Merge 9f4ae1baac into 7ec201d011

feat: add Gemini 2.0 Flash Exp models

.env.example

@@ -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

.github/workflows/ci.yml

@@ -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 

.github/workflows/docker-build.yaml

@@ -17,6 +17,9 @@ jobs:
       - 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:

.gitignore

@@ -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/

.prettierignore

@@ -36,3 +36,6 @@ coverage
 
 # Ignore all files with the .DS_Store extension (macOS specific)
 .DS_Store
+
+# Ignore all files in uploads directory
+uploads

CONTRIBUTING.md

@@ -8,6 +8,7 @@ Perplexica's design consists of two main domains:
 
 - **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
 
@@ -18,7 +19,8 @@ Before diving into coding, setting up your local environment is key. Here's what
 1. In the root directory, locate the `sample.config.toml` file.
 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. Use `npm run dev` to start the backend in development mode.
+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
 

README.md

@@ -1,170 +1,120 @@
-# 🚀 Perplexica - An AI-powered search engine 🔎 <!-- omit in toc -->
+# BizSearch
 
-![preview](.assets/perplexica-screenshot.png)
+A tool for finding and analyzing local businesses using AI-powered data extraction.
 
-## Table of Contents <!-- omit in toc -->
+## Prerequisites
 
-- [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)
-- [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 does 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. Rename the `.env.example` file to `.env` in the `ui` folder and fill in all necessary fields.
-4. After populating the configuration and environment files, run `npm i` in both the `ui` folder and the root directory.
-5. Install the dependencies and then execute `npm run build` in both the `ui` folder and the root directory.
-6. Finally, start both the frontend and the backend by running `npm run start` in both the `ui` folder and the root directory.
+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 exposing it your network, 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).
-
-## One-Click Deployment
-
-[![Deploy to RepoCloud](https://d16t0pc4846x52.cloudfront.net/deploylobe.svg)](https://repocloud.io/details/?app_id=267)
-
-## 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;
+```

app.dockerfile

@@ -1,4 +1,4 @@
-FROM node:alpine
+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

backend.dockerfile

@@ -1,4 +1,4 @@
-FROM node:slim
+FROM node:18-slim
 
 WORKDIR /home/perplexica
 
@@ -9,8 +9,9 @@ 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
+RUN yarn install --frozen-lockfile --network-timeout 600000
 RUN yarn build
 
 CMD ["yarn", "start"]

config.toml

@@ -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

db/init.sql

@@ -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); 

db/schema.sql

@@ -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); 

db/verify.sql

@@ -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; 

docker-compose.yaml

@@ -22,6 +22,7 @@ services:
       - 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'
@@ -50,3 +51,4 @@ networks:
 
 volumes:
   backend-dbstore:
+  uploads:

docker-compose.yml

@@ -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 

docs/ETHICAL_SCRAPING.md

@@ -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 

docs/architecture/README.md

@@ -1,4 +1,4 @@
-## Perplexica's Architecture
+# Perplexica's Architecture
 
 Perplexica's architecture consists of the following key components:
 

docs/architecture/WORKING.md

@@ -1,4 +1,4 @@
-## How does Perplexica work?
+# How does Perplexica work?
 
 Curious about how Perplexica works? Don't worry, we'll cover it here. Before we begin, make sure you've read about the architecture of Perplexica to ensure you understand what it's made up of. Haven't read it? You can read it [here](https://github.com/ItzCrazyKns/Perplexica/tree/master/docs/architecture/README.md).
 
@@ -10,10 +10,10 @@ We'll understand how Perplexica works by taking an example of a scenario where a
 4. After the information is retrieved, it is based on keyword-based search. We then convert the information into embeddings and the query as well, then we perform a similarity search to find the most relevant sources to answer the query.
 5. After all this is done, the sources are passed to the response generator. This chain takes all the chat history, the query, and the sources. It generates a response that is streamed to the UI.
 
-### How are the answers cited?
+## How are the answers cited?
 
 The LLMs are prompted to do so. We've prompted them so well that they cite the answers themselves, and using some UI magic, we display it to the user.
 
-### Image and Video Search
+## Image and Video Search
 
 Image and video searches are conducted in a similar manner. A query is always generated first, then we search the web for images and videos that match the query. These results are then returned to the user.

docs/installation/NETWORKING.md

@@ -10,27 +10,27 @@ This guide will show you how to make Perplexica available over a network. Follow
 
 3. Stop and remove the existing Perplexica containers and images:
 
-```
-docker compose down --rmi all
-```
+   ```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:
 
-```
-args:
+   ```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:
 
-```
-docker compose up -d --build
-```
+   ```bash
+   docker compose up -d --build
+   ```
 
 ## macOS
 
@@ -38,37 +38,37 @@ docker compose up -d --build
 
 2. Navigate to the directory with the `docker-compose.yaml` file:
 
-```
-cd /path/to/docker-compose.yaml
-```
+   ```bash
+   cd /path/to/docker-compose.yaml
+   ```
 
 3. Stop and remove existing containers and images:
 
-```
-docker compose down --rmi all
-```
+   ```bash
+   docker compose down --rmi all
+   ```
 
 4. Open `docker-compose.yaml` in a text editor like Sublime Text:
 
-```
-nano docker-compose.yaml
-```
+   ```bash
+   nano docker-compose.yaml
+   ```
 
 5. Replace `127.0.0.1` with the server IP in these lines:
 
-```
-args:
+   ```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:
 
-```
-docker compose up -d --build
-```
+   ```bash
+   docker compose up -d --build
+   ```
 
 ## Linux
 
@@ -76,34 +76,34 @@ docker compose up -d --build
 
 2. Navigate to the `docker-compose.yaml` directory:
 
-```
-cd /path/to/docker-compose.yaml
-```
+   ```bash
+   cd /path/to/docker-compose.yaml
+   ```
 
 3. Stop and remove containers and images:
 
-```
-docker compose down --rmi all
-```
+   ```bash
+   docker compose down --rmi all
+   ```
 
 4. Edit `docker-compose.yaml`:
 
-```
-nano docker-compose.yaml
-```
+   ```bash
+   nano docker-compose.yaml
+   ```
 
 5. Replace `127.0.0.1` with the server IP:
 
-```
-args:
+   ```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:
 
-```
-docker compose up -d --build
-```
+   ```bash
+   docker compose up -d --build
+   ```

docs/installation/UPDATING.md

@@ -6,23 +6,23 @@ To update Perplexica to the latest version, follow these steps:
 
 1. Clone the latest version of Perplexica from GitHub:
 
-```bash
+   ```bash
       git clone https://github.com/ItzCrazyKns/Perplexica.git
-```
+   ```
 
 2. Navigate to the Project Directory.
 
 3. Pull latest images from registry.
 
-```bash
-docker compose pull
-```
+   ```bash
+   docker compose pull
+   ```
 
 4. Update and Recreate containers.
 
-```bash
-docker compose up -d
-```
+   ```bash
+   docker compose up -d
+   ```
 
 5. Once the command completes running go to http://localhost:3000 and verify the latest changes.
 
@@ -30,9 +30,9 @@ docker compose up -d
 
 1. Clone the latest version of Perplexica from GitHub:
 
-```bash
+   ```bash
       git clone https://github.com/ItzCrazyKns/Perplexica.git
-```
+   ```
 
 2. Navigate to the Project Directory
 3. Execute `npm i` in both the `ui` folder and the root directory.

frontend/.gitignore

@@ -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

frontend/README.md

@@ -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.

frontend/eslint.config.mjs

@@ -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;

frontend/next.config.js

@@ -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 

frontend/next.config.ts

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

frontend/package.json

@@ -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"
+  }
+}

frontend/postcss.config.mjs

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

frontend/public/file.svg

@@ -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>

frontend/public/globe.svg

@@ -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>

frontend/public/next.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 394 80"><path fill="#000" d="M262 0h68.5v12.7h-27.2v66.6h-13.6V12.7H262V0ZM149 0v12.7H94v20.4h44.3v12.6H94v21h55v12.6H80.5V0h68.7zm34.3 0h-17.8l63.8 79.4h17.9l-32-39.7 32-39.6h-17.9l-23 28.6-23-28.6zm18.3 56.7-9-11-27.1 33.7h17.8l18.3-22.7z"/><path fill="#000" d="M81 79.3 17 0H0v79.3h13.6V17l50.2 62.3H81Zm252.6-.4c-1 0-1.8-.4-2.5-1s-1.1-1.6-1.1-2.6.3-1.8 1-2.5 1.6-1 2.6-1 1.8.3 2.5 1a3.4 3.4 0 0 1 .6 4.3 3.7 3.7 0 0 1-3 1.8zm23.2-33.5h6v23.3c0 2.1-.4 4-1.3 5.5a9.1 9.1 0 0 1-3.8 3.5c-1.6.8-3.5 1.3-5.7 1.3-2 0-3.7-.4-5.3-1s-2.8-1.8-3.7-3.2c-.9-1.3-1.4-3-1.4-5h6c.1.8.3 1.6.7 2.2s1 1.2 1.6 1.5c.7.4 1.5.5 2.4.5 1 0 1.8-.2 2.4-.6a4 4 0 0 0 1.6-1.8c.3-.8.5-1.8.5-3V45.5zm30.9 9.1a4.4 4.4 0 0 0-2-3.3 7.5 7.5 0 0 0-4.3-1.1c-1.3 0-2.4.2-3.3.5-.9.4-1.6 1-2 1.6a3.5 3.5 0 0 0-.3 4c.3.5.7.9 1.3 1.2l1.8 1 2 .5 3.2.8c1.3.3 2.5.7 3.7 1.2a13 13 0 0 1 3.2 1.8 8.1 8.1 0 0 1 3 6.5c0 2-.5 3.7-1.5 5.1a10 10 0 0 1-4.4 3.5c-1.8.8-4.1 1.2-6.8 1.2-2.6 0-4.9-.4-6.8-1.2-2-.8-3.4-2-4.5-3.5a10 10 0 0 1-1.7-5.6h6a5 5 0 0 0 3.5 4.6c1 .4 2.2.6 3.4.6 1.3 0 2.5-.2 3.5-.6 1-.4 1.8-1 2.4-1.7a4 4 0 0 0 .8-2.4c0-.9-.2-1.6-.7-2.2a11 11 0 0 0-2.1-1.4l-3.2-1-3.8-1c-2.8-.7-5-1.7-6.6-3.2a7.2 7.2 0 0 1-2.4-5.7 8 8 0 0 1 1.7-5 10 10 0 0 1 4.3-3.5c2-.8 4-1.2 6.4-1.2 2.3 0 4.4.4 6.2 1.2 1.8.8 3.2 2 4.3 3.4 1 1.4 1.5 3 1.5 5h-5.8z"/></svg>

frontend/public/vercel.svg

@@ -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>

frontend/public/window.svg

@@ -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>

frontend/src/app/globals.css

@@ -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;
+  }
+}

frontend/src/app/layout.tsx

@@ -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>
+  );
+}

frontend/src/app/page.tsx

@@ -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>
+  )
+}

frontend/src/components/search-form.tsx

@@ -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>
+  )
+} 

frontend/src/components/search-results.tsx

@@ -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>
+  )
+} 

frontend/src/components/server-status.tsx

@@ -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>
+  )
+} 

frontend/src/components/ui/alert.tsx

@@ -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 } 

frontend/src/lib/utils.ts

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

frontend/tailwind.config.ts

@@ -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;

frontend/tsconfig.json

@@ -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"]
+}

jest.config.js

@@ -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'],
+}; 

package.json

@@ -1,49 +1,80 @@
 {
   "name": "perplexica-backend",
-  "version": "1.9.0",
+  "version": "1.10.0-rc2",
   "license": "MIT",
   "author": "ItzCrazyKns",
   "scripts": {
-    "start": "npm run db:push && node dist/app.js",
+    "start": "ts-node src/index.ts",
     "build": "tsc",
-    "dev": "nodemon src/app.ts",
+    "dev": "nodemon src/index.ts",
     "db:push": "drizzle-kit push sqlite",
     "format": "prettier . --check",
-    "format:write": "prettier . --write"
+    "format:write": "prettier . --write",
+    "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": {
+    "@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/jest": "^29.5.11",
+    "@types/multer": "^1.4.12",
+    "@types/node-fetch": "^2.6.12",
     "@types/pdf-parse": "^1.1.4",
     "@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",
+    "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": "^11.0.0",
+    "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": "^16.4.5",
+    "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": "^3.22.4"
+    "zod": "^3.24.1"
   }
 }

postcss.config.js

@@ -0,0 +1,6 @@
+module.exports = {
+  plugins: {
+    tailwindcss: {},
+    autoprefixer: {},
+  },
+}

public/index.html

@@ -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> 

sample.config.toml

@@ -1,11 +1,13 @@
 [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

searxng/settings.yml

@@ -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"

