) as newest_entry,
count(distinct category) as unique_categories,
count(distinct location) as unique_locations
from public.search_cache;
-- Grant permissions to access the view
grant select on cache_stats to postgres;
-- Create table if not exists businesses
create table if not exists businesses (
id text primary key,
name text not null,
phone text,
email text,
address text,
rating numeric,
website text,
logo text,
source text,
description text,
latitude numeric,
longitude numeric,
last_updated timestamp with time zone default timezone('utc'::text, now()),
search_count integer default 1,
created_at timestamp with time zone default timezone('utc'::text, now())
);
-- Create indexes for common queries
create index if not exists businesses_name_idx on businesses (name);
create index if not exists businesses_rating_idx on businesses (rating desc);
create index if not exists businesses_search_count_idx on businesses (search_count desc);
create index if not exists businesses_last_updated_idx on businesses (last_updated desc);
-- Create tables if they don't exist
CREATE TABLE IF NOT EXISTS businesses (
id TEXT PRIMARY KEY,
name TEXT NOT NULL,
phone TEXT,
email TEXT,
address TEXT,
rating INTEGER,
website TEXT,
logo TEXT,
source TEXT,
description TEXT,
location JSONB,
place_id TEXT,
photos TEXT[],
opening_hours TEXT[],
distance JSONB,
last_updated TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
search_count INTEGER DEFAULT 0
);
CREATE TABLE IF NOT EXISTS searches (
id SERIAL PRIMARY KEY,
query TEXT NOT NULL,
location TEXT NOT NULL,
timestamp TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
results_count INTEGER
);
CREATE TABLE IF NOT EXISTS cache (
key TEXT PRIMARY KEY,
value JSONB NOT NULL,
created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
expires_at TIMESTAMP WITH TIME ZONE NOT NULL
);
-- Create indexes
CREATE INDEX IF NOT EXISTS idx_businesses_location ON businesses USING GIN (location);
CREATE INDEX IF NOT EXISTS idx_businesses_search ON businesses USING GIN (to_tsvector('english', name || ' ' || COALESCE(description, '')));
CREATE INDEX IF NOT EXISTS idx_cache_expires ON cache (expires_at);
-- Set up RLS (Row Level Security)
ALTER TABLE businesses ENABLE ROW LEVEL SECURITY;
ALTER TABLE searches ENABLE ROW LEVEL SECURITY;
ALTER TABLE cache ENABLE ROW LEVEL SECURITY;
-- Create policies
CREATE POLICY "Allow anonymous select" ON businesses FOR SELECT USING (true);
CREATE POLICY "Allow service role insert" ON businesses FOR INSERT WITH CHECK (true);
CREATE POLICY "Allow service role update" ON businesses FOR UPDATE USING (true);
CREATE POLICY "Allow anonymous select" ON searches FOR SELECT USING (true);
CREATE POLICY "Allow service role insert" ON searches FOR INSERT WITH CHECK (true);
CREATE POLICY "Allow anonymous select" ON cache FOR SELECT USING (true);
CREATE POLICY "Allow service role all" ON cache USING (true);
-- Add place_id column to businesses table if it doesn't exist
ALTER TABLE businesses ADD COLUMN IF NOT EXISTS place_id TEXT;
CREATE INDEX IF NOT EXISTS idx_businesses_place_id ON businesses(place_id);
-- Create a unique constraint on place_id (excluding nulls)
CREATE UNIQUE INDEX IF NOT EXISTS idx_businesses_place_id_unique
ON businesses(place_id)
WHERE place_id IS NOT NULL;
CREATE TABLE IF NOT EXISTS businesses (
id TEXT PRIMARY KEY,
name TEXT NOT NULL,
address TEXT NOT NULL,
phone TEXT NOT NULL,
description TEXT NOT NULL,
website TEXT,
source TEXT NOT NULL,
rating REAL,
lat REAL,
lng REAL,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
CREATE INDEX IF NOT EXISTS idx_businesses_source ON businesses(source);
CREATE INDEX IF NOT EXISTS idx_businesses_rating ON businesses(rating);

44
db/schema.sql Normal file
View File

@@ -0,0 +1,44 @@
-- Create the businesses table
create table businesses (
id uuid primary key,
name text not null,
phone text,
address text,
city text,
state text,
zip text,
category text[],
rating numeric,
review_count integer,
license text,
services text[],
hours jsonb,
website text,
email text,
verified boolean default false,
last_updated timestamp with time zone,
search_query text,
search_location text,
search_timestamp timestamp with time zone,
reliability_score integer,
-- Create a composite index for deduplication
constraint unique_business unique (phone, address)
);
-- Create indexes for common queries
create index idx_business_location on businesses (city, state);
create index idx_business_category on businesses using gin (category);
create index idx_search_query on businesses using gin (search_query gin_trgm_ops);
create index idx_search_location on businesses using gin (search_location gin_trgm_ops);
create index idx_reliability on businesses (reliability_score);
-- Enable full text search
alter table businesses add column search_vector tsvector
generated always as (
setweight(to_tsvector('english', coalesce(name, '')), 'A') ||
setweight(to_tsvector('english', coalesce(search_query, '')), 'B') ||
setweight(to_tsvector('english', coalesce(search_location, '')), 'C')
) stored;
create index idx_business_search on businesses using gin(search_vector);

15
db/verify.sql Normal file
View File

@@ -0,0 +1,15 @@
-- Check if table exists
SELECT EXISTS (
SELECT FROM information_schema.tables
WHERE table_schema = 'public'
AND table_name = 'businesses'
);
-- Check table structure
SELECT column_name, data_type, is_nullable
FROM information_schema.columns
WHERE table_schema = 'public'
AND table_name = 'businesses';
-- Check row count
SELECT COUNT(*) as count FROM businesses;

View File

@@ -9,22 +9,41 @@ services:
- perplexica-network
restart: unless-stopped
app:
image: itzcrazykns1337/perplexica:main
perplexica-backend:
build:
context: .
dockerfile: app.dockerfile
dockerfile: backend.dockerfile
image: itzcrazykns1337/perplexica-backend:main
environment:
- SEARXNG_API_URL=http://searxng:8080
- DATA_DIR=/home/perplexica
depends_on:
- searxng
ports:
- 3000:3000
networks:
- perplexica-network
- 3001:3001
volumes:
- backend-dbstore:/home/perplexica/data
- uploads:/home/perplexica/uploads
- ./config.toml:/home/perplexica/config.toml
extra_hosts:
- 'host.docker.internal:host-gateway'
networks:
- perplexica-network
restart: unless-stopped
perplexica-frontend:
build:
context: .
dockerfile: app.dockerfile
args:
- NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
- NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
image: itzcrazykns1337/perplexica-frontend:main
depends_on:
- perplexica-backend
ports:
- 3000:3000
networks:
- perplexica-network
restart: unless-stopped
networks:

26
docker-compose.yml Normal file
View File

@@ -0,0 +1,26 @@
version: '3'
services:
searxng:
image: searxng/searxng
ports:
- "4000:8080"
volumes:
- ./searxng:/etc/searxng
environment:
- INSTANCE_NAME=perplexica-searxng
- BASE_URL=http://localhost:4000/
- SEARXNG_SECRET=your_secret_key_here
restart: unless-stopped
app:
build:
context: .
dockerfile: backend.dockerfile
ports:
- "3000:3000"
environment:
- SEARXNG_URL=http://searxng:8080
volumes:
- ./config.toml:/home/perplexica/config.toml
depends_on:
- searxng

View File

@@ -6,9 +6,9 @@ Perplexicas Search API makes it easy to use our AI-powered search engine. You
## Endpoint
### **POST** `http://localhost:3000/api/search`
### **POST** `http://localhost:3001/api/search`
**Note**: Replace `3000` with any other port if you've changed the default PORT
**Note**: Replace `3001` with any other port if you've changed the default PORT
### Request
@@ -20,11 +20,11 @@ The API accepts a JSON object in the request body, where you define the focus mo
{
"chatModel": {
"provider": "openai",
"name": "gpt-4o-mini"
"model": "gpt-4o-mini"
},
"embeddingModel": {
"provider": "openai",
"name": "text-embedding-3-large"
"model": "text-embedding-3-large"
},
"optimizationMode": "speed",
"focusMode": "webSearch",
@@ -32,26 +32,24 @@ The API accepts a JSON object in the request body, where you define the focus mo
"history": [
["human", "Hi, how are you?"],
["assistant", "I am doing well, how can I help you today?"]
],
"systemInstructions": "Focus on providing technical details about Perplexica's architecture.",
"stream": false
]
}
```
### Request Parameters
- **`chatModel`** (object, optional): Defines the chat model to be used for the query. For model details you can send a GET request at `http://localhost:3000/api/models`. Make sure to use the key value (For example "gpt-4o-mini" instead of the display name "GPT 4 omni mini").
- **`chatModel`** (object, optional): Defines the chat model to be used for the query. For model details you can send a GET request at `http://localhost:3001/api/models`. Make sure to use the key value (For example "gpt-4o-mini" instead of the display name "GPT 4 omni mini").
- `provider`: Specifies the provider for the chat model (e.g., `openai`, `ollama`).
- `name`: The specific model from the chosen provider (e.g., `gpt-4o-mini`).
- `model`: The specific model from the chosen provider (e.g., `gpt-4o-mini`).
- Optional fields for custom OpenAI configuration:
- `customOpenAIBaseURL`: If youre using a custom OpenAI instance, provide the base URL.
- `customOpenAIKey`: The API key for a custom OpenAI instance.
- **`embeddingModel`** (object, optional): Defines the embedding model for similarity-based searching. For model details you can send a GET request at `http://localhost:3000/api/models`. Make sure to use the key value (For example "text-embedding-3-large" instead of the display name "Text Embedding 3 Large").
- **`embeddingModel`** (object, optional): Defines the embedding model for similarity-based searching. For model details you can send a GET request at `http://localhost:3001/api/models`. Make sure to use the key value (For example "text-embedding-3-large" instead of the display name "Text Embedding 3 Large").
- `provider`: The provider for the embedding model (e.g., `openai`).
- `name`: The specific embedding model (e.g., `text-embedding-3-large`).
- `model`: The specific embedding model (e.g., `text-embedding-3-large`).
- **`focusMode`** (string, required): Specifies which focus mode to use. Available modes:
@@ -64,8 +62,6 @@ The API accepts a JSON object in the request body, where you define the focus mo
- **`query`** (string, required): The search query or question.
- **`systemInstructions`** (string, optional): Custom instructions provided by the user to guide the AI's response. These instructions are treated as user preferences and have lower priority than the system's core instructions. For example, you can specify a particular writing style, format, or focus area.
- **`history`** (array, optional): An array of message pairs representing the conversation history. Each pair consists of a role (either 'human' or 'assistant') and the message content. This allows the system to use the context of the conversation to refine results. Example:
```json
@@ -75,13 +71,11 @@ The API accepts a JSON object in the request body, where you define the focus mo
]
```
- **`stream`** (boolean, optional): When set to `true`, enables streaming responses. Default is `false`.
### Response
The response from the API includes both the final message and the sources used to generate that message.
#### Standard Response (stream: false)
#### Example Response
```json
{
@@ -106,28 +100,6 @@ The response from the API includes both the final message and the sources used t
}
```
#### Streaming Response (stream: true)
When streaming is enabled, the API returns a stream of newline-delimited JSON objects. Each line contains a complete, valid JSON object. The response has Content-Type: application/json.
Example of streamed response objects:
```
{"type":"init","data":"Stream connected"}
{"type":"sources","data":[{"pageContent":"...","metadata":{"title":"...","url":"..."}},...]}
{"type":"response","data":"Perplexica is an "}
{"type":"response","data":"innovative, open-source "}
{"type":"response","data":"AI-powered search engine..."}
{"type":"done"}
```
Clients should process each line as a separate JSON object. The different message types include:
- **`init`**: Initial connection message
- **`sources`**: All sources used for the response
- **`response`**: Chunks of the generated answer text
- **`done`**: Indicates the stream is complete
### Fields in the Response
- **`message`** (string): The search result, generated based on the query and focus mode.

108
docs/ETHICAL_SCRAPING.md Normal file
View File

@@ -0,0 +1,108 @@
# Ethical Web Scraping Guidelines
## Core Principles
1. **Respect Robots.txt**
- Always check and honor robots.txt directives
- Cache robots.txt to reduce server load
- Default to conservative behavior when uncertain
2. **Proper Identification**
- Use clear, identifiable User-Agent strings
- Provide contact information
- Be transparent about your purpose
3. **Rate Limiting**
- Implement conservative rate limits
- Use exponential backoff for errors
- Distribute requests over time
4. **Data Usage**
- Only collect publicly available business information
- Respect privacy and data protection laws
- Provide clear opt-out mechanisms
- Keep data accurate and up-to-date
5. **Technical Considerations**
- Cache results to minimize requests
- Handle errors gracefully
- Monitor and log access patterns
- Use structured data when available
## Implementation
1. **Request Headers**
```typescript
const headers = {
'User-Agent': 'BizSearch/1.0 (+https://bizsearch.com/about)',
'Accept': 'text/html,application/xhtml+xml',
'From': 'contact@bizsearch.com'
};
```
2. **Rate Limiting**
```typescript
const rateLimits = {
requestsPerMinute: 10,
requestsPerHour: 100,
requestsPerDomain: 20
};
```
3. **Caching**
```typescript
const cacheSettings = {
ttl: 24 * 60 * 60, // 24 hours
maxSize: 1000 // entries
};
```
## Opt-Out Process
1. Business owners can opt-out by:
- Submitting a form on our website
- Emailing opt-out@bizsearch.com
- Adding a meta tag: `<meta name="bizsearch" content="noindex">`
2. We honor opt-outs within:
- 24 hours for direct requests
- 72 hours for cached data
## Legal Compliance
1. **Data Protection**
- GDPR compliance for EU businesses
- CCPA compliance for California businesses
- Regular data audits and cleanup
2. **Attribution**
- Clear source attribution
- Last-updated timestamps
- Data accuracy disclaimers
## Best Practices
1. **Before Scraping**
- Check robots.txt
- Verify site status
- Review terms of service
- Look for API alternatives
2. **During Scraping**
- Monitor response codes
- Respect server hints
- Implement backoff strategies
- Log access patterns
3. **After Scraping**
- Verify data accuracy
- Update cache entries
- Clean up old data
- Monitor opt-out requests
## Contact
For questions or concerns about our scraping practices:
- Email: ethics@bizsearch.com
- Phone: (555) 123-4567
- Web: https://bizsearch.com/ethics

View File

@@ -4,7 +4,7 @@ Curious about how Perplexica works? Don't worry, we'll cover it here. Before we
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:
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.
1. The message is sent via WS to the backend server 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.

View File

@@ -0,0 +1,109 @@
# Expose Perplexica to a network
This guide will show you how to make Perplexica available over a network. Follow these steps to allow computers on the same network to interact with Perplexica. Choose the instructions that match the operating system you are using.
## Windows
1. Open PowerShell as Administrator
2. Navigate to the directory containing the `docker-compose.yaml` file
3. Stop and remove the existing Perplexica containers and images:
```bash
docker compose down --rmi all
```
4. Open the `docker-compose.yaml` file in a text editor like Notepad++
5. Replace `127.0.0.1` with the IP address of the server Perplexica is running on in these two lines:
```bash
args:
- NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
- NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
```
6. Save and close the `docker-compose.yaml` file
7. Rebuild and restart the Perplexica container:
```bash
docker compose up -d --build
```
## macOS
1. Open the Terminal application
2. Navigate to the directory with the `docker-compose.yaml` file:
```bash
cd /path/to/docker-compose.yaml
```
3. Stop and remove existing containers and images:
```bash
docker compose down --rmi all
```
4. Open `docker-compose.yaml` in a text editor like Sublime Text:
```bash
nano docker-compose.yaml
```
5. Replace `127.0.0.1` with the server IP in these lines:
```bash
args:
- NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
- NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
```
6. Save and exit the editor
7. Rebuild and restart Perplexica:
```bash
docker compose up -d --build
```
## Linux
1. Open the terminal
2. Navigate to the `docker-compose.yaml` directory:
```bash
cd /path/to/docker-compose.yaml
```
3. Stop and remove containers and images:
```bash
docker compose down --rmi all
```
4. Edit `docker-compose.yaml`:
```bash
nano docker-compose.yaml
```
5. Replace `127.0.0.1` with the server IP:
```bash
args:
- NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
- NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
```
6. Save and exit the editor
7. Rebuild and restart Perplexica:
```bash
docker compose up -d --build
```

View File

@@ -7,40 +7,34 @@ To update Perplexica to the latest version, follow these steps:
1. Clone the latest version of Perplexica from GitHub:
```bash
git clone https://github.com/ItzCrazyKns/Perplexica.git
git clone https://github.com/ItzCrazyKns/Perplexica.git
```
2. Navigate to the project directory.
2. Navigate to the Project Directory.
3. Check for changes in the configuration files. If the `sample.config.toml` file contains new fields, delete your existing `config.toml` file, rename `sample.config.toml` to `config.toml`, and update the configuration accordingly.
4. Pull the latest images from the registry.
3. Pull latest images from registry.
```bash
docker compose pull
```
5. Update and recreate the containers.
4. Update and Recreate containers.
```bash
docker compose up -d
```
6. Once the command completes, go to http://localhost:3000 and verify the latest changes.
5. Once the command completes running go to http://localhost:3000 and verify the latest changes.
## For non-Docker users
## For non Docker users
1. Clone the latest version of Perplexica from GitHub:
```bash
git clone https://github.com/ItzCrazyKns/Perplexica.git
git clone https://github.com/ItzCrazyKns/Perplexica.git
```
2. Navigate to the project directory.
3. Check for changes in the configuration files. If the `sample.config.toml` file contains new fields, delete your existing `config.toml` file, rename `sample.config.toml` to `config.toml`, and update the configuration accordingly.
4. After populating the configuration run `npm i`.
5. Install the dependencies and then execute `npm run build`.
6. Finally, start the app by running `npm rum start`
---
2. Navigate to the Project Directory
3. Execute `npm i` in both the `ui` folder and the root directory.
4. Once packages are updated, execute `npm run build` in both the `ui` folder and the root directory.
5. Finally, start both the frontend and the backend by running `npm run start` in both the `ui` folder and the root directory.

View File

@@ -1,11 +1,10 @@
import { defineConfig } from 'drizzle-kit';
import path from 'path';
export default defineConfig({
dialect: 'sqlite',
schema: './src/lib/db/schema.ts',
schema: './src/db/schema.ts',
out: './drizzle',
dbCredentials: {
url: path.join(process.cwd(), 'data', 'db.sqlite'),
url: './data/db.sqlite',
},
});

View File

@@ -1,16 +0,0 @@
CREATE TABLE IF NOT EXISTS `chats` (
`id` text PRIMARY KEY NOT NULL,
`title` text NOT NULL,
`createdAt` text NOT NULL,
`focusMode` text NOT NULL,
`files` text DEFAULT '[]'
);
--> statement-breakpoint
CREATE TABLE IF NOT EXISTS `messages` (
`id` integer PRIMARY KEY NOT NULL,
`content` text NOT NULL,
`chatId` text NOT NULL,
`messageId` text NOT NULL,
`type` text,
`metadata` text
);

View File

@@ -1,116 +0,0 @@
{
"version": "6",
"dialect": "sqlite",
"id": "ef3a044b-0f34-40b5-babb-2bb3a909ba27",
"prevId": "00000000-0000-0000-0000-000000000000",
"tables": {
"chats": {
"name": "chats",
"columns": {
"id": {
"name": "id",
"type": "text",
"primaryKey": true,
"notNull": true,
"autoincrement": false
},
"title": {
"name": "title",
"type": "text",
"primaryKey": false,
"notNull": true,
"autoincrement": false
},
"createdAt": {
"name": "createdAt",
"type": "text",
"primaryKey": false,
"notNull": true,
"autoincrement": false
},
"focusMode": {
"name": "focusMode",
"type": "text",
"primaryKey": false,
"notNull": true,
"autoincrement": false
},
"files": {
"name": "files",
"type": "text",
"primaryKey": false,
"notNull": false,
"autoincrement": false,
"default": "'[]'"
}
},
"indexes": {},
"foreignKeys": {},
"compositePrimaryKeys": {},
"uniqueConstraints": {},
"checkConstraints": {}
},
"messages": {
"name": "messages",
"columns": {
"id": {
"name": "id",
"type": "integer",
"primaryKey": true,
"notNull": true,
"autoincrement": false
},
"content": {
"name": "content",
"type": "text",
"primaryKey": false,
"notNull": true,
"autoincrement": false
},
"chatId": {
"name": "chatId",
"type": "text",
"primaryKey": false,
"notNull": true,
"autoincrement": false
},
"messageId": {
"name": "messageId",
"type": "text",
"primaryKey": false,
"notNull": true,
"autoincrement": false
},
"type": {
"name": "type",
"type": "text",
"primaryKey": false,
"notNull": false,
"autoincrement": false
},
"metadata": {
"name": "metadata",
"type": "text",
"primaryKey": false,
"notNull": false,
"autoincrement": false
}
},
"indexes": {},
"foreignKeys": {},
"compositePrimaryKeys": {},
"uniqueConstraints": {},
"checkConstraints": {}
}
},
"views": {},
"enums": {},
"_meta": {
"schemas": {},
"tables": {},
"columns": {}
},
"internal": {
"indexes": {}
}
}

View File

@@ -1,13 +0,0 @@
{
"version": "7",
"dialect": "sqlite",
"entries": [
{
"idx": 0,
"version": "6",
"when": 1748405503809,
"tag": "0000_fuzzy_randall",
"breakpoints": true
}
]
}

View File

@@ -1,6 +0,0 @@
#!/bin/sh
set -e
node migrate.js
exec node server.js

41
frontend/.gitignore vendored Normal file
View File

@@ -0,0 +1,41 @@
# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
# dependencies
/node_modules
/.pnp
.pnp.*
.yarn/*
!.yarn/patches
!.yarn/plugins
!.yarn/releases
!.yarn/versions
# testing
/coverage
# next.js
/.next/
/out/
# production
/build
# misc
.DS_Store
*.pem
# debug
npm-debug.log*
yarn-debug.log*
yarn-error.log*
.pnpm-debug.log*
# env files (can opt-in for committing if needed)
.env*
# vercel
.vercel
# typescript
*.tsbuildinfo
next-env.d.ts

36
frontend/README.md Normal file
View File

@@ -0,0 +1,36 @@
This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app).
