Tyler
150 lines
5.0 KiB
Markdown
150 lines
5.0 KiB
Markdown
<div align="center">
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# ollama-ai-answers-searxng
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**Local AI search overviews for SearXNG, powered by Ollama.**
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[](https://github.com/TySP-Dev/ollama-ai-answers-searxng)
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</div>
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## One-line Install
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```bash
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bash <(curl -fsSL https://raw.githubusercontent.com/TySP-Dev/ollama-ai-answers-searxng/master/install.sh)
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```
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## Features
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- AI Overview box at the top of every search result page
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- Powered entirely by your local Ollama instance — no external API calls
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- Page content fetching — enriches context beyond SearXNG snippets
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- Model selector dropdown — switch models per-search without restarting
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- Inline citations with clickable source links
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- Citation footer listing all referenced sources
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- Follow-up questions with conversation history
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- Copy and Regenerate buttons
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- Typewriter animation (granian-compatible buffered response)
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- Ollama-only — no OpenAI, Gemini, or other provider bloat
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## Requirements
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- SearXNG installed via Docker Compose
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- Ollama running and accessible from the SearXNG container
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- Python 3.8+ (for `build.py` and `install.sh`)
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- Docker + Docker Compose
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## Install
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### One-line (recommended)
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```bash
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bash <(curl -fsSL https://raw.githubusercontent.com/TySP-Dev/ollama-ai-answers-searxng/master/install.sh)
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```
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The script will clone the repo, build the plugin, detect your SearXNG Docker Compose installation, copy the plugin, update `docker-compose.yml` and `settings.yml`, and optionally restart SearXNG.
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### Manual
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```bash
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git clone https://github.com/TySP-Dev/ollama-ai-answers-searxng
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cd ollama-ai-answers-searxng
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python3 build.py
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bash install.sh
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```
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Or manually copy the built plugin and update your config:
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```yaml
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# docker-compose.yml — searxng service
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environment:
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- LLM_URL=http://ollama:11434/v1/chat/completions
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- LLM_MODEL=qwen3.5:9b
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volumes:
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- ./plugins/ollama_answers.py:/usr/local/searxng/searx/plugins/ollama_answers.py:Z
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```
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```yaml
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# settings.yml
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plugins:
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searx.plugins.ollama_answers.SXNGPlugin:
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active: true
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```
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## Configuration
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All configuration is done via environment variables on the SearXNG container.
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| Variable | Default | Description |
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|---|---|---|
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| `LLM_URL` | `http://ollama:11434/v1/chat/completions` | Ollama endpoint |
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| `LLM_MODEL` | `qwen3.5:9b` | Default model |
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| `LLM_MAX_TOKENS` | `200` | Max response tokens |
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| `LLM_TEMPERATURE` | `0.2` | Response temperature |
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| `LLM_TABS` | `general,science,it,news` | Search tabs to show AI overview on |
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| `LLM_QUESTION_MARK_REQUIRED` | `false` | Only trigger on queries ending with `?` |
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| `LLM_INTERACTIVE` | `true` | Show copy/regenerate/follow-up UI |
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| `LLM_SYSTEM_PROMPT` | *(built-in)* | Override the system prompt |
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| `LLM_CONTEXT_DEEP_COUNT` | `5` | Results fetched for full page content |
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| `LLM_CONTEXT_SHALLOW_COUNT` | `15` | Results used as headline-only context |
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## Project Structure
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```
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ollama-ai-answers-searxng/
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├── ollama_answers.py # Source plugin — reads UI from assets/
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├── build.py # Assembles dist/ollama_answers.py (self-contained)
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├── install.sh # Full automated Docker Compose installer
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├── assets/
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│ ├── ui.css # Interactive widget styles
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│ ├── ui.html # Interactive widget HTML (copy/regen/follow-up bar)
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│ └── ui.js # Frontend JS (typewriter, citations, streaming)
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├── dist/ # Output of build.py — gitignored
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│ └── ollama_answers.py # Self-contained, ready to deploy
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├── dev/
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│ └── dev.py # Local Flask dev server (no SearXNG required)
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└── README.md
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```
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## Development
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```bash
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# Edit source files
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vim ollama_answers.py
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vim assets/ui.css
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# Build dist file for deployment
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python3 build.py
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# Deploy to server
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cp dist/ollama_answers.py ~/searxng/plugins/ollama_answers.py
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cd ~/searxng && docker compose up -d --force-recreate core
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# Run local dev server
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PYTHONPATH=. python3 dev/dev.py
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```
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The dev server mocks the SearXNG plugin environment so you can test the full UI without a running SearXNG instance. Open [http://127.0.0.1:5000/](http://127.0.0.1:5000/) after starting it.
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> **Note:** Use `127.0.0.1:5000`, not `localhost:5000` — macOS AirPlay Receiver can occupy the IPv6 loopback on port 5000.
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## How It Works
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1. User searches on SearXNG
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2. `post_search` hook fires after results are fetched
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3. Top result URLs are fetched in parallel for full page content
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4. Context is assembled from page content + snippets + infoboxes
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5. A signed token is generated and injected into the page
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6. The browser POSTs to `/ai-stream` with the token and context
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7. The server calls Ollama with the enriched context
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8. The response is returned as JSON and animated with a typewriter effect
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9. Citations are rendered inline and collected in a footer
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## License
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MIT License
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