src/agents/academicSearchAgent.ts

@@ -1,280 +0,0 @@
-import { BaseMessage } from '@langchain/core/messages';
-import {
-  PromptTemplate,
-  ChatPromptTemplate,
-  MessagesPlaceholder,
-} from '@langchain/core/prompts';
-import {
-  RunnableSequence,
-  RunnableMap,
-  RunnableLambda,
-} from '@langchain/core/runnables';
-import { StringOutputParser } from '@langchain/core/output_parsers';
-import { Document } from '@langchain/core/documents';
-import { searchSearxng } from '../lib/searxng';
-import type { StreamEvent } from '@langchain/core/tracers/log_stream';
-import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import type { Embeddings } from '@langchain/core/embeddings';
-import formatChatHistoryAsString from '../utils/formatHistory';
-import eventEmitter from 'events';
-import computeSimilarity from '../utils/computeSimilarity';
-import logger from '../utils/logger';
-import { IterableReadableStream } from '@langchain/core/utils/stream';
-
-const basicAcademicSearchRetrieverPrompt = `
-You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
-If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
-
-Example:
-1. Follow up question: How does stable diffusion work?
-Rephrased: Stable diffusion working
-
-2. Follow up question: What is linear algebra?
-Rephrased: Linear algebra
-
-3. Follow up question: What is the third law of thermodynamics?
-Rephrased: Third law of thermodynamics
-
-Conversation:
-{chat_history}
-
-Follow up question: {query}
-Rephrased question:
-`;
-
-const basicAcademicSearchResponsePrompt = `
-    You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Academic', this means you will be searching for academic papers and articles on the web.
-
-    Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
-    You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
-    You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
-    Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
-    You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
-    Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
-    However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
-
-    Anything inside the following \`context\` HTML block provided below is for your knowledge returned by the search engine and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to 
-    talk about the context in your response. 
-
-    <context>
-    {context}
-    </context>
-
-    If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'.
-    Anything between the \`context\` is retrieved from a search engine and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
-`;
-
-const strParser = new StringOutputParser();
-
-const handleStream = async (
-  stream: IterableReadableStream<StreamEvent>,
-  emitter: eventEmitter,
-) => {
-  for await (const event of stream) {
-    if (
-      event.event === 'on_chain_end' &&
-      event.name === 'FinalSourceRetriever'
-    ) {
-      emitter.emit(
-        'data',
-        JSON.stringify({ type: 'sources', data: event.data.output }),
-      );
-    }
-    if (
-      event.event === 'on_chain_stream' &&
-      event.name === 'FinalResponseGenerator'
-    ) {
-      emitter.emit(
-        'data',
-        JSON.stringify({ type: 'response', data: event.data.chunk }),
-      );
-    }
-    if (
-      event.event === 'on_chain_end' &&
-      event.name === 'FinalResponseGenerator'
-    ) {
-      emitter.emit('end');
-    }
-  }
-};
-
-type BasicChainInput = {
-  chat_history: BaseMessage[];
-  query: string;
-};
-
-const createBasicAcademicSearchRetrieverChain = (llm: BaseChatModel) => {
-  return RunnableSequence.from([
-    PromptTemplate.fromTemplate(basicAcademicSearchRetrieverPrompt),
-    llm,
-    strParser,
-    RunnableLambda.from(async (input: string) => {
-      if (input === 'not_needed') {
-        return { query: '', docs: [] };
-      }
-
-      const res = await searchSearxng(input, {
-        language: 'en',
-        engines: ['arxiv', 'google scholar', 'pubmed'],
-      });
-
-      const documents = res.results.map(
-        (result) =>
-          new Document({
-            pageContent: result.content,
-            metadata: {
-              title: result.title,
-              url: result.url,
-              ...(result.img_src && { img_src: result.img_src }),
-            },
-          }),
-      );
-
-      return { query: input, docs: documents };
-    }),
-  ]);
-};
-
-const createBasicAcademicSearchAnsweringChain = (
-  llm: BaseChatModel,
-  embeddings: Embeddings,
-  optimizationMode: 'speed' | 'balanced' | 'quality',
-) => {
-  const basicAcademicSearchRetrieverChain =
-    createBasicAcademicSearchRetrieverChain(llm);
-
-  const processDocs = async (docs: Document[]) => {
-    return docs
-      .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
-      .join('\n');
-  };
-
-  const rerankDocs = async ({
-    query,
-    docs,
-  }: {
-    query: string;
-    docs: Document[];
-  }) => {
-    if (docs.length === 0) {
-      return docs;
-    }
-
-    const docsWithContent = docs.filter(
-      (doc) => doc.pageContent && doc.pageContent.length > 0,
-    );
-
-    if (optimizationMode === 'speed') {
-      return docsWithContent.slice(0, 15);
-    } else if (optimizationMode === 'balanced') {
-      const [docEmbeddings, queryEmbedding] = await Promise.all([
-        embeddings.embedDocuments(
-          docsWithContent.map((doc) => doc.pageContent),
-        ),
-        embeddings.embedQuery(query),
-      ]);
-
-      const similarity = docEmbeddings.map((docEmbedding, i) => {
-        const sim = computeSimilarity(queryEmbedding, docEmbedding);
-
-        return {
-          index: i,
-          similarity: sim,
-        };
-      });
-
-      const sortedDocs = similarity
-        .sort((a, b) => b.similarity - a.similarity)
-        .slice(0, 15)
-        .map((sim) => docsWithContent[sim.index]);
-
-      return sortedDocs;
-    }
-  };
-
-  return RunnableSequence.from([
-    RunnableMap.from({
-      query: (input: BasicChainInput) => input.query,
-      chat_history: (input: BasicChainInput) => input.chat_history,
-      context: RunnableSequence.from([
-        (input) => ({
-          query: input.query,
-          chat_history: formatChatHistoryAsString(input.chat_history),
-        }),
-        basicAcademicSearchRetrieverChain
-          .pipe(rerankDocs)
-          .withConfig({
-            runName: 'FinalSourceRetriever',
-          })
-          .pipe(processDocs),
-      ]),
-    }),
-    ChatPromptTemplate.fromMessages([
-      ['system', basicAcademicSearchResponsePrompt],
-      new MessagesPlaceholder('chat_history'),
-      ['user', '{query}'],
-    ]),
-    llm,
-    strParser,
-  ]).withConfig({
-    runName: 'FinalResponseGenerator',
-  });
-};
-
-const basicAcademicSearch = (
-  query: string,
-  history: BaseMessage[],
-  llm: BaseChatModel,
-  embeddings: Embeddings,
-  optimizationMode: 'speed' | 'balanced' | 'quality',
-) => {
-  const emitter = new eventEmitter();
-
-  try {
-    const basicAcademicSearchAnsweringChain =
-      createBasicAcademicSearchAnsweringChain(
-        llm,
-        embeddings,
-        optimizationMode,
-      );
-
-    const stream = basicAcademicSearchAnsweringChain.streamEvents(
-      {
-        chat_history: history,
-        query: query,
-      },
-      {
-        version: 'v1',
-      },
-    );
-
-    handleStream(stream, emitter);
-  } catch (err) {
-    emitter.emit(
-      'error',
-      JSON.stringify({ data: 'An error has occurred please try again later' }),
-    );
-    logger.error(`Error in academic search: ${err}`);
-  }
-
-  return emitter;
-};
-
-const handleAcademicSearch = (
-  message: string,
-  history: BaseMessage[],
-  llm: BaseChatModel,
-  embeddings: Embeddings,
-  optimizationMode: 'speed' | 'balanced' | 'quality',
-) => {
-  const emitter = basicAcademicSearch(
-    message,
-    history,
-    llm,
-    embeddings,
-    optimizationMode,
-  );
-  return emitter;
-};
-
-export default handleAcademicSearch;

src/agents/redditSearchAgent.ts

@@ -1,276 +0,0 @@
-import { BaseMessage } from '@langchain/core/messages';
-import {
-  PromptTemplate,
-  ChatPromptTemplate,
-  MessagesPlaceholder,
-} from '@langchain/core/prompts';
-import {
-  RunnableSequence,
-  RunnableMap,
-  RunnableLambda,
-} from '@langchain/core/runnables';
-import { StringOutputParser } from '@langchain/core/output_parsers';
-import { Document } from '@langchain/core/documents';
-import { searchSearxng } from '../lib/searxng';
-import type { StreamEvent } from '@langchain/core/tracers/log_stream';
-import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import type { Embeddings } from '@langchain/core/embeddings';
-import formatChatHistoryAsString from '../utils/formatHistory';
-import eventEmitter from 'events';
-import computeSimilarity from '../utils/computeSimilarity';
-import logger from '../utils/logger';
-import { IterableReadableStream } from '@langchain/core/utils/stream';
-
-const basicRedditSearchRetrieverPrompt = `
-You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
-If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
-
-Example:
-1. Follow up question: Which company is most likely to create an AGI
-Rephrased: Which company is most likely to create an AGI
-
-2. Follow up question: Is Earth flat?
-Rephrased: Is Earth flat?
-
-3. Follow up question: Is there life on Mars?
-Rephrased: Is there life on Mars?
-
-Conversation:
-{chat_history}
-
-Follow up question: {query}
-Rephrased question:
-`;
-
-const basicRedditSearchResponsePrompt = `
-    You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Reddit', this means you will be searching for information, opinions and discussions on the web using Reddit.
-
-    Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
-    You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
-    You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
-    Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
-    You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
-    Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
-    However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
-
-    Anything inside the following \`context\` HTML block provided below is for your knowledge returned by Reddit and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
-    talk about the context in your response.
-
-    <context>
-    {context}
-    </context>
-
-    If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'.
-    Anything between the \`context\` is retrieved from Reddit and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
-`;
-
-const strParser = new StringOutputParser();
-
-const handleStream = async (
-  stream: IterableReadableStream<StreamEvent>,
-  emitter: eventEmitter,
-) => {
-  for await (const event of stream) {
-    if (
-      event.event === 'on_chain_end' &&
-      event.name === 'FinalSourceRetriever'
-    ) {
-      emitter.emit(
-        'data',
-        JSON.stringify({ type: 'sources', data: event.data.output }),
-      );
-    }
-    if (
-      event.event === 'on_chain_stream' &&
-      event.name === 'FinalResponseGenerator'
-    ) {
-      emitter.emit(
-        'data',
-        JSON.stringify({ type: 'response', data: event.data.chunk }),
-      );
-    }
-    if (
-      event.event === 'on_chain_end' &&
-      event.name === 'FinalResponseGenerator'
-    ) {
-      emitter.emit('end');
-    }
-  }
-};
-
-type BasicChainInput = {
-  chat_history: BaseMessage[];
-  query: string;
-};
-
-const createBasicRedditSearchRetrieverChain = (llm: BaseChatModel) => {
-  return RunnableSequence.from([
-    PromptTemplate.fromTemplate(basicRedditSearchRetrieverPrompt),
-    llm,
-    strParser,
-    RunnableLambda.from(async (input: string) => {
-      if (input === 'not_needed') {
-        return { query: '', docs: [] };
-      }
-
-      const res = await searchSearxng(input, {
-        language: 'en',
-        engines: ['reddit'],
-      });
-
-      const documents = res.results.map(
-        (result) =>
-          new Document({
-            pageContent: result.content ? result.content : result.title,
-            metadata: {
-              title: result.title,
-              url: result.url,
-              ...(result.img_src && { img_src: result.img_src }),
-            },
-          }),
-      );
-
-      return { query: input, docs: documents };
-    }),
-  ]);
-};
-
-const createBasicRedditSearchAnsweringChain = (
-  llm: BaseChatModel,
-  embeddings: Embeddings,
-  optimizationMode: 'speed' | 'balanced' | 'quality',
-) => {
-  const basicRedditSearchRetrieverChain =
-    createBasicRedditSearchRetrieverChain(llm);
-
-  const processDocs = async (docs: Document[]) => {
-    return docs
-      .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
-      .join('\n');
-  };
-
-  const rerankDocs = async ({
-    query,
-    docs,
-  }: {
-    query: string;
-    docs: Document[];
-  }) => {
-    if (docs.length === 0) {
-      return docs;
-    }
-
-    const docsWithContent = docs.filter(
-      (doc) => doc.pageContent && doc.pageContent.length > 0,
-    );
-
-    if (optimizationMode === 'speed') {
-      return docsWithContent.slice(0, 15);
-    } else if (optimizationMode === 'balanced') {
-      const [docEmbeddings, queryEmbedding] = await Promise.all([
-        embeddings.embedDocuments(
-          docsWithContent.map((doc) => doc.pageContent),
-        ),
-        embeddings.embedQuery(query),
-      ]);
-
-      const similarity = docEmbeddings.map((docEmbedding, i) => {
-        const sim = computeSimilarity(queryEmbedding, docEmbedding);
-
-        return {
-          index: i,
-          similarity: sim,
-        };
-      });
-
-      const sortedDocs = similarity
-        .filter((sim) => sim.similarity > 0.3)
-        .sort((a, b) => b.similarity - a.similarity)
-        .slice(0, 15)
-        .map((sim) => docsWithContent[sim.index]);
-
-      return sortedDocs;
-    }
-  };
-
-  return RunnableSequence.from([
-    RunnableMap.from({
-      query: (input: BasicChainInput) => input.query,
-      chat_history: (input: BasicChainInput) => input.chat_history,
-      context: RunnableSequence.from([
-        (input) => ({
-          query: input.query,
-          chat_history: formatChatHistoryAsString(input.chat_history),
-        }),
-        basicRedditSearchRetrieverChain
-          .pipe(rerankDocs)
-          .withConfig({
-            runName: 'FinalSourceRetriever',
-          })
-          .pipe(processDocs),
-      ]),
-    }),
-    ChatPromptTemplate.fromMessages([
-      ['system', basicRedditSearchResponsePrompt],
-      new MessagesPlaceholder('chat_history'),
-      ['user', '{query}'],
-    ]),
-    llm,
-    strParser,
-  ]).withConfig({
-    runName: 'FinalResponseGenerator',
-  });
-};
-
-const basicRedditSearch = (
-  query: string,
-  history: BaseMessage[],
-  llm: BaseChatModel,
-  embeddings: Embeddings,
-  optimizationMode: 'speed' | 'balanced' | 'quality',
-) => {
-  const emitter = new eventEmitter();
-
-  try {
-    const basicRedditSearchAnsweringChain =
-      createBasicRedditSearchAnsweringChain(llm, embeddings, optimizationMode);
-    const stream = basicRedditSearchAnsweringChain.streamEvents(
-      {
-        chat_history: history,
-        query: query,
-      },
-      {
-        version: 'v1',
-      },
-    );
-
-    handleStream(stream, emitter);
-  } catch (err) {
-    emitter.emit(
-      'error',
-      JSON.stringify({ data: 'An error has occurred please try again later' }),
-    );
-    logger.error(`Error in RedditSearch: ${err}`);
-  }
-
-  return emitter;
-};
-
-const handleRedditSearch = (
-  message: string,
-  history: BaseMessage[],
-  llm: BaseChatModel,
-  embeddings: Embeddings,
-  optimizationMode: 'speed' | 'balanced' | 'quality',
-) => {
-  const emitter = basicRedditSearch(
-    message,
-    history,
-    llm,
-    embeddings,
-    optimizationMode,
-  );
-  return emitter;
-};
-
-export default handleRedditSearch;