## Getting Started
First, run the development server:
```bash
npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev
```
Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.
This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel.
## Learn More
To learn more about Next.js, take a look at the following resources:
- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome!
## Deploy on Vercel
The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.
Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details.

View File

@@ -0,0 +1,16 @@
import { dirname } from "path";
import { fileURLToPath } from "url";
import { FlatCompat } from "@eslint/eslintrc";
const __filename = fileURLToPath(import.meta.url);
const __dirname = dirname(__filename);
const compat = new FlatCompat({
baseDirectory: __dirname,
});
const eslintConfig = [
...compat.extends("next/core-web-vitals", "next/typescript"),
];
export default eslintConfig;

13
frontend/next.config.js Normal file
View File

@@ -0,0 +1,13 @@
/** @type {import('next').NextConfig} */
const nextConfig = {
async rewrites() {
return [
{
source: '/api/:path*',
destination: 'http://localhost:3000/api/:path*',
},
]
}
}
module.exports = nextConfig

7
frontend/next.config.ts Normal file
View File

@@ -0,0 +1,7 @@
import type { NextConfig } from "next";
const nextConfig: NextConfig = {
/* config options here */
};
export default nextConfig;

5848
frontend/package-lock.json generated Normal file

File diff suppressed because it is too large Load Diff

33
frontend/package.json Normal file
View File

@@ -0,0 +1,33 @@
{
"name": "frontend",
"version": "0.1.0",
"private": true,
"scripts": {
"dev": "next dev",
"build": "next build",
"start": "next start",
"lint": "next lint"
},
"dependencies": {
"@radix-ui/react-icons": "^1.3.2",
"class-variance-authority": "^0.7.1",
"clsx": "^2.1.1",
"lucide-react": "^0.469.0",
"next": "15.1.3",
"react": "^19.0.0",
"react-dom": "^19.0.0",
"tailwind-merge": "^2.6.0",
"tailwindcss-animate": "^1.0.7"
},
"devDependencies": {
"@eslint/eslintrc": "^3",
"@types/node": "^20",
"@types/react": "^19",
"@types/react-dom": "^19",
"eslint": "^9",
"eslint-config-next": "15.1.3",
"postcss": "^8",
"tailwindcss": "^3.4.1",
"typescript": "^5"
}
}

View File

@@ -0,0 +1,8 @@
/** @type {import('postcss-load-config').Config} */
const config = {
plugins: {
tailwindcss: {},
},
};
export default config;

1
frontend/public/file.svg Normal file
View File

@@ -0,0 +1 @@
<svg fill="none" viewBox="0 0 16 16" xmlns="http://www.w3.org/2000/svg"><path d="M14.5 13.5V5.41a1 1 0 0 0-.3-.7L9.8.29A1 1 0 0 0 9.08 0H1.5v13.5A2.5 2.5 0 0 0 4 16h8a2.5 2.5 0 0 0 2.5-2.5m-1.5 0v-7H8v-5H3v12a1 1 0 0 0 1 1h8a1 1 0 0 0 1-1M9.5 5V2.12L12.38 5zM5.13 5h-.62v1.25h2.12V5zm-.62 3h7.12v1.25H4.5zm.62 3h-.62v1.25h7.12V11z" clip-rule="evenodd" fill="#666" fill-rule="evenodd"/></svg>

After

Width:  |  Height:  |  Size: 391 B

View File

@@ -0,0 +1 @@
<svg fill="none" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><g clip-path="url(#a)"><path fill-rule="evenodd" clip-rule="evenodd" d="M10.27 14.1a6.5 6.5 0 0 0 3.67-3.45q-1.24.21-2.7.34-.31 1.83-.97 3.1M8 16A8 8 0 1 0 8 0a8 8 0 0 0 0 16m.48-1.52a7 7 0 0 1-.96 0H7.5a4 4 0 0 1-.84-1.32q-.38-.89-.63-2.08a40 40 0 0 0 3.92 0q-.25 1.2-.63 2.08a4 4 0 0 1-.84 1.31zm2.94-4.76q1.66-.15 2.95-.43a7 7 0 0 0 0-2.58q-1.3-.27-2.95-.43a18 18 0 0 1 0 3.44m-1.27-3.54a17 17 0 0 1 0 3.64 39 39 0 0 1-4.3 0 17 17 0 0 1 0-3.64 39 39 0 0 1 4.3 0m1.1-1.17q1.45.13 2.69.34a6.5 6.5 0 0 0-3.67-3.44q.65 1.26.98 3.1M8.48 1.5l.01.02q.41.37.84 1.31.38.89.63 2.08a40 40 0 0 0-3.92 0q.25-1.2.63-2.08a4 4 0 0 1 .85-1.32 7 7 0 0 1 .96 0m-2.75.4a6.5 6.5 0 0 0-3.67 3.44 29 29 0 0 1 2.7-.34q.31-1.83.97-3.1M4.58 6.28q-1.66.16-2.95.43a7 7 0 0 0 0 2.58q1.3.27 2.95.43a18 18 0 0 1 0-3.44m.17 4.71q-1.45-.12-2.69-.34a6.5 6.5 0 0 0 3.67 3.44q-.65-1.27-.98-3.1" fill="#666"/></g><defs><clipPath id="a"><path fill="#fff" d="M0 0h16v16H0z"/></clipPath></defs></svg>

After

Width:  |  Height:  |  Size: 1.0 KiB

View File

Before

Width:  |  Height:  |  Size: 1.3 KiB

After

Width:  |  Height:  |  Size: 1.3 KiB

View File

@@ -0,0 +1 @@
<svg fill="none" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1155 1000"><path d="m577.3 0 577.4 1000H0z" fill="#fff"/></svg>

After

Width:  |  Height:  |  Size: 128 B

View File

@@ -0,0 +1 @@
<svg fill="none" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><path fill-rule="evenodd" clip-rule="evenodd" d="M1.5 2.5h13v10a1 1 0 0 1-1 1h-11a1 1 0 0 1-1-1zM0 1h16v11.5a2.5 2.5 0 0 1-2.5 2.5h-11A2.5 2.5 0 0 1 0 12.5zm3.75 4.5a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5M7 4.75a.75.75 0 1 1-1.5 0 .75.75 0 0 1 1.5 0m1.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5" fill="#666"/></svg>

After

Width:  |  Height:  |  Size: 385 B

View File

Before

Width:  |  Height:  |  Size: 25 KiB

After

Width:  |  Height:  |  Size: 25 KiB

View File

@@ -0,0 +1,76 @@
@tailwind base;
@tailwind components;
@tailwind utilities;
@layer base {
:root {
--background: 0 0% 100%;
--foreground: 222.2 84% 4.9%;
--card: 0 0% 100%;
--card-foreground: 222.2 84% 4.9%;
--popover: 0 0% 100%;
--popover-foreground: 222.2 84% 4.9%;
--primary: 222.2 47.4% 11.2%;
--primary-foreground: 210 40% 98%;
--secondary: 210 40% 96.1%;
--secondary-foreground: 222.2 47.4% 11.2%;
--muted: 210 40% 96.1%;
--muted-foreground: 215.4 16.3% 46.9%;
--accent: 210 40% 96.1%;
--accent-foreground: 222.2 47.4% 11.2%;
--destructive: 0 84.2% 60.2%;
--destructive-foreground: 210 40% 98%;
--border: 214.3 31.8% 91.4%;
--input: 214.3 31.8% 91.4%;
--ring: 222.2 84% 4.9%;
--radius: 0.5rem;
}
.dark {
--background: 222.2 84% 4.9%;
--foreground: 210 40% 98%;
--card: 222.2 84% 4.9%;
--card-foreground: 210 40% 98%;
--popover: 222.2 84% 4.9%;
--popover-foreground: 210 40% 98%;
--primary: 210 40% 98%;
--primary-foreground: 222.2 47.4% 11.2%;
--secondary: 217.2 32.6% 17.5%;
--secondary-foreground: 210 40% 98%;
--muted: 217.2 32.6% 17.5%;
--muted-foreground: 215 20.2% 65.1%;
--accent: 217.2 32.6% 17.5%;
--accent-foreground: 210 40% 98%;
--destructive: 0 62.8% 30.6%;
--destructive-foreground: 210 40% 98%;
--border: 217.2 32.6% 17.5%;
--input: 217.2 32.6% 17.5%;
--ring: 212.7 26.8% 83.9%;
}
}
@layer base {
* {
@apply border-border;
}
body {
@apply bg-background text-foreground;
}
}

View File

@@ -0,0 +1,34 @@
import type { Metadata } from "next";
import { Geist, Geist_Mono } from "next/font/google";
import "./globals.css";
const geistSans = Geist({
variable: "--font-geist-sans",
subsets: ["latin"],
});
const geistMono = Geist_Mono({
variable: "--font-geist-mono",
subsets: ["latin"],
});
export const metadata: Metadata = {
title: "Create Next App",
description: "Generated by create next app",
};
export default function RootLayout({
children,
}: Readonly<{
children: React.ReactNode;
}>) {
return (
<html lang="en">
<body
className={`${geistSans.variable} ${geistMono.variable} antialiased`}
>
{children}
</body>
</html>
);
}

26
frontend/src/app/page.tsx Normal file
View File

@@ -0,0 +1,26 @@
'use client'
import { ServerStatus } from "@/components/server-status"
import { SearchForm } from "@/components/search-form"
import { SearchResults } from "@/components/search-results"
import { useState } from "react"
export default function Home() {
const [searchResults, setSearchResults] = useState([])
const [isSearching, setIsSearching] = useState(false)
const services = [
{ name: "Ollama", status: "running" as const },
{ name: "SearxNG", status: "running" as const },
{ name: "Supabase", status: "running" as const }
]
return (
<main className="container mx-auto p-4">
<h1 className="text-4xl font-bold text-center mb-8">Business Search</h1>
<SearchForm onSearch={setSearchResults} onSearchingChange={setIsSearching} />
<SearchResults results={searchResults} isLoading={isSearching} />
<ServerStatus services={services} />
</main>
)
}

View File

@@ -0,0 +1,79 @@
import { Search } from "lucide-react"
import { useState } from "react"
interface SearchFormProps {
onSearch: (results: any[]) => void;
onSearchingChange: (isSearching: boolean) => void;
}
export function SearchForm({ onSearch, onSearchingChange }: SearchFormProps) {
const [query, setQuery] = useState("")
const [error, setError] = useState<string | null>(null)
const handleSearch = async (e: React.FormEvent) => {
e.preventDefault()
if (!query.trim()) return
setError(null)
onSearchingChange(true)
try {
const response = await fetch("/api/search", {
method: "POST",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify({ query: query.trim() }),
})
if (!response.ok) {
throw new Error("Search failed")
}
const data = await response.json()
onSearch(data.results || [])
} catch (error) {
console.error("Search error:", error)
onSearch([])
setError("Failed to perform search. Please try again.")
} finally {
onSearchingChange(false)
}
}
return (
<div className="w-full max-w-2xl mx-auto mt-8 mb-12">
<div className="flex flex-col gap-4">
<div className="flex flex-col gap-2">
<label htmlFor="search" className="text-lg font-medium text-center">
Find local businesses
</label>
<form onSubmit={handleSearch} className="relative">
<input
id="search"
type="text"
value={query}
onChange={(e) => setQuery(e.target.value)}
placeholder="e.g. plumbers in Denver, CO"
className="w-full px-4 py-3 text-lg rounded-lg border border-border bg-background focus:outline-none focus:ring-2 focus:ring-primary"
/>
<button
type="submit"
disabled={!query.trim()}
className="absolute right-2 top-1/2 -translate-y-1/2 p-3 rounded-md bg-primary text-primary-foreground hover:bg-primary/90 transition-colors disabled:opacity-50 disabled:cursor-not-allowed"
aria-label="Search"
>
<Search className="h-5 w-5" />
</button>
</form>
{error && (
<p className="text-sm text-destructive text-center">{error}</p>
)}
<p className="text-sm text-muted-foreground text-center mt-2">
Try searching for: restaurants, dentists, electricians, etc.
</p>
</div>
</div>
</div>
)
}

View File

@@ -0,0 +1,76 @@
interface Business {
id: string;
name: string;
address: string;
phone: string;
website?: string;
email?: string;
description?: string;
rating?: number;
}
interface SearchResultsProps {
results: Business[];
isLoading: boolean;
}
export function SearchResults({ results, isLoading }: SearchResultsProps) {
if (isLoading) {
return (
<div className="w-full max-w-4xl mx-auto mt-8">
<div className="animate-pulse space-y-4">
{[...Array(3)].map((_, i) => (
<div key={i} className="bg-muted rounded-lg p-6">
<div className="h-4 bg-muted-foreground/20 rounded w-3/4 mb-4"></div>
<div className="h-3 bg-muted-foreground/20 rounded w-1/2"></div>
</div>
))}
</div>
</div>
)
}
if (!results.length) {
return null
}
return (
<div className="w-full max-w-4xl mx-auto mt-8">
<div className="space-y-4">
{results.map((business) => (
<div key={business.id} className="bg-card rounded-lg p-6 shadow-sm">
<h3 className="text-xl font-semibold mb-2">{business.name}</h3>
{business.address && (
<p className="text-muted-foreground mb-2">{business.address}</p>
)}
<div className="flex flex-wrap gap-4 text-sm">
{business.phone && (
<a
href={`tel:${business.phone}`}
className="text-primary hover:underline"
>
{business.phone}
</a>
)}
{business.website && (
<a
href={business.website}
target="_blank"
rel="noopener noreferrer"
className="text-primary hover:underline"
>
Visit Website
</a>
)}
</div>
{business.description && (
<p className="mt-4 text-sm text-muted-foreground">
{business.description}
</p>
)}
</div>
))}
</div>
</div>
)
}

View File

@@ -0,0 +1,59 @@
import { CheckCircle2, XCircle, AlertCircle } from "lucide-react"
import { Alert, AlertDescription, AlertTitle } from "@/components/ui/alert"
interface ServiceStatus {
name: string
status: "running" | "error" | "warning"
}
interface ServerStatusProps {
services: ServiceStatus[]
error?: string
}
export function ServerStatus({ services, error }: ServerStatusProps) {
if (error) {
return (
<Alert variant="destructive" className="max-w-md mx-auto mt-4">
<XCircle className="h-4 w-4" />
<AlertTitle>Server Error</AlertTitle>
<AlertDescription>{error}</AlertDescription>
</Alert>
)
}
return (
<div className="space-y-4 max-w-md mx-auto mt-4">
<h2 className="text-xl font-semibold text-center mb-6">Service Status</h2>
<div className="space-y-3">
{services.map((service) => (
<Alert
key={service.name}
variant={service.status === "error" ? "destructive" : "default"}
className="flex items-center justify-between hover:bg-accent/50 transition-colors"
>
<div className="flex items-center gap-3">
{service.status === "running" && (
<CheckCircle2 className="h-5 w-5 text-green-500 shrink-0" />
)}
{service.status === "error" && (
<XCircle className="h-5 w-5 text-red-500 shrink-0" />
)}
{service.status === "warning" && (
<AlertCircle className="h-5 w-5 text-yellow-500 shrink-0" />
)}
<AlertTitle className="font-medium">{service.name}</AlertTitle>
</div>
<span className={`text-sm ${
service.status === "running" ? "text-green-600" :
service.status === "error" ? "text-red-600" :
"text-yellow-600"
}`}>
{service.status.charAt(0).toUpperCase() + service.status.slice(1)}
</span>
</Alert>
))}
</div>
</div>
)
}

View File

@@ -0,0 +1,58 @@
import * as React from "react"
import { cva, type VariantProps } from "class-variance-authority"
import { cn } from "@/lib/utils"
const alertVariants = cva(
"relative w-full rounded-lg border p-4 [&>svg~*]:pl-7 [&>svg+div]:translate-y-[-3px] [&>svg]:absolute [&>svg]:left-4 [&>svg]:top-4 [&>svg]:text-foreground",
{
variants: {
variant: {
default: "bg-background text-foreground",
destructive:
"border-destructive/50 text-destructive dark:border-destructive [&>svg]:text-destructive",
},
},
defaultVariants: {
variant: "default",
},
}
)
const Alert = React.forwardRef<
HTMLDivElement,
React.HTMLAttributes<HTMLDivElement> & VariantProps<typeof alertVariants>
>(({ className, variant, ...props }, ref) => (
<div
ref={ref}
role="alert"
className={cn(alertVariants({ variant }), className)}
{...props}
/>
))
Alert.displayName = "Alert"
const AlertTitle = React.forwardRef<
HTMLParagraphElement,
React.HTMLAttributes<HTMLHeadingElement>
>(({ className, ...props }, ref) => (
<h5
ref={ref}
className={cn("mb-1 font-medium leading-none tracking-tight", className)}
{...props}
/>
))
AlertTitle.displayName = "AlertTitle"
const AlertDescription = React.forwardRef<
HTMLParagraphElement,
React.HTMLAttributes<HTMLParagraphElement>
>(({ className, ...props }, ref) => (
<div
ref={ref}
className={cn("text-sm [&_p]:leading-relaxed", className)}
{...props}
/>
))
AlertDescription.displayName = "AlertDescription"
export { Alert, AlertTitle, AlertDescription }

View File

@@ -0,0 +1,6 @@
import { type ClassValue, clsx } from "clsx"
import { twMerge } from "tailwind-merge"
export function cn(...inputs: ClassValue[]) {
return twMerge(clsx(inputs))
}

View File

@@ -0,0 +1,79 @@
import type { Config } from "tailwindcss";
const config: Config = {
darkMode: ["class"],
content: [
'./pages/**/*.{ts,tsx}',
'./components/**/*.{ts,tsx}',
'./app/**/*.{ts,tsx}',
'./src/**/*.{ts,tsx}',
],
theme: {
container: {
center: true,
padding: "2rem",
screens: {
"2xl": "1400px",
},
},
extend: {
colors: {
border: "hsl(var(--border))",
input: "hsl(var(--input))",
ring: "hsl(var(--ring))",
background: "hsl(var(--background))",
foreground: "hsl(var(--foreground))",
primary: {
DEFAULT: "hsl(var(--primary))",
foreground: "hsl(var(--primary-foreground))",
},
secondary: {
DEFAULT: "hsl(var(--secondary))",
foreground: "hsl(var(--secondary-foreground))",
},
destructive: {
DEFAULT: "hsl(var(--destructive))",
foreground: "hsl(var(--destructive-foreground))",
},
muted: {
DEFAULT: "hsl(var(--muted))",
foreground: "hsl(var(--muted-foreground))",
},
accent: {
DEFAULT: "hsl(var(--accent))",
foreground: "hsl(var(--accent-foreground))",
},
popover: {
DEFAULT: "hsl(var(--popover))",
foreground: "hsl(var(--popover-foreground))",
},
card: {
DEFAULT: "hsl(var(--card))",
foreground: "hsl(var(--card-foreground))",
},
},
borderRadius: {
lg: "var(--radius)",
md: "calc(var(--radius) - 2px)",
sm: "calc(var(--radius) - 4px)",
},
keyframes: {
"accordion-down": {
from: { height: "0" },
to: { height: "var(--radix-accordion-content-height)" },
},
"accordion-up": {
from: { height: "var(--radix-accordion-content-height)" },
to: { height: "0" },
},
},
animation: {
"accordion-down": "accordion-down 0.2s ease-out",
"accordion-up": "accordion-up 0.2s ease-out",
},
},
},
plugins: [require("tailwindcss-animate")],
}
export default config;

27
frontend/tsconfig.json Normal file
View File

@@ -0,0 +1,27 @@
{
"compilerOptions": {
"target": "ES2017",
"lib": ["dom", "dom.iterable", "esnext"],
"allowJs": true,
"skipLibCheck": true,
"strict": true,
"noEmit": true,
"esModuleInterop": true,
"module": "esnext",
"moduleResolution": "bundler",
"resolveJsonModule": true,
"isolatedModules": true,
"jsx": "preserve",
"incremental": true,
"plugins": [
{
"name": "next"
}
],
"paths": {
"@/*": ["./src/*"]
}
},
"include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
"exclude": ["node_modules"]
}

17
jest.config.js Normal file
View File

@@ -0,0 +1,17 @@
module.exports = {
preset: 'ts-jest',
testEnvironment: 'node',
roots: ['<rootDir>/src'],
testMatch: ['**/__tests__/**/*.ts', '**/?(*.)+(spec|test).ts'],
transform: {
'^.+\\.ts$': 'ts-jest',
},
moduleFileExtensions: ['ts', 'js', 'json', 'node'],
collectCoverageFrom: [
'src/**/*.{ts,js}',
'!src/tests/**',
'!**/node_modules/**',
],
coverageDirectory: 'coverage',
setupFilesAfterEnv: ['<rootDir>/src/tests/setup.ts'],
};

5
next-env.d.ts vendored
View File

@@ -1,5 +0,0 @@
/// <reference types="next" />
/// <reference types="next/image-types/global" />
// NOTE: This file should not be edited
// see https://nextjs.org/docs/app/api-reference/config/typescript for more information.

