Ollama AI Answers Plugin for SearXNG

Based on ai-answers-searxng by cra88y

A SearXNG plugin that generates local AI overviews powered by Ollama, using search results as RAG context.

Features:

• Token-by-token UI streaming
• Clickable inline citations
• Interactive mode: continue summary, ask follow-ups, copy, or regenerate
• Simple response mode with no extras
• Internally called low-latency RAG for follow-ups (bypasses HTTP loopback)
• Native network integration via searx.network (respects proxy/SSL settings)
• Stateless conversation persistence/shareability via URL hash
• Model selector in the AI overview widget
• Does not slow down result loading
• One file install

Installation

Place ollama_answers.py into the searx/plugins directory of your SearXNG instance (or mount it in a container) and enable it in settings.yml:

plugins:
  searx.plugins.ollama_answers.SXNGPlugin:
    active: true

Configuration

Configure via environment variables.

Required

Variable	Description	Default
LLM_URL	Ollama chat completions endpoint	http://ollama:11434/v1/chat/completions
LLM_MODEL	Model name as listed in Ollama	qwen3.5:9b

Optional

Variable	Description	Default
LLM_SYSTEM_PROMPT	Overrides the default system prompt	You are a direct, citation-accurate search synthesis engine.
LLM_MAX_TOKENS	Max tokens in the AI response	200
LLM_TEMPERATURE	Sampling temperature	0.2
LLM_CONTEXT_DEEP_COUNT	Results used with full snippets	5
LLM_CONTEXT_SHALLOW_COUNT	Results with headlines only (breadth)	15
LLM_TABS	Comma-delimited tab whitelist	general,science,it,news
LLM_INTERACTIVE	Interactive UI mode (copy, regenerate, follow-up)	true
LLM_QUESTION_MARK_REQUIRED	Only trigger on queries containing ?	false

How It Works

1. User performs a search
2. Results return server-side
3. post_search plugin hook fires
4. Token-optimized context is extracted from results
5. UI/logic shell injected into the standard answers object
6. Client-side script calls a signed endpoint (/ai-stream)
7. Ollama streams a response token-by-token in the UI

Docker Compose Example

services:
  searxng:
    environment:
      - LLM_URL=http://ollama:11434/v1/chat/completions
      - LLM_MODEL=qwen3.5:9b
    volumes:
      - ./ollama_answers.py:/usr/local/searxng/searx/plugins/ollama_answers.py

  ollama:
    image: ollama/ollama
    volumes:
      - ollama_data:/root/.ollama

volumes:
  ollama_data:

Remote Ollama

If your Ollama instance is remote or behind a reverse proxy, set LLM_URL to the full endpoint and provide an API key if required. The plugin supports Bearer token auth and follows HTTP redirects.

environment:
  - LLM_URL=https://ollama.example.com/v1/chat/completions
  - LLM_API_KEY=your-bearer-token

Development — Dev Server

A standalone Flask dev server is included in tests/dev.py. It mocks the SearXNG plugin environment so you can test the full UI without a running SearXNG instance.

Setup

pip install flask flask-babel certifi

Run

python tests/dev.py

Then open http://127.0.0.1:5000/ in your browser.

Note: Use 127.0.0.1:5000, not localhost:5000 — macOS AirPlay Receiver can occupy the IPv6 loopback on port 5000.

Usage

• Type a query in the search bar and hit Search to trigger an AI overview.
• Expand Ollama Configuration at the top to change the endpoint URL or Bearer token for the current session. Click Apply to save and re-run the current query.
• The model selector in the AI overview widget (loaded from /ai-models) shows all models available on the configured Ollama server and persists your choice in the session URL.

Environment Variables (dev)

The dev reads the same variables as the plugin:

LLM_URL=http://localhost:11434/v1/chat/completions \
LLM_MODEL=qwen3.5:9b \
python tests/dev.py

Or export them before running. Any values set in the config panel at runtime take priority for that session.