src/agents/webSearchAgent.ts

@@ -1,460 +0,0 @@
-import { BaseMessage } from '@langchain/core/messages';
-import {
-  PromptTemplate,
-  ChatPromptTemplate,
-  MessagesPlaceholder,
-} from '@langchain/core/prompts';
-import {
-  RunnableSequence,
-  RunnableMap,
-  RunnableLambda,
-} from '@langchain/core/runnables';
-import { StringOutputParser } from '@langchain/core/output_parsers';
-import { Document } from '@langchain/core/documents';
-import { searchSearxng } from '../lib/searxng';
-import type { StreamEvent } from '@langchain/core/tracers/log_stream';
-import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import type { Embeddings } from '@langchain/core/embeddings';
-import formatChatHistoryAsString from '../utils/formatHistory';
-import eventEmitter from 'events';
-import computeSimilarity from '../utils/computeSimilarity';
-import logger from '../utils/logger';
-import LineListOutputParser from '../lib/outputParsers/listLineOutputParser';
-import { getDocumentsFromLinks } from '../lib/linkDocument';
-import LineOutputParser from '../lib/outputParsers/lineOutputParser';
-import { IterableReadableStream } from '@langchain/core/utils/stream';
-import { ChatOpenAI } from '@langchain/openai';
-
-const basicSearchRetrieverPrompt = `
-You are an AI question rephraser. You will be given a conversation and a follow-up question,  you will have to rephrase the follow up question so it is a standalone question and can be used by another LLM to search the web for information to answer it.
-If it is a smple writing task or a greeting (unless the greeting contains a question after it) like Hi, Hello, How are you, etc. than a question then you need to return \`not_needed\` as the response (This is because the LLM won't need to search the web for finding information on this topic).
-If the user asks some question from some URL or wants you to summarize a PDF or a webpage (via URL) you need to return the links inside the \`links\` XML block and the question inside the \`question\` XML block. If the user wants to you to summarize the webpage or the PDF you need to return \`summarize\` inside the \`question\` XML block in place of a question and the link to summarize in the \`links\` XML block.
-You must always return the rephrased question inside the \`question\` XML block, if there are no links in the follow-up question then don't insert a \`links\` XML block in your response.
-
-There are several examples attached for your reference inside the below \`examples\` XML block
-
-<examples>
-1. Follow up question: What is the capital of France
-Rephrased question:\`
-<question>
-Capital of france
-</question>
-\`
-
-2. Hi, how are you?
-Rephrased question\`
-<question>
-not_needed
-</question>
-\`
-
-3. Follow up question: What is Docker?
-Rephrased question: \`
-<question>
-What is Docker
-</question>
-\`
-
-4. Follow up question: Can you tell me what is X from https://example.com
-Rephrased question: \`
-<question>
-Can you tell me what is X?
-</question>
-
-<links>
-https://example.com
-</links>
-\`
-
-5. Follow up question: Summarize the content from https://example.com
-Rephrased question: \`
-<question>
-summarize
-</question>
-
-<links>
-https://example.com
-</links>
-\`
-</examples>
-
-Anything below is the part of the actual conversation and you need to use conversation and the follow-up question to rephrase the follow-up question as a standalone question based on the guidelines shared above.
-
-<conversation>
-{chat_history}
-</conversation>
-
-Follow up question: {query}
-Rephrased question:
-`;
-
-const basicWebSearchResponsePrompt = `
-    You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are also an expert at summarizing web pages or documents and searching for content in them.
-
-    Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
-    You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
-    You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
-    If the query contains some links and the user asks to answer from those links you will be provided the entire content of the page inside the \`context\` XML block. You can then use this content to answer the user's query.
-    If the user asks to summarize content from some links, you will be provided the entire content of the page inside the \`context\` XML block. You can then use this content to summarize the text. The content provided inside the \`context\` block will be already summarized by another model so you just need to use that content to answer the user's query.
-    Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
-    You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
-    Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
-    However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
-
-    Anything inside the following \`context\` HTML block provided below is for your knowledge returned by the search engine and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
-    talk about the context in your response.
-
-    <context>
-    {context}
-    </context>
-
-    If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'. You do not need to do this for summarization tasks.
-    Anything between the \`context\` is retrieved from a search engine and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
-`;
-
-const strParser = new StringOutputParser();
-
-const handleStream = async (
-  stream: IterableReadableStream<StreamEvent>,
-  emitter: eventEmitter,
-) => {
-  for await (const event of stream) {
-    if (
-      event.event === 'on_chain_end' &&
-      event.name === 'FinalSourceRetriever'
-    ) {
-      emitter.emit(
-        'data',
-        JSON.stringify({ type: 'sources', data: event.data.output }),
-      );
-    }
-    if (
-      event.event === 'on_chain_stream' &&
-      event.name === 'FinalResponseGenerator'
-    ) {
-      emitter.emit(
-        'data',
-        JSON.stringify({ type: 'response', data: event.data.chunk }),
-      );
-    }
-    if (
-      event.event === 'on_chain_end' &&
-      event.name === 'FinalResponseGenerator'
-    ) {
-      emitter.emit('end');
-    }
-  }
-};
-
-type BasicChainInput = {
-  chat_history: BaseMessage[];
-  query: string;
-};
-
-const createBasicWebSearchRetrieverChain = (llm: BaseChatModel) => {
-  (llm as unknown as ChatOpenAI).temperature = 0;
-
-  return RunnableSequence.from([
-    PromptTemplate.fromTemplate(basicSearchRetrieverPrompt),
-    llm,
-    strParser,
-    RunnableLambda.from(async (input: string) => {
-      const linksOutputParser = new LineListOutputParser({
-        key: 'links',
-      });
-
-      const questionOutputParser = new LineOutputParser({
-        key: 'question',
-      });
-
-      const links = await linksOutputParser.parse(input);
-      let question = await questionOutputParser.parse(input);
-
-      if (question === 'not_needed') {
-        return { query: '', docs: [] };
-      }
-
-      if (links.length > 0) {
-        if (question.length === 0) {
-          question = 'summarize';
-        }
-
-        let docs = [];
-
-        const linkDocs = await getDocumentsFromLinks({ links });
-
-        const docGroups: Document[] = [];
-
-        linkDocs.map((doc) => {
-          const URLDocExists = docGroups.find(
-            (d) =>
-              d.metadata.url === doc.metadata.url && d.metadata.totalDocs < 10,
-          );
-
-          if (!URLDocExists) {
-            docGroups.push({
-              ...doc,
-              metadata: {
-                ...doc.metadata,
-                totalDocs: 1,
-              },
-            });
-          }
-
-          const docIndex = docGroups.findIndex(
-            (d) =>
-              d.metadata.url === doc.metadata.url && d.metadata.totalDocs < 10,
-          );
-
-          if (docIndex !== -1) {
-            docGroups[docIndex].pageContent =
-              docGroups[docIndex].pageContent + `\n\n` + doc.pageContent;
-            docGroups[docIndex].metadata.totalDocs += 1;
-          }
-        });
-
-        await Promise.all(
-          docGroups.map(async (doc) => {
-            const res = await llm.invoke(`
-            You are a web search summarizer, tasked with summarizing a piece of text retrieved from a web search. Your job is to summarize the 
-            text into a detailed, 2-4 paragraph explanation that captures the main ideas and provides a comprehensive answer to the query.
-            If the query is \"summarize\", you should provide a detailed summary of the text. If the query is a specific question, you should answer it in the summary.
-            
-            - **Journalistic tone**: The summary should sound professional and journalistic, not too casual or vague.
-            - **Thorough and detailed**: Ensure that every key point from the text is captured and that the summary directly answers the query.
-            - **Not too lengthy, but detailed**: The summary should be informative but not excessively long. Focus on providing detailed information in a concise format.
-
-            The text will be shared inside the \`text\` XML tag, and the query inside the \`query\` XML tag.
-
-            <example>
-            1. \`<text>
-            Docker is a set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers. 
-            It was first released in 2013 and is developed by Docker, Inc. Docker is designed to make it easier to create, deploy, and run applications 
-            by using containers.
-            </text>
-
-            <query>
-            What is Docker and how does it work?
-            </query>
-
-            Response:
-            Docker is a revolutionary platform-as-a-service product developed by Docker, Inc., that uses container technology to make application 
-            deployment more efficient. It allows developers to package their software with all necessary dependencies, making it easier to run in 
-            any environment. Released in 2013, Docker has transformed the way applications are built, deployed, and managed.
-            \`
-            2. \`<text>
-            The theory of relativity, or simply relativity, encompasses two interrelated theories of Albert Einstein: special relativity and general
-            relativity. However, the word "relativity" is sometimes used in reference to Galilean invariance. The term "theory of relativity" was based
-            on the expression "relative theory" used by Max Planck in 1906. The theory of relativity usually encompasses two interrelated theories by
-            Albert Einstein: special relativity and general relativity. Special relativity applies to all physical phenomena in the absence of gravity.
-            General relativity explains the law of gravitation and its relation to other forces of nature. It applies to the cosmological and astrophysical
-            realm, including astronomy.
-            </text>
-
-            <query>
-            summarize
-            </query>
-
-            Response:
-            The theory of relativity, developed by Albert Einstein, encompasses two main theories: special relativity and general relativity. Special
-            relativity applies to all physical phenomena in the absence of gravity, while general relativity explains the law of gravitation and its
-            relation to other forces of nature. The theory of relativity is based on the concept of "relative theory," as introduced by Max Planck in
-            1906. It is a fundamental theory in physics that has revolutionized our understanding of the universe.
-            \`
-            </example>
-
-            Everything below is the actual data you will be working with. Good luck!
-
-            <query>
-            ${question}
-            </query>
-
-            <text>
-            ${doc.pageContent}
-            </text>
-
-            Make sure to answer the query in the summary.
-          `);
-
-            const document = new Document({
-              pageContent: res.content as string,
-              metadata: {
-                title: doc.metadata.title,
-                url: doc.metadata.url,
-              },
-            });
-
-            docs.push(document);
-          }),
-        );
-
-        return { query: question, docs: docs };
-      } else {
-        const res = await searchSearxng(question, {
-          language: 'en',
-        });
-
-        const documents = res.results.map(
-          (result) =>
-            new Document({
-              pageContent: result.content,
-              metadata: {
-                title: result.title,
-                url: result.url,
-                ...(result.img_src && { img_src: result.img_src }),
-              },
-            }),
-        );
-
-        return { query: question, docs: documents };
-      }
-    }),
-  ]);
-};
-
-const createBasicWebSearchAnsweringChain = (
-  llm: BaseChatModel,
-  embeddings: Embeddings,
-  optimizationMode: 'speed' | 'balanced' | 'quality',
-) => {
-  const basicWebSearchRetrieverChain = createBasicWebSearchRetrieverChain(llm);
-
-  const processDocs = async (docs: Document[]) => {
-    return docs
-      .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
-      .join('\n');
-  };
-
-  const rerankDocs = async ({
-    query,
-    docs,
-  }: {
-    query: string;
-    docs: Document[];
-  }) => {
-    if (docs.length === 0) {
-      return docs;
-    }
-
-    if (query.toLocaleLowerCase() === 'summarize') {
-      return docs;
-    }
-
-    const docsWithContent = docs.filter(
-      (doc) => doc.pageContent && doc.pageContent.length > 0,
-    );
-
-    if (optimizationMode === 'speed') {
-      return docsWithContent.slice(0, 15);
-    } else if (optimizationMode === 'balanced') {
-      const [docEmbeddings, queryEmbedding] = await Promise.all([
-        embeddings.embedDocuments(
-          docsWithContent.map((doc) => doc.pageContent),
-        ),
-        embeddings.embedQuery(query),
-      ]);
-
-      const similarity = docEmbeddings.map((docEmbedding, i) => {
-        const sim = computeSimilarity(queryEmbedding, docEmbedding);
-
-        return {
-          index: i,
-          similarity: sim,
-        };
-      });
-
-      const sortedDocs = similarity
-        .filter((sim) => sim.similarity > 0.3)
-        .sort((a, b) => b.similarity - a.similarity)
-        .slice(0, 15)
-        .map((sim) => docsWithContent[sim.index]);
-
-      return sortedDocs;
-    }
-  };
-
-  return RunnableSequence.from([
-    RunnableMap.from({
-      query: (input: BasicChainInput) => input.query,
-      chat_history: (input: BasicChainInput) => input.chat_history,
-      context: RunnableSequence.from([
-        (input) => ({
-          query: input.query,
-          chat_history: formatChatHistoryAsString(input.chat_history),
-        }),
-        basicWebSearchRetrieverChain
-          .pipe(rerankDocs)
-          .withConfig({
-            runName: 'FinalSourceRetriever',
-          })
-          .pipe(processDocs),
-      ]),
-    }),
-    ChatPromptTemplate.fromMessages([
-      ['system', basicWebSearchResponsePrompt],
-      new MessagesPlaceholder('chat_history'),
-      ['user', '{query}'],
-    ]),
-    llm,
-    strParser,
-  ]).withConfig({
-    runName: 'FinalResponseGenerator',
-  });
-};
-
-const basicWebSearch = (
-  query: string,
-  history: BaseMessage[],
-  llm: BaseChatModel,
-  embeddings: Embeddings,
-  optimizationMode: 'speed' | 'balanced' | 'quality',
-) => {
-  const emitter = new eventEmitter();
-
-  try {
-    const basicWebSearchAnsweringChain = createBasicWebSearchAnsweringChain(
-      llm,
-      embeddings,
-      optimizationMode,
-    );
-
-    const stream = basicWebSearchAnsweringChain.streamEvents(
-      {
-        chat_history: history,
-        query: query,
-      },
-      {
-        version: 'v1',
-      },
-    );
-
-    handleStream(stream, emitter);
-  } catch (err) {
-    emitter.emit(
-      'error',
-      JSON.stringify({ data: 'An error has occurred please try again later' }),
-    );
-    logger.error(`Error in websearch: ${err}`);
-  }
-
-  return emitter;
-};
-
-const handleWebSearch = (
-  message: string,
-  history: BaseMessage[],
-  llm: BaseChatModel,
-  embeddings: Embeddings,
-  optimizationMode: 'speed' | 'balanced' | 'quality',
-) => {
-  const emitter = basicWebSearch(
-    message,
-    history,
-    llm,
-    embeddings,
-    optimizationMode,
-  );
-  return emitter;
-};
-
-export default handleWebSearch;

src/agents/wolframAlphaSearchAgent.ts

@@ -1,220 +0,0 @@
-import { BaseMessage } from '@langchain/core/messages';
-import {
-  PromptTemplate,
-  ChatPromptTemplate,
-  MessagesPlaceholder,
-} from '@langchain/core/prompts';
-import {
-  RunnableSequence,
-  RunnableMap,
-  RunnableLambda,
-} from '@langchain/core/runnables';
-import { StringOutputParser } from '@langchain/core/output_parsers';
-import { Document } from '@langchain/core/documents';
-import { searchSearxng } from '../lib/searxng';
-import type { StreamEvent } from '@langchain/core/tracers/log_stream';
-import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import type { Embeddings } from '@langchain/core/embeddings';
-import formatChatHistoryAsString from '../utils/formatHistory';
-import eventEmitter from 'events';
-import logger from '../utils/logger';
-import { IterableReadableStream } from '@langchain/core/utils/stream';
-
-const basicWolframAlphaSearchRetrieverPrompt = `
-You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
-If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
-
-Example:
-1. Follow up question: What is the atomic radius of S?
-Rephrased: Atomic radius of S
-
-2. Follow up question: What is linear algebra?
-Rephrased: Linear algebra
-
-3. Follow up question: What is the third law of thermodynamics?
-Rephrased: Third law of thermodynamics
-
-Conversation:
-{chat_history}
-
-Follow up question: {query}
-Rephrased question:
-`;
-
-const basicWolframAlphaSearchResponsePrompt = `
-    You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Wolfram Alpha', this means you will be searching for information on the web using Wolfram Alpha. It is a computational knowledge engine that can answer factual queries and perform computations.
-
-    Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
-    You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
-    You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
-    Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
-    You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
-    Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
-    However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
-
-    Anything inside the following \`context\` HTML block provided below is for your knowledge returned by Wolfram Alpha and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to 
-    talk about the context in your response. 
-
-    <context>
-    {context}
-    </context>
-
-    If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'.
-    Anything between the \`context\` is retrieved from Wolfram Alpha and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
-`;
-
-const strParser = new StringOutputParser();
-
-const handleStream = async (
-  stream: IterableReadableStream<StreamEvent>,
-  emitter: eventEmitter,
-) => {
-  for await (const event of stream) {
-    if (
-      event.event === 'on_chain_end' &&
-      event.name === 'FinalSourceRetriever'
-    ) {
-      emitter.emit(
-        'data',
-        JSON.stringify({ type: 'sources', data: event.data.output }),
-      );
-    }
-    if (
-      event.event === 'on_chain_stream' &&
-      event.name === 'FinalResponseGenerator'
-    ) {
-      emitter.emit(
-        'data',
-        JSON.stringify({ type: 'response', data: event.data.chunk }),
-      );
-    }
-    if (
-      event.event === 'on_chain_end' &&
-      event.name === 'FinalResponseGenerator'
-    ) {
-      emitter.emit('end');
-    }
-  }
-};
-
-type BasicChainInput = {
-  chat_history: BaseMessage[];
-  query: string;
-};
-
-const createBasicWolframAlphaSearchRetrieverChain = (llm: BaseChatModel) => {
-  return RunnableSequence.from([
-    PromptTemplate.fromTemplate(basicWolframAlphaSearchRetrieverPrompt),
-    llm,
-    strParser,
-    RunnableLambda.from(async (input: string) => {
-      if (input === 'not_needed') {
-        return { query: '', docs: [] };
-      }
-
-      const res = await searchSearxng(input, {
-        language: 'en',
-        engines: ['wolframalpha'],
-      });
-
-      const documents = res.results.map(
-        (result) =>
-          new Document({
-            pageContent: result.content,
-            metadata: {
-              title: result.title,
-              url: result.url,
-              ...(result.img_src && { img_src: result.img_src }),
-            },
-          }),
-      );
-
-      return { query: input, docs: documents };
-    }),
-  ]);
-};
-
-const createBasicWolframAlphaSearchAnsweringChain = (llm: BaseChatModel) => {
-  const basicWolframAlphaSearchRetrieverChain =
-    createBasicWolframAlphaSearchRetrieverChain(llm);
-
-  const processDocs = (docs: Document[]) => {
-    return docs
-      .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
-      .join('\n');
-  };
-
-  return RunnableSequence.from([
-    RunnableMap.from({
-      query: (input: BasicChainInput) => input.query,
-      chat_history: (input: BasicChainInput) => input.chat_history,
-      context: RunnableSequence.from([
-        (input) => ({
-          query: input.query,
-          chat_history: formatChatHistoryAsString(input.chat_history),
-        }),
-        basicWolframAlphaSearchRetrieverChain
-          .pipe(({ query, docs }) => {
-            return docs;
-          })
-          .withConfig({
-            runName: 'FinalSourceRetriever',
-          })
-          .pipe(processDocs),
-      ]),
-    }),
-    ChatPromptTemplate.fromMessages([
-      ['system', basicWolframAlphaSearchResponsePrompt],
-      new MessagesPlaceholder('chat_history'),
-      ['user', '{query}'],
-    ]),
-    llm,
-    strParser,
-  ]).withConfig({
-    runName: 'FinalResponseGenerator',
-  });
-};
-
-const basicWolframAlphaSearch = (
-  query: string,
-  history: BaseMessage[],
-  llm: BaseChatModel,
-) => {
-  const emitter = new eventEmitter();
-
-  try {
-    const basicWolframAlphaSearchAnsweringChain =
-      createBasicWolframAlphaSearchAnsweringChain(llm);
-    const stream = basicWolframAlphaSearchAnsweringChain.streamEvents(
-      {
-        chat_history: history,
-        query: query,
-      },
-      {
-        version: 'v1',
-      },
-    );
-
-    handleStream(stream, emitter);
-  } catch (err) {
-    emitter.emit(
-      'error',
-      JSON.stringify({ data: 'An error has occurred please try again later' }),
-    );
-    logger.error(`Error in WolframAlphaSearch: ${err}`);
-  }
-
-  return emitter;
-};
-
-const handleWolframAlphaSearch = (
-  message: string,
-  history: BaseMessage[],
-  llm: BaseChatModel,
-  embeddings: Embeddings,
-) => {
-  const emitter = basicWolframAlphaSearch(message, history, llm);
-  return emitter;
-};
-
-export default handleWolframAlphaSearch;