14318
package-lock.json generated Normal file

File diff suppressed because it is too large Load Diff

View File

@@ -1,68 +1,80 @@
{
"name": "perplexica-frontend",
"version": "1.10.2",
"name": "perplexica-backend",
"version": "1.10.0-rc2",
"license": "MIT",
"author": "ItzCrazyKns",
"scripts": {
"dev": "next dev",
"build": "npm run db:push && next build",
"start": "next start",
"lint": "next lint",
"start": "ts-node src/index.ts",
"build": "tsc",
"dev": "nodemon src/index.ts",
"db:push": "drizzle-kit push sqlite",
"format": "prettier . --check",
"format:write": "prettier . --write",
"db:push": "drizzle-kit push"
},
"dependencies": {
"@headlessui/react": "^2.2.0",
"@iarna/toml": "^2.2.5",
"@icons-pack/react-simple-icons": "^12.3.0",
"@langchain/anthropic": "^0.3.15",
"@langchain/community": "^0.3.36",
"@langchain/core": "^0.3.42",
"@langchain/google-genai": "^0.1.12",
"@langchain/openai": "^0.0.25",
"@langchain/textsplitters": "^0.1.0",
"@tailwindcss/typography": "^0.5.12",
"@xenova/transformers": "^2.17.2",
"axios": "^1.8.3",
"better-sqlite3": "^11.9.1",
"clsx": "^2.1.0",
"compute-cosine-similarity": "^1.1.0",
"compute-dot": "^1.1.0",
"drizzle-orm": "^0.40.1",
"html-to-text": "^9.0.5",
"jspdf": "^3.0.1",
"langchain": "^0.1.30",
"lucide-react": "^0.363.0",
"mammoth": "^1.9.1",
"markdown-to-jsx": "^7.7.2",
"next": "^15.2.2",
"next-themes": "^0.3.0",
"pdf-parse": "^1.1.1",
"react": "^18",
"react-dom": "^18",
"react-text-to-speech": "^0.14.5",
"react-textarea-autosize": "^8.5.3",
"sonner": "^1.4.41",
"tailwind-merge": "^2.2.2",
"winston": "^3.17.0",
"yet-another-react-lightbox": "^3.17.2",
"zod": "^3.22.4"
"test:search": "ts-node src/tests/testSearch.ts",
"test:supabase": "ts-node src/tests/supabaseTest.ts",
"test:deepseek": "ts-node src/tests/testDeepseek.ts",
"test:ollama": "ts-node src/tests/testOllama.ts",
"test": "jest",
"test:watch": "jest --watch",
"test:coverage": "jest --coverage",
"build:css": "tailwindcss -i ./src/styles/input.css -o ./public/styles/output.css",
"watch:css": "tailwindcss -i ./src/styles/input.css -o ./public/styles/output.css --watch"
},
"devDependencies": {
"@types/better-sqlite3": "^7.6.12",
"@testing-library/jest-dom": "^6.1.5",
"@types/better-sqlite3": "^7.6.10",
"@types/cors": "^2.8.17",
"@types/express": "^4.17.21",
"@types/html-to-text": "^9.0.4",
"@types/jspdf": "^2.0.0",
"@types/node": "^20",
"@types/jest": "^29.5.11",
"@types/multer": "^1.4.12",
"@types/node-fetch": "^2.6.12",
"@types/pdf-parse": "^1.1.4",
"@types/react": "^18",
"@types/react-dom": "^18",
"autoprefixer": "^10.0.1",
"drizzle-kit": "^0.30.5",
"eslint": "^8",
"eslint-config-next": "14.1.4",
"postcss": "^8",
"@types/readable-stream": "^4.0.11",
"@types/supertest": "^6.0.2",
"@types/ws": "^8.5.12",
"autoprefixer": "^10.4.20",
"drizzle-kit": "^0.22.7",
"jest": "^29.7.0",
"nodemon": "^3.1.0",
"postcss": "^8.4.49",
"prettier": "^3.2.5",
"tailwindcss": "^3.3.0",
"typescript": "^5"
"supertest": "^7.0.0",
"tailwindcss": "^3.4.17",
"ts-jest": "^29.1.1",
"ts-node": "^10.9.2",
"typescript": "^5.4.3"
},
"dependencies": {
"@huggingface/transformers": "latest",
"@iarna/toml": "^2.2.5",
"@langchain/anthropic": "^0.2.3",
"@langchain/community": "^0.2.16",
"@langchain/google-genai": "^0.0.23",
"@langchain/openai": "^0.0.25",
"@shadcn/ui": "^0.0.4",
"@supabase/supabase-js": "^2.47.10",
"@xenova/transformers": "^2.17.1",
"axios": "^1.6.8",
"better-sqlite3": "^11.7.0",
"cheerio": "^1.0.0",
"compute-cosine-similarity": "^1.1.0",
"compute-dot": "^1.1.0",
"cors": "^2.8.5",
"dotenv": "^16.4.7",
"drizzle-orm": "^0.31.2",
"express": "^4.19.2",
"html-to-text": "^9.0.5",
"langchain": "^0.1.30",
"mammoth": "^1.8.0",
"multer": "^1.4.5-lts.1",
"node-fetch": "^2.7.0",
"pdf-parse": "^1.1.1",
"robots-parser": "^3.0.1",
"tesseract.js": "^4.1.4",
"torch": "latest",
"winston": "^3.13.0",
"ws": "^8.17.1",
"zod": "^3.24.1"
}
}

View File

@@ -3,4 +3,4 @@ module.exports = {
tailwindcss: {},
autoprefixer: {},
},
};
}

214
public/index.html Normal file
View File

@@ -0,0 +1,214 @@
<!DOCTYPE html>
<html lang="en" class="h-full bg-gray-50">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>OffMarket Pro - Business Search</title>
<link href="/styles/output.css" rel="stylesheet">
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet">
</head>
<body class="min-h-full">
<div class="bg-white">
<!-- Navigation -->
<nav class="bg-white shadow-sm">
<div class="mx-auto max-w-7xl px-4 sm:px-6 lg:px-8">
<div class="flex h-16 justify-between items-center">
<div class="flex-shrink-0 flex items-center">
<h1 class="text-xl font-bold text-gray-900">OffMarket Pro</h1>
</div>
</div>
</div>
</nav>
<!-- Main Content -->
<main class="mx-auto max-w-7xl px-4 sm:px-6 lg:px-8 py-8">
<!-- Search Form -->
<div class="mb-8">
<h2 class="text-2xl font-bold text-gray-900 mb-6">Find Off-Market Property Services</h2>
<div class="grid grid-cols-1 gap-4 sm:grid-cols-2">
<div>
<label for="searchQuery" class="block text-sm font-medium text-gray-700">Service Type</label>
<input type="text" id="searchQuery" class="mt-1 block w-full rounded-md border-gray-300 shadow-sm focus:border-primary focus:ring-primary sm:text-sm" placeholder="e.g. plumber, electrician">
</div>
<div>
<label for="searchLocation" class="block text-sm font-medium text-gray-700">Location</label>
<input type="text" id="searchLocation" class="mt-1 block w-full rounded-md border-gray-300 shadow-sm focus:border-primary focus:ring-primary sm:text-sm" placeholder="e.g. Denver, CO">
</div>
</div>
<div class="mt-4">
<button onclick="performSearch()" class="inline-flex items-center px-4 py-2 border border-transparent text-sm font-medium rounded-md shadow-sm text-white bg-primary hover:bg-primary-hover focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-primary">
Search
</button>
</div>
</div>
<!-- Progress Indicator -->
<div id="searchProgress" class="hidden mb-8">
<div class="bg-white shadow sm:rounded-lg">
<div class="px-4 py-5 sm:p-6">
<h3 class="text-lg font-medium leading-6 text-gray-900">Search Progress</h3>
<div class="mt-4">
<div class="relative pt-1">
<div class="overflow-hidden h-2 mb-4 text-xs flex rounded bg-gray-200">
<div id="progressBar" class="shadow-none flex flex-col text-center whitespace-nowrap text-white justify-center bg-primary transition-all duration-500" style="width: 0%"></div>
</div>
<div id="progressText" class="text-sm text-gray-600"></div>
</div>
</div>
</div>
</div>
</div>
<!-- Error Display -->
<div id="errorDisplay" class="hidden mb-8">
<div class="rounded-md bg-red-50 p-4">
<div class="flex">
<div class="flex-shrink-0">
<svg class="h-5 w-5 text-red-400" viewBox="0 0 20 20" fill="currentColor">
<path fill-rule="evenodd" d="M10 18a8 8 0 100-16 8 8 0 000 16zM8.707 7.293a1 1 0 00-1.414 1.414L8.586 10l-1.293 1.293a1 1 0 101.414 1.414L10 11.414l1.293 1.293a1 1 0 001.414-1.414L11.414 10l1.293-1.293a1 1 0 00-1.414-1.414L10 8.586 8.707 7.293z" clip-rule="evenodd"/>
</svg>
</div>
<div class="ml-3">
<h3 class="text-sm font-medium text-red-800">Error</h3>
<div class="mt-2 text-sm text-red-700">
<p id="errorMessage"></p>
</div>
</div>
</div>
</div>
</div>
<!-- Results Table -->
<div id="resultsContainer" class="hidden">
<div class="bg-white shadow overflow-hidden sm:rounded-lg">
<div class="px-4 py-5 sm:px-6">
<h3 class="text-lg leading-6 font-medium text-gray-900">Search Results</h3>
</div>
<div class="border-t border-gray-200">
<div class="overflow-x-auto">
<table class="min-w-full divide-y divide-gray-200">
<thead class="bg-gray-50">
<tr>
<th scope="col" class="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">Business</th>
<th scope="col" class="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">Contact</th>
<th scope="col" class="px-6 py-3 text-left text-xs font-medium text-gray-500 uppercase tracking-wider">Actions</th>
</tr>
</thead>
<tbody id="resultsBody" class="bg-white divide-y divide-gray-200">
<!-- Results will be inserted here -->
</tbody>
</table>
</div>
</div>
</div>
</div>
</main>
</div>
<script>
class SearchProgress {
constructor() {
this.progressBar = document.getElementById('progressBar');
this.progressText = document.getElementById('progressText');
this.container = document.getElementById('searchProgress');
}
show() {
this.container.classList.remove('hidden');
this.setProgress(0, 'Starting search...');
}
hide() {
this.container.classList.add('hidden');
}
setProgress(percent, message) {
this.progressBar.style.width = `${percent}%`;
this.progressText.textContent = message;
}
showError(message) {
this.setProgress(100, `Error: ${message}`);
this.progressBar.classList.remove('bg-primary');
this.progressBar.classList.add('bg-red-500');
}
}
async function performSearch() {
const query = document.getElementById('searchQuery').value;
const location = document.getElementById('searchLocation').value;
if (!query || !location) {
showError('Please enter both search query and location');
return;
}
const progress = new SearchProgress();
progress.show();
try {
document.getElementById('errorDisplay').classList.add('hidden');
document.getElementById('resultsContainer').classList.add('hidden');
const response = await fetch('/api/search', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({ query, location })
});
const data = await response.json();
if (!data.success) {
throw new Error(data.error || 'Search failed');
}
displayResults(data.results);
progress.hide();
} catch (error) {
console.error('Search error:', error);
progress.showError(error.message);
showError(error.message);
}
}
function showError(message) {
const errorDisplay = document.getElementById('errorDisplay');
const errorMessage = document.getElementById('errorMessage');
errorMessage.textContent = message;
errorDisplay.classList.remove('hidden');
}
function displayResults(results) {
const container = document.getElementById('resultsContainer');
const tbody = document.getElementById('resultsBody');
tbody.innerHTML = results.map(business => `
<tr>
<td class="px-6 py-4">
<div class="text-sm font-medium text-gray-900">${business.name}</div>
<div class="text-sm text-gray-500">${business.description}</div>
</td>
<td class="px-6 py-4">
<div class="text-sm text-gray-900">${business.address}</div>
<div class="text-sm text-gray-500">${business.phone}</div>
</td>
<td class="px-6 py-4">
${business.website ?
`<a href="${business.website}" target="_blank"
class="inline-flex items-center px-3 py-2 border border-transparent text-sm leading-4 font-medium rounded-md text-white bg-primary hover:bg-primary-hover focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-primary">
Visit Website
</a>` :
'<span class="text-sm text-gray-500">No website available</span>'
}
</td>
</tr>
`).join('');
container.classList.remove('hidden');
}
</script>
</body>
</html>

View File

@@ -1,32 +1,14 @@
[GENERAL]
PORT = 3001 # Port to run the server on
SIMILARITY_MEASURE = "cosine" # "cosine" or "dot"
KEEP_ALIVE = "5m" # How long to keep Ollama models loaded into memory. (Instead of using -1 use "-1m")
[MODELS.OPENAI]
API_KEY = ""
[MODELS.GROQ]
API_KEY = ""
[MODELS.ANTHROPIC]
API_KEY = ""
[MODELS.GEMINI]
API_KEY = ""
[MODELS.CUSTOM_OPENAI]
API_KEY = ""
API_URL = ""
MODEL_NAME = ""
[MODELS.OLLAMA]
API_URL = "" # Ollama API URL - http://host.docker.internal:11434
[MODELS.DEEPSEEK]
API_KEY = ""
[MODELS.LM_STUDIO]
API_URL = "" # LM Studio API URL - http://host.docker.internal:1234
[API_KEYS]
OPENAI = "" # OpenAI API key - sk-1234567890abcdef1234567890abcdef
GROQ = "" # Groq API key - gsk_1234567890abcdef1234567890abcdef
ANTHROPIC = "" # Anthropic API key - sk-ant-1234567890abcdef1234567890abcdef
GEMINI = "" # Gemini API key - sk-1234567890abcdef1234567890abcdef
[API_ENDPOINTS]
SEARXNG = "" # SearxNG API URL - http://localhost:32768
SEARXNG = "http://localhost:32768" # SearxNG API URL
OLLAMA = "" # Ollama API URL - http://host.docker.internal:11434

View File

@@ -11,7 +11,49 @@ search:
server:
secret_key: 'a2fb23f1b02e6ee83875b09826990de0f6bd908b6638e8c10277d415f6ab852b' # Is overwritten by ${SEARXNG_SECRET}
port: 8080
bind_address: "0.0.0.0"
base_url: http://localhost:8080/
engines:
- name: wolframalpha
disabled: false
- name: google
engine: google
shortcut: g
disabled: false
- name: bing
engine: bing
shortcut: b
disabled: false
- name: duckduckgo
engine: duckduckgo
shortcut: d
disabled: false
- name: yelp
engine: yelp
shortcut: y
disabled: false
ui:
static_path: ""
templates_path: ""
default_theme: simple
default_locale: en
results_on_new_tab: false
outgoing:
request_timeout: 6.0
max_request_timeout: 10.0
pool_connections: 100
pool_maxsize: 10
enable_http2: true
server:
limiter: false
image_proxy: false
http_protocol_version: "1.0"

16
src/app.ts Normal file
View File

@@ -0,0 +1,16 @@
import express from 'express';
import cors from 'cors';
import searchRoutes from './routes/search';
import businessRoutes from './routes/business';
const app = express();
// Middleware
app.use(cors());
app.use(express.json());
// Routes
app.use('/api/search', searchRoutes);
app.use('/api/business', businessRoutes);
export default app;

View File

@@ -1,306 +0,0 @@
import prompts from '@/lib/prompts';
import MetaSearchAgent from '@/lib/search/metaSearchAgent';
import crypto from 'crypto';
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
import { EventEmitter } from 'stream';
import {
chatModelProviders,
embeddingModelProviders,
getAvailableChatModelProviders,
getAvailableEmbeddingModelProviders,
} from '@/lib/providers';
import db from '@/lib/db';
import { chats, messages as messagesSchema } from '@/lib/db/schema';
import { and, eq, gt } from 'drizzle-orm';
import { getFileDetails } from '@/lib/utils/files';
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
import { ChatOpenAI } from '@langchain/openai';
import {
getCustomOpenaiApiKey,
getCustomOpenaiApiUrl,
getCustomOpenaiModelName,
} from '@/lib/config';
import { searchHandlers } from '@/lib/search';
export const runtime = 'nodejs';
export const dynamic = 'force-dynamic';
type Message = {
messageId: string;
chatId: string;
content: string;
};
type ChatModel = {
provider: string;
name: string;
};
type EmbeddingModel = {
provider: string;
name: string;
};
type Body = {
message: Message;
optimizationMode: 'speed' | 'balanced' | 'quality';
focusMode: string;
history: Array<[string, string]>;
files: Array<string>;
chatModel: ChatModel;
embeddingModel: EmbeddingModel;
systemInstructions: string;
};
const handleEmitterEvents = async (
stream: EventEmitter,
writer: WritableStreamDefaultWriter,
encoder: TextEncoder,
aiMessageId: string,
chatId: string,
) => {
let recievedMessage = '';
let sources: any[] = [];
stream.on('data', (data) => {
const parsedData = JSON.parse(data);
if (parsedData.type === 'response') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'message',
data: parsedData.data,
messageId: aiMessageId,
}) + '\n',
),
);
recievedMessage += parsedData.data;
} else if (parsedData.type === 'sources') {
writer.write(
encoder.encode(
JSON.stringify({
type: 'sources',
data: parsedData.data,
messageId: aiMessageId,
}) + '\n',
),
);
sources = parsedData.data;
}
});
stream.on('end', () => {
writer.write(
encoder.encode(
JSON.stringify({
type: 'messageEnd',
messageId: aiMessageId,
}) + '\n',
),
);
writer.close();
db.insert(messagesSchema)
.values({
content: recievedMessage,
chatId: chatId,
messageId: aiMessageId,
role: 'assistant',
metadata: JSON.stringify({
createdAt: new Date(),
...(sources && sources.length > 0 && { sources }),
}),
})
.execute();
});
stream.on('error', (data) => {
const parsedData = JSON.parse(data);
writer.write(
encoder.encode(
JSON.stringify({
type: 'error',
data: parsedData.data,
}),
),
);
writer.close();
});
};
const handleHistorySave = async (
message: Message,
humanMessageId: string,
focusMode: string,
files: string[],
) => {
const chat = await db.query.chats.findFirst({
where: eq(chats.id, message.chatId),
});
if (!chat) {
await db
.insert(chats)
.values({
id: message.chatId,
title: message.content,
createdAt: new Date().toString(),
focusMode: focusMode,
files: files.map(getFileDetails),
})
.execute();
}
const messageExists = await db.query.messages.findFirst({
where: eq(messagesSchema.messageId, humanMessageId),
});
if (!messageExists) {
await db
.insert(messagesSchema)
.values({
content: message.content,
chatId: message.chatId,
messageId: humanMessageId,
role: 'user',
metadata: JSON.stringify({
createdAt: new Date(),
}),
})
.execute();
} else {
await db
.delete(messagesSchema)
.where(
and(
gt(messagesSchema.id, messageExists.id),
eq(messagesSchema.chatId, message.chatId),
),
)
.execute();
}
};
export const POST = async (req: Request) => {
try {
const body = (await req.json()) as Body;
const { message } = body;
if (message.content === '') {
return Response.json(
{
message: 'Please provide a message to process',
},
{ status: 400 },
);
}
const [chatModelProviders, embeddingModelProviders] = await Promise.all([
getAvailableChatModelProviders(),
getAvailableEmbeddingModelProviders(),
]);
const chatModelProvider =
chatModelProviders[
body.chatModel?.provider || Object.keys(chatModelProviders)[0]
];
const chatModel =
chatModelProvider[
body.chatModel?.name || Object.keys(chatModelProvider)[0]
];
const embeddingProvider =
embeddingModelProviders[
body.embeddingModel?.provider || Object.keys(embeddingModelProviders)[0]
];
const embeddingModel =
embeddingProvider[
body.embeddingModel?.name || Object.keys(embeddingProvider)[0]
];
let llm: BaseChatModel | undefined;
let embedding = embeddingModel.model;
if (body.chatModel?.provider === 'custom_openai') {
llm = new ChatOpenAI({
openAIApiKey: getCustomOpenaiApiKey(),
modelName: getCustomOpenaiModelName(),
temperature: 0.7,
configuration: {
baseURL: getCustomOpenaiApiUrl(),
},
}) as unknown as BaseChatModel;
} else if (chatModelProvider && chatModel) {
llm = chatModel.model;
}
if (!llm) {
return Response.json({ error: 'Invalid chat model' }, { status: 400 });
}
if (!embedding) {
return Response.json(
{ error: 'Invalid embedding model' },
{ status: 400 },
);
}
const humanMessageId =
message.messageId ?? crypto.randomBytes(7).toString('hex');
const aiMessageId = crypto.randomBytes(7).toString('hex');
const history: BaseMessage[] = body.history.map((msg) => {
if (msg[0] === 'human') {
return new HumanMessage({
content: msg[1],
});
} else {
return new AIMessage({
content: msg[1],
});
}
});
const handler = searchHandlers[body.focusMode];
if (!handler) {
return Response.json(
{
message: 'Invalid focus mode',
},
{ status: 400 },
);
}
const stream = await handler.searchAndAnswer(
message.content,
history,
llm,
embedding,
body.optimizationMode,
body.files,
body.systemInstructions,
);
const responseStream = new TransformStream();
const writer = responseStream.writable.getWriter();
const encoder = new TextEncoder();
handleEmitterEvents(stream, writer, encoder, aiMessageId, message.chatId);
handleHistorySave(message, humanMessageId, body.focusMode, body.files);
return new Response(responseStream.readable, {
headers: {
'Content-Type': 'text/event-stream',
Connection: 'keep-alive',
'Cache-Control': 'no-cache, no-transform',
},
});
} catch (err) {
console.error('An error occurred while processing chat request:', err);
return Response.json(
{ message: 'An error occurred while processing chat request' },
{ status: 500 },
);
}
};

View File

@@ -1,69 +0,0 @@
import db from '@/lib/db';
import { chats, messages } from '@/lib/db/schema';
import { eq } from 'drizzle-orm';
export const GET = async (
