Ollama AI Answers Plugin for SearXNG

Single file install
Does not block result loading time
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 to 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/sharability via URL

Installation

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

plugins:
  searx.plugins.ai_answers.SXNGPlugin:  
    active: true

Configuration

Configure via environment variables:

Required

• LLM_URL: Ollama chat completions endpoint. Default: http://ollama:11434/v1/chat/completions
• LLM_MODEL: Model name as listed in Ollama. Default: llama3.2

Optional

• LLM_SYSTEM_PROMPT: Overrides the system prompt. Default: You are a direct, citation-accurate search synthesis engine.
• LLM_MAX_TOKENS: Default 200.
• LLM_TEMPERATURE: Default 0.2.
• LLM_CONTEXT_DEEP_COUNT: Results used as context with full snippets. Default 5.
• LLM_CONTEXT_SHALLOW_COUNT: Results with headlines only (additional breadth). Default 15.
• LLM_TABS: Tab whitelist, comma delimited. Default general,science,it,news.
• LLM_INTERACTIVE: UI mode. Default true (interactive: copy, regenerate, follow up). Set to false for simple response only.
• LLM_QUESTION_MARK_REQUIRED: Only trigger AI answers when the query contains ?. Default false.

How It Works

1. User performs initial search
2. Results return server side
3. post_search plugin hook fires
4. Token-optimized context extracted from results
5. UI/logic shell injected into the standard results answer object
6. Client-side script calls custom endpoint with a signed token
7. Ollama response renders token by token in the UI

Example

Docker Compose

environment:
  - LLM_URL=http://ollama:11434/v1/chat/completions
  - LLM_MODEL=llama3.2

Environment variables

LLM_URL=http://ollama:11434/v1/chat/completions
LLM_MODEL=llama3.2

Development

pip install flask flask-babel
python tests/demo.py   # UI demo at localhost:5000