src/agents/writingAssistant.ts

@@ -1,91 +0,0 @@
-import { BaseMessage } from '@langchain/core/messages';
-import {
-  ChatPromptTemplate,
-  MessagesPlaceholder,
-} from '@langchain/core/prompts';
-import { RunnableSequence } from '@langchain/core/runnables';
-import { StringOutputParser } from '@langchain/core/output_parsers';
-import type { StreamEvent } from '@langchain/core/tracers/log_stream';
-import eventEmitter from 'events';
-import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import type { Embeddings } from '@langchain/core/embeddings';
-import logger from '../utils/logger';
-import { IterableReadableStream } from '@langchain/core/utils/stream';
-
-const writingAssistantPrompt = `
-You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are currently set on focus mode 'Writing Assistant', this means you will be helping the user write a response to a given query. 
-Since you are a writing assistant, you would not perform web searches. If you think you lack information to answer the query, you can ask the user for more information or suggest them to switch to a different focus mode.
-`;
-
-const strParser = new StringOutputParser();
-
-const handleStream = async (
-  stream: IterableReadableStream<StreamEvent>,
-  emitter: eventEmitter,
-) => {
-  for await (const event of stream) {
-    if (
-      event.event === 'on_chain_stream' &&
-      event.name === 'FinalResponseGenerator'
-    ) {
-      emitter.emit(
-        'data',
-        JSON.stringify({ type: 'response', data: event.data.chunk }),
-      );
-    }
-    if (
-      event.event === 'on_chain_end' &&
-      event.name === 'FinalResponseGenerator'
-    ) {
-      emitter.emit('end');
-    }
-  }
-};
-
-const createWritingAssistantChain = (llm: BaseChatModel) => {
-  return RunnableSequence.from([
-    ChatPromptTemplate.fromMessages([
-      ['system', writingAssistantPrompt],
-      new MessagesPlaceholder('chat_history'),
-      ['user', '{query}'],
-    ]),
-    llm,
-    strParser,
-  ]).withConfig({
-    runName: 'FinalResponseGenerator',
-  });
-};
-
-const handleWritingAssistant = (
-  query: string,
-  history: BaseMessage[],
-  llm: BaseChatModel,
-  embeddings: Embeddings,
-) => {
-  const emitter = new eventEmitter();
-
-  try {
-    const writingAssistantChain = createWritingAssistantChain(llm);
-    const stream = writingAssistantChain.streamEvents(
-      {
-        chat_history: history,
-        query: query,
-      },
-      {
-        version: 'v1',
-      },
-    );
-
-    handleStream(stream, emitter);
-  } catch (err) {
-    emitter.emit(
-      'error',
-      JSON.stringify({ data: 'An error has occurred please try again later' }),
-    );
-    logger.error(`Error in writing assistant: ${err}`);
-  }
-
-  return emitter;
-};
-
-export default handleWritingAssistant;

src/agents/youtubeSearchAgent.ts

@@ -1,277 +0,0 @@
-import { BaseMessage } from '@langchain/core/messages';
-import {
-  PromptTemplate,
-  ChatPromptTemplate,
-  MessagesPlaceholder,
-} from '@langchain/core/prompts';
-import {
-  RunnableSequence,
-  RunnableMap,
-  RunnableLambda,
-} from '@langchain/core/runnables';
-import { StringOutputParser } from '@langchain/core/output_parsers';
-import { Document } from '@langchain/core/documents';
-import { searchSearxng } from '../lib/searxng';
-import type { StreamEvent } from '@langchain/core/tracers/log_stream';
-import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import type { Embeddings } from '@langchain/core/embeddings';
-import formatChatHistoryAsString from '../utils/formatHistory';
-import eventEmitter from 'events';
-import computeSimilarity from '../utils/computeSimilarity';
-import logger from '../utils/logger';
-import { IterableReadableStream } from '@langchain/core/utils/stream';
-
-const basicYoutubeSearchRetrieverPrompt = `
-You will be given a conversation below and a follow up question. You need to rephrase the follow-up question if needed so it is a standalone question that can be used by the LLM to search the web for information.
-If it is a writing task or a simple hi, hello rather than a question, you need to return \`not_needed\` as the response.
-
-Example:
-1. Follow up question: How does an A.C work?
-Rephrased: A.C working
-
-2. Follow up question: Linear algebra explanation video
-Rephrased: What is linear algebra?
-
-3. Follow up question: What is theory of relativity?
-Rephrased: What is theory of relativity?
-
-Conversation:
-{chat_history}
-
-Follow up question: {query}
-Rephrased question:
-`;
-
-const basicYoutubeSearchResponsePrompt = `
-    You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are set on focus mode 'Youtube', this means you will be searching for videos on the web using Youtube and providing information based on the video's transcript.
-
-    Generate a response that is informative and relevant to the user's query based on provided context (the context consits of search results containing a brief description of the content of that page).
-    You must use this context to answer the user's query in the best way possible. Use an unbaised and journalistic tone in your response. Do not repeat the text.
-    You must not tell the user to open any link or visit any website to get the answer. You must provide the answer in the response itself. If the user asks for links you can provide them.
-    Your responses should be medium to long in length be informative and relevant to the user's query. You can use markdowns to format your response. You should use bullet points to list the information. Make sure the answer is not short and is informative.
-    You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
-    Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
-    However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
-
-    Anything inside the following \`context\` HTML block provided below is for your knowledge returned by Youtube and is not shared by the user. You have to answer question on the basis of it and cite the relevant information from it but you do not have to
-    talk about the context in your response.
-
-    <context>
-    {context}
-    </context>
-
-    If you think there's nothing relevant in the search results, you can say that 'Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?'.
-    Anything between the \`context\` is retrieved from Youtube and is not a part of the conversation with the user. Today's date is ${new Date().toISOString()}
-`;
-
-const strParser = new StringOutputParser();
-
-const handleStream = async (
-  stream: IterableReadableStream<StreamEvent>,
-  emitter: eventEmitter,
-) => {
-  for await (const event of stream) {
-    if (
-      event.event === 'on_chain_end' &&
-      event.name === 'FinalSourceRetriever'
-    ) {
-      emitter.emit(
-        'data',
-        JSON.stringify({ type: 'sources', data: event.data.output }),
-      );
-    }
-    if (
-      event.event === 'on_chain_stream' &&
-      event.name === 'FinalResponseGenerator'
-    ) {
-      emitter.emit(
-        'data',
-        JSON.stringify({ type: 'response', data: event.data.chunk }),
-      );
-    }
-    if (
-      event.event === 'on_chain_end' &&
-      event.name === 'FinalResponseGenerator'
-    ) {
-      emitter.emit('end');
-    }
-  }
-};
-
-type BasicChainInput = {
-  chat_history: BaseMessage[];
-  query: string;
-};
-
-const createBasicYoutubeSearchRetrieverChain = (llm: BaseChatModel) => {
-  return RunnableSequence.from([
-    PromptTemplate.fromTemplate(basicYoutubeSearchRetrieverPrompt),
-    llm,
-    strParser,
-    RunnableLambda.from(async (input: string) => {
-      if (input === 'not_needed') {
-        return { query: '', docs: [] };
-      }
-
-      const res = await searchSearxng(input, {
-        language: 'en',
-        engines: ['youtube'],
-      });
-
-      const documents = res.results.map(
-        (result) =>
-          new Document({
-            pageContent: result.content ? result.content : result.title,
-            metadata: {
-              title: result.title,
-              url: result.url,
-              ...(result.img_src && { img_src: result.img_src }),
-            },
-          }),
-      );
-
-      return { query: input, docs: documents };
-    }),
-  ]);
-};
-
-const createBasicYoutubeSearchAnsweringChain = (
-  llm: BaseChatModel,
-  embeddings: Embeddings,
-  optimizationMode: 'speed' | 'balanced' | 'quality',
-) => {
-  const basicYoutubeSearchRetrieverChain =
-    createBasicYoutubeSearchRetrieverChain(llm);
-
-  const processDocs = async (docs: Document[]) => {
-    return docs
-      .map((_, index) => `${index + 1}. ${docs[index].pageContent}`)
-      .join('\n');
-  };
-
-  const rerankDocs = async ({
-    query,
-    docs,
-  }: {
-    query: string;
-    docs: Document[];
-  }) => {
-    if (docs.length === 0) {
-      return docs;
-    }
-
-    const docsWithContent = docs.filter(
-      (doc) => doc.pageContent && doc.pageContent.length > 0,
-    );
-
-    if (optimizationMode === 'speed') {
-      return docsWithContent.slice(0, 15);
-    } else {
-      const [docEmbeddings, queryEmbedding] = await Promise.all([
-        embeddings.embedDocuments(
-          docsWithContent.map((doc) => doc.pageContent),
-        ),
-        embeddings.embedQuery(query),
-      ]);
-
-      const similarity = docEmbeddings.map((docEmbedding, i) => {
-        const sim = computeSimilarity(queryEmbedding, docEmbedding);
-
-        return {
-          index: i,
-          similarity: sim,
-        };
-      });
-
-      const sortedDocs = similarity
-        .filter((sim) => sim.similarity > 0.3)
-        .sort((a, b) => b.similarity - a.similarity)
-        .slice(0, 15)
-        .map((sim) => docsWithContent[sim.index]);
-
-      return sortedDocs;
-    }
-  };
-
-  return RunnableSequence.from([
-    RunnableMap.from({
-      query: (input: BasicChainInput) => input.query,
-      chat_history: (input: BasicChainInput) => input.chat_history,
-      context: RunnableSequence.from([
-        (input) => ({
-          query: input.query,
-          chat_history: formatChatHistoryAsString(input.chat_history),
-        }),
-        basicYoutubeSearchRetrieverChain
-          .pipe(rerankDocs)
-          .withConfig({
-            runName: 'FinalSourceRetriever',
-          })
-          .pipe(processDocs),
-      ]),
-    }),
-    ChatPromptTemplate.fromMessages([
-      ['system', basicYoutubeSearchResponsePrompt],
-      new MessagesPlaceholder('chat_history'),
-      ['user', '{query}'],
-    ]),
-    llm,
-    strParser,
-  ]).withConfig({
-    runName: 'FinalResponseGenerator',
-  });
-};
-
-const basicYoutubeSearch = (
-  query: string,
-  history: BaseMessage[],
-  llm: BaseChatModel,
-  embeddings: Embeddings,
-  optimizationMode: 'speed' | 'balanced' | 'quality',
-) => {
-  const emitter = new eventEmitter();
-
-  try {
-    const basicYoutubeSearchAnsweringChain =
-      createBasicYoutubeSearchAnsweringChain(llm, embeddings, optimizationMode);
-
-    const stream = basicYoutubeSearchAnsweringChain.streamEvents(
-      {
-        chat_history: history,
-        query: query,
-      },
-      {
-        version: 'v1',
-      },
-    );
-
-    handleStream(stream, emitter);
-  } catch (err) {
-    emitter.emit(
-      'error',
-      JSON.stringify({ data: 'An error has occurred please try again later' }),
-    );
-    logger.error(`Error in youtube search: ${err}`);
-  }
-
-  return emitter;
-};
-
-const handleYoutubeSearch = (
-  message: string,
-  history: BaseMessage[],
-  llm: BaseChatModel,
-  embeddings: Embeddings,
-  optimizationMode: 'speed' | 'balanced' | 'quality',
-) => {
-  const emitter = basicYoutubeSearch(
-    message,
-    history,
-    llm,
-    embeddings,
-    optimizationMode,
-  );
-  return emitter;
-};
-
-export default handleYoutubeSearch;

src/app.ts

@@ -1,38 +1,16 @@
-import { startWebSocketServer } from './websocket';
 import express from 'express';
 import cors from 'cors';
-import http from 'http';
-import routes from './routes';
-import { getPort } from './config';
-import logger from './utils/logger';
-
-const port = getPort();
+import searchRoutes from './routes/search';
+import businessRoutes from './routes/business';
 
 const app = express();
-const server = http.createServer(app);
 
-const corsOptions = {
-  origin: '*',
-};
-
-app.use(cors(corsOptions));
+// Middleware
+app.use(cors());
 app.use(express.json());
 
-app.use('/api', routes);
-app.get('/api', (_, res) => {
-  res.status(200).json({ status: 'ok' });
-});
+// Routes
+app.use('/api/search', searchRoutes);
+app.use('/api/business', businessRoutes);
 
-server.listen(port, () => {
-  logger.info(`Server is running on port ${port}`);
-});
-
-startWebSocketServer(server);
-
-process.on('uncaughtException', (err, origin) => {
-  logger.error(`Uncaught Exception at ${origin}: ${err}`);
-});
-
-process.on('unhandledRejection', (reason, promise) => {
-  logger.error(`Unhandled Rejection at: ${promise}, reason: ${reason}`);
-});
+export default app;

src/config.ts

@@ -8,11 +8,13 @@ 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;
@@ -34,12 +36,16 @@ 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;
 
@@ -71,3 +77,16 @@ export const updateConfig = (config: RecursivePartial<Config>) => {
     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
+};

src/config/env.ts

@@ -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 }; 

src/config/index.ts

@@ -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 }; 

src/db/schema.ts

@@ -1,3 +1,4 @@
+import { sql } from 'drizzle-orm';
 import { text, integer, sqliteTable } from 'drizzle-orm/sqlite-core';
 
 export const messages = sqliteTable('messages', {
@@ -11,9 +12,17 @@ export const messages = sqliteTable('messages', {
   }),
 });
 
+interface File {
+  name: string;
+  fileId: string;
+}
+
 export const chats = sqliteTable('chats', {
   id: text('id').primaryKey(),
   title: text('title').notNull(),
   createdAt: text('createdAt').notNull(),
   focusMode: text('focusMode').notNull(),
+  files: text('files', { mode: 'json' })
+    .$type<File[]>()
+    .default(sql`'[]'`),
 });

src/index.ts

@@ -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); 

src/lib/categories.ts

@@ -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' }
+    ]
+  }
+]; 

src/lib/db/optOutDb.ts

@@ -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();
+  }
+} 

src/lib/db/supabase.ts

@@ -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;
+  }
+} 

src/lib/emailScraper.ts

@@ -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;
+  }
+} 

src/lib/outputParsers/lineOutputParser.ts

@@ -19,6 +19,8 @@ class LineOutputParser extends BaseOutputParser<string> {
   lc_namespace = ['langchain', 'output_parsers', 'line_output_parser'];
 
   async parse(text: string): Promise<string> {
+    text = text.trim() || '';
+
     const regex = /^(\s*(-|\*|\d+\.\s|\d+\)\s|\u2022)\s*)+/;
     const startKeyIndex = text.indexOf(`<${this.key}>`);
     const endKeyIndex = text.indexOf(`</${this.key}>`);

src/lib/outputParsers/listLineOutputParser.ts

@@ -19,6 +19,8 @@ class LineListOutputParser extends BaseOutputParser<string[]> {
   lc_namespace = ['langchain', 'output_parsers', 'line_list_output_parser'];
 
   async parse(text: string): Promise<string[]> {
+    text = text.trim() || '';
+
     const regex = /^(\s*(-|\*|\d+\.\s|\d+\)\s|\u2022)\s*)+/;
     const startKeyIndex = text.indexOf(`<${this.key}>`);
     const endKeyIndex = text.indexOf(`</${this.key}>`);

src/lib/providers/anthropic.ts

@@ -9,12 +9,20 @@ export const loadAnthropicChatModels = async () => {
 
   try {
     const chatModels = {
-      'claude-3-5-sonnet-20240620': {
+      'claude-3-5-sonnet-20241022': {
         displayName: 'Claude 3.5 Sonnet',
         model: new ChatAnthropic({
           temperature: 0.7,
           anthropicApiKey: anthropicApiKey,
-          model: 'claude-3-5-sonnet-20240620',
+          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': {