req: Request,
{ params }: { params: Promise<{ id: string }> },
) => {
try {
const { id } = await params;
const chatExists = await db.query.chats.findFirst({
where: eq(chats.id, id),
});
if (!chatExists) {
return Response.json({ message: 'Chat not found' }, { status: 404 });
}
const chatMessages = await db.query.messages.findMany({
where: eq(messages.chatId, id),
});
return Response.json(
{
chat: chatExists,
messages: chatMessages,
},
{ status: 200 },
);
} catch (err) {
console.error('Error in getting chat by id: ', err);
return Response.json(
{ message: 'An error has occurred.' },
{ status: 500 },
);
}
};
export const DELETE = async (
req: Request,
{ params }: { params: Promise<{ id: string }> },
) => {
try {
const { id } = await params;
const chatExists = await db.query.chats.findFirst({
where: eq(chats.id, id),
});
if (!chatExists) {
return Response.json({ message: 'Chat not found' }, { status: 404 });
}
await db.delete(chats).where(eq(chats.id, id)).execute();
await db.delete(messages).where(eq(messages.chatId, id)).execute();
return Response.json(
{ message: 'Chat deleted successfully' },
{ status: 200 },
);
} catch (err) {
console.error('Error in deleting chat by id: ', err);
return Response.json(
{ message: 'An error has occurred.' },
{ status: 500 },
);
}
};

View File

@@ -1,15 +0,0 @@
import db from '@/lib/db';
export const GET = async (req: Request) => {
try {
let chats = await db.query.chats.findMany();
chats = chats.reverse();
return Response.json({ chats: chats }, { status: 200 });
} catch (err) {
console.error('Error in getting chats: ', err);
return Response.json(
{ message: 'An error has occurred.' },
{ status: 500 },
);
}
};

View File

@@ -1,119 +0,0 @@
import {
getAnthropicApiKey,
getCustomOpenaiApiKey,
getCustomOpenaiApiUrl,
getCustomOpenaiModelName,
getGeminiApiKey,
getGroqApiKey,
getOllamaApiEndpoint,
getOpenaiApiKey,
getDeepseekApiKey,
getLMStudioApiEndpoint,
updateConfig,
} from '@/lib/config';
import {
getAvailableChatModelProviders,
getAvailableEmbeddingModelProviders,
} from '@/lib/providers';
export const GET = async (req: Request) => {
try {
const config: Record<string, any> = {};
const [chatModelProviders, embeddingModelProviders] = await Promise.all([
getAvailableChatModelProviders(),
getAvailableEmbeddingModelProviders(),
]);
config['chatModelProviders'] = {};
config['embeddingModelProviders'] = {};
for (const provider in chatModelProviders) {
config['chatModelProviders'][provider] = Object.keys(
chatModelProviders[provider],
).map((model) => {
return {
name: model,
displayName: chatModelProviders[provider][model].displayName,
};
});
}
for (const provider in embeddingModelProviders) {
config['embeddingModelProviders'][provider] = Object.keys(
embeddingModelProviders[provider],
).map((model) => {
return {
name: model,
displayName: embeddingModelProviders[provider][model].displayName,
};
});
}
config['openaiApiKey'] = getOpenaiApiKey();
config['ollamaApiUrl'] = getOllamaApiEndpoint();
config['lmStudioApiUrl'] = getLMStudioApiEndpoint();
config['anthropicApiKey'] = getAnthropicApiKey();
config['groqApiKey'] = getGroqApiKey();
config['geminiApiKey'] = getGeminiApiKey();
config['deepseekApiKey'] = getDeepseekApiKey();
config['customOpenaiApiUrl'] = getCustomOpenaiApiUrl();
config['customOpenaiApiKey'] = getCustomOpenaiApiKey();
config['customOpenaiModelName'] = getCustomOpenaiModelName();
return Response.json({ ...config }, { status: 200 });
} catch (err) {
console.error('An error occurred while getting config:', err);
return Response.json(
{ message: 'An error occurred while getting config' },
{ status: 500 },
);
}
};
export const POST = async (req: Request) => {
try {
const config = await req.json();
const updatedConfig = {
MODELS: {
OPENAI: {
API_KEY: config.openaiApiKey,
},
GROQ: {
API_KEY: config.groqApiKey,
},
ANTHROPIC: {
API_KEY: config.anthropicApiKey,
},
GEMINI: {
API_KEY: config.geminiApiKey,
},
OLLAMA: {
API_URL: config.ollamaApiUrl,
},
DEEPSEEK: {
API_KEY: config.deepseekApiKey,
},
LM_STUDIO: {
API_URL: config.lmStudioApiUrl,
},
CUSTOM_OPENAI: {
API_URL: config.customOpenaiApiUrl,
API_KEY: config.customOpenaiApiKey,
MODEL_NAME: config.customOpenaiModelName,
},
},
};
updateConfig(updatedConfig);
return Response.json({ message: 'Config updated' }, { status: 200 });
} catch (err) {
console.error('An error occurred while updating config:', err);
return Response.json(
{ message: 'An error occurred while updating config' },
{ status: 500 },
);
}
};

View File

@@ -1,61 +0,0 @@
import { searchSearxng } from '@/lib/searxng';
const articleWebsites = [
'yahoo.com',
'www.exchangewire.com',
'businessinsider.com',
/* 'wired.com',
'mashable.com',
'theverge.com',
'gizmodo.com',
'cnet.com',
'venturebeat.com', */
];
const topics = ['AI', 'tech']; /* TODO: Add UI to customize this */
export const GET = async (req: Request) => {
try {
const data = (
await Promise.all([
...new Array(articleWebsites.length * topics.length)
.fill(0)
.map(async (_, i) => {
return (
await searchSearxng(
`site:${articleWebsites[i % articleWebsites.length]} ${
topics[i % topics.length]
}`,
{
engines: ['bing news'],
pageno: 1,
},
)
).results;
}),
])
)
.map((result) => result)
.flat()
.sort(() => Math.random() - 0.5);
return Response.json(
{
blogs: data,
},
{
status: 200,
},
);
} catch (err) {
console.error(`An error occurred in discover route: ${err}`);
return Response.json(
{
message: 'An error has occurred',
},
{
status: 500,
},
);
}
};

View File

@@ -1,83 +0,0 @@
import handleImageSearch from '@/lib/chains/imageSearchAgent';
import {
getCustomOpenaiApiKey,
getCustomOpenaiApiUrl,
getCustomOpenaiModelName,
} from '@/lib/config';
import { getAvailableChatModelProviders } from '@/lib/providers';
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
import { ChatOpenAI } from '@langchain/openai';
interface ChatModel {
provider: string;
model: string;
}
interface ImageSearchBody {
query: string;
chatHistory: any[];
chatModel?: ChatModel;
}
export const POST = async (req: Request) => {
try {
const body: ImageSearchBody = await req.json();
const chatHistory = body.chatHistory
.map((msg: any) => {
if (msg.role === 'user') {
return new HumanMessage(msg.content);
} else if (msg.role === 'assistant') {
return new AIMessage(msg.content);
}
})
.filter((msg) => msg !== undefined) as BaseMessage[];
const chatModelProviders = await getAvailableChatModelProviders();
const chatModelProvider =
chatModelProviders[
body.chatModel?.provider || Object.keys(chatModelProviders)[0]
];
const chatModel =
chatModelProvider[
body.chatModel?.model || Object.keys(chatModelProvider)[0]
];
let llm: BaseChatModel | undefined;
if (body.chatModel?.provider === 'custom_openai') {
llm = new ChatOpenAI({
openAIApiKey: getCustomOpenaiApiKey(),
modelName: getCustomOpenaiModelName(),
temperature: 0.7,
configuration: {
baseURL: getCustomOpenaiApiUrl(),
},
}) as unknown as BaseChatModel;
} else if (chatModelProvider && chatModel) {
llm = chatModel.model;
}
if (!llm) {
return Response.json({ error: 'Invalid chat model' }, { status: 400 });
}
const images = await handleImageSearch(
{
chat_history: chatHistory,
query: body.query,
},
llm,
);
return Response.json({ images }, { status: 200 });
} catch (err) {
console.error(`An error occurred while searching images: ${err}`);
return Response.json(
{ message: 'An error occurred while searching images' },
{ status: 500 },
);
}
};

View File

@@ -1,47 +0,0 @@
import {
getAvailableChatModelProviders,
getAvailableEmbeddingModelProviders,
} from '@/lib/providers';
export const GET = async (req: Request) => {
try {
const [chatModelProviders, embeddingModelProviders] = await Promise.all([
getAvailableChatModelProviders(),
getAvailableEmbeddingModelProviders(),
]);
Object.keys(chatModelProviders).forEach((provider) => {
Object.keys(chatModelProviders[provider]).forEach((model) => {
delete (chatModelProviders[provider][model] as { model?: unknown })
.model;
});
});
Object.keys(embeddingModelProviders).forEach((provider) => {
Object.keys(embeddingModelProviders[provider]).forEach((model) => {
delete (embeddingModelProviders[provider][model] as { model?: unknown })
.model;
});
});
return Response.json(
{
chatModelProviders,
embeddingModelProviders,
},
{
status: 200,
},
);
} catch (err) {
console.error('An error occurred while fetching models', err);
return Response.json(
{
message: 'An error has occurred.',
},
{
status: 500,
},
);
}
};

View File

@@ -1,270 +0,0 @@
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
import type { Embeddings } from '@langchain/core/embeddings';
import { ChatOpenAI } from '@langchain/openai';
import {
getAvailableChatModelProviders,
getAvailableEmbeddingModelProviders,
} from '@/lib/providers';
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
import { MetaSearchAgentType } from '@/lib/search/metaSearchAgent';
import {
getCustomOpenaiApiKey,
getCustomOpenaiApiUrl,
getCustomOpenaiModelName,
} from '@/lib/config';
import { searchHandlers } from '@/lib/search';
interface chatModel {
provider: string;
name: string;
customOpenAIKey?: string;
customOpenAIBaseURL?: string;
}
interface embeddingModel {
provider: string;
name: string;
}
interface ChatRequestBody {
optimizationMode: 'speed' | 'balanced';
focusMode: string;
chatModel?: chatModel;
embeddingModel?: embeddingModel;
query: string;
history: Array<[string, string]>;
stream?: boolean;
systemInstructions?: string;
}
export const POST = async (req: Request) => {
try {
const body: ChatRequestBody = await req.json();
if (!body.focusMode || !body.query) {
return Response.json(
{ message: 'Missing focus mode or query' },
{ status: 400 },
);
}
body.history = body.history || [];
body.optimizationMode = body.optimizationMode || 'balanced';
body.stream = body.stream || false;
const history: BaseMessage[] = body.history.map((msg) => {
return msg[0] === 'human'
? new HumanMessage({ content: msg[1] })
: new AIMessage({ content: msg[1] });
});
const [chatModelProviders, embeddingModelProviders] = await Promise.all([
getAvailableChatModelProviders(),
getAvailableEmbeddingModelProviders(),
]);
const chatModelProvider =
body.chatModel?.provider || Object.keys(chatModelProviders)[0];
const chatModel =
body.chatModel?.name ||
Object.keys(chatModelProviders[chatModelProvider])[0];
const embeddingModelProvider =
body.embeddingModel?.provider || Object.keys(embeddingModelProviders)[0];
const embeddingModel =
body.embeddingModel?.name ||
Object.keys(embeddingModelProviders[embeddingModelProvider])[0];
let llm: BaseChatModel | undefined;
let embeddings: Embeddings | undefined;
if (body.chatModel?.provider === 'custom_openai') {
llm = new ChatOpenAI({
modelName: body.chatModel?.name || getCustomOpenaiModelName(),
openAIApiKey:
body.chatModel?.customOpenAIKey || getCustomOpenaiApiKey(),
temperature: 0.7,
configuration: {
baseURL:
body.chatModel?.customOpenAIBaseURL || getCustomOpenaiApiUrl(),
},
}) as unknown as BaseChatModel;
} else if (
chatModelProviders[chatModelProvider] &&
chatModelProviders[chatModelProvider][chatModel]
) {
llm = chatModelProviders[chatModelProvider][chatModel]
.model as unknown as BaseChatModel | undefined;
}
if (
embeddingModelProviders[embeddingModelProvider] &&
embeddingModelProviders[embeddingModelProvider][embeddingModel]
) {
embeddings = embeddingModelProviders[embeddingModelProvider][
embeddingModel
].model as Embeddings | undefined;
}
if (!llm || !embeddings) {
return Response.json(
{ message: 'Invalid model selected' },
{ status: 400 },
);
}
const searchHandler: MetaSearchAgentType = searchHandlers[body.focusMode];
if (!searchHandler) {
return Response.json({ message: 'Invalid focus mode' }, { status: 400 });
}
const emitter = await searchHandler.searchAndAnswer(
body.query,
history,
llm,
embeddings,
body.optimizationMode,
[],
body.systemInstructions || '',
);
if (!body.stream) {
return new Promise(
(
resolve: (value: Response) => void,
reject: (value: Response) => void,
) => {
let message = '';
let sources: any[] = [];
emitter.on('data', (data: string) => {
try {
const parsedData = JSON.parse(data);
if (parsedData.type === 'response') {
message += parsedData.data;
} else if (parsedData.type === 'sources') {
sources = parsedData.data;
}
} catch (error) {
reject(
Response.json(
{ message: 'Error parsing data' },
{ status: 500 },
),
);
}
});
emitter.on('end', () => {
resolve(Response.json({ message, sources }, { status: 200 }));
});
emitter.on('error', (error: any) => {
reject(
Response.json(
{ message: 'Search error', error },
{ status: 500 },
),
);
});
},
);
}
const encoder = new TextEncoder();
const abortController = new AbortController();
const { signal } = abortController;
const stream = new ReadableStream({
start(controller) {
let sources: any[] = [];
controller.enqueue(
encoder.encode(
JSON.stringify({
type: 'init',
data: 'Stream connected',
}) + '\n',
),
);
signal.addEventListener('abort', () => {
emitter.removeAllListeners();
try {
controller.close();
} catch (error) {}
});
emitter.on('data', (data: string) => {
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',
),
);
}
} catch (error) {
controller.error(error);
}
});
emitter.on('end', () => {
if (signal.aborted) return;
controller.enqueue(
encoder.encode(
JSON.stringify({
type: 'done',
}) + '\n',
),
);
controller.close();
});
emitter.on('error', (error: any) => {
if (signal.aborted) return;
controller.error(error);
});
},
cancel() {
abortController.abort();
},
});
return new Response(stream, {
headers: {
'Content-Type': 'text/event-stream',
'Cache-Control': 'no-cache, no-transform',
Connection: 'keep-alive',
},
});
} catch (err: any) {
console.error(`Error in getting search results: ${err.message}`);
return Response.json(
{ message: 'An error has occurred.' },
{ status: 500 },
);
}
};

View File

@@ -1,81 +0,0 @@
import generateSuggestions from '@/lib/chains/suggestionGeneratorAgent';
import {
getCustomOpenaiApiKey,
getCustomOpenaiApiUrl,
getCustomOpenaiModelName,
} from '@/lib/config';
import { getAvailableChatModelProviders } from '@/lib/providers';
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
import { ChatOpenAI } from '@langchain/openai';
interface ChatModel {
provider: string;
model: string;
}
interface SuggestionsGenerationBody {
chatHistory: any[];
chatModel?: ChatModel;
}
export const POST = async (req: Request) => {
try {
const body: SuggestionsGenerationBody = await req.json();
const chatHistory = body.chatHistory
.map((msg: any) => {
if (msg.role === 'user') {
return new HumanMessage(msg.content);
} else if (msg.role === 'assistant') {
return new AIMessage(msg.content);
}
})
.filter((msg) => msg !== undefined) as BaseMessage[];
const chatModelProviders = await getAvailableChatModelProviders();
const chatModelProvider =
chatModelProviders[
body.chatModel?.provider || Object.keys(chatModelProviders)[0]
];
const chatModel =
chatModelProvider[
body.chatModel?.model || Object.keys(chatModelProvider)[0]
];
let llm: BaseChatModel | undefined;
if (body.chatModel?.provider === 'custom_openai') {
llm = new ChatOpenAI({
openAIApiKey: getCustomOpenaiApiKey(),
modelName: getCustomOpenaiModelName(),
temperature: 0.7,
configuration: {
baseURL: getCustomOpenaiApiUrl(),
},
}) as unknown as BaseChatModel;
} else if (chatModelProvider && chatModel) {
llm = chatModel.model;
}
if (!llm) {
return Response.json({ error: 'Invalid chat model' }, { status: 400 });
}
const suggestions = await generateSuggestions(
{
chat_history: chatHistory,
},
llm,
);
return Response.json({ suggestions }, { status: 200 });
} catch (err) {
console.error(`An error occurred while generating suggestions: ${err}`);
return Response.json(
{ message: 'An error occurred while generating suggestions' },
{ status: 500 },
);
}
};

View File

@@ -1,134 +0,0 @@
import { NextResponse } from 'next/server';
import fs from 'fs';
import path from 'path';
import crypto from 'crypto';
import { getAvailableEmbeddingModelProviders } from '@/lib/providers';
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/document';
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,
});
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');
const embedding_model_provider = formData.get('embedding_model_provider');
if (!embedding_model || !embedding_model_provider) {
return NextResponse.json(
{ message: 'Missing embedding model or provider' },
{ status: 400 },
);
}
const embeddingModels = await getAvailableEmbeddingModelProviders();
const provider =
embedding_model_provider ?? Object.keys(embeddingModels)[0];
const embeddingModel =
embedding_model ?? Object.keys(embeddingModels[provider as string])[0];
let embeddingsModel =
embeddingModels[provider as string]?.[embeddingModel as string]?.model;
if (!embeddingsModel) {
return NextResponse.json(
{ message: 'Invalid embedding model selected' },
{ status: 400 },
);
}
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+$/, '-extracted.json');
fs.writeFileSync(
extractedDataPath,
JSON.stringify({
title: file.name,
contents: splitted.map((doc) => doc.pageContent),
}),
);
const embeddings = await embeddingsModel.embedDocuments(
splitted.map((doc) => doc.pageContent),
);
const embeddingsDataPath = filePath.replace(
/\.\w+$/,
'-embeddings.json',
);
fs.writeFileSync(
embeddingsDataPath,
JSON.stringify({
title: file.name,
embeddings,
}),
);
processedFiles.push({
fileName: file.name,
fileExtension: fileExtension,
fileId: uniqueFileName.replace(/\.\w+$/, ''),
});
}),
);
return NextResponse.json({
files: processedFiles,
});
} catch (error) {
console.error('Error uploading file:', error);
return NextResponse.json(
{ message: 'An error has occurred.' },
{ status: 500 },
);
}
}

View File

@@ -1,83 +0,0 @@
import handleVideoSearch from '@/lib/chains/videoSearchAgent';
import {
getCustomOpenaiApiKey,
getCustomOpenaiApiUrl,
getCustomOpenaiModelName,
} from '@/lib/config';
import { getAvailableChatModelProviders } from '@/lib/providers';
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
import { ChatOpenAI } from '@langchain/openai';
interface ChatModel {
provider: string;
model: string;
}
interface VideoSearchBody {
query: string;
chatHistory: any[];
chatModel?: ChatModel;
}
export const POST = async (req: Request) => {
try {
const body: VideoSearchBody = await req.json();
const chatHistory = body.chatHistory
.map((msg: any) => {
if (msg.role === 'user') {
return new HumanMessage(msg.content);
} else if (msg.role === 'assistant') {
return new AIMessage(msg.content);
}
})
.filter((msg) => msg !== undefined) as BaseMessage[];
const chatModelProviders = await getAvailableChatModelProviders();
const chatModelProvider =
chatModelProviders[
body.chatModel?.provider || Object.keys(chatModelProviders)[0]
];
const chatModel =
chatModelProvider[
body.chatModel?.model || Object.keys(chatModelProvider)[0]
];
let llm: BaseChatModel | undefined;
if (body.chatModel?.provider === 'custom_openai') {
llm = new ChatOpenAI({
openAIApiKey: getCustomOpenaiApiKey(),
modelName: getCustomOpenaiModelName(),
temperature: 0.7,
configuration: {
baseURL: getCustomOpenaiApiUrl(),
},
}) as unknown as BaseChatModel;
} else if (chatModelProvider && chatModel) {
llm = chatModel.model;
}
if (!llm) {
return Response.json({ error: 'Invalid chat model' }, { status: 400 });
}
const videos = await handleVideoSearch(
{
chat_history: chatHistory,
query: body.query,
},
llm,
);
return Response.json({ videos }, { status: 200 });
} catch (err) {
console.error(`An error occurred while searching videos: ${err}`);
return Response.json(