src/lib/providers/business/index.ts

@@ -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;
+  }
+} 

src/lib/providers/business/webScraper.ts

@@ -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
+    };
+  }
+} 

src/lib/providers/gemini.ts

@@ -0,0 +1,85 @@
+import {
+  ChatGoogleGenerativeAI,
+  GoogleGenerativeAIEmbeddings,
+} from '@langchain/google-genai';
+import { getGeminiApiKey } from '../../config';
+import logger from '../../utils/logger';
+
+export const loadGeminiChatModels = async () => {
+  const geminiApiKey = getGeminiApiKey();
+
+  if (!geminiApiKey) return {};
+
+  try {
+    const chatModels = {
+      'gemini-1.5-flash': {
+        displayName: 'Gemini 1.5 Flash',
+        model: new ChatGoogleGenerativeAI({
+          modelName: 'gemini-1.5-flash',
+          temperature: 0.7,
+          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,
+        }),
+      },
+      'gemini-2.0-flash-exp': {
+        displayName: 'Gemini 2.0 Flash Exp',
+        model: new ChatGoogleGenerativeAI({
+          modelName: 'gemini-2.0-flash-exp',
+          temperature: 0.7,
+          apiKey: geminiApiKey,
+        }),
+      },
+      'gemini-2.0-flash-thinking-exp-01-21': {
+        displayName: 'Gemini 2.0 Flash Thinking Exp 01-21',
+        model: new ChatGoogleGenerativeAI({
+          modelName: 'gemini-2.0-flash-thinking-exp-01-21',
+          temperature: 0.7,
+          apiKey: geminiApiKey,
+        }),
+      },
+    };
+
+    return chatModels;
+  } catch (err) {
+    logger.error(`Error loading Gemini models: ${err}`);
+    return {};
+  }
+};
+
+export const loadGeminiEmbeddingsModels = async () => {
+  const geminiApiKey = getGeminiApiKey();
+
+  if (!geminiApiKey) return {};
+
+  try {
+    const embeddingModels = {
+      'text-embedding-004': {
+        displayName: 'Text Embedding',
+        model: new GoogleGenerativeAIEmbeddings({
+          apiKey: geminiApiKey,
+          modelName: 'text-embedding-004',
+        }),
+      },
+    };
+
+    return embeddingModels;
+  } catch (err) {
+    logger.error(`Error loading Gemini embeddings model: ${err}`);
+    return {};
+  }
+};

src/lib/providers/groq.ts

@@ -9,6 +9,19 @@ export const loadGroqChatModels = async () => {
 
   try {
     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',
+          },
+        ),
+      },
       'llama-3.2-3b-preview': {
         displayName: 'Llama 3.2 3B',
         model: new ChatOpenAI(
@@ -22,12 +35,12 @@ export const loadGroqChatModels = async () => {
           },
         ),
       },
-      'llama-3.2-11b-text-preview': {
-        displayName: 'Llama 3.2 11B Text',
+      'llama-3.2-11b-vision-preview': {
+        displayName: 'Llama 3.2 11B Vision',
         model: new ChatOpenAI(
           {
             openAIApiKey: groqApiKey,
-            modelName: 'llama-3.2-11b-text-preview',
+            modelName: 'llama-3.2-11b-vision-preview',
             temperature: 0.7,
           },
           {
@@ -35,25 +48,12 @@ export const loadGroqChatModels = async () => {
           },
         ),
       },
-      'llama-3.2-90b-text-preview': {
-        displayName: 'Llama 3.2 90B Text',
+      'llama-3.2-90b-vision-preview': {
+        displayName: 'Llama 3.2 90B Vision',
         model: new ChatOpenAI(
           {
             openAIApiKey: groqApiKey,
-            modelName: 'llama-3.2-90b-text-preview',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
-      'llama-3.1-70b-versatile': {
-        displayName: 'Llama 3.1 70B',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'llama-3.1-70b-versatile',
+            modelName: 'llama-3.2-90b-vision-preview',
             temperature: 0.7,
           },
           {
@@ -113,19 +113,6 @@ export const loadGroqChatModels = async () => {
           },
         ),
       },
-      'gemma-7b-it': {
-        displayName: 'Gemma 7B',
-        model: new ChatOpenAI(
-          {
-            openAIApiKey: groqApiKey,
-            modelName: 'gemma-7b-it',
-            temperature: 0.7,
-          },
-          {
-            baseURL: 'https://api.groq.com/openai/v1',
-          },
-        ),
-      },
       'gemma2-9b-it': {
         displayName: 'Gemma2 9B',
         model: new ChatOpenAI(

src/lib/providers/index.ts

@@ -3,18 +3,21 @@ import { loadOllamaChatModels, loadOllamaEmbeddingsModels } from './ollama';
 import { loadOpenAIChatModels, loadOpenAIEmbeddingsModels } from './openai';
 import { loadAnthropicChatModels } from './anthropic';
 import { loadTransformersEmbeddingsModels } from './transformers';
+import { loadGeminiChatModels, loadGeminiEmbeddingsModels } from './gemini';
 
 const chatModelProviders = {
   openai: loadOpenAIChatModels,
   groq: loadGroqChatModels,
   ollama: loadOllamaChatModels,
   anthropic: loadAnthropicChatModels,
+  gemini: loadGeminiChatModels,
 };
 
 const embeddingModelProviders = {
   openai: loadOpenAIEmbeddingsModels,
   local: loadTransformersEmbeddingsModels,
   ollama: loadOllamaEmbeddingsModels,
+  gemini: loadGeminiEmbeddingsModels,
 };
 
 export const getAvailableChatModelProviders = async () => {

src/lib/providers/ollama.ts

@@ -1,21 +1,23 @@
 import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
-import { getOllamaApiEndpoint } from '../../config';
+import { getKeepAlive, getOllamaApiEndpoint } from '../../config';
 import logger from '../../utils/logger';
 import { ChatOllama } from '@langchain/community/chat_models/ollama';
+import axios from 'axios';
 
 export const loadOllamaChatModels = async () => {
   const ollamaEndpoint = getOllamaApiEndpoint();
+  const keepAlive = getKeepAlive();
 
   if (!ollamaEndpoint) return {};
 
   try {
-    const response = await fetch(`${ollamaEndpoint}/api/tags`, {
+    const response = await axios.get(`${ollamaEndpoint}/api/tags`, {
       headers: {
         'Content-Type': 'application/json',
       },
     });
 
-    const { models: ollamaModels } = (await response.json()) as any;
+    const { models: ollamaModels } = response.data;
 
     const chatModels = ollamaModels.reduce((acc, model) => {
       acc[model.model] = {
@@ -24,6 +26,7 @@ export const loadOllamaChatModels = async () => {
           baseUrl: ollamaEndpoint,
           model: model.model,
           temperature: 0.7,
+          keepAlive: keepAlive,
         }),
       };
 
@@ -43,13 +46,13 @@ export const loadOllamaEmbeddingsModels = async () => {
   if (!ollamaEndpoint) return {};
 
   try {
-    const response = await fetch(`${ollamaEndpoint}/api/tags`, {
+    const response = await axios.get(`${ollamaEndpoint}/api/tags`, {
       headers: {
         'Content-Type': 'application/json',
       },
     });
 
-    const { models: ollamaModels } = (await response.json()) as any;
+    const { models: ollamaModels } = response.data;
 
     const embeddingsModels = ollamaModels.reduce((acc, model) => {
       acc[model.model] = {

src/lib/search.ts

@@ -0,0 +1,54 @@
+import axios from 'axios';
+import { config } from '../config';
+
+interface SearchOptions {
+  maxResults?: number;
+  type?: 'general' | 'news';
+  engines?: string[];
+}
+
+interface SearchResult {
+  url: string;
+  title: string;
+  content: string;
+  score?: number;
+}
+
+export async function searchWeb(
+  query: string,
+  options: SearchOptions = {}
+): Promise<SearchResult[]> {
+  const {
+    maxResults = 20,
+    type = 'general',
+    engines = ['google', 'bing', 'duckduckgo']
+  } = options;
+
+  try {
+    const response = await axios.get(`${config.search.searxngUrl || process.env.SEARXNG_URL}/search`, {
+      params: {
+        q: query,
+        format: 'json',
+        categories: type,
+        engines: engines.join(','),
+        limit: maxResults
+      }
+    });
+
+    if (!response.data || !response.data.results) {
+      console.error('Invalid response from SearxNG:', response.data);
+      return [];
+    }
+
+    return response.data.results.map((result: any) => ({
+      url: result.url,
+      title: result.title,
+      content: result.content || result.snippet || '',
+      score: result.score
+    }));
+
+  } catch (error) {
+    console.error('Search failed:', error);
+    throw error;
+  }
+} 

src/lib/searxng.ts

@@ -1,47 +1,313 @@
 import axios from 'axios';
-import { getSearxngApiEndpoint } from '../config';
+import * as cheerio from 'cheerio';
+import { createWorker } from 'tesseract.js';
+import { env } from '../config/env';
+import { OllamaService } from './services/ollamaService';
+import { BusinessData } from './types';
+import { db } from './services/databaseService';
+import { generateBusinessId } from './utils';
+import { extractContactFromHtml, extractCleanAddress } from './utils/scraper';
+import { GeocodingService } from './services/geocodingService';
+import { cleanAddress, formatPhoneNumber, cleanEmail, cleanDescription } from './utils/dataCleanup';
+import { CleanupService } from './services/cleanupService';
 
-interface SearxngSearchOptions {
-  categories?: string[];
-  engines?: string[];
-  language?: string;
-  pageno?: number;
-}
-
-interface SearxngSearchResult {
-  title: string;
+// Define interfaces used only in this file
+interface SearchResult {
     url: string;
-  img_src?: string;
-  thumbnail_src?: string;
-  thumbnail?: string;
-  content?: string;
-  author?: string;
-  iframe_src?: string;
+    title: string;
+    content: string;
+    phone?: string;
+    email?: string;
+    address?: string;
+    website?: string;
+    rating?: number;
+    coordinates?: {
+        lat: number;
+        lng: number;
+    };
 }
 
-export const searchSearxng = async (
+interface ContactInfo {
+    phone?: string;
+    email?: string;
+    address?: string;
+    description?: string;
+    openingHours?: string[];
+}
+
+// Export the main search function
+export async function searchBusinesses(
     query: string,
-  opts?: SearxngSearchOptions,
-) => {
-  const searxngURL = getSearxngApiEndpoint();
-
-  const url = new URL(`${searxngURL}/search?format=json`);
-  url.searchParams.append('q', query);
-
-  if (opts) {
-    Object.keys(opts).forEach((key) => {
-      if (Array.isArray(opts[key])) {
-        url.searchParams.append(key, opts[key].join(','));
-        return;
+    options: { onProgress?: (status: string, progress: number) => void } = {}
+): Promise<BusinessData[]> {
+    try {
+        console.log('Processing search query:', query);
+        const [searchTerm, location] = query.split(' in ').map(s => s.trim());
+        if (!searchTerm || !location) {
+            throw new Error('Invalid search query format. Use: "search term in location"');
+        }
+
+        options.onProgress?.('Checking cache', 0);
+
+        // Check cache first
+        const cacheKey = `search:${searchTerm}:${location}`;
+        let results = await db.getFromCache(cacheKey);
+        
+        if (!results) {
+            // Check database for existing businesses
+            console.log('Searching database for:', searchTerm, 'in', location);
+            const existingBusinesses = await db.searchBusinesses(searchTerm, location);
+            
+            // Start search immediately
+            console.log('Starting web search');
+            const searchPromise = performSearch(searchTerm, location, options);
+            
+            if (existingBusinesses.length > 0) {
+                console.log(`Found ${existingBusinesses.length} existing businesses`);
+                options.onProgress?.('Retrieved from database', 50);
+            }
+
+            // Wait for new results
+            const newResults = await searchPromise;
+            console.log(`Got ${newResults.length} new results from search`);
+            
+            // Merge results, removing duplicates by ID
+            const allResults = [...existingBusinesses];
+            for (const result of newResults) {
+                if (!allResults.some(b => b.id === result.id)) {
+                    allResults.push(result);
+                }
+            }
+            
+            console.log(`Total unique results: ${allResults.length}`);
+            
+            // Cache combined results
+            await db.saveToCache(cacheKey, allResults, env.cache.durationHours * 60 * 60 * 1000);
+            
+            console.log(`Returning ${allResults.length} total results (${existingBusinesses.length} existing + ${newResults.length} new)`);
+            results = allResults;
+        }
+
+        // Clean all results using LLM
+        options.onProgress?.('Cleaning data', 75);
+        const cleanedResults = await CleanupService.cleanBusinessRecords(results);
+
+        options.onProgress?.('Search complete', 100);
+        return cleanedResults;
+    } catch (error) {
+        console.error('Search error:', error);
+        return [];
+    }
+}
+
+async function performSearch(
+    searchTerm: string, 
+    location: string, 
+    options: any
+): Promise<BusinessData[]> {
+    const queries = [
+        searchTerm + ' ' + location,
+        searchTerm + ' business near ' + location,
+        searchTerm + ' services ' + location,
+        'local ' + searchTerm + ' ' + location
+    ];
+
+    options.onProgress?.('Searching multiple sources', 25);
+
+    let allResults: SearchResult[] = [];
+    const seenUrls = new Set<string>();
+
+    for (const q of queries) {
+        try {
+            const response = await axios.get(`${env.searxng.currentUrl}/search`, {
+                params: {
+                    q,
+                    format: 'json',
+                    engines: 'google,google_maps',
+                    language: 'en-US',
+                    time_range: '',
+                    safesearch: 1
                 }
-      url.searchParams.append(key, opts[key]);
             });
+
+            if (response.data?.results) {
+                // Deduplicate results
+                const newResults = response.data.results.filter((result: SearchResult) => {
+                    if (seenUrls.has(result.url)) {
+                        return false;
+                    }
+                    seenUrls.add(result.url);
+                    return true;
+                });
+
+                console.log(`Found ${newResults.length} unique results from ${response.data.results[0]?.engine}`);
+                allResults = allResults.concat(newResults);
+            }
+        } catch (error) {
+            console.error(`Search failed for query "${q}":`, error);
+        }
     }
 
-  const res = await axios.get(url.toString());
+    options.onProgress?.('Processing results', 50);
 
-  const results: SearxngSearchResult[] = res.data.results;
-  const suggestions: string[] = res.data.suggestions;
+    const filteredResults = allResults.filter(isValidBusinessResult);
+    const processedResults = await processResults(filteredResults, location);
 
-  return { results, suggestions };
-};
+    // Save results to database
+    for (const result of processedResults) {
+        await db.saveBusiness(result).catch(console.error);
+    }
+
+    options.onProgress?.('Search complete', 100);
+    return processedResults;
+}
+
+// Add other necessary functions (isValidBusinessResult, processResults, etc.)
+function isValidBusinessResult(result: SearchResult): boolean {
+    // Skip listing/directory pages and search results
+    const skipPatterns = [
+        'tripadvisor.com',
+        'yelp.com',
+        'opentable.com',
+        'restaurants-for-sale',
+        'guide.michelin.com',
+        'denver.org',
+        '/blog/',
+        '/maps/',
+        'search?',
+        'features/',
+        '/lists/',
+        'reddit.com',
+        'eater.com'
+    ];
+
+    if (skipPatterns.some(pattern => result.url.toLowerCase().includes(pattern))) {
+        console.log(`Skipping listing page: ${result.url}`);
+        return false;
+    }
+
+    // Must have a title
+    if (!result.title || result.title.length < 2) {
+        return false;
+    }
+
+    // Skip results that look like articles or lists
+    const articlePatterns = [
+        'Best',
+        'Top',
+        'Guide',
+        'Where to',
+        'Welcome to',
+        'Updated',
+        'Near',
+        'Restaurants in'
+    ];
+
+    if (articlePatterns.some(pattern => result.title.includes(pattern))) {
+        console.log(`Skipping article: ${result.title}`);
+        return false;
+    }
+
+    // Only accept results that look like actual business pages
+    const businessPatterns = [
+        'menu',
+        'reservation',
+        'location',
+        'contact',
+        'about-us',
+        'home'
+    ];
+
+    const hasBusinessPattern = businessPatterns.some(pattern => 
+        result.url.toLowerCase().includes(pattern) || 
+        result.content.toLowerCase().includes(pattern)
+    );
+
+    if (!hasBusinessPattern) {
+        console.log(`Skipping non-business page: ${result.url}`);
+        return false;
+    }
+
+    return true;
+}
+
+async function processResults(results: SearchResult[], location: string): Promise<BusinessData[]> {
+    const processedResults: BusinessData[] = [];
+    
+    // Get coordinates for the location
+    const locationGeo = await GeocodingService.geocode(location);
+    const defaultCoords = locationGeo || { lat: 39.7392, lng: -104.9903 };
+
+    for (const result of results) {
+        try {
+            // Extract contact info from webpage
+            const contactInfo = await extractContactFromHtml(result.url);
+            
+            // Create initial business record
+            const business: BusinessData = {
+                id: generateBusinessId(result),
+                name: cleanBusinessName(result.title),
+                phone: result.phone || contactInfo.phone || '',
+                email: result.email || contactInfo.email || '',
+                address: result.address || contactInfo.address || '',
+                rating: result.rating || 0,
+                website: result.website || result.url || '',
+                logo: '',
+                source: 'web',
+                description: result.content || contactInfo.description || '',
+                location: defaultCoords,
+                openingHours: contactInfo.openingHours
+            };
+
+            // Clean up the record using LLM
+            const cleanedBusiness = await CleanupService.cleanBusinessRecord(business);
+
+            // Get coordinates for cleaned address
+            if (cleanedBusiness.address) {
+                const addressGeo = await GeocodingService.geocode(cleanedBusiness.address);
+                if (addressGeo) {
+                    cleanedBusiness.location = addressGeo;
+                }
+            }
+
+            // Only add if we have at least a name and either phone or address
+            if (cleanedBusiness.name && (cleanedBusiness.phone || cleanedBusiness.address)) {
+                processedResults.push(cleanedBusiness);
+            }
+
+        } catch (error) {
+            console.error(`Error processing result ${result.title}:`, error);
+        }
+    }
+
+    return processedResults;
+}
+
+// Helper functions
+function cleanBusinessName(name: string): string {
+    // Remove common suffixes and prefixes
+    const cleanName = name
+        .replace(/^(The|A|An)\s+/i, '')
+        .replace(/\s+(-|–|—|:).*$/, '')
+        .replace(/\s*\([^)]*\)/g, '')
+        .trim();
+    
+    return cleanName;
+}
+
+async function getLocationCoordinates(address: string): Promise<{lat: number, lng: number}> {
+    // Implement geocoding here
+    // For now, return default coordinates for Denver
+    return { lat: 39.7392, lng: -104.9903 };
+}
+
+async function searchAndUpdateInBackground(searchTerm: string, location: string) {
+    try {
+        const results = await performSearch(searchTerm, location, {});
+        console.log(`Updated ${results.length} businesses in background`);
+    } catch (error) {
+        console.error('Background search error:', error);
+    }
+}
+
+// ... rest of the file remains the same

src/lib/services/businessCrawler.ts

@@ -0,0 +1,111 @@
+import axios from 'axios';
+import * as cheerio from 'cheerio';
+import { Cache } from '../utils/cache';
+import { RateLimiter } from '../utils/rateLimiter';
+
+interface CrawlResult {
+  mainContent: string;
+  contactInfo: string;
+  aboutInfo: string;
+  structuredData: any;
+}
+
+export class BusinessCrawler {
+  private cache: Cache<CrawlResult>;
+  private rateLimiter: RateLimiter;
+
+  constructor() {
+    this.cache = new Cache<CrawlResult>(60); // 1 hour cache
+    this.rateLimiter = new RateLimiter();
+  }
+
+  async crawlBusinessSite(url: string): Promise<CrawlResult> {
+    // Check cache first
+    const cached = this.cache.get(url);
+    if (cached) return cached;
+
+    await this.rateLimiter.waitForSlot();
+
+    try {
+      const mainPage = await this.fetchPage(url);
+      const $ = cheerio.load(mainPage);
+      
+      // Get all important URLs
+      const contactUrl = this.findContactPage($, url);
+      const aboutUrl = this.findAboutPage($, url);
+
+      // Crawl additional pages
+      const [contactPage, aboutPage] = await Promise.all([
+        contactUrl ? this.fetchPage(contactUrl) : '',
+        aboutUrl ? this.fetchPage(aboutUrl) : ''
+      ]);
+
+      // Extract structured data
+      const structuredData = this.extractStructuredData($);
+
+      const result = {
+        mainContent: $('body').text(),
+        contactInfo: contactPage,
+        aboutInfo: aboutPage,
+        structuredData
+      };
+
+      this.cache.set(url, result);
+      return result;
+    } catch (error) {
+      console.error(`Failed to crawl ${url}:`, error);
+      return {
+        mainContent: '',
+        contactInfo: '',
+        aboutInfo: '',
+        structuredData: {}
+      };
+    }
+  }
+
+  private async fetchPage(url: string): Promise<string> {
+    try {
+      const response = await axios.get(url, {
+        timeout: 10000,
+        headers: {
+          'User-Agent': 'Mozilla/5.0 (compatible; BizSearch/1.0; +http://localhost:3000/about)',
+        }
+      });
+      return response.data;
+    } catch (error) {
+      console.error(`Failed to fetch ${url}:`, error);
+      return '';
+    }
+  }
+
+  private findContactPage($: cheerio.CheerioAPI, baseUrl: string): string | null {
+    const contactLinks = $('a[href*="contact"], a:contains("Contact")');
+    if (contactLinks.length > 0) {
+      const href = contactLinks.first().attr('href');
+      return href ? new URL(href, baseUrl).toString() : null;
+    }
+    return null;
+  }
+
+  private findAboutPage($: cheerio.CheerioAPI, baseUrl: string): string | null {
+    const aboutLinks = $('a[href*="about"], a:contains("About")');
+    if (aboutLinks.length > 0) {
+      const href = aboutLinks.first().attr('href');
+      return href ? new URL(href, baseUrl).toString() : null;
+    }
+    return null;
+  }
+
+  private extractStructuredData($: cheerio.CheerioAPI): any {
+    const structuredData: any[] = [];
+    $('script[type="application/ld+json"]').each((_, element) => {
+      try {
+        const data = JSON.parse($(element).html() || '{}');
+        structuredData.push(data);
+      } catch (error) {
+        console.error('Failed to parse structured data:', error);
+      }
+    });
+    return structuredData;
+  }
+} 

src/lib/services/cacheService.ts

@@ -0,0 +1,71 @@
+import { supabase } from '../supabase';
+import { BusinessData } from '../searxng';
+
+export class CacheService {
+  static async getCachedResults(category: string, location: string): Promise<BusinessData[] | null> {
+    try {
+      const { data, error } = await supabase
+        .from('search_cache')
+        .select('results')
+        .eq('category', category.toLowerCase())
+        .eq('location', location.toLowerCase())
+        .gt('expires_at', new Date().toISOString())
+        .order('created_at', { ascending: false })
+        .limit(1)
+        .single();
+
+      if (error) throw error;
+      return data ? data.results : null;
+    } catch (error) {
+      console.error('Cache lookup failed:', error);
+      return null;
+    }
+  }
+
+  static async cacheResults(
+    category: string, 
+    location: string, 
+    results: BusinessData[],
+    expiresInDays: number = 7
+  ): Promise<void> {
+    try {
+      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) throw error;
+    } catch (error) {
+      console.error('Failed to cache results:', error);
+    }
+  }
+
+  static async updateCache(
+    category: string,
+    location: string,
+    newResults: BusinessData[]
+  ): Promise<void> {
+    try {
+      const { error } = await supabase
+        .from('search_cache')
+        .update({
+          results: newResults,
+          updated_at: new Date().toISOString()
+        })
+        .eq('category', category.toLowerCase())
+        .eq('location', location.toLowerCase());
+
+      if (error) throw error;
+    } catch (error) {
+      console.error('Failed to update cache:', error);
+    }
+  }
+} 

src/lib/services/cleanupService.ts

@@ -0,0 +1,235 @@
+import { DeepSeekService } from './deepseekService';
+import { Business } from '../types';
+import { db } from './databaseService';
+
+// Constants for validation and scoring
+const BATCH_SIZE = 3; // Process businesses in small batches to avoid overwhelming LLM
+const LLM_TIMEOUT = 30000; // 30 second timeout for LLM requests
+const MIN_CONFIDENCE_SCORE = 0.7; // Minimum score required to cache results
+const VALID_EMAIL_REGEX = /^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$/;
+const VALID_PHONE_REGEX = /^\(\d{3}\) \d{3}-\d{4}$/;
+const VALID_ADDRESS_REGEX = /^\d+.*(?:street|st|avenue|ave|road|rd|boulevard|blvd|lane|ln|drive|dr|court|ct|circle|cir|way|parkway|pkwy|place|pl),?\s+[a-z ]+,\s*[a-z]{2}\s+\d{5}$/i;
+
+export class CleanupService {
+    /**
+     * Attempts to clean business data using LLM with timeout protection.
+     * Falls back to original data if LLM fails or times out.
+     */
+    private static async cleanWithLLM(prompt: string, originalBusiness: Business): Promise<string> {
+        try {
+            const timeoutPromise = new Promise((_, reject) => {
+                setTimeout(() => reject(new Error('LLM timeout')), LLM_TIMEOUT);
+            });
+
+            const llmPromise = DeepSeekService.chat([{
+                role: 'user',
+                content: prompt
+            }]);
+
+            const response = await Promise.race([llmPromise, timeoutPromise]);
+            return (response as string).trim();
+        } catch (error) {
+            console.error('LLM cleanup error:', error);
+            // On timeout, return the original values
+            return `
+                Address: ${originalBusiness.address}
+                Phone: ${originalBusiness.phone}
+                Email: ${originalBusiness.email}
+                Description: ${originalBusiness.description}
+            `;
+        }
+    }
+
+    /**
+     * Calculates a confidence score (0-1) for the cleaned business data.
+     * Score is based on:
+     * - Valid email format (0.25)
+     * - Valid phone format (0.25)
+     * - Valid address format (0.25)
+     * - Description quality (0.25)
+     */
+    private static calculateConfidenceScore(business: Business): number {
+        let score = 0;
+        
+        // Valid email adds 0.25
+        if (business.email && VALID_EMAIL_REGEX.test(business.email)) {
+            score += 0.25;
+        }
+        
+        // Valid phone adds 0.25
+        if (business.phone && VALID_PHONE_REGEX.test(business.phone)) {
+            score += 0.25;
+        }
+        
+        // Valid address adds 0.25
+        if (business.address && VALID_ADDRESS_REGEX.test(business.address)) {
+            score += 0.25;
+        }
+        
+        // Description quality checks (0.25 max)
+        if (business.description) {
+            // Length check (0.1)
+            if (business.description.length > 30 && business.description.length < 200) {
+                score += 0.1;
+            }
+            
+            // Relevance check (0.1)
+            const businessType = this.getBusinessType(business.name);
+            if (business.description.toLowerCase().includes(businessType)) {
+                score += 0.1;
+            }
+            
+            // No HTML/markdown (0.05)
+            if (!/[<>[\]()]/.test(business.description)) {
+                score += 0.05;
+            }
+        }
+
+        return score;
+    }
+
+    /**
+     * Determines the type of business based on name keywords.
+     * Used for validating and generating descriptions.
+     */
+    private static getBusinessType(name: string): string {
+        const types = [
+            'restaurant', 'plumber', 'electrician', 'cafe', 'bar', 
+            'salon', 'shop', 'store', 'service'
+        ];
+        
+        const nameLower = name.toLowerCase();
+        return types.find(type => nameLower.includes(type)) || 'business';
+    }
+
+    /**
+     * Parses LLM response into structured business data.
+     * Expects format: "field: value" for each line.
+     */
+    private static parseResponse(response: string): Partial<Business> {
+        const cleaned: Partial<Business> = {};
+        const lines = response.split('\n');
+        
+        for (const line of lines) {
+            const [field, ...values] = line.split(':');
+            const value = values.join(':').trim();
+            
+            switch (field.toLowerCase().trim()) {
+                case 'address':
+                    cleaned.address = value;
+                    break;
+                case 'phone':
+                    cleaned.phone = value;
+                    break;
+                case 'email':
+                    cleaned.email = value;
+                    break;
+                case 'description':
+                    cleaned.description = value;
+                    break;
+            }
+        }
+
+        return cleaned;
+    }
+
+    /**
+     * Applies validation rules and cleaning to each field.
+     * - Standardizes formats
+     * - Removes invalid data
+     * - Ensures consistent formatting
+     */
+    private static validateAndClean(business: Business): Business {
+        const cleaned = { ...business };
+
+        // Email validation and cleaning
+        if (cleaned.email) {
+            cleaned.email = cleaned.email
+                .toLowerCase()
+                .replace(/\[|\]|\(mailto:.*?\)/g, '')
+                .replace(/^\d+-\d+/, '')
+                .trim();
+            
+            if (!VALID_EMAIL_REGEX.test(cleaned.email) || 
+                ['none', 'n/a', 'union office', ''].includes(cleaned.email.toLowerCase())) {
+                cleaned.email = '';
+            }
+        }
+
+        // Phone validation and cleaning
+        if (cleaned.phone) {
+            const digits = cleaned.phone.replace(/\D/g, '');
+            if (digits.length === 10) {
+                cleaned.phone = `(${digits.slice(0,3)}) ${digits.slice(3,6)}-${digits.slice(6)}`;
+            } else {
+                cleaned.phone = '';
+            }
+        }
+
+        // Address validation and cleaning
+        if (cleaned.address) {
+            cleaned.address = cleaned.address
+                .replace(/^.*?(?=\d|[A-Z])/s, '')
+                .replace(/^(Sure!.*?:|The business.*?:|.*?address.*?:)(?:\s*\\n)*\s*/si, '')
+                .replace(/\s+/g, ' ')
+                .trim();
+            
+            // Standardize state abbreviations
+            cleaned.address = cleaned.address.replace(/\b(Colorado|Colo|Col)\b/gi, 'CO');
+        }
+
+        // Description validation and cleaning
+        if (cleaned.description) {
+            cleaned.description = cleaned.description
+                .replace(/\$\d+(\.\d{2})?/g, '') // Remove prices
+                .replace(/\b(call|email|website|click|visit)\b.*$/i, '') // Remove calls to action
+                .replace(/\s+/g, ' ')
+                .trim();
+
+            const businessType = this.getBusinessType(cleaned.name);
+            if (businessType !== 'business' && 
+                !cleaned.description.toLowerCase().includes(businessType)) {
+                cleaned.description = `${businessType.charAt(0).toUpperCase() + businessType.slice(1)} services in the Denver area.`;
+            }
+        }
+
+        return cleaned;
+    }
+
+    static async cleanBusinessRecord(business: Business): Promise<Business> {
+        // Check cache first
+        const cacheKey = `clean:${business.id}`;
+        const cached = await db.getFromCache(cacheKey);
+        if (cached) {
+            console.log('Using cached clean data for:', business.name);
+            return cached;
+        }
+
+        // Clean using DeepSeek
+        const cleaned = await DeepSeekService.cleanBusinessData(business);
+        const validated = this.validateAndClean({ ...business, ...cleaned });
+        
+        // Only cache if confidence score is high enough
+        const confidence = this.calculateConfidenceScore(validated);
+        if (confidence >= MIN_CONFIDENCE_SCORE) {
+            await db.saveToCache(cacheKey, validated, 24 * 60 * 60 * 1000);
+        }
+
+        return validated;
+    }
+
+    static async cleanBusinessRecords(businesses: Business[]): Promise<Business[]> {
+        const cleanedBusinesses: Business[] = [];
+        
+        // Process in batches
+        for (let i = 0; i < businesses.length; i += BATCH_SIZE) {
+            const batch = businesses.slice(i, i + BATCH_SIZE);
+            const cleanedBatch = await Promise.all(
+                batch.map(business => this.cleanBusinessRecord(business))
+            );
+            cleanedBusinesses.push(...cleanedBatch);
+        }
+        
+        return cleanedBusinesses;
+    }
+} 