{ message: 'An error occurred while searching videos' },
{ status: 500 },
);
}
};

View File

@@ -1,9 +0,0 @@
import ChatWindow from '@/components/ChatWindow';
import React from 'react';
const Page = ({ params }: { params: Promise<{ chatId: string }> }) => {
const { chatId } = React.use(params);
return <ChatWindow id={chatId} />;
};
export default Page;

View File

@@ -1,892 +0,0 @@
'use client';
import { Settings as SettingsIcon, ArrowLeft, Loader2 } from 'lucide-react';
import { useEffect, useState } from 'react';
import { cn } from '@/lib/utils';
import { Switch } from '@headlessui/react';
import ThemeSwitcher from '@/components/theme/Switcher';
import { ImagesIcon, VideoIcon } from 'lucide-react';
import Link from 'next/link';
import { PROVIDER_METADATA } from '@/lib/providers';
interface SettingsType {
chatModelProviders: {
[key: string]: [Record<string, any>];
};
embeddingModelProviders: {
[key: string]: [Record<string, any>];
};
openaiApiKey: string;
groqApiKey: string;
anthropicApiKey: string;
geminiApiKey: string;
ollamaApiUrl: string;
lmStudioApiUrl: string;
deepseekApiKey: string;
customOpenaiApiKey: string;
customOpenaiApiUrl: string;
customOpenaiModelName: string;
}
interface InputProps extends React.InputHTMLAttributes<HTMLInputElement> {
isSaving?: boolean;
onSave?: (value: string) => void;
}
const Input = ({ className, isSaving, onSave, ...restProps }: InputProps) => {
return (
<div className="relative">
<input
{...restProps}
className={cn(
'bg-light-secondary dark:bg-dark-secondary w-full px-3 py-2 flex items-center overflow-hidden border border-light-200 dark:border-dark-200 dark:text-white rounded-lg text-sm',
isSaving && 'pr-10',
className,
)}
onBlur={(e) => onSave?.(e.target.value)}
/>
{isSaving && (
<div className="absolute right-3 top-1/2 -translate-y-1/2">
<Loader2
size={16}
className="animate-spin text-black/70 dark:text-white/70"
/>
</div>
)}
</div>
);
};
interface TextareaProps extends React.InputHTMLAttributes<HTMLTextAreaElement> {
isSaving?: boolean;
onSave?: (value: string) => void;
}
const Textarea = ({
className,
isSaving,
onSave,
...restProps
}: TextareaProps) => {
return (
<div className="relative">
<textarea
placeholder="Any special instructions for the LLM"
className="placeholder:text-sm text-sm w-full flex items-center justify-between p-3 bg-light-secondary dark:bg-dark-secondary rounded-lg hover:bg-light-200 dark:hover:bg-dark-200 transition-colors"
rows={4}
onBlur={(e) => onSave?.(e.target.value)}
{...restProps}
/>
{isSaving && (
<div className="absolute right-3 top-3">
<Loader2
size={16}
className="animate-spin text-black/70 dark:text-white/70"
/>
</div>
)}
</div>
);
};
const Select = ({
className,
options,
...restProps
}: React.SelectHTMLAttributes<HTMLSelectElement> & {
options: { value: string; label: string; disabled?: boolean }[];
}) => {
return (
<select
{...restProps}
className={cn(
'bg-light-secondary dark:bg-dark-secondary px-3 py-2 flex items-center overflow-hidden border border-light-200 dark:border-dark-200 dark:text-white rounded-lg text-sm',
className,
)}
>
{options.map(({ label, value, disabled }) => (
<option key={value} value={value} disabled={disabled}>
{label}
</option>
))}
</select>
);
};
const SettingsSection = ({
title,
children,
}: {
title: string;
children: React.ReactNode;
}) => (
<div className="flex flex-col space-y-4 p-4 bg-light-secondary/50 dark:bg-dark-secondary/50 rounded-xl border border-light-200 dark:border-dark-200">
<h2 className="text-black/90 dark:text-white/90 font-medium">{title}</h2>
{children}
</div>
);
const Page = () => {
const [config, setConfig] = useState<SettingsType | null>(null);
const [chatModels, setChatModels] = useState<Record<string, any>>({});
const [embeddingModels, setEmbeddingModels] = useState<Record<string, any>>(
{},
);
const [selectedChatModelProvider, setSelectedChatModelProvider] = useState<
string | null
>(null);
const [selectedChatModel, setSelectedChatModel] = useState<string | null>(
null,
);
const [selectedEmbeddingModelProvider, setSelectedEmbeddingModelProvider] =
useState<string | null>(null);
const [selectedEmbeddingModel, setSelectedEmbeddingModel] = useState<
string | null
>(null);
const [isLoading, setIsLoading] = useState(true);
const [automaticImageSearch, setAutomaticImageSearch] = useState(false);
const [automaticVideoSearch, setAutomaticVideoSearch] = useState(false);
const [systemInstructions, setSystemInstructions] = useState<string>('');
const [savingStates, setSavingStates] = useState<Record<string, boolean>>({});
useEffect(() => {
const fetchConfig = async () => {
const res = await fetch(`/api/config`, {
headers: {
'Content-Type': 'application/json',
},
});
const data = (await res.json()) as SettingsType;
setConfig(data);
const chatModelProvidersKeys = Object.keys(data.chatModelProviders || {});
const embeddingModelProvidersKeys = Object.keys(
data.embeddingModelProviders || {},
);
const defaultChatModelProvider =
chatModelProvidersKeys.length > 0 ? chatModelProvidersKeys[0] : '';
const defaultEmbeddingModelProvider =
embeddingModelProvidersKeys.length > 0
? embeddingModelProvidersKeys[0]
: '';
const chatModelProvider =
localStorage.getItem('chatModelProvider') ||
defaultChatModelProvider ||
'';
const chatModel =
localStorage.getItem('chatModel') ||
(data.chatModelProviders &&
data.chatModelProviders[chatModelProvider]?.length > 0
? data.chatModelProviders[chatModelProvider][0].name
: undefined) ||
'';
const embeddingModelProvider =
localStorage.getItem('embeddingModelProvider') ||
defaultEmbeddingModelProvider ||
'';
const embeddingModel =
localStorage.getItem('embeddingModel') ||
(data.embeddingModelProviders &&
data.embeddingModelProviders[embeddingModelProvider]?.[0].name) ||
'';
setSelectedChatModelProvider(chatModelProvider);
setSelectedChatModel(chatModel);
setSelectedEmbeddingModelProvider(embeddingModelProvider);
setSelectedEmbeddingModel(embeddingModel);
setChatModels(data.chatModelProviders || {});
setEmbeddingModels(data.embeddingModelProviders || {});
setAutomaticImageSearch(
localStorage.getItem('autoImageSearch') === 'true',
);
setAutomaticVideoSearch(
localStorage.getItem('autoVideoSearch') === 'true',
);
setSystemInstructions(localStorage.getItem('systemInstructions')!);
setIsLoading(false);
};
fetchConfig();
}, []);
const saveConfig = async (key: string, value: any) => {
setSavingStates((prev) => ({ ...prev, [key]: true }));
try {
const updatedConfig = {
...config,
[key]: value,
} as SettingsType;
const response = await fetch(`/api/config`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify(updatedConfig),
});
if (!response.ok) {
throw new Error('Failed to update config');
}
setConfig(updatedConfig);
if (
key.toLowerCase().includes('api') ||
key.toLowerCase().includes('url')
) {
const res = await fetch(`/api/config`, {
headers: {
'Content-Type': 'application/json',
},
});
if (!res.ok) {
throw new Error('Failed to fetch updated config');
}
const data = await res.json();
setChatModels(data.chatModelProviders || {});
setEmbeddingModels(data.embeddingModelProviders || {});
const currentChatProvider = selectedChatModelProvider;
const newChatProviders = Object.keys(data.chatModelProviders || {});
if (!currentChatProvider && newChatProviders.length > 0) {
const firstProvider = newChatProviders[0];
const firstModel = data.chatModelProviders[firstProvider]?.[0]?.name;
if (firstModel) {
setSelectedChatModelProvider(firstProvider);
setSelectedChatModel(firstModel);
localStorage.setItem('chatModelProvider', firstProvider);
localStorage.setItem('chatModel', firstModel);
}
} else if (
currentChatProvider &&
(!data.chatModelProviders ||
!data.chatModelProviders[currentChatProvider] ||
!Array.isArray(data.chatModelProviders[currentChatProvider]) ||
data.chatModelProviders[currentChatProvider].length === 0)
) {
const firstValidProvider = Object.entries(
data.chatModelProviders || {},
).find(
([_, models]) => Array.isArray(models) && models.length > 0,
)?.[0];
if (firstValidProvider) {
setSelectedChatModelProvider(firstValidProvider);
setSelectedChatModel(
data.chatModelProviders[firstValidProvider][0].name,
);
localStorage.setItem('chatModelProvider', firstValidProvider);
localStorage.setItem(
'chatModel',
data.chatModelProviders[firstValidProvider][0].name,
);
} else {
setSelectedChatModelProvider(null);
setSelectedChatModel(null);
localStorage.removeItem('chatModelProvider');
localStorage.removeItem('chatModel');
}
}
const currentEmbeddingProvider = selectedEmbeddingModelProvider;
const newEmbeddingProviders = Object.keys(
data.embeddingModelProviders || {},
);
if (!currentEmbeddingProvider && newEmbeddingProviders.length > 0) {
const firstProvider = newEmbeddingProviders[0];
const firstModel =
data.embeddingModelProviders[firstProvider]?.[0]?.name;
if (firstModel) {
setSelectedEmbeddingModelProvider(firstProvider);
setSelectedEmbeddingModel(firstModel);
localStorage.setItem('embeddingModelProvider', firstProvider);
localStorage.setItem('embeddingModel', firstModel);
}
} else if (
currentEmbeddingProvider &&
(!data.embeddingModelProviders ||
!data.embeddingModelProviders[currentEmbeddingProvider] ||
!Array.isArray(
data.embeddingModelProviders[currentEmbeddingProvider],
) ||
data.embeddingModelProviders[currentEmbeddingProvider].length === 0)
) {
const firstValidProvider = Object.entries(
data.embeddingModelProviders || {},
).find(
([_, models]) => Array.isArray(models) && models.length > 0,
)?.[0];
if (firstValidProvider) {
setSelectedEmbeddingModelProvider(firstValidProvider);
setSelectedEmbeddingModel(
data.embeddingModelProviders[firstValidProvider][0].name,
);
localStorage.setItem('embeddingModelProvider', firstValidProvider);
localStorage.setItem(
'embeddingModel',
data.embeddingModelProviders[firstValidProvider][0].name,
);
} else {
setSelectedEmbeddingModelProvider(null);
setSelectedEmbeddingModel(null);
localStorage.removeItem('embeddingModelProvider');
localStorage.removeItem('embeddingModel');
}
}
setConfig(data);
}
if (key === 'automaticImageSearch') {
localStorage.setItem('autoImageSearch', value.toString());
} else if (key === 'automaticVideoSearch') {
localStorage.setItem('autoVideoSearch', value.toString());
} else if (key === 'chatModelProvider') {
localStorage.setItem('chatModelProvider', value);
} else if (key === 'chatModel') {
localStorage.setItem('chatModel', value);
} else if (key === 'embeddingModelProvider') {
localStorage.setItem('embeddingModelProvider', value);
} else if (key === 'embeddingModel') {
localStorage.setItem('embeddingModel', value);
} else if (key === 'systemInstructions') {
localStorage.setItem('systemInstructions', value);
}
} catch (err) {
console.error('Failed to save:', err);
setConfig((prev) => ({ ...prev! }));
} finally {
setTimeout(() => {
setSavingStates((prev) => ({ ...prev, [key]: false }));
}, 500);
}
};
return (
<div className="max-w-3xl mx-auto">
<div className="flex flex-col pt-4">
<div className="flex items-center space-x-2">
<Link href="/" className="lg:hidden">
<ArrowLeft className="text-black/70 dark:text-white/70" />
</Link>
<div className="flex flex-row space-x-0.5 items-center">
<SettingsIcon size={23} />
<h1 className="text-3xl font-medium p-2">Settings</h1>
</div>
</div>
<hr className="border-t border-[#2B2C2C] my-4 w-full" />
</div>
{isLoading ? (
<div className="flex flex-row items-center justify-center min-h-[50vh]">
<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>
) : (
config && (
<div className="flex flex-col space-y-6 pb-28 lg:pb-8">
<SettingsSection title="Appearance">
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Theme
</p>
<ThemeSwitcher />
</div>
</SettingsSection>
<SettingsSection title="Automatic Search">
<div className="flex flex-col space-y-4">
<div className="flex items-center justify-between p-3 bg-light-secondary dark:bg-dark-secondary rounded-lg hover:bg-light-200 dark:hover:bg-dark-200 transition-colors">
<div className="flex items-center space-x-3">
<div className="p-2 bg-light-200 dark:bg-dark-200 rounded-lg">
<ImagesIcon
size={18}
className="text-black/70 dark:text-white/70"
/>
</div>
<div>
<p className="text-sm text-black/90 dark:text-white/90 font-medium">
Automatic Image Search
</p>
<p className="text-xs text-black/60 dark:text-white/60 mt-0.5">
Automatically search for relevant images in chat
responses
</p>
</div>
</div>
<Switch
checked={automaticImageSearch}
onChange={(checked) => {
setAutomaticImageSearch(checked);
saveConfig('automaticImageSearch', checked);
}}
className={cn(
automaticImageSearch
? 'bg-[#24A0ED]'
: 'bg-light-200 dark:bg-dark-200',
'relative inline-flex h-6 w-11 items-center rounded-full transition-colors focus:outline-none',
)}
>
<span
className={cn(
automaticImageSearch
? 'translate-x-6'
: 'translate-x-1',
'inline-block h-4 w-4 transform rounded-full bg-white transition-transform',
)}
/>
</Switch>
</div>
<div className="flex items-center justify-between p-3 bg-light-secondary dark:bg-dark-secondary rounded-lg hover:bg-light-200 dark:hover:bg-dark-200 transition-colors">
<div className="flex items-center space-x-3">
<div className="p-2 bg-light-200 dark:bg-dark-200 rounded-lg">
<VideoIcon
size={18}
className="text-black/70 dark:text-white/70"
/>
</div>
<div>
<p className="text-sm text-black/90 dark:text-white/90 font-medium">
Automatic Video Search
</p>
<p className="text-xs text-black/60 dark:text-white/60 mt-0.5">
Automatically search for relevant videos in chat
responses
</p>
</div>
</div>
<Switch
checked={automaticVideoSearch}
onChange={(checked) => {
setAutomaticVideoSearch(checked);
saveConfig('automaticVideoSearch', checked);
}}
className={cn(
automaticVideoSearch
? 'bg-[#24A0ED]'
: 'bg-light-200 dark:bg-dark-200',
'relative inline-flex h-6 w-11 items-center rounded-full transition-colors focus:outline-none',
)}
>
<span
className={cn(
automaticVideoSearch
? 'translate-x-6'
: 'translate-x-1',
'inline-block h-4 w-4 transform rounded-full bg-white transition-transform',
)}
/>
</Switch>
</div>
</div>
</SettingsSection>
<SettingsSection title="System Instructions">
<div className="flex flex-col space-y-4">
<Textarea
value={systemInstructions}
isSaving={savingStates['systemInstructions']}
onChange={(e) => {
setSystemInstructions(e.target.value);
}}
onSave={(value) => saveConfig('systemInstructions', value)}
/>
</div>
</SettingsSection>
<SettingsSection title="Model Settings">
{config.chatModelProviders && (
<div className="flex flex-col space-y-4">
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Chat Model Provider
</p>
<Select
value={selectedChatModelProvider ?? undefined}
onChange={(e) => {
const value = e.target.value;
setSelectedChatModelProvider(value);
saveConfig('chatModelProvider', value);
const firstModel =
config.chatModelProviders[value]?.[0]?.name;
if (firstModel) {
setSelectedChatModel(firstModel);
saveConfig('chatModel', firstModel);
}
}}
options={Object.keys(config.chatModelProviders).map(
(provider) => ({
value: provider,
label:
(PROVIDER_METADATA as any)[provider]?.displayName ||
provider.charAt(0).toUpperCase() +
provider.slice(1),
}),
)}
/>
</div>
{selectedChatModelProvider &&
selectedChatModelProvider != 'custom_openai' && (
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Chat Model
</p>
<Select
value={selectedChatModel ?? undefined}
onChange={(e) => {
const value = e.target.value;
setSelectedChatModel(value);
saveConfig('chatModel', value);
}}
options={(() => {
const chatModelProvider =
config.chatModelProviders[
selectedChatModelProvider
];
return chatModelProvider
? chatModelProvider.length > 0
? chatModelProvider.map((model) => ({
value: model.name,
label: model.displayName,
}))
: [
{
value: '',
label: 'No models available',
disabled: true,
},
]
: [
{
value: '',
label:
'Invalid provider, please check backend logs',
disabled: true,
},
];
})()}
/>
</div>
)}
</div>
)}
{selectedChatModelProvider &&
selectedChatModelProvider === 'custom_openai' && (
<div className="flex flex-col space-y-4">
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Model Name
</p>
<Input
type="text"
placeholder="Model name"
value={config.customOpenaiModelName}
isSaving={savingStates['customOpenaiModelName']}
onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
setConfig((prev) => ({
...prev!,
customOpenaiModelName: e.target.value,
}));
}}
onSave={(value) =>
saveConfig('customOpenaiModelName', value)
}
/>
</div>
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Custom OpenAI API Key
</p>
<Input
type="text"
placeholder="Custom OpenAI API Key"
value={config.customOpenaiApiKey}
isSaving={savingStates['customOpenaiApiKey']}
onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
setConfig((prev) => ({
...prev!,
customOpenaiApiKey: e.target.value,
}));
}}
onSave={(value) =>
saveConfig('customOpenaiApiKey', value)
}
/>
</div>
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Custom OpenAI Base URL
</p>
<Input
type="text"
placeholder="Custom OpenAI Base URL"
value={config.customOpenaiApiUrl}
isSaving={savingStates['customOpenaiApiUrl']}
onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
setConfig((prev) => ({
...prev!,
customOpenaiApiUrl: e.target.value,
}));
}}
onSave={(value) =>
saveConfig('customOpenaiApiUrl', value)
}
/>
</div>
</div>
)}
{config.embeddingModelProviders && (
<div className="flex flex-col space-y-4 mt-4 pt-4 border-t border-light-200 dark:border-dark-200">
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Embedding Model Provider
</p>
<Select
value={selectedEmbeddingModelProvider ?? undefined}
onChange={(e) => {
const value = e.target.value;
setSelectedEmbeddingModelProvider(value);
saveConfig('embeddingModelProvider', value);
const firstModel =
config.embeddingModelProviders[value]?.[0]?.name;
if (firstModel) {
setSelectedEmbeddingModel(firstModel);
saveConfig('embeddingModel', firstModel);
}
}}
options={Object.keys(config.embeddingModelProviders).map(
(provider) => ({
value: provider,
label:
(PROVIDER_METADATA as any)[provider]?.displayName ||
provider.charAt(0).toUpperCase() +
provider.slice(1),
}),
)}
/>
</div>
{selectedEmbeddingModelProvider && (
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Embedding Model
</p>
<Select
value={selectedEmbeddingModel ?? undefined}
onChange={(e) => {
const value = e.target.value;
setSelectedEmbeddingModel(value);
saveConfig('embeddingModel', value);
}}
options={(() => {
const embeddingModelProvider =
config.embeddingModelProviders[
selectedEmbeddingModelProvider
];
return embeddingModelProvider
? embeddingModelProvider.length > 0
? embeddingModelProvider.map((model) => ({
value: model.name,
label: model.displayName,
}))
: [
{
value: '',
label: 'No models available',
disabled: true,
},
]
: [
{
value: '',
label:
'Invalid provider, please check backend logs',
disabled: true,
},
];
})()}
/>
</div>
)}
</div>
)}
</SettingsSection>
<SettingsSection title="API Keys">
<div className="flex flex-col space-y-4">
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
OpenAI API Key
</p>
<Input
type="text"
placeholder="OpenAI API Key"
value={config.openaiApiKey}
isSaving={savingStates['openaiApiKey']}
onChange={(e) => {
setConfig((prev) => ({
...prev!,
openaiApiKey: e.target.value,
}));
}}
onSave={(value) => saveConfig('openaiApiKey', value)}
/>
</div>
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Ollama API URL
</p>
<Input
type="text"
placeholder="Ollama API URL"
value={config.ollamaApiUrl}
isSaving={savingStates['ollamaApiUrl']}
onChange={(e) => {
setConfig((prev) => ({
...prev!,
ollamaApiUrl: e.target.value,