src/lib/services/dataValidation.ts

@@ -0,0 +1,107 @@
+import { OllamaService } from './ollamaService';
+
+interface ValidatedBusinessData {
+  name: string;
+  phone: string;
+  email: string;
+  address: string;
+  description: string;
+  hours?: string;
+  isValid: boolean;
+}
+
+export class DataValidationService {
+  private ollama: OllamaService;
+
+  constructor() {
+    this.ollama = new OllamaService();
+  }
+
+  async validateAndCleanData(rawText: string): Promise<ValidatedBusinessData> {
+    try {
+      const prompt = `
+        You are a business data validation expert. Extract and validate business information from the following text.
+        Return ONLY a JSON object with the following format, nothing else:
+        {
+          "name": "verified business name",
+          "phone": "formatted phone number or N/A",
+          "email": "verified email address or N/A",
+          "address": "verified physical address or N/A",
+          "description": "short business description",
+          "hours": "business hours if available",
+          "isValid": boolean
+        }
+
+        Rules:
+        1. Phone numbers should be in (XXX) XXX-XXXX format
+        2. Addresses should be properly formatted with street, city, state, zip
+        3. Remove any irrelevant text from descriptions
+        4. Set isValid to true only if name and at least one contact method is found
+        5. Clean up any obvious formatting issues
+        6. Validate email addresses for proper format
+
+        Text to analyze:
+        ${rawText}
+      `;
+
+      const response = await this.ollama.generateResponse(prompt);
+      
+      try {
+        // Find the JSON object in the response
+        const jsonMatch = response.match(/\{[\s\S]*\}/);
+        if (!jsonMatch) {
+          throw new Error('No JSON found in response');
+        }
+        
+        const result = JSON.parse(jsonMatch[0]);
+        return this.validateResult(result);
+      } catch (parseError) {
+        console.error('Failed to parse Ollama response:', parseError);
+        throw parseError;
+      }
+    } catch (error) {
+      console.error('Data validation failed:', error);
+      return {
+        name: 'Unknown',
+        phone: 'N/A',
+        email: 'N/A',
+        address: 'N/A',
+        description: '',
+        hours: '',
+        isValid: false
+      };
+    }
+  }
+
+  private validateResult(result: any): ValidatedBusinessData {
+    // Ensure all required fields are present
+    const validated: ValidatedBusinessData = {
+      name: this.cleanField(result.name) || 'Unknown',
+      phone: this.formatPhone(result.phone) || 'N/A',
+      email: this.cleanField(result.email) || 'N/A',
+      address: this.cleanField(result.address) || 'N/A',
+      description: this.cleanField(result.description) || '',
+      hours: this.cleanField(result.hours),
+      isValid: Boolean(result.isValid)
+    };
+
+    return validated;
+  }
+
+  private cleanField(value: any): string {
+    if (!value || typeof value !== 'string') return '';
+    return value.trim().replace(/\s+/g, ' ');
+  }
+
+  private formatPhone(phone: string): string {
+    if (!phone || phone === 'N/A') return 'N/A';
+    
+    // Extract digits
+    const digits = phone.replace(/\D/g, '');
+    if (digits.length === 10) {
+      return `(${digits.slice(0,3)}) ${digits.slice(3,6)}-${digits.slice(6)}`;
+    }
+    
+    return phone;
+  }
+} 

src/lib/services/databaseService.ts

@@ -0,0 +1,80 @@
+import { createClient } from '@supabase/supabase-js';
+import { Business } from '../types';
+import env from '../../config/env';
+
+interface PartialBusiness {
+    name: string;
+    address: string;
+    phone: string;
+    description: string;
+    website?: string;
+    rating?: number;
+    source?: string;
+    location?: {
+        lat: number;
+        lng: number;
+    };
+}
+
+export class DatabaseService {
+    private supabase;
+    
+    constructor() {
+        this.supabase = createClient(env.SUPABASE_URL, env.SUPABASE_KEY);
+    }
+
+    async saveBusiness(business: PartialBusiness): Promise<Business> {
+        const { data, error } = await this.supabase
+            .from('businesses')
+            .upsert({
+                name: business.name,
+                address: business.address,
+                phone: business.phone,
+                description: business.description,
+                website: business.website,
+                source: business.source || 'deepseek',
+                rating: business.rating || 4.5,
+                location: business.location ? `(${business.location.lng},${business.location.lat})` : '(0,0)'
+            })
+            .select()
+            .single();
+
+        if (error) {
+            console.error('Error saving business:', error);
+            throw new Error('Failed to save business');
+        }
+
+        return data;
+    }
+
+    async findBusinessesByQuery(query: string, location: string): Promise<Business[]> {
+        const { data, error } = await this.supabase
+            .from('businesses')
+            .select('*')
+            .or(`name.ilike.%${query}%,description.ilike.%${query}%`)
+            .ilike('address', `%${location}%`)
+            .order('rating', { ascending: false });
+
+        if (error) {
+            console.error('Error finding businesses:', error);
+            throw new Error('Failed to find businesses');
+        }
+
+        return data || [];
+    }
+
+    async getBusinessById(id: string): Promise<Business | null> {
+        const { data, error } = await this.supabase
+            .from('businesses')
+            .select('*')
+            .eq('id', id)
+            .single();
+
+        if (error) {
+            console.error('Error getting business:', error);
+            return null;
+        }
+
+        return data;
+    }
+} 

src/lib/services/deepseekService.ts

@@ -0,0 +1,285 @@
+import axios from 'axios';
+import EventEmitter from 'events';
+import { Business } from '../types';
+
+interface PartialBusiness {
+    name: string;
+    address: string;
+    phone: string;
+    description: string;
+    website?: string;
+    rating?: number;
+}
+
+export class DeepSeekService extends EventEmitter {
+    private readonly baseUrl: string;
+    private readonly model: string;
+    
+    constructor() {
+        super();
+        this.baseUrl = process.env.OLLAMA_URL || 'http://localhost:11434';
+        this.model = process.env.OLLAMA_MODEL || 'deepseek-coder:6.7b';
+        console.log('DeepSeekService initialized with:', {
+            baseUrl: this.baseUrl,
+            model: this.model
+        });
+    }
+
+    async streamChat(messages: any[], onResult: (business: PartialBusiness) => Promise<void>): Promise<void> {
+        try {
+            console.log('\nStarting streaming chat request...');
+            
+            // Enhanced system prompt with more explicit instructions
+            const enhancedMessages = [
+                {
+                    role: "system",
+                    content: `You are a business search assistant powered by Deepseek Coder. Your task is to generate sample business listings in JSON format.
+
+When asked about businesses in a location, return business listings one at a time in this exact JSON format:
+
+\`\`\`json
+{
+    "name": "Example Plumbing Co",
+    "address": "123 Main St, Denver, CO 80202",
+    "phone": "(303) 555-0123",
+    "description": "Licensed plumbing contractor specializing in residential and commercial services",
+    "website": "https://exampleplumbing.com",
+    "rating": 4.8
+}
+\`\`\`
+
+Important rules:
+1. Return ONE business at a time in JSON format
+2. Generate realistic but fictional business data
+3. Use proper formatting for phone numbers and addresses
+4. Include ratings from 1-5 stars (can use decimals)
+5. When sorting by rating, return highest rated first
+6. Make each business unique with different names, addresses, and phone numbers
+7. Keep descriptions concise and professional
+8. Use realistic website URLs based on business names
+9. Return exactly the number of businesses requested`
+                },
+                ...messages
+            ];
+
+            console.log('Sending streaming request to Ollama with messages:', JSON.stringify(enhancedMessages, null, 2));
+
+            const response = await axios.post(`${this.baseUrl}/api/chat`, {
+                model: this.model,
+                messages: enhancedMessages,
+                stream: true,
+                temperature: 0.7,
+                max_tokens: 1000,
+                system: "You are a business search assistant that returns one business at a time in JSON format."
+            }, {
+                responseType: 'stream'
+            });
+
+            let currentJson = '';
+            response.data.on('data', async (chunk: Buffer) => {
+                const text = chunk.toString();
+                currentJson += text;
+
+                // Try to find and process complete JSON objects
+                try {
+                    const business = await this.extractNextBusiness(currentJson);
+                    if (business) {
+                        currentJson = ''; // Reset for next business
+                        await onResult(business);
+                    }
+                } catch (error) {
+                    // Continue collecting more data if JSON is incomplete
+                    console.debug('Collecting more data for complete JSON');
+                }
+            });
+
+            return new Promise((resolve, reject) => {
+                response.data.on('end', () => resolve());
+                response.data.on('error', (error: Error) => reject(error));
+            });
+
+        } catch (error) {
+            console.error('\nDeepseek streaming chat error:', error);
+            if (error instanceof Error) {
+                console.error('Error stack:', error.stack);
+                throw new Error(`AI model streaming error: ${error.message}`);
+            }
+            throw new Error('Failed to get streaming response from AI model');
+        }
+    }
+
+    private async extractNextBusiness(text: string): Promise<PartialBusiness | null> {
+        // Try to find a complete JSON object
+        const jsonMatch = text.match(/\{[^{]*\}/);
+        if (!jsonMatch) return null;
+
+        try {
+            const jsonStr = jsonMatch[0];
+            const business = JSON.parse(jsonStr);
+            
+            // Validate required fields
+            if (!business.name || !business.address || !business.phone || !business.description) {
+                return null;
+            }
+
+            return business;
+        } catch (e) {
+            return null;
+        }
+    }
+
+    async chat(messages: any[]): Promise<any> {
+        try {
+            console.log('\nStarting chat request...');
+            
+            // Enhanced system prompt with more explicit instructions
+            const enhancedMessages = [
+                {
+                    role: "system",
+                    content: `You are a business search assistant powered by Deepseek Coder. Your task is to generate sample business listings in JSON format.
+
+When asked about businesses in a location, return business listings in this exact JSON format, with no additional text or comments:
+
+\`\`\`json
+[
+    {
+        "name": "Example Plumbing Co",
+        "address": "123 Main St, Denver, CO 80202",
+        "phone": "(303) 555-0123",
+        "description": "Licensed plumbing contractor specializing in residential and commercial services",
+        "website": "https://exampleplumbing.com",
+        "rating": 4.8
+    }
+]
+\`\`\`
+
+Important rules:
+1. Return ONLY the JSON array inside code blocks - no explanations or comments
+2. Generate realistic but fictional business data
+3. Use proper formatting for phone numbers (e.g., "(303) 555-XXXX") and addresses
+4. Include ratings from 1-5 stars (can use decimals, e.g., 4.8)
+5. When sorting by rating, sort from highest to lowest rating
+6. When asked for a specific number of results, always return exactly that many
+7. Make each business unique with different names, addresses, and phone numbers
+8. Keep descriptions concise and professional
+9. Use realistic website URLs based on business names`
+                },
+                ...messages
+            ];
+
+            console.log('Sending request to Ollama with messages:', JSON.stringify(enhancedMessages, null, 2));
+
+            const response = await axios.post(`${this.baseUrl}/api/chat`, {
+                model: this.model,
+                messages: enhancedMessages,
+                stream: false,
+                temperature: 0.7,
+                max_tokens: 1000,
+                system: "You are a business search assistant that always responds with JSON data."
+            });
+
+            if (!response.data) {
+                throw new Error('Empty response from AI model');
+            }
+
+            console.log('\nRaw response data:', JSON.stringify(response.data, null, 2));
+
+            if (!response.data.message?.content) {
+                throw new Error('No content in AI model response');
+            }
+
+            console.log('\nParsing AI response...');
+            const results = await this.sanitizeJsonResponse(response.data.message.content);
+            console.log('Parsed results:', JSON.stringify(results, null, 2));
+            
+            return results;
+
+        } catch (error) {
+            console.error('\nDeepseek chat error:', error);
+            if (error instanceof Error) {
+                console.error('Error stack:', error.stack);
+                throw new Error(`AI model error: ${error.message}`);
+            }
+            throw new Error('Failed to get response from AI model');
+        }
+    }
+
+    private async sanitizeJsonResponse(text: string): Promise<PartialBusiness[]> {
+        console.log('Attempting to parse response:', text);
+
+        // First try to find JSON blocks
+        const jsonBlockMatch = text.match(/```(?:json)?\s*([\s\S]*?)\s*```/);
+        if (jsonBlockMatch) {
+            try {
+                const jsonStr = jsonBlockMatch[1].trim();
+                console.log('Found JSON block:', jsonStr);
+                const parsed = JSON.parse(jsonStr);
+                return Array.isArray(parsed) ? parsed : [parsed];
+            } catch (e) {
+                console.error('Failed to parse JSON block:', e);
+            }
+        }
+
+        // Then try to find any JSON-like structure
+        const jsonPatterns = [
+            /\[\s*\{[\s\S]*\}\s*\]/,  // Array of objects
+            /\{[\s\S]*\}/             // Single object
+        ];
+
+        for (const pattern of jsonPatterns) {
+            const match = text.match(pattern);
+            if (match) {
+                try {
+                    const jsonStr = match[0].trim();
+                    console.log('Found JSON pattern:', jsonStr);
+                    const parsed = JSON.parse(jsonStr);
+                    return Array.isArray(parsed) ? parsed : [parsed];
+                } catch (e) {
+                    console.error('Failed to parse JSON pattern:', e);
+                    continue;
+                }
+            }
+        }
+
+        // If no valid JSON found, try to extract structured data
+        try {
+            const extractedData = this.extractBusinessData(text);
+            if (extractedData) {
+                console.log('Extracted business data:', extractedData);
+                return [extractedData];
+            }
+        } catch (e) {
+            console.error('Failed to extract business data:', e);
+        }
+
+        throw new Error('No valid JSON or business information found in response');
+    }
+
+    private extractBusinessData(text: string): PartialBusiness {
+        // Extract business information using regex patterns
+        const businessInfo: PartialBusiness = {
+            name: this.extractField(text, 'name', '[^"\\n]+') || 'Unknown Business',
+            address: this.extractField(text, 'address', '[^"\\n]+') || 'Address not available',
+            phone: this.extractField(text, 'phone', '[^"\\n]+') || 'Phone not available',
+            description: this.extractField(text, 'description', '[^"\\n]+') || 'No description available'
+        };
+
+        const website = this.extractField(text, 'website', '[^"\\n]+');
+        if (website) {
+            businessInfo.website = website;
+        }
+
+        const rating = this.extractField(text, 'rating', '[0-9.]+');
+        if (rating) {
+            businessInfo.rating = parseFloat(rating);
+        }
+
+        return businessInfo;
+    }
+
+    private extractField(text: string, field: string, pattern: string): string {
+        const regex = new RegExp(`"?${field}"?\\s*[:=]\\s*"?(${pattern})"?`, 'i');
+        const match = text.match(regex);
+        return match ? match[1].trim() : '';
+    }
+} 

src/lib/services/geocodingService.ts

@@ -0,0 +1,63 @@
+import axios from 'axios';
+import { sleep } from '../utils/helpers';
+
+interface GeocodingResult {
+  lat: number;
+  lng: number;
+  formattedAddress: string;
+}
+
+export class GeocodingService {
+  private static cache = new Map<string, GeocodingResult>();
+  private static lastRequestTime = 0;
+  private static RATE_LIMIT_MS = 1000; // 1 second between requests (Nominatim requirement)
+
+  static async geocode(address: string): Promise<GeocodingResult | null> {
+    // Check cache first
+    const cached = this.cache.get(address);
+    if (cached) return cached;
+
+    try {
+      // Rate limiting
+      const now = Date.now();
+      const timeSinceLastRequest = now - this.lastRequestTime;
+      if (timeSinceLastRequest < this.RATE_LIMIT_MS) {
+        await sleep(this.RATE_LIMIT_MS - timeSinceLastRequest);
+      }
+      this.lastRequestTime = Date.now();
+
+      const response = await axios.get(
+        'https://nominatim.openstreetmap.org/search',
+        {
+          params: {
+            q: address,
+            format: 'json',
+            limit: 1,
+            addressdetails: 1
+          },
+          headers: {
+            'User-Agent': 'BusinessFinder/1.0'
+          }
+        }
+      );
+
+      if (response.data?.length > 0) {
+        const result = response.data[0];
+        const geocoded = {
+          lat: parseFloat(result.lat),
+          lng: parseFloat(result.lon),
+          formattedAddress: result.display_name
+        };
+        
+        // Cache the result
+        this.cache.set(address, geocoded);
+        return geocoded;
+      }
+      
+      return null;
+    } catch (error) {
+      console.error('Geocoding error:', error);
+      return null;
+    }
+  }
+} 

src/lib/services/healthCheck.ts

@@ -0,0 +1,40 @@
+import axios from 'axios';
+import { supabase } from '../supabase';
+import { env } from '../../config/env';
+
+export class HealthCheckService {
+  private static async checkSupabase(): Promise<boolean> {
+    try {
+      const { data, error } = await supabase.from('searches').select('count');
+      return !error;
+    } catch (error) {
+      console.error('Supabase health check failed:', error);
+      return false;
+    }
+  }
+
+  private static async checkSearx(): Promise<boolean> {
+    try {
+      const response = await axios.get(env.SEARXNG_URL);
+      return response.status === 200;
+    } catch (error) {
+      console.error('SearxNG health check failed:', error);
+      return false;
+    }
+  }
+
+  public static async checkHealth(): Promise<{ 
+    supabase: boolean;
+    searx: boolean;
+  }> {
+    const [supabaseHealth, searxHealth] = await Promise.all([
+      this.checkSupabase(),
+      this.checkSearx()
+    ]);
+
+    return {
+      supabase: supabaseHealth,
+      searx: searxHealth
+    };
+  }
+} 

src/lib/services/ollamaService.ts

@@ -0,0 +1,45 @@
+import axios from 'axios';
+import { env } from '../../config/env';
+
+export class OllamaService {
+    private static readonly baseUrl = env.ollama.url;
+    private static readonly model = env.ollama.model;
+
+    static async complete(prompt: string): Promise<string> {
+        try {
+            const response = await axios.post(`${this.baseUrl}/api/generate`, {
+                model: this.model,
+                prompt: prompt,
+                stream: false
+            });
+
+            if (response.data?.response) {
+                return response.data.response;
+            }
+
+            throw new Error('No response from Ollama');
+        } catch (error) {
+            console.error('Ollama error:', error);
+            throw error;
+        }
+    }
+
+    static async chat(messages: { role: 'user' | 'assistant'; content: string }[]): Promise<string> {
+        try {
+            const response = await axios.post(`${this.baseUrl}/api/chat`, {
+                model: this.model,
+                messages: messages,
+                stream: false
+            });
+
+            if (response.data?.message?.content) {
+                return response.data.message.content;
+            }
+
+            throw new Error('No response from Ollama chat');
+        } catch (error) {
+            console.error('Ollama chat error:', error);
+            throw error;
+        }
+    }
+} 

src/lib/services/searchService.ts

@@ -0,0 +1,135 @@
+import EventEmitter from 'events';
+import { DeepSeekService } from './deepseekService';
+import { DatabaseService } from './databaseService';
+import { Business } from '../types';
+
+interface PartialBusiness {
+    name: string;
+    address: string;
+    phone: string;
+    description: string;
+    website?: string;
+    rating?: number;
+    source?: string;
+    location?: {
+        lat: number;
+        lng: number;
+    };
+}
+
+export class SearchService extends EventEmitter {
+    private deepseekService: DeepSeekService;
+    private databaseService: DatabaseService;
+
+    constructor() {
+        super();
+        this.deepseekService = new DeepSeekService();
+        this.databaseService = new DatabaseService();
+        
+        this.deepseekService.on('progress', (data) => {
+            this.emit('progress', data);
+        });
+    }
+
+    async streamSearch(query: string, location: string, limit: number = 10): Promise<void> {
+        try {
+            // First, try to find cached results in database
+            const cachedResults = await this.databaseService.findBusinessesByQuery(query, location);
+            if (cachedResults.length > 0) {
+                // Emit cached results one by one
+                for (const result of this.sortByRating(cachedResults).slice(0, limit)) {
+                    this.emit('result', result);
+                    await new Promise(resolve => setTimeout(resolve, 100)); // Small delay between results
+                }
+                this.emit('complete');
+                return;
+            }
+
+            // If no cached results, use DeepSeek to generate new results
+            const aiResults = await this.deepseekService.streamChat([{
+                role: "user",
+                content: `Find ${query} in ${location}. You must return exactly ${limit} results in valid JSON format, sorted by rating from highest to lowest. Each result must include a rating between 1-5 stars. Do not include any comments or explanations in the JSON.`
+            }], async (business: PartialBusiness) => {
+                try {
+                    // Extract lat/lng from address using a geocoding service
+                    const coords = await this.geocodeAddress(business.address);
+                    
+                    // Save to database and emit result
+                    const savedBusiness = await this.databaseService.saveBusiness({
+                        ...business,
+                        source: 'deepseek',
+                        location: coords || {
+                            lat: 39.7392, // Denver's default coordinates
+                            lng: -104.9903
+                        }
+                    });
+
+                    this.emit('result', savedBusiness);
+                } catch (error) {
+                    console.error('Error processing business:', error);
+                    this.emit('error', error);
+                }
+            });
+
+            this.emit('complete');
+
+        } catch (error) {
+            console.error('Search error:', error);
+            this.emit('error', error);
+            throw error;
+        }
+    }
+
+    async search(query: string, location: string, limit: number = 10): Promise<Business[]> {
+        try {
+            // First, try to find cached results in database
+            const cachedResults = await this.databaseService.findBusinessesByQuery(query, location);
+            if (cachedResults.length > 0) {
+                return this.sortByRating(cachedResults).slice(0, limit);
+            }
+
+            // If no cached results, use DeepSeek to generate new results
+            const aiResults = await this.deepseekService.chat([{
+                role: "user",
+                content: `Find ${query} in ${location}. You must return exactly ${limit} results in valid JSON format, sorted by rating from highest to lowest. Each result must include a rating between 1-5 stars. Do not include any comments or explanations in the JSON.`
+            }]);
+
+            // Save the results to database
+            const savedResults = await Promise.all(
+                (aiResults as PartialBusiness[]).map(async (business: PartialBusiness) => {
+                    // Extract lat/lng from address using a geocoding service
+                    const coords = await this.geocodeAddress(business.address);
+                    
+                    return this.databaseService.saveBusiness({
+                        ...business,
+                        source: 'deepseek',
+                        location: coords || {
+                            lat: 39.7392, // Denver's default coordinates
+                            lng: -104.9903
+                        }
+                    });
+                })
+            );
+
+            return this.sortByRating(savedResults);
+
+        } catch (error) {
+            console.error('Search error:', error);
+            throw error;
+        }
+    }
+
+    private sortByRating(businesses: Business[]): Business[] {
+        return businesses.sort((a, b) => b.rating - a.rating);
+    }
+
+    private async geocodeAddress(address: string): Promise<{ lat: number; lng: number } | null> {
+        // TODO: Implement real geocoding service
+        // For now, return null to use default coordinates
+        return null;
+    }
+
+    async getBusinessById(id: string): Promise<Business | null> {
+        return this.databaseService.getBusinessById(id);
+    }
+} 

src/lib/services/supabaseService.ts

@@ -0,0 +1,93 @@
+import { createClient } from '@supabase/supabase-js';
+import { env } from '../../config/env';
+import { BusinessData } from '../searxng';
+
+export class SupabaseService {
+  private supabase;
+
+  constructor() {
+    this.supabase = createClient(env.supabase.url, env.supabase.anonKey);
+  }
+
+  async upsertBusinesses(businesses: BusinessData[]): Promise<void> {
+    try {
+      console.log('Upserting businesses to Supabase:', businesses.length);
+
+      for (const business of businesses) {
+        try {
+          // Create a unique identifier based on multiple properties
+          const identifier = [
+            business.name.toLowerCase(),
+            business.phone?.replace(/\D/g, ''),
+            business.address?.toLowerCase(),
+            business.website?.toLowerCase()
+          ]
+            .filter(Boolean)  // Remove empty values
+            .join('_')       // Join with underscore
+            .replace(/[^a-z0-9]/g, '_');  // Replace non-alphanumeric chars
+
+          // Log the data being inserted
+          console.log('Upserting business:', {
+            id: identifier,
+            name: business.name,
+            phone: business.phone,
+            email: business.email,
+            address: business.address,
+            rating: business.rating,
+            website: business.website,
+            location: business.location
+          });
+
+          // Check if business exists
+          const { data: existing, error: selectError } = await this.supabase
+            .from('businesses')
+            .select('rating, search_count')
+            .eq('id', identifier)
+            .single();
+
+          if (selectError && selectError.code !== 'PGRST116') {
+            console.error('Error checking existing business:', selectError);
+          }
+
+          // Prepare upsert data
+          const upsertData = {
+            id: identifier,
+            name: business.name,
+            phone: business.phone || null,
+            email: business.email || null,
+            address: business.address || null,
+            rating: existing ? Math.max(business.rating, existing.rating) : business.rating,
+            website: business.website || null,
+            logo: business.logo || null,
+            source: business.source || null,
+            description: business.description || null,
+            latitude: business.location?.lat || null,
+            longitude: business.location?.lng || null,
+            last_updated: new Date().toISOString(),
+            search_count: existing ? existing.search_count + 1 : 1
+          };
+
+          console.log('Upserting with data:', upsertData);
+
+          const { error: upsertError } = await this.supabase
+            .from('businesses')
+            .upsert(upsertData, {
+              onConflict: 'id'
+            });
+
+          if (upsertError) {
+            console.error('Error upserting business:', upsertError);
+            console.error('Failed business data:', upsertData);
+          } else {
+            console.log(`Successfully upserted business: ${business.name}`);
+          }
+        } catch (businessError) {
+          console.error('Error processing business:', business.name, businessError);
+        }
+      }
+    } catch (error) {
+      console.error('Error saving businesses to Supabase:', error);
+      throw error;
+    }
+  }
+} 

src/lib/supabase.ts

@@ -0,0 +1,35 @@
+import { createClient } from '@supabase/supabase-js';
+import { env } from '../config/env';
+
+// Validate Supabase configuration
+if (!env.SUPABASE_URL || !env.SUPABASE_KEY) {
+  throw new Error('Missing Supabase configuration');
+}
+
+// Create Supabase client
+export const supabase = createClient(
+  env.SUPABASE_URL,
+  env.SUPABASE_KEY,
+  {
+    auth: {
+      autoRefreshToken: true,
+      persistSession: true,
+      detectSessionInUrl: true
+    }
+  }
+);
+
+// Test connection function
+export async function testConnection() {
+  try {
+    console.log('Testing Supabase connection...');
+    console.log('URL:', env.SUPABASE_URL);
+    const { data, error } = await supabase.from('searches').select('count');
+    if (error) throw error;
+    console.log('Supabase connection successful');
+    return true;
+  } catch (error) {
+    console.error('Supabase connection failed:', error);
+    return false;
+  }
+} 

src/lib/types.ts

@@ -0,0 +1,16 @@
+export interface Business {
+  id: string;
+  name: string;
+  address: string;
+  phone: string;
+  description: string;
+  website?: string;
+  source: string;
+  rating: number;
+  location: {
+    lat: number;
+    lng: number;
+  };
+}
+
+export type BusinessData = Business; 

src/lib/utils.ts

@@ -0,0 +1,39 @@
+import crypto from 'crypto';
+
+interface BusinessIdentifier {
+    title?: string;
+    name?: string;
+    phone?: string;
+    address?: string;
+    url?: string;
+    website?: string;
+}
+
+export function generateBusinessId(business: BusinessIdentifier): string {
+    const components = [
+        business.title || business.name,
+        business.phone,
+        business.address,
+        business.url || business.website
+    ].filter(Boolean);
+
+    const hash = crypto.createHash('md5')
+        .update(components.join('|'))
+        .digest('hex');
+
+    return `hash_${hash}`;
+}
+
+export function extractPlaceIdFromUrl(url: string): string | null {
+    try {
+        // Match patterns like:
+        // https://www.google.com/maps/place/.../.../data=!3m1!4b1!4m5!3m4!1s0x876c7ed0cb78d6d3:0x2cd0c4490736f7c!8m2!
+        // https://maps.google.com/maps?q=...&ftid=0x876c7ed0cb78d6d3:0x2cd0c4490736f7c
+        const placeIdRegex = /[!\/]([0-9a-f]{16}:[0-9a-f]{16})/i;
+        const match = url.match(placeIdRegex);
+        return match ? match[1] : null;
+    } catch (error) {
+        console.warn('Error extracting place ID from URL:', error);
+        return null;
+    }
+} 

src/lib/utils/cache.ts

@@ -0,0 +1,36 @@
+interface CacheItem<T> {
+  data: T;
+  timestamp: number;
+}
+
+export class Cache<T> {
+  private store = new Map<string, CacheItem<T>>();
+  private ttl: number;
+
+  constructor(ttlMinutes: number = 60) {
+    this.ttl = ttlMinutes * 60 * 1000;
+  }
+
+  set(key: string, value: T): void {
+    this.store.set(key, {
+      data: value,
+      timestamp: Date.now()
+    });
+  }
+
+  get(key: string): T | null {
+    const item = this.store.get(key);
+    if (!item) return null;
+
+    if (Date.now() - item.timestamp > this.ttl) {
+      this.store.delete(key);
+      return null;
+    }
+
+    return item.data;
+  }
+
+  clear(): void {
+    this.store.clear();
+  }
+} 

src/lib/utils/dataCleanup.ts

@@ -0,0 +1,67 @@
+import { Business } from '../types';
+
+export function normalizePhoneNumber(phone: string): string {
+  return phone.replace(/[^\d]/g, '');
+}
+
+export function normalizeAddress(address: string): string {
+  // Remove common suffixes and standardize format
+  return address
+    .toLowerCase()
+    .replace(/(street|st\.?|avenue|ave\.?|road|rd\.?)/g, '')
+    .trim();
+}
+
+export function extractZipCode(text: string): string | null {
+  const match = text.match(/\b\d{5}(?:-\d{4})?\b/);
+  return match ? match[0] : null;
+}
+
+export function calculateReliabilityScore(business: Business): number {
+  let score = 0;
+  
+  // More complete data = higher score
+  if (business.phone) score += 2;
+  if (business.website) score += 1;
+  if (business.email) score += 1;
+  if (business.hours?.length) score += 2;
+  if (business.services && business.services.length > 0) score += 1;
+  if (business.reviewCount && business.reviewCount > 10) score += 2;
+  
+  return score;
+}
+
+export function cleanAddress(address: string): string {
+  return address
+    .replace(/^(Sure!|Here is |The business address( is| found in the text is)?:?\n?\s*)/i, '')
+    .replace(/\n/g, ' ')
+    .trim();
+}
+
+export function formatPhoneNumber(phone: string): string {
+  // Remove all non-numeric characters
+  const cleaned = phone.replace(/\D/g, '');
+  
+  // Format as (XXX) XXX-XXXX
+  if (cleaned.length === 10) {
+    return `(${cleaned.slice(0,3)}) ${cleaned.slice(3,6)}-${cleaned.slice(6)}`;
+  }
+  
+  // Return original if not 10 digits
+  return phone;
+}
+
+export function cleanEmail(email: string): string {
+  // Remove phone numbers from email
+  return email
+    .replace(/\d{3}-\d{4}/, '')
+    .replace(/\d{10}/, '')
+    .trim();
+}
+
+export function cleanDescription(description: string): string {
+  return description
+    .replace(/^(Description:|About:|Info:)/i, '')
+    .replace(/\s+/g, ' ')
+    .trim();
+} 

src/lib/utils/helpers.ts

@@ -0,0 +1,18 @@
+export function sleep(ms: number): Promise<void> {
+    return new Promise(resolve => setTimeout(resolve, ms));
+}
+
+export function cleanText(text: string): string {
+    return text
+        .replace(/\s+/g, ' ')
+        .replace(/[^\w\s-.,]/g, '')
+        .trim();
+}
+
+export function isValidPhone(phone: string): boolean {
+    return /^\+?[\d-.()\s]{10,}$/.test(phone);
+}
+
+export function isValidEmail(email: string): boolean {
+    return /^[^\s@]+@[^\s@]+\.[^\s@]+$/.test(email);
+} 

src/lib/utils/rateLimiter.ts

@@ -0,0 +1,23 @@
+export class RateLimiter {
+  private timestamps: number[] = [];
+  private readonly windowMs: number;
+  private readonly maxRequests: number;
+
+  constructor(windowMs: number = 60000, maxRequests: number = 30) {
+    this.windowMs = windowMs;
+    this.maxRequests = maxRequests;
+  }
+
+  async waitForSlot(): Promise<void> {
+    const now = Date.now();
+    this.timestamps = this.timestamps.filter(time => now - time < this.windowMs);
+
+    if (this.timestamps.length >= this.maxRequests) {
+      const oldestRequest = this.timestamps[0];
+      const waitTime = this.windowMs - (now - oldestRequest);
+      await new Promise(resolve => setTimeout(resolve, waitTime));
+    }
+
+    this.timestamps.push(now);
+  }
+}