}));
}}
onSave={(value) => saveConfig('ollamaApiUrl', value)}
/>
</div>
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
GROQ API Key
</p>
<Input
type="text"
placeholder="GROQ API Key"
value={config.groqApiKey}
isSaving={savingStates['groqApiKey']}
onChange={(e) => {
setConfig((prev) => ({
...prev!,
groqApiKey: e.target.value,
}));
}}
onSave={(value) => saveConfig('groqApiKey', value)}
/>
</div>
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Anthropic API Key
</p>
<Input
type="text"
placeholder="Anthropic API key"
value={config.anthropicApiKey}
isSaving={savingStates['anthropicApiKey']}
onChange={(e) => {
setConfig((prev) => ({
...prev!,
anthropicApiKey: e.target.value,
}));
}}
onSave={(value) => saveConfig('anthropicApiKey', value)}
/>
</div>
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Gemini API Key
</p>
<Input
type="text"
placeholder="Gemini API key"
value={config.geminiApiKey}
isSaving={savingStates['geminiApiKey']}
onChange={(e) => {
setConfig((prev) => ({
...prev!,
geminiApiKey: e.target.value,
}));
}}
onSave={(value) => saveConfig('geminiApiKey', value)}
/>
</div>
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
Deepseek API Key
</p>
<Input
type="text"
placeholder="Deepseek API Key"
value={config.deepseekApiKey}
isSaving={savingStates['deepseekApiKey']}
onChange={(e) => {
setConfig((prev) => ({
...prev!,
deepseekApiKey: e.target.value,
}));
}}
onSave={(value) => saveConfig('deepseekApiKey', value)}
/>
</div>
<div className="flex flex-col space-y-1">
<p className="text-black/70 dark:text-white/70 text-sm">
LM Studio API URL
</p>
<Input
type="text"
placeholder="LM Studio API URL"
value={config.lmStudioApiUrl}
isSaving={savingStates['lmStudioApiUrl']}
onChange={(e) => {
setConfig((prev) => ({
...prev!,
lmStudioApiUrl: e.target.value,
}));
}}
onSave={(value) => saveConfig('lmStudioApiUrl', value)}
/>
</div>
</div>
</SettingsSection>
</div>
)
)}
</div>
);
};
export default Page;

View File

@@ -7,7 +7,7 @@ import { PromptTemplate } from '@langchain/core/prompts';
import formatChatHistoryAsString from '../utils/formatHistory';
import { BaseMessage } from '@langchain/core/messages';
import { StringOutputParser } from '@langchain/core/output_parsers';
import { searchSearxng } from '../searxng';
import { searchSearxng } from '../lib/searxng';
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
const imageSearchChainPrompt = `
@@ -36,12 +36,6 @@ type ImageSearchChainInput = {
query: string;
};
interface ImageSearchResult {
img_src: string;
url: string;
title: string;
}
const strParser = new StringOutputParser();
const createImageSearchChain = (llm: BaseChatModel) => {
@@ -58,13 +52,11 @@ const createImageSearchChain = (llm: BaseChatModel) => {
llm,
strParser,
RunnableLambda.from(async (input: string) => {
input = input.replace(/<think>.*?<\/think>/g, '');
const res = await searchSearxng(input, {
engines: ['bing images', 'google images'],
});
const images: ImageSearchResult[] = [];
const images = [];
res.results.forEach((result) => {
if (result.img_src && result.url && result.title) {

View File

@@ -1,5 +1,5 @@
import { RunnableSequence, RunnableMap } from '@langchain/core/runnables';
import ListLineOutputParser from '../outputParsers/listLineOutputParser';
import ListLineOutputParser from '../lib/outputParsers/listLineOutputParser';
import { PromptTemplate } from '@langchain/core/prompts';
import formatChatHistoryAsString from '../utils/formatHistory';
import { BaseMessage } from '@langchain/core/messages';

View File

@@ -7,7 +7,7 @@ import { PromptTemplate } from '@langchain/core/prompts';
import formatChatHistoryAsString from '../utils/formatHistory';
import { BaseMessage } from '@langchain/core/messages';
import { StringOutputParser } from '@langchain/core/output_parsers';
import { searchSearxng } from '../searxng';
import { searchSearxng } from '../lib/searxng';
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
const VideoSearchChainPrompt = `
@@ -36,13 +36,6 @@ type VideoSearchChainInput = {
query: string;
};
interface VideoSearchResult {
img_src: string;
url: string;
title: string;
iframe_src: string;
}
const strParser = new StringOutputParser();
const createVideoSearchChain = (llm: BaseChatModel) => {
@@ -59,13 +52,11 @@ const createVideoSearchChain = (llm: BaseChatModel) => {
llm,
strParser,
RunnableLambda.from(async (input: string) => {
input = input.replace(/<think>.*?<\/think>/g, '');
const res = await searchSearxng(input, {
engines: ['youtube'],
});
const videos: VideoSearchResult[] = [];
const videos = [];
res.results.forEach((result) => {
if (

View File

@@ -1,612 +0,0 @@
'use client';
import { useEffect, useRef, useState } from 'react';
import { Document } from '@langchain/core/documents';
import Navbar from './Navbar';
import Chat from './Chat';
import EmptyChat from './EmptyChat';
import crypto from 'crypto';
import { toast } from 'sonner';
import { useSearchParams } from 'next/navigation';
import { getSuggestions } from '@/lib/actions';
import { Settings } from 'lucide-react';
import Link from 'next/link';
import NextError from 'next/error';
export type Message = {
messageId: string;
chatId: string;
createdAt: Date;
content: string;
role: 'user' | 'assistant';
suggestions?: string[];
sources?: Document[];
};
export interface File {
fileName: string;
fileExtension: string;
fileId: string;
}
interface ChatModelProvider {
name: string;
provider: string;
}
interface EmbeddingModelProvider {
name: string;
provider: string;
}
const checkConfig = async (
setChatModelProvider: (provider: ChatModelProvider) => void,
setEmbeddingModelProvider: (provider: EmbeddingModelProvider) => void,
setIsConfigReady: (ready: boolean) => void,
setHasError: (hasError: boolean) => void,
) => {
try {
let chatModel = localStorage.getItem('chatModel');
let chatModelProvider = localStorage.getItem('chatModelProvider');
let embeddingModel = localStorage.getItem('embeddingModel');
let embeddingModelProvider = localStorage.getItem('embeddingModelProvider');
const autoImageSearch = localStorage.getItem('autoImageSearch');
const autoVideoSearch = localStorage.getItem('autoVideoSearch');
if (!autoImageSearch) {
localStorage.setItem('autoImageSearch', 'true');
}
if (!autoVideoSearch) {
localStorage.setItem('autoVideoSearch', 'false');
}
const providers = await fetch(`/api/models`, {
headers: {
'Content-Type': 'application/json',
},
}).then(async (res) => {
if (!res.ok)
throw new Error(
`Failed to fetch models: ${res.status} ${res.statusText}`,
);
return res.json();
});
if (
!chatModel ||
!chatModelProvider ||
!embeddingModel ||
!embeddingModelProvider
) {
if (!chatModel || !chatModelProvider) {
const chatModelProviders = providers.chatModelProviders;
chatModelProvider =
chatModelProvider || Object.keys(chatModelProviders)[0];
chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
if (!chatModelProviders || Object.keys(chatModelProviders).length === 0)
return toast.error('No chat models available');
}
if (!embeddingModel || !embeddingModelProvider) {
const embeddingModelProviders = providers.embeddingModelProviders;
if (
!embeddingModelProviders ||
Object.keys(embeddingModelProviders).length === 0
)
return toast.error('No embedding models available');
embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
embeddingModel = Object.keys(
embeddingModelProviders[embeddingModelProvider],
)[0];
}
localStorage.setItem('chatModel', chatModel!);
localStorage.setItem('chatModelProvider', chatModelProvider);
localStorage.setItem('embeddingModel', embeddingModel!);
localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
} else {
const chatModelProviders = providers.chatModelProviders;
const embeddingModelProviders = providers.embeddingModelProviders;
if (
Object.keys(chatModelProviders).length > 0 &&
!chatModelProviders[chatModelProvider]
) {
const chatModelProvidersKeys = Object.keys(chatModelProviders);
chatModelProvider =
chatModelProvidersKeys.find(
(key) => Object.keys(chatModelProviders[key]).length > 0,
) || chatModelProvidersKeys[0];
localStorage.setItem('chatModelProvider', chatModelProvider);
}
if (
chatModelProvider &&
!chatModelProviders[chatModelProvider][chatModel]
) {
chatModel = Object.keys(
chatModelProviders[
Object.keys(chatModelProviders[chatModelProvider]).length > 0
? chatModelProvider
: Object.keys(chatModelProviders)[0]
],
)[0];
localStorage.setItem('chatModel', chatModel);
}
if (
Object.keys(embeddingModelProviders).length > 0 &&
!embeddingModelProviders[embeddingModelProvider]
) {
embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
}
if (
embeddingModelProvider &&
!embeddingModelProviders[embeddingModelProvider][embeddingModel]
) {
embeddingModel = Object.keys(
embeddingModelProviders[embeddingModelProvider],
)[0];
localStorage.setItem('embeddingModel', embeddingModel);
}
}
setChatModelProvider({
name: chatModel!,
provider: chatModelProvider,
});
setEmbeddingModelProvider({
name: embeddingModel!,
provider: embeddingModelProvider,
});
setIsConfigReady(true);
} catch (err) {
console.error('An error occurred while checking the configuration:', err);
setIsConfigReady(false);
setHasError(true);
}
};
const loadMessages = async (
chatId: string,
setMessages: (messages: Message[]) => void,
setIsMessagesLoaded: (loaded: boolean) => void,
setChatHistory: (history: [string, string][]) => void,
setFocusMode: (mode: string) => void,
setNotFound: (notFound: boolean) => void,
setFiles: (files: File[]) => void,
setFileIds: (fileIds: string[]) => void,
) => {
const res = await fetch(`/api/chats/${chatId}`, {
method: 'GET',
headers: {
'Content-Type': 'application/json',
},
});
if (res.status === 404) {
setNotFound(true);
setIsMessagesLoaded(true);
return;
}
const data = await res.json();
const messages = data.messages.map((msg: any) => {
return {
...msg,
...JSON.parse(msg.metadata),
};
}) as Message[];
setMessages(messages);
const history = messages.map((msg) => {
return [msg.role, msg.content];
}) as [string, string][];
console.debug(new Date(), 'app:messages_loaded');
document.title = messages[0].content;
const files = data.chat.files.map((file: any) => {
return {
fileName: file.name,
fileExtension: file.name.split('.').pop(),
fileId: file.fileId,
};
});
setFiles(files);
setFileIds(files.map((file: File) => file.fileId));
setChatHistory(history);
setFocusMode(data.chat.focusMode);
setIsMessagesLoaded(true);
};
const ChatWindow = ({ id }: { id?: string }) => {
const searchParams = useSearchParams();
const initialMessage = searchParams.get('q');
const [chatId, setChatId] = useState<string | undefined>(id);
const [newChatCreated, setNewChatCreated] = useState(false);
const [chatModelProvider, setChatModelProvider] = useState<ChatModelProvider>(
{
name: '',
provider: '',
},
);
const [embeddingModelProvider, setEmbeddingModelProvider] =
useState<EmbeddingModelProvider>({
name: '',
provider: '',
});
const [isConfigReady, setIsConfigReady] = useState(false);
const [hasError, setHasError] = useState(false);
const [isReady, setIsReady] = useState(false);
useEffect(() => {
checkConfig(
setChatModelProvider,
setEmbeddingModelProvider,
setIsConfigReady,
setHasError,
);
// eslint-disable-next-line react-hooks/exhaustive-deps
}, []);
const [loading, setLoading] = useState(false);
const [messageAppeared, setMessageAppeared] = useState(false);
const [chatHistory, setChatHistory] = useState<[string, string][]>([]);
const [messages, setMessages] = useState<Message[]>([]);
const [files, setFiles] = useState<File[]>([]);
const [fileIds, setFileIds] = useState<string[]>([]);
const [focusMode, setFocusMode] = useState('webSearch');
const [optimizationMode, setOptimizationMode] = useState('speed');
const [isMessagesLoaded, setIsMessagesLoaded] = useState(false);
const [notFound, setNotFound] = useState(false);
useEffect(() => {
if (
chatId &&
!newChatCreated &&
!isMessagesLoaded &&
messages.length === 0
) {
loadMessages(
chatId,
setMessages,
setIsMessagesLoaded,
setChatHistory,
setFocusMode,
setNotFound,
setFiles,
setFileIds,
);
} else if (!chatId) {
setNewChatCreated(true);
setIsMessagesLoaded(true);
setChatId(crypto.randomBytes(20).toString('hex'));
}
// eslint-disable-next-line react-hooks/exhaustive-deps
}, []);
const messagesRef = useRef<Message[]>([]);
useEffect(() => {
messagesRef.current = messages;
}, [messages]);
useEffect(() => {
if (isMessagesLoaded && isConfigReady) {
setIsReady(true);
console.debug(new Date(), 'app:ready');
} else {
setIsReady(false);
}
}, [isMessagesLoaded, isConfigReady]);
const sendMessage = async (message: string, messageId?: string) => {
if (loading) return;
if (!isConfigReady) {
toast.error('Cannot send message before the configuration is ready');
return;
}
setLoading(true);
setMessageAppeared(false);
let sources: Document[] | undefined = undefined;
let recievedMessage = '';
let added = false;
messageId = messageId ?? crypto.randomBytes(7).toString('hex');
setMessages((prevMessages) => [
...prevMessages,
{
content: message,
messageId: messageId,
chatId: chatId!,
role: 'user',
createdAt: new Date(),
},
]);
const messageHandler = async (data: any) => {
if (data.type === 'error') {
toast.error(data.data);
setLoading(false);
return;
}
if (data.type === 'sources') {
sources = data.data;
if (!added) {
setMessages((prevMessages) => [
...prevMessages,
{
content: '',
messageId: data.messageId,
chatId: chatId!,
role: 'assistant',
sources: sources,
createdAt: new Date(),
},
]);
added = true;
}
setMessageAppeared(true);
}
if (data.type === 'message') {
if (!added) {
setMessages((prevMessages) => [
...prevMessages,
{
content: data.data,
messageId: data.messageId,
chatId: chatId!,
role: 'assistant',
sources: sources,
createdAt: new Date(),
},
]);
added = true;
}
setMessages((prev) =>
prev.map((message) => {
if (message.messageId === data.messageId) {
return { ...message, content: message.content + data.data };
}
return message;
}),
);
recievedMessage += data.data;
setMessageAppeared(true);
}
if (data.type === 'messageEnd') {
setChatHistory((prevHistory) => [
...prevHistory,
['human', message],
['assistant', recievedMessage],
]);
setLoading(false);
const lastMsg = messagesRef.current[messagesRef.current.length - 1];
const autoImageSearch = localStorage.getItem('autoImageSearch');
const autoVideoSearch = localStorage.getItem('autoVideoSearch');
if (autoImageSearch === 'true') {
document
.getElementById(`search-images-${lastMsg.messageId}`)
?.click();
}
if (autoVideoSearch === 'true') {
document
.getElementById(`search-videos-${lastMsg.messageId}`)
?.click();
}
if (
lastMsg.role === 'assistant' &&
lastMsg.sources &&
lastMsg.sources.length > 0 &&
!lastMsg.suggestions
) {
const suggestions = await getSuggestions(messagesRef.current);
setMessages((prev) =>
prev.map((msg) => {
if (msg.messageId === lastMsg.messageId) {
return { ...msg, suggestions: suggestions };
}
return msg;
}),
);
}
}
};
const res = await fetch('/api/chat', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
content: message,
message: {
messageId: messageId,
chatId: chatId!,
content: message,
},
chatId: chatId!,
files: fileIds,
focusMode: focusMode,
optimizationMode: optimizationMode,
history: chatHistory,
chatModel: {
name: chatModelProvider.name,
provider: chatModelProvider.provider,
},
embeddingModel: {
name: embeddingModelProvider.name,
provider: embeddingModelProvider.provider,
},
systemInstructions: localStorage.getItem('systemInstructions'),
}),
});
if (!res.body) throw new Error('No response body');
const reader = res.body?.getReader();
const decoder = new TextDecoder('utf-8');
let partialChunk = '';
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...');
}
}
};
const rewrite = (messageId: string) => {
const index = messages.findIndex((msg) => msg.messageId === messageId);
if (index === -1) return;
const message = messages[index - 1];
setMessages((prev) => {
return [...prev.slice(0, messages.length > 2 ? index - 1 : 0)];
});
setChatHistory((prev) => {
return [...prev.slice(0, messages.length > 2 ? index - 1 : 0)];
});
sendMessage(message.content, message.messageId);
};
useEffect(() => {
if (isReady && initialMessage && isConfigReady) {
sendMessage(initialMessage);
}
// eslint-disable-next-line react-hooks/exhaustive-deps
}, [isConfigReady, isReady, initialMessage]);
if (hasError) {
return (
<div className="relative">
<div className="absolute w-full flex flex-row items-center justify-end mr-5 mt-5">
<Link href="/settings">
<Settings className="cursor-pointer lg:hidden" />
</Link>
</div>
<div className="flex flex-col items-center justify-center min-h-screen">
<p className="dark:text-white/70 text-black/70 text-sm">
Failed to connect to the server. Please try again later.
</p>
</div>
</div>
);
}
return isReady ? (
notFound ? (
<NextError statusCode={404} />
) : (
<div>
{messages.length > 0 ? (
<>
<Navbar chatId={chatId!} messages={messages} />
<Chat
loading={loading}
messages={messages}
sendMessage={sendMessage}
messageAppeared={messageAppeared}
rewrite={rewrite}
fileIds={fileIds}
setFileIds={setFileIds}
files={files}
setFiles={setFiles}
/>
</>
) : (
<EmptyChat
sendMessage={sendMessage}
focusMode={focusMode}
setFocusMode={setFocusMode}
optimizationMode={optimizationMode}
setOptimizationMode={setOptimizationMode}
fileIds={fileIds}
setFileIds={setFileIds}
files={files}
setFiles={setFiles}
/>
)}
</div>
)
) : (
<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>
);
};
export default ChatWindow;

View File

@@ -1,215 +0,0 @@
import { Clock, Edit, Share, Trash, FileText, FileDown } from 'lucide-react';
import { Message } from './ChatWindow';
import { useEffect, useState, Fragment } from 'react';
import { formatTimeDifference } from '@/lib/utils';
import DeleteChat from './DeleteChat';
import {
Popover,
PopoverButton,
PopoverPanel,
Transition,
} from '@headlessui/react';
import jsPDF from 'jspdf';
const downloadFile = (filename: string, content: string, type: string) => {
const blob = new Blob([content], { type });
const url = URL.createObjectURL(blob);
const a = document.createElement('a');
a.href = url;
a.download = filename;
document.body.appendChild(a);
a.click();
setTimeout(() => {
document.body.removeChild(a);
URL.revokeObjectURL(url);
}, 0);
};
const exportAsMarkdown = (messages: Message[], title: string) => {
const date = new Date(messages[0]?.createdAt || Date.now()).toLocaleString();
let md = `# 💬 Chat Export: ${title}\n\n`;
md += `*Exported on: ${date}*\n\n---\n`;
messages.forEach((msg, idx) => {
md += `\n---\n`;
md += `**${msg.role === 'user' ? '🧑 User' : '🤖 Assistant'}**
`;
md += `*${new Date(msg.createdAt).toLocaleString()}*\n\n`;
md += `> ${msg.content.replace(/\n/g, '\n> ')}\n`;
if (msg.sources && msg.sources.length > 0) {
md += `\n**Citations:**\n`;
msg.sources.forEach((src: any, i: number) => {
const url = src.metadata?.url || '';
md += `- [${i + 1}] [${url}](${url})\n`;
});
}
});
md += '\n---\n';
downloadFile(`${title || 'chat'}.md`, md, 'text/markdown');
};
const exportAsPDF = (messages: Message[], title: string) => {
const doc = new jsPDF();
const date = new Date(messages[0]?.createdAt || Date.now()).toLocaleString();
let y = 15;
const pageHeight = doc.internal.pageSize.height;
doc.setFontSize(18);
doc.text(`Chat Export: ${title}`, 10, y);
y += 8;
doc.setFontSize(11);
doc.setTextColor(100);
doc.text(`Exported on: ${date}`, 10, y);
y += 8;
doc.setDrawColor(200);
doc.line(10, y, 200, y);
y += 6;
doc.setTextColor(30);
messages.forEach((msg, idx) => {
if (y > pageHeight - 30) {
doc.addPage();
y = 15;
}
doc.setFont('helvetica', 'bold');
doc.text(`${msg.role === 'user' ? 'User' : 'Assistant'}`, 10, y);
doc.setFont('helvetica', 'normal');
doc.setFontSize(10);
doc.setTextColor(120);
doc.text(`${new Date(msg.createdAt).toLocaleString()}`, 40, y);
y += 6;
doc.setTextColor(30);
doc.setFontSize(12);
const lines = doc.splitTextToSize(msg.content, 180);
for (let i = 0; i < lines.length; i++) {
if (y > pageHeight - 20) {
doc.addPage();
y = 15;
}
doc.text(lines[i], 12, y);
y += 6;
}
if (msg.sources && msg.sources.length > 0) {
doc.setFontSize(11);
doc.setTextColor(80);
if (y > pageHeight - 20) {
doc.addPage();
y = 15;
}
doc.text('Citations:', 12, y);
y += 5;
msg.sources.forEach((src: any, i: number) => {
const url = src.metadata?.url || '';
if (y > pageHeight - 15) {
doc.addPage();
y = 15;
}
doc.text(`- [${i + 1}] ${url}`, 15, y);
y += 5;
});
doc.setTextColor(30);
}
y += 6;
doc.setDrawColor(230);
if (y > pageHeight - 10) {
doc.addPage();
y = 15;
}
doc.line(10, y, 200, y);
y += 4;
});
doc.save(`${title || 'chat'}.pdf`);
};
const Navbar = ({
chatId,
messages,
}: {
messages: Message[];
chatId: string;
}) => {
const [title, setTitle] = useState<string>('');
const [timeAgo, setTimeAgo] = useState<string>('');
useEffect(() => {
if (messages.length > 0) {
const newTitle =
messages[0].content.length > 20
? `${messages[0].content.substring(0, 20).trim()}...`
: messages[0].content;
setTitle(newTitle);
const newTimeAgo = formatTimeDifference(
new Date(),
messages[0].createdAt,
);
setTimeAgo(newTimeAgo);
}
}, [messages]);
useEffect(() => {
const intervalId = setInterval(() => {
if (messages.length > 0) {
const newTimeAgo = formatTimeDifference(
new Date(),
messages[0].createdAt,
);
setTimeAgo(newTimeAgo);
}
}, 1000);
return () => clearInterval(intervalId);
// eslint-disable-next-line react-hooks/exhaustive-deps
}, []);
return (
<div className="fixed z-40 top-0 left-0 right-0 px-4 lg:pl-[104px] lg:pr-6 lg:px-8 flex flex-row items-center justify-between w-full py-4 text-sm text-black dark:text-white/70 border-b bg-light-primary dark:bg-dark-primary border-light-100 dark:border-dark-200">
<a
href="/"
className="active:scale-95 transition duration-100 cursor-pointer lg:hidden"
>
<Edit size={17} />
</a>
<div className="hidden lg:flex flex-row items-center justify-center space-x-2">
<Clock size={17} />
<p className="text-xs">{timeAgo} ago</p>
</div>
<p className="hidden lg:flex">{title}</p>
<div className="flex flex-row items-center space-x-4">
<Popover className="relative">
<PopoverButton className="active:scale-95 transition duration-100 cursor-pointer p-2 rounded-full hover:bg-light-secondary dark:hover:bg-dark-secondary">
<Share size={17} />
</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-75"
leaveFrom="opacity-100 translate-y-0"
leaveTo="opacity-0 translate-y-1"
>
<PopoverPanel className="absolute right-0 mt-2 w-64 rounded-xl shadow-xl bg-light-primary dark:bg-dark-primary border border-light-200 dark:border-dark-200 z-50">
<div className="flex flex-col py-3 px-3 gap-2">
<button
className="flex items-center gap-2 px-4 py-2 text-left hover:bg-light-secondary dark:hover:bg-dark-secondary transition-colors text-black dark:text-white rounded-lg font-medium"
onClick={() => exportAsMarkdown(messages, title || '')}
>
<FileText size={17} className="text-[#24A0ED]" />
Export as Markdown
</button>
<button
className="flex items-center gap-2 px-4 py-2 text-left hover:bg-light-secondary dark:hover:bg-dark-secondary transition-colors text-black dark:text-white rounded-lg font-medium"
onClick={() => exportAsPDF(messages, title || '')}
>
<FileDown size={17} className="text-[#24A0ED]" />
Export as PDF
</button>
</div>
</PopoverPanel>
</Transition>
</Popover>
<DeleteChat redirect chatId={chatId} chats={[]} setChats={() => {}} />
</div>
</div>
);
};
export default Navbar;

View File

@@ -1,43 +0,0 @@
'use client';
import { useState } from 'react';
import { cn } from '@/lib/utils';
import { ChevronDown, ChevronUp, BrainCircuit } from 'lucide-react';
interface ThinkBoxProps {
content: string;
}
const ThinkBox = ({ content }: ThinkBoxProps) => {
const [isExpanded, setIsExpanded] = useState(false);
return (
<div className="my-4 bg-light-secondary/50 dark:bg-dark-secondary/50 rounded-xl border border-light-200 dark:border-dark-200 overflow-hidden">
<button
onClick={() => setIsExpanded(!isExpanded)}
className="w-full flex items-center justify-between px-4 py-1 text-black/90 dark:text-white/90 hover:bg-light-200 dark:hover:bg-dark-200 transition duration-200"
>
<div className="flex items-center space-x-2">
<BrainCircuit
size={20}
className="text-[#9C27B0] dark:text-[#CE93D8]"
/>
<p className="font-medium text-sm">Thinking Process</p>
</div>
{isExpanded ? (
<ChevronUp size={18} className="text-black/70 dark:text-white/70" />
) : (
<ChevronDown size={18} className="text-black/70 dark:text-white/70" />
)}
</button>
{isExpanded && (
<div className="px-4 py-3 text-black/80 dark:text-white/80 text-sm border-t border-light-200 dark:border-dark-200 bg-light-100/50 dark:bg-dark-100/50 whitespace-pre-wrap">
{content}
</div>
)}
</div>
);
};
export default ThinkBox;

View File

@@ -1,28 +0,0 @@
import { cn } from '@/lib/utils';
import { SelectHTMLAttributes } from 'react';
interface SelectProps extends SelectHTMLAttributes<HTMLSelectElement> {
options: { value: string; label: string; disabled?: boolean }[];
}
export const Select = ({ className, options, ...restProps }: SelectProps) => {
return (
<select
{...restProps}
className={cn(
'bg-light-secondary dark:bg-dark-secondary px-3 py-2 flex items-center overflow-hidden border border-light-200 dark:border-dark-200 dark:text-white rounded-lg text-sm',
className,
)}
>
{options.map(({ label, value, disabled }) => {
return (
<option key={value} value={value} disabled={disabled}>
{label}
</option>
);
})}
</select>
);
};
export default Select;

92
src/config.ts Normal file
View File

@@ -0,0 +1,92 @@
import fs from 'fs';
import path from 'path';
import toml from '@iarna/toml';
const configFileName = 'config.toml';
interface Config {
GENERAL: {
PORT: number;
SIMILARITY_MEASURE: string;
KEEP_ALIVE: string;
};
API_KEYS: {
OPENAI: string;
GROQ: string;
ANTHROPIC: string;
GEMINI: string;
};
API_ENDPOINTS: {
SEARXNG: string;
OLLAMA: string;
};
}
type RecursivePartial<T> = {
[P in keyof T]?: RecursivePartial<T[P]>;
};
const loadConfig = () =>
toml.parse(
fs.readFileSync(path.join(__dirname, `../${configFileName}`), 'utf-8'),
) as any as Config;
export const getPort = () => loadConfig().GENERAL.PORT;
export const getSimilarityMeasure = () =>
loadConfig().GENERAL.SIMILARITY_MEASURE;
export const getKeepAlive = () => loadConfig().GENERAL.KEEP_ALIVE;
export const getOpenaiApiKey = () => loadConfig().API_KEYS.OPENAI;
export const getGroqApiKey = () => loadConfig().API_KEYS.GROQ;
export const getAnthropicApiKey = () => loadConfig().API_KEYS.ANTHROPIC;
export const getGeminiApiKey = () => loadConfig().API_KEYS.GEMINI;
export const getSearxngApiEndpoint = () =>
process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;
export const getOllamaApiEndpoint = () => loadConfig().API_ENDPOINTS.OLLAMA;
export const updateConfig = (config: RecursivePartial<Config>) => {
const currentConfig = loadConfig();
for (const key in currentConfig) {
if (!config[key]) config[key] = {};
if (typeof currentConfig[key] === 'object' && currentConfig[key] !== null) {
for (const nestedKey in currentConfig[key]) {
if (
!config[key][nestedKey] &&
currentConfig[key][nestedKey] &&
config[key][nestedKey] !== ''
) {
config[key][nestedKey] = currentConfig[key][nestedKey];
}
}
} else if (currentConfig[key] && config[key] !== '') {
config[key] = currentConfig[key];
}
}
fs.writeFileSync(
path.join(__dirname, `../${configFileName}`),
toml.stringify(config),
);
};
export const config = {
ollama: {
url: process.env.OLLAMA_URL || 'http://localhost:11434',
model: process.env.OLLAMA_MODEL || 'mistral',
options: {
temperature: 0.1,
top_p: 0.9,
timeout: 30000 // 30 seconds timeout
}
},
// ... other config
};

40
src/config/env.ts Normal file
View File

@@ -0,0 +1,40 @@
import dotenv from 'dotenv';
// Load environment variables
dotenv.config();
// Environment configuration
const env = {
// Supabase Configuration
SUPABASE_URL: process.env.SUPABASE_URL || '',
SUPABASE_KEY: process.env.SUPABASE_KEY || '',
// Server Configuration
PORT: parseInt(process.env.PORT || '3001', 10),
NODE_ENV: process.env.NODE_ENV || 'development',
// Search Configuration
MAX_RESULTS_PER_QUERY: parseInt(process.env.MAX_RESULTS_PER_QUERY || '50', 10),
CACHE_DURATION_HOURS: parseInt(process.env.CACHE_DURATION_HOURS || '24', 10),
CACHE_DURATION_DAYS: parseInt(process.env.CACHE_DURATION_DAYS || '7', 10),
// SearxNG Configuration
SEARXNG_URL: process.env.SEARXNG_URL || 'http://localhost:4000',
// Ollama Configuration
OLLAMA_URL: process.env.OLLAMA_URL || 'http://localhost:11434',
OLLAMA_MODEL: process.env.OLLAMA_MODEL || 'deepseek-coder:6.7b',
// Hugging Face Configuration
HUGGING_FACE_API_KEY: process.env.HUGGING_FACE_API_KEY || ''
};
// Validate required environment variables
const requiredEnvVars = ['SUPABASE_URL', 'SUPABASE_KEY', 'SEARXNG_URL'];
for (const envVar of requiredEnvVars) {
if (!env[envVar as keyof typeof env]) {
throw new Error(`Missing required environment variable: ${envVar}`);
}
}
export { env };

77
src/config/index.ts Normal file
View File

@@ -0,0 +1,77 @@
import dotenv from 'dotenv';
import path from 'path';
// Load .env file
dotenv.config({ path: path.resolve(__dirname, '../../.env') });
export interface Config {
supabase: {
url: string;
anonKey: string;
};
server: {
port: number;
nodeEnv: string;
};
search: {
maxResultsPerQuery: number;
cacheDurationHours: number;
searxngUrl?: string;
};
rateLimit: {
windowMs: number;
maxRequests: number;
};
security: {
corsOrigin: string;
jwtSecret: string;
};
proxy?: {
http?: string;
https?: string;
};
logging: {
level: string;
};
}
const config: Config = {
supabase: {
url: process.env.SUPABASE_URL || '',
anonKey: process.env.SUPABASE_ANON_KEY || '',
},
server: {
port: parseInt(process.env.PORT || '3000', 10),
nodeEnv: process.env.NODE_ENV || 'development',
},
search: {
maxResultsPerQuery: parseInt(process.env.MAX_RESULTS_PER_QUERY || '20', 10),
cacheDurationHours: parseInt(process.env.CACHE_DURATION_HOURS || '24', 10),
searxngUrl: process.env.SEARXNG_URL
},
rateLimit: {
windowMs: parseInt(process.env.RATE_LIMIT_WINDOW_MS || '900000', 10),
maxRequests: parseInt(process.env.RATE_LIMIT_MAX_REQUESTS || '100', 10),
},
security: {
corsOrigin: process.env.CORS_ORIGIN || 'http://localhost:3000',
jwtSecret: process.env.JWT_SECRET || 'your_jwt_secret_key',
},
logging: {
level: process.env.LOG_LEVEL || 'info',
},
};
// Validate required configuration
const validateConfig = () => {
if (!config.supabase.url) {
throw new Error('SUPABASE_URL is required');
}
if (!config.supabase.anonKey) {
throw new Error('SUPABASE_ANON_KEY is required');
}
};
validateConfig();
export { config };

View File

@@ -1,10 +1,8 @@
import { drizzle } from 'drizzle-orm/better-sqlite3';
import Database from 'better-sqlite3';
import * as schema from './schema';
import path from 'path';
const DATA_DIR = process.env.DATA_DIR || process.cwd();
const sqlite = new Database(path.join(DATA_DIR, './data/db.sqlite'));
const sqlite = new Database('data/db.sqlite');
const db = drizzle(sqlite, {
schema: schema,
});

24
src/index.ts Normal file
View File

@@ -0,0 +1,24 @@
import './config/env'; // Load environment variables first
import { startServer } from './server';
import { isPortAvailable } from './utils/portCheck';
import { testConnection } from './lib/supabase';
const PORT = process.env.PORT || 3001;
const init = async () => {
if (!await isPortAvailable(PORT)) {
console.error(`Port ${PORT} is in use. Please try a different port or free up the current one.`);
process.exit(1);
}
// Test Supabase connection
const isConnected = await testConnection();
if (!isConnected) {
console.error('Failed to connect to Supabase. Please check your configuration.');
process.exit(1);
}
startServer();
};
init().catch(console.error);

116
src/lib/categories.ts Normal file
View File

@@ -0,0 +1,116 @@
export interface Category {
id: string;
name: string;
icon: string;
subcategories: SubCategory[];
}
export interface SubCategory {
id: string;
name: string;
}
export const categories: Category[] = [
{
id: 'real-estate-pros',
name: 'Real Estate Professionals',
icon: '🏢',
subcategories: [
{ id: 'wholesalers', name: 'Real Estate Wholesalers' },
{ id: 'agents', name: 'Real Estate Agents' },
{ id: 'attorneys', name: 'Real Estate Attorneys' },
{ id: 'scouts', name: 'Property Scouts' },
{ id: 'brokers', name: 'Real Estate Brokers' },
{ id: 'consultants', name: 'Real Estate Consultants' }
]
},
{
id: 'legal-title',
name: 'Legal & Title Services',
icon: '⚖️',
subcategories: [
{ id: 'title-companies', name: 'Title Companies' },
{ id: 'closing-attorneys', name: 'Closing Attorneys' },
{ id: 'zoning-consultants', name: 'Zoning Consultants' },
{ id: 'probate-specialists', name: 'Probate Specialists' },
{ id: 'eviction-specialists', name: 'Eviction Specialists' }
]
},
{
id: 'financial',
name: 'Financial Services',
icon: '💰',
subcategories: [
{ id: 'hard-money', name: 'Hard Money Lenders' },
{ id: 'private-equity', name: 'Private Equity Investors' },
{ id: 'mortgage-brokers', name: 'Mortgage Brokers' },
{ id: 'tax-advisors', name: 'Tax Advisors' },
{ id: 'appraisers', name: 'Appraisers' }
]
},
{
id: 'contractors',
name: 'Specialist Contractors',
icon: '🔨',
subcategories: [
{ id: 'general', name: 'General Contractors' },
{ id: 'plumbers', name: 'Plumbers' },
{ id: 'electricians', name: 'Electricians' },
{ id: 'hvac', name: 'HVAC Technicians' },
{ id: 'roofers', name: 'Roofers' },
{ id: 'foundation', name: 'Foundation Specialists' },
{ id: 'asbestos', name: 'Asbestos Removal' },
{ id: 'mold', name: 'Mold Remediation' }
]
},
{
id: 'property-services',
name: 'Property Services',
icon: '🏠',
subcategories: [
{ id: 'surveyors', name: 'Surveyors' },
{ id: 'inspectors', name: 'Inspectors' },
{ id: 'property-managers', name: 'Property Managers' },
{ id: 'environmental', name: 'Environmental Consultants' },
{ id: 'junk-removal', name: 'Junk Removal Services' },
{ id: 'cleaning', name: 'Property Cleaning' }
]
},
{
id: 'marketing',
name: 'Marketing & Lead Gen',
icon: '📢',
subcategories: [
{ id: 'direct-mail', name: 'Direct Mail Services' },
{ id: 'social-media', name: 'Social Media Marketing' },
{ id: 'seo', name: 'SEO Specialists' },
{ id: 'ppc', name: 'PPC Advertising' },
{ id: 'lead-gen', name: 'Lead Generation' },
{ id: 'skip-tracing', name: 'Skip Tracing Services' }
]
},
{
id: 'data-tech',
name: 'Data & Technology',
icon: '💻',
subcategories: [
{ id: 'data-providers', name: 'Property Data Providers' },
{ id: 'crm', name: 'CRM Systems' },
{ id: 'valuation', name: 'Valuation Tools' },
{ id: 'virtual-tours', name: 'Virtual Tour Services' },
{ id: 'automation', name: 'Automation Tools' }
]
},
{
id: 'specialty',
name: 'Specialty Services',
icon: '🎯',
subcategories: [
{ id: 'auction', name: 'Auction Companies' },
{ id: 'relocation', name: 'Relocation Services' },
{ id: 'staging', name: 'Home Staging' },
{ id: 'photography', name: 'Real Estate Photography' },
{ id: 'virtual-assistant', name: 'Virtual Assistants' }
]
}
];

View File

@@ -1,141 +0,0 @@
import toml from '@iarna/toml';
// Use dynamic imports for Node.js modules to prevent client-side errors
let fs: any;
let path: any;
if (typeof window === 'undefined') {
// We're on the server
fs = require('fs');
path = require('path');
}
const configFileName = 'config.toml';
interface Config {
GENERAL: {
SIMILARITY_MEASURE: string;
KEEP_ALIVE: string;
};
MODELS: {
OPENAI: {
API_KEY: string;
};
GROQ: {
API_KEY: string;
};
ANTHROPIC: {
API_KEY: string;
};
GEMINI: {
API_KEY: string;
};
OLLAMA: {
API_URL: string;
};
DEEPSEEK: {
API_KEY: string;
};
LM_STUDIO: {
API_URL: string;
};
CUSTOM_OPENAI: {
API_URL: string;
API_KEY: string;
MODEL_NAME: string;
};
};
API_ENDPOINTS: {
SEARXNG: string;
};
}
type RecursivePartial<T> = {
[P in keyof T]?: RecursivePartial<T[P]>;
};
const loadConfig = () => {
// Server-side only
if (typeof window === 'undefined') {
return toml.parse(
fs.readFileSync(path.join(process.cwd(), `${configFileName}`), 'utf-8'),
) as any as Config;
}
// Client-side fallback - settings will be loaded via API
return {} as Config;
};
export const getSimilarityMeasure = () =>
loadConfig().GENERAL.SIMILARITY_MEASURE;
export const getKeepAlive = () => loadConfig().GENERAL.KEEP_ALIVE;
export const getOpenaiApiKey = () => loadConfig().MODELS.OPENAI.API_KEY;
export const getGroqApiKey = () => loadConfig().MODELS.GROQ.API_KEY;
export const getAnthropicApiKey = () => loadConfig().MODELS.ANTHROPIC.API_KEY;
export const getGeminiApiKey = () => loadConfig().MODELS.GEMINI.API_KEY;
export const getSearxngApiEndpoint = () =>
process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;
export const getOllamaApiEndpoint = () => loadConfig().MODELS.OLLAMA.API_URL;
export const getDeepseekApiKey = () => loadConfig().MODELS.DEEPSEEK.API_KEY;
export const getCustomOpenaiApiKey = () =>
loadConfig().MODELS.CUSTOM_OPENAI.API_KEY;
export const getCustomOpenaiApiUrl = () =>
loadConfig().MODELS.CUSTOM_OPENAI.API_URL;
export const getCustomOpenaiModelName = () =>
loadConfig().MODELS.CUSTOM_OPENAI.MODEL_NAME;
export const getLMStudioApiEndpoint = () =>
loadConfig().MODELS.LM_STUDIO.API_URL;
const mergeConfigs = (current: any, update: any): any => {
if (update === null || update === undefined) {
return current;
}
if (typeof current !== 'object' || current === null) {
return update;
}
const result = { ...current };
for (const key in update) {
if (Object.prototype.hasOwnProperty.call(update, key)) {
const updateValue = update[key];
if (
typeof updateValue === 'object' &&
updateValue !== null &&
typeof result[key] === 'object' &&
result[key] !== null
) {
result[key] = mergeConfigs(result[key], updateValue);
} else if (updateValue !== undefined) {
result[key] = updateValue;
}
}
}
return result;
};
export const updateConfig = (config: RecursivePartial<Config>) => {
// Server-side only
if (typeof window === 'undefined') {
const currentConfig = loadConfig();
const mergedConfig = mergeConfigs(currentConfig, config);
fs.writeFileSync(
path.join(path.join(process.cwd(), `${configFileName}`)),
toml.stringify(mergedConfig),
);
}
};

View File

@@ -1,5 +0,0 @@
import db from './';
import { migrate } from 'drizzle-orm/better-sqlite3/migrator';
import path from 'path';
migrate(db, { migrationsFolder: path.join(process.cwd(), 'drizzle') });

51
src/lib/db/optOutDb.ts Normal file
View File

@@ -0,0 +1,51 @@
import { Database } from 'better-sqlite3';
import path from 'path';
interface OptOutEntry {
domain: string;
email: string;
reason?: string;
timestamp: Date;
}
export class OptOutDatabase {
private db: Database;
constructor() {
this.db = new Database(path.join(__dirname, '../../../data/optout.db'));
this.initializeDatabase();
}
private initializeDatabase() {
this.db.exec(`
CREATE TABLE IF NOT EXISTS opt_outs (
domain TEXT PRIMARY KEY,
email TEXT NOT NULL,
reason TEXT,
timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
);
CREATE INDEX IF NOT EXISTS idx_domain ON opt_outs(domain);
`);
}
async addOptOut(entry: OptOutEntry): Promise<void> {
const stmt = this.db.prepare(
'INSERT OR REPLACE INTO opt_outs (domain, email, reason, timestamp) VALUES (?, ?, ?, ?)'
);
stmt.run(entry.domain, entry.email, entry.reason, entry.timestamp.toISOString());
}
isOptedOut(domain: string): boolean {
const stmt = this.db.prepare('SELECT 1 FROM opt_outs WHERE domain = ?');
return stmt.get(domain) !== undefined;
}
removeOptOut(domain: string): void {
const stmt = this.db.prepare('DELETE FROM opt_outs WHERE domain = ?');
stmt.run(domain);
}
getOptOutList(): OptOutEntry[] {
return this.db.prepare('SELECT * FROM opt_outs').all();
}
}

74
src/lib/db/supabase.ts Normal file
View File

@@ -0,0 +1,74 @@
import { createClient } from '@supabase/supabase-js';
import { BusinessData } from '../searxng';
import { env } from '../../config/env';
// Create the Supabase client with validated environment variables
export const supabase = createClient(
env.supabase.url,
env.supabase.anonKey,
{
auth: {
persistSession: false // Since this is a server environment
}
}
);
// Define the cache record type
export interface CacheRecord {
id: string;
query: string;
results: BusinessData[];
location: string;
category: string;
created_at: string;
updated_at: string;
expires_at: string;
}
// Export database helper functions
export async function getCacheEntry(
category: string,
location: string
): Promise<CacheRecord | null> {
const { data, error } = await supabase
.from('search_cache')
.select('*')
.eq('category', category.toLowerCase())
.eq('location', location.toLowerCase())
.gt('expires_at', new Date().toISOString())
.order('created_at', { ascending: false })
.limit(1)
.single();
if (error) {
console.error('Cache lookup failed:', error);
return null;
}
return data;
}
export async function saveCacheEntry(
category: string,
location: string,
results: BusinessData[],
expiresInDays: number = 7
): Promise<void> {
const expiresAt = new Date();
expiresAt.setDate(expiresAt.getDate() + expiresInDays);
const { error } = await supabase
.from('search_cache')
.insert({
query: `${category} in ${location}`,
category: category.toLowerCase(),
location: location.toLowerCase(),
results,
expires_at: expiresAt.toISOString()
});
if (error) {
console.error('Failed to save cache entry:', error);
throw error;
}
}

195
src/lib/emailScraper.ts Normal file
View File

@@ -0,0 +1,195 @@
import axios from 'axios';
import * as cheerio from 'cheerio';
import { Cache } from './utils/cache';
import { RateLimiter } from './utils/rateLimiter';
import robotsParser from 'robots-parser';
interface ScrapingResult {
emails: string[];
phones: string[];
addresses: string[];
socialLinks: string[];
source: string;
timestamp: Date;
attribution: string;
}
export class EmailScraper {
private cache: Cache<ScrapingResult>;
private rateLimiter: RateLimiter;
private robotsCache = new Map<string, any>();
constructor(private options = {
timeout: 5000,
cacheTTL: 60,
rateLimit: { windowMs: 60000, maxRequests: 10 }, // More conservative rate limiting
userAgent: 'BizSearch/1.0 (+https://your-domain.com/about) - Business Directory Service'
}) {
this.cache = new Cache<ScrapingResult>(options.cacheTTL);
this.rateLimiter = new RateLimiter(options.rateLimit.windowMs, options.rateLimit.maxRequests);
}
private async checkRobotsPermission(url: string): Promise<boolean> {
try {
const { protocol, host } = new URL(url);
const robotsUrl = `${protocol}//${host}/robots.txt`;
let parser = this.robotsCache.get(host);
if (!parser) {
const response = await axios.get(robotsUrl);
parser = robotsParser(robotsUrl, response.data);
this.robotsCache.set(host, parser);
}
return parser.isAllowed(url, this.options.userAgent);
} catch (error) {
console.warn(`Could not check robots.txt for ${url}:`, error);
return true; // Assume allowed if robots.txt is unavailable
}
}
async scrapeEmails(url: string): Promise<ScrapingResult> {
// Check cache first
const cached = this.cache.get(url);
if (cached) return cached;
// Check robots.txt
const allowed = await this.checkRobotsPermission(url);
if (!allowed) {
console.log(`Respecting robots.txt disallow for ${url}`);
return {
emails: [],
phones: [],
addresses: [],
socialLinks: [],
source: url,
timestamp: new Date(),
attribution: 'Restricted by robots.txt'
};
}
// Wait for rate limiting slot
await this.rateLimiter.waitForSlot();
try {
const response = await axios.get(url, {
timeout: this.options.timeout,
headers: {
'User-Agent': this.options.userAgent,
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
}
});
// Check for noindex meta tag
const $ = cheerio.load(response.data);
if ($('meta[name="robots"][content*="noindex"]').length > 0) {
return {
emails: [],
phones: [],
addresses: [],
socialLinks: [],
source: url,
timestamp: new Date(),
attribution: 'Respecting noindex directive'
};
}
// Only extract contact information from public contact pages or structured data
const isContactPage = /contact|about/i.test(url) ||
$('h1, h2').text().toLowerCase().includes('contact');
const result = {
emails: new Set<string>(),
phones: new Set<string>(),
addresses: new Set<string>(),
socialLinks: new Set<string>(),
source: url,
timestamp: new Date(),
attribution: `Data from public business listing at ${new URL(url).hostname}`
};
// Extract from structured data (Schema.org)
$('script[type="application/ld+json"]').each((_, element) => {
try {
const data = JSON.parse($(element).html() || '{}');
if (data['@type'] === 'LocalBusiness' || data['@type'] === 'Organization') {
if (data.email) result.emails.add(data.email.toLowerCase());
if (data.telephone) result.phones.add(this.formatPhoneNumber(data.telephone));
if (data.address) {
const fullAddress = this.formatAddress(data.address);
if (fullAddress) result.addresses.add(fullAddress);
}
}
} catch (e) {
console.error('Error parsing JSON-LD:', e);
}
});
// Only scrape additional info if it's a contact page
if (isContactPage) {
// Extract clearly marked contact information
$('[itemprop="email"], .contact-email, .email').each((_, element) => {
const email = $(element).text().trim();
if (this.isValidEmail(email)) {
result.emails.add(email.toLowerCase());
}
});
$('[itemprop="telephone"], .phone, .contact-phone').each((_, element) => {
const phone = $(element).text().trim();
const formatted = this.formatPhoneNumber(phone);
if (formatted) result.phones.add(formatted);
});
}
const finalResult = {
...result,
emails: Array.from(result.emails),
phones: Array.from(result.phones),
addresses: Array.from(result.addresses),
socialLinks: Array.from(result.socialLinks)
};
this.cache.set(url, finalResult);
return finalResult;
} catch (error) {
console.error(`Failed to scrape ${url}:`, error);
return {
emails: [],
phones: [],
addresses: [],
socialLinks: [],
source: url,
timestamp: new Date(),
attribution: 'Error accessing page'
};
}
}
private isValidEmail(email: string): boolean {
return /^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$/.test(email);
}
private formatPhoneNumber(phone: string): string {
const digits = phone.replace(/\D/g, '');
if (digits.length === 10) {
return `(${digits.slice(0,3)}) ${digits.slice(3,6)}-${digits.slice(6)}`;
}
return phone;
}
private formatAddress(address: any): string | null {
if (typeof address === 'string') return address;
if (typeof address === 'object') {
const parts = [
address.streetAddress,
address.addressLocality,
address.addressRegion,
address.postalCode
].filter(Boolean);
if (parts.length > 0) return parts.join(', ');
}
return null;
}
}

View File

@@ -28,7 +28,7 @@ export class HuggingFaceTransformersEmbeddings
timeout?: number;
private pipelinePromise: Promise<any> | undefined;
private pipelinePromise: Promise<any>;
constructor(fields?: Partial<HuggingFaceTransformersEmbeddingsParams>) {
super(fields ?? {});

View File

@@ -9,7 +9,7 @@ class LineOutputParser extends BaseOutputParser<string> {
constructor(args?: LineOutputParserArgs) {
super();
this.key = args?.key ?? this.key;
this.key = args.key ?? this.key;
}
static lc_name() {

View File

@@ -9,7 +9,7 @@ class LineListOutputParser extends BaseOutputParser<string[]> {
constructor(args?: LineListOutputParserArgs) {
super();
this.key = args?.key ?? this.key;
this.key = args.key ?? this.key;
}
static lc_name() {

View File

@@ -1,43 +1,6 @@
import { ChatAnthropic } from '@langchain/anthropic';
import { ChatModel } from '.';
import { getAnthropicApiKey } from '../config';
export const PROVIDER_INFO = {
key: 'anthropic',
displayName: 'Anthropic',
};
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
const anthropicChatModels: Record<string, string>[] = [
{
displayName: 'Claude 3.7 Sonnet',
key: 'claude-3-7-sonnet-20250219',
},
{
displayName: 'Claude 3.5 Haiku',
key: 'claude-3-5-haiku-20241022',
},
{
displayName: 'Claude 3.5 Sonnet v2',
key: 'claude-3-5-sonnet-20241022',
},
{
displayName: 'Claude 3.5 Sonnet',
key: 'claude-3-5-sonnet-20240620',
},
{
displayName: 'Claude 3 Opus',
key: 'claude-3-opus-20240229',
},
{
displayName: 'Claude 3 Sonnet',
key: 'claude-3-sonnet-20240229',
},
{
displayName: 'Claude 3 Haiku',
key: 'claude-3-haiku-20240307',
},
];
import { getAnthropicApiKey } from '../../config';
import logger from '../../utils/logger';
export const loadAnthropicChatModels = async () => {
const anthropicApiKey = getAnthropicApiKey();
@@ -45,22 +8,52 @@ export const loadAnthropicChatModels = async () => {
if (!anthropicApiKey) return {};
try {
const chatModels: Record<string, ChatModel> = {};
anthropicChatModels.forEach((model) => {
chatModels[model.key] = {
displayName: model.displayName,
const chatModels = {
'claude-3-5-sonnet-20241022': {
displayName: 'Claude 3.5 Sonnet',
model: new ChatAnthropic({
apiKey: anthropicApiKey,
modelName: model.key,
temperature: 0.7,
}) as unknown as BaseChatModel,
};
});
anthropicApiKey: anthropicApiKey,
model: 'claude-3-5-sonnet-20241022',
}),
},
'claude-3-5-haiku-20241022': {
displayName: 'Claude 3.5 Haiku',
model: new ChatAnthropic({
temperature: 0.7,
anthropicApiKey: anthropicApiKey,
model: 'claude-3-5-haiku-20241022',
}),
},
'claude-3-opus-20240229': {
displayName: 'Claude 3 Opus',
model: new ChatAnthropic({
temperature: 0.7,
anthropicApiKey: anthropicApiKey,
model: 'claude-3-opus-20240229',
}),
},
'claude-3-sonnet-20240229': {
displayName: 'Claude 3 Sonnet',
model: new ChatAnthropic({
temperature: 0.7,
anthropicApiKey: anthropicApiKey,
model: 'claude-3-sonnet-20240229',
}),
},
'claude-3-haiku-20240307': {
displayName: 'Claude 3 Haiku',
model: new ChatAnthropic({
temperature: 0.7,
anthropicApiKey: anthropicApiKey,
model: 'claude-3-haiku-20240307',
}),
},
};
return chatModels;
} catch (err) {
console.error(`Error loading Anthropic models: ${err}`);
logger.error(`Error loading Anthropic models: ${err}`);
return {};
}
};

View File

@@ -0,0 +1,19 @@
import { Business, SearchParams } from '../../../types/business';
import { WebScraperProvider } from './webScraper';
export class BusinessProvider {
private scraper: WebScraperProvider;
constructor() {
this.scraper = new WebScraperProvider();
}
async search(params: SearchParams): Promise<Business[]> {
return this.scraper.search(params);
}
async getDetails(businessId: string): Promise<Business | null> {
// Implement detailed business lookup using stored data or additional scraping
return null;
}
}

View File

@@ -0,0 +1,111 @@
import { Business, SearchParams } from '../../../types/business';
import { searchWeb } from '../search'; // This is Perplexica's existing search function
import { parseHTML } from '../utils/parser';
export class WebScraperProvider {
async search(params: SearchParams): Promise<Business[]> {
const searchQueries = this.generateQueries(params);
const businesses: Business[] = [];
for (const query of searchQueries) {
// Use Perplexica's existing search functionality
const results = await searchWeb(query, {
maxResults: 20,
type: 'general' // or 'news' depending on what we want
});
for (const result of results) {
try {
const html = await fetch(result.url).then(res => res.text());
const businessData = await this.extractBusinessData(html, result.url);
if (businessData) {
businesses.push(businessData);
}
} catch (error) {
console.error(`Failed to extract data from ${result.url}:`, error);
}
}
}
return this.deduplicateBusinesses(businesses);
}
private generateQueries(params: SearchParams): string[] {
const { location, category } = params;
return [
`${category} in ${location}`,
`${category} business ${location}`,
`best ${category} near ${location}`,
`${category} services ${location} reviews`
];
}
private async extractBusinessData(html: string, sourceUrl: string): Promise<Business | null> {
const $ = parseHTML(html);
// Different extraction logic based on source
if (sourceUrl.includes('yelp.com')) {
return this.extractYelpData($);
} else if (sourceUrl.includes('yellowpages.com')) {
return this.extractYellowPagesData($);
}
// ... other source-specific extractors
return null;
}
private extractYelpData($: any): Business | null {
try {
return {
id: crypto.randomUUID(),
name: $('.business-name').text().trim(),
phone: $('.phone-number').text().trim(),
address: $('.address').text().trim(),
city: $('.city').text().trim(),
state: $('.state').text().trim(),
zip: $('.zip').text().trim(),
category: $('.category-str-list').text().split(',').map(s => s.trim()),
rating: parseFloat($('.rating').text()),
reviewCount: parseInt($('.review-count').text()),
services: $('.services-list').text().split(',').map(s => s.trim()),
hours: this.extractHours($),
website: $('.website-link').attr('href'),
verified: false,
lastUpdated: new Date()
};
} catch (error) {
return null;
}
}
private deduplicateBusinesses(businesses: Business[]): Business[] {
// Group by phone number and address to identify duplicates
const uniqueBusinesses = new Map<string, Business>();
for (const business of businesses) {
const key = `${business.phone}-${business.address}`.toLowerCase();
if (!uniqueBusinesses.has(key)) {
uniqueBusinesses.set(key, business);
} else {
// Merge data if we have additional information
const existing = uniqueBusinesses.get(key)!;
uniqueBusinesses.set(key, this.mergeBusinessData(existing, business));
}
}
return Array.from(uniqueBusinesses.values());
}
private mergeBusinessData(existing: Business, newData: Business): Business {
return {
...existing,
services: [...new Set([...existing.services, ...newData.services])],
rating: (existing.rating + newData.rating) / 2,
reviewCount: existing.reviewCount + newData.reviewCount,
// Keep the most complete data for other fields
website: existing.website || newData.website,
email: existing.email || newData.email,
hours: existing.hours || newData.hours
};
}
}

View File

@@ -1,49 +0,0 @@
import { ChatOpenAI } from '@langchain/openai';
import { getDeepseekApiKey } from '../config';
import { ChatModel } from '.';
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
export const PROVIDER_INFO = {
key: 'deepseek',
displayName: 'Deepseek AI',
};
const deepseekChatModels: Record<string, string>[] = [
{
displayName: 'Deepseek Chat (Deepseek V3)',
key: 'deepseek-chat',
},
{
displayName: 'Deepseek Reasoner (Deepseek R1)',
key: 'deepseek-reasoner',
},
];
export const loadDeepseekChatModels = async () => {
const deepseekApiKey = getDeepseekApiKey();
if (!deepseekApiKey) return {};
try {
const chatModels: Record<string, ChatModel> = {};
deepseekChatModels.forEach((model) => {
chatModels[model.key] = {
displayName: model.displayName,
model: new ChatOpenAI({
openAIApiKey: deepseekApiKey,
modelName: model.key,
temperature: 0.7,
configuration: {
baseURL: 'https://api.deepseek.com',
},
}) as unknown as BaseChatModel,
};
});
return chatModels;
} catch (err) {
console.error(`Error loading Deepseek models: ${err}`);
return {};
}
};

View File

@@ -2,57 +2,8 @@ import {
ChatGoogleGenerativeAI,
GoogleGenerativeAIEmbeddings,
} from '@langchain/google-genai';
import { getGeminiApiKey } from '../config';
import { ChatModel, EmbeddingModel } from '.';
export const PROVIDER_INFO = {
key: 'gemini',
displayName: 'Google Gemini',
};
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
import { Embeddings } from '@langchain/core/embeddings';
const geminiChatModels: Record<string, string>[] = [
{
displayName: 'Gemini 2.5 Pro Experimental',
key: 'gemini-2.5-pro-exp-03-25',
},
{
displayName: 'Gemini 2.0 Flash',
key: 'gemini-2.0-flash',
},
{
displayName: 'Gemini 2.0 Flash-Lite',
key: 'gemini-2.0-flash-lite',
},
{
displayName: 'Gemini 2.0 Flash Thinking Experimental',
key: 'gemini-2.0-flash-thinking-exp-01-21',
},
{
displayName: 'Gemini 1.5 Flash',
key: 'gemini-1.5-flash',
},
{
displayName: 'Gemini 1.5 Flash-8B',
key: 'gemini-1.5-flash-8b',
},
{
displayName: 'Gemini 1.5 Pro',
key: 'gemini-1.5-pro',
},
];
const geminiEmbeddingModels: Record<string, string>[] = [
{
displayName: 'Text Embedding 004',
key: 'models/text-embedding-004',
},
{
displayName: 'Embedding 001',
key: 'models/embedding-001',
},
];
import { getGeminiApiKey } from '../../config';
import logger from '../../utils/logger';
export const loadGeminiChatModels = async () => {
const geminiApiKey = getGeminiApiKey();
@@ -60,47 +11,59 @@ export const loadGeminiChatModels = async () => {
if (!geminiApiKey) return {};
try {
const chatModels: Record<string, ChatModel> = {};
geminiChatModels.forEach((model) => {
chatModels[model.key] = {
displayName: model.displayName,
const chatModels = {
'gemini-1.5-flash': {
displayName: 'Gemini 1.5 Flash',
model: new ChatGoogleGenerativeAI({
apiKey: geminiApiKey,
modelName: model.key,
modelName: 'gemini-1.5-flash',
temperature: 0.7,
}) as unknown as BaseChatModel,
};
});
apiKey: geminiApiKey,
}),
},
'gemini-1.5-flash-8b': {
displayName: 'Gemini 1.5 Flash 8B',
model: new ChatGoogleGenerativeAI({
modelName: 'gemini-1.5-flash-8b',
temperature: 0.7,
apiKey: geminiApiKey,
}),
},
'gemini-1.5-pro': {
displayName: 'Gemini 1.5 Pro',
model: new ChatGoogleGenerativeAI({
modelName: 'gemini-1.5-pro',
temperature: 0.7,
apiKey: geminiApiKey,
}),
},
};
return chatModels;
} catch (err) {
console.error(`Error loading Gemini models: ${err}`);
logger.error(`Error loading Gemini models: ${err}`);
return {};
}
};
export const loadGeminiEmbeddingModels = async () => {
export const loadGeminiEmbeddingsModels = async () => {
const geminiApiKey = getGeminiApiKey();
if (!geminiApiKey) return {};
try {
const embeddingModels: Record<string, EmbeddingModel> = {};
geminiEmbeddingModels.forEach((model) => {
embeddingModels[model.key] = {
displayName: model.displayName,
const embeddingModels = {
'text-embedding-004': {
displayName: 'Text Embedding',
model: new GoogleGenerativeAIEmbeddings({
apiKey: geminiApiKey,
modelName: model.key,
}) as unknown as Embeddings,
};
});
modelName: 'text-embedding-004',
}),
},
};
return embeddingModels;
} catch (err) {
console.error(`Error loading OpenAI embeddings models: ${err}`);
logger.error(`Error loading Gemini embeddings model: ${err}`);
return {};
}
};

View File

@@ -1,47 +1,136 @@
import { ChatOpenAI } from '@langchain/openai';
import { getGroqApiKey } from '../config';
import { ChatModel } from '.';
export const PROVIDER_INFO = {
key: 'groq',
displayName: 'Groq',
};
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
import { getGroqApiKey } from '../../config';
import logger from '../../utils/logger';
export const loadGroqChatModels = async () => {
const groqApiKey = getGroqApiKey();
if (!groqApiKey) return {};
try {
const res = await fetch('https://api.groq.com/openai/v1/models', {
method: 'GET',
headers: {
Authorization: `bearer ${groqApiKey}`,
'Content-Type': 'application/json',
},
});
const groqChatModels = (await res.json()).data;
const chatModels: Record<string, ChatModel> = {};
groqChatModels.forEach((model: any) => {
chatModels[model.id] = {
displayName: model.id,
model: new ChatOpenAI({
openAIApiKey: groqApiKey,
modelName: model.id,
temperature: 0.7,
configuration: {
const chatModels = {
'llama-3.3-70b-versatile': {
displayName: 'Llama 3.3 70B',
model: new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'llama-3.3-70b-versatile',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
}) as unknown as BaseChatModel,
};
});
),
},
'llama-3.2-3b-preview': {
displayName: 'Llama 3.2 3B',
model: new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'llama-3.2-3b-preview',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
},
'llama-3.2-11b-vision-preview': {
displayName: 'Llama 3.2 11B Vision',
model: new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'llama-3.2-11b-vision-preview',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
},
'llama-3.2-90b-vision-preview': {
displayName: 'Llama 3.2 90B Vision',
model: new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'llama-3.2-90b-vision-preview',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
},
'llama-3.1-8b-instant': {
displayName: 'Llama 3.1 8B',
model: new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'llama-3.1-8b-instant',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
},
'llama3-8b-8192': {
displayName: 'LLaMA3 8B',
model: new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'llama3-8b-8192',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
},
'llama3-70b-8192': {
displayName: 'LLaMA3 70B',
model: new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'llama3-70b-8192',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
},
'mixtral-8x7b-32768': {
displayName: 'Mixtral 8x7B',
model: new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'mixtral-8x7b-32768',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
},
'gemma2-9b-it': {
displayName: 'Gemma2 9B',
model: new ChatOpenAI(
{
openAIApiKey: groqApiKey,
modelName: 'gemma2-9b-it',
temperature: 0.7,
},
{
baseURL: 'https://api.groq.com/openai/v1',
},
),
},
};
return chatModels;
} catch (err) {
console.error(`Error loading Groq models: ${err}`);
logger.error(`Error loading Groq models: ${err}`);
return {};
}
};

View File

@@ -1,97 +1,27 @@
import { Embeddings } from '@langchain/core/embeddings';
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
import {
loadOpenAIChatModels,
loadOpenAIEmbeddingModels,
PROVIDER_INFO as OpenAIInfo,
PROVIDER_INFO,
} from './openai';
import {
getCustomOpenaiApiKey,
getCustomOpenaiApiUrl,
getCustomOpenaiModelName,
} from '../config';
import { ChatOpenAI } from '@langchain/openai';
import {
loadOllamaChatModels,
loadOllamaEmbeddingModels,
PROVIDER_INFO as OllamaInfo,
} from './ollama';
import { loadGroqChatModels, PROVIDER_INFO as GroqInfo } from './groq';
import {
loadAnthropicChatModels,
PROVIDER_INFO as AnthropicInfo,
} from './anthropic';
import {
loadGeminiChatModels,
loadGeminiEmbeddingModels,
PROVIDER_INFO as GeminiInfo,
} from './gemini';
import {
loadTransformersEmbeddingsModels,
PROVIDER_INFO as TransformersInfo,
} from './transformers';
import {
loadDeepseekChatModels,
PROVIDER_INFO as DeepseekInfo,
} from './deepseek';
import {
loadLMStudioChatModels,
loadLMStudioEmbeddingsModels,
PROVIDER_INFO as LMStudioInfo,
} from './lmstudio';
import { loadGroqChatModels } from './groq';
import { loadOllamaChatModels, loadOllamaEmbeddingsModels } from './ollama';
import { loadOpenAIChatModels, loadOpenAIEmbeddingsModels } from './openai';
import { loadAnthropicChatModels } from './anthropic';
import { loadTransformersEmbeddingsModels } from './transformers';
import { loadGeminiChatModels, loadGeminiEmbeddingsModels } from './gemini';
export const PROVIDER_METADATA = {
openai: OpenAIInfo,
ollama: OllamaInfo,
groq: GroqInfo,
anthropic: AnthropicInfo,
gemini: GeminiInfo,
transformers: TransformersInfo,
deepseek: DeepseekInfo,
lmstudio: LMStudioInfo,
custom_openai: {
key: 'custom_openai',
displayName: 'Custom OpenAI',
},
};
export interface ChatModel {
displayName: string;
model: BaseChatModel;
}
export interface EmbeddingModel {
displayName: string;
model: Embeddings;
}
export const chatModelProviders: Record<
string,
() => Promise<Record<string, ChatModel>>
> = {
const chatModelProviders = {
openai: loadOpenAIChatModels,
ollama: loadOllamaChatModels,
groq: loadGroqChatModels,
ollama: loadOllamaChatModels,
anthropic: loadAnthropicChatModels,
gemini: loadGeminiChatModels,
deepseek: loadDeepseekChatModels,
lmstudio: loadLMStudioChatModels,
};
export const embeddingModelProviders: Record<
string,
() => Promise<Record<string, EmbeddingModel>>
> = {
openai: loadOpenAIEmbeddingModels,
ollama: loadOllamaEmbeddingModels,
gemini: loadGeminiEmbeddingModels,
transformers: loadTransformersEmbeddingsModels,
lmstudio: loadLMStudioEmbeddingsModels,
const embeddingModelProviders = {
openai: loadOpenAIEmbeddingsModels,
local: loadTransformersEmbeddingsModels,
ollama: loadOllamaEmbeddingsModels,
gemini: loadGeminiEmbeddingsModels,
};
export const getAvailableChatModelProviders = async () => {
const models: Record<string, Record<string, ChatModel>> = {};
const models = {};
for (const provider in chatModelProviders) {
const providerModels = await chatModelProviders[provider]();
@@ -100,33 +30,13 @@ export const getAvailableChatModelProviders = async () => {
}
}
const customOpenAiApiKey = getCustomOpenaiApiKey();
const customOpenAiApiUrl = getCustomOpenaiApiUrl();
const customOpenAiModelName = getCustomOpenaiModelName();
models['custom_openai'] = {
...(customOpenAiApiKey && customOpenAiApiUrl && customOpenAiModelName
? {
[customOpenAiModelName]: {
displayName: customOpenAiModelName,
model: new ChatOpenAI({
openAIApiKey: customOpenAiApiKey,
modelName: customOpenAiModelName,
temperature: 0.7,
configuration: {
baseURL: customOpenAiApiUrl,
},
}) as unknown as BaseChatModel,
},
}
: {}),
};
models['custom_openai'] = {};
return models;
};
export const getAvailableEmbeddingModelProviders = async () => {
const models: Record<string, Record<string, EmbeddingModel>> = {};
const models = {};
for (const provider in embeddingModelProviders) {
const providerModels = await embeddingModelProviders[provider]();

Some files were not shown because too many files have changed in this diff Show More