Tyler
78 lines
2.5 KiB
Markdown
78 lines
2.5 KiB
Markdown
# Ollama AI Answers Plugin for SearXNG
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**Single file install**
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**Does not block result loading time**
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**Based on [ai-answers-searxng](https://github.com/cra88y/ai-answers-searxng) by [cra88y](https://github.com/cra88y)
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A SearXNG plugin that generates local AI overviews powered by Ollama, using search results as RAG context.
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Features:
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- token-by-token UI streaming
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- clickable inline citations
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- interactive mode to continue summary, ask follow ups, copy, or regenerate
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- simple response mode with no extras
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- internally called low-latency RAG for follow ups (bypasses http loopback)
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- native network integration via `searx.network` (respects proxy/SSL settings)
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- stateless conversation persistence/sharability via URL
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## Installation
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Place `ai_answers.py` into the `searx/plugins` directory of your instance (or mount it in a container) and enable it in `settings.yml`:
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```yaml
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plugins:
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searx.plugins.ai_answers.SXNGPlugin:
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active: true
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```
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## Configuration
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Configure via environment variables:
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### Required
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- `LLM_URL`: Ollama chat completions endpoint. Default: `http://ollama:11434/v1/chat/completions`
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- `LLM_MODEL`: Model name as listed in Ollama. Default: `llama3.2`
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### Optional
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- `LLM_SYSTEM_PROMPT`: Overrides the system prompt. Default: `You are a direct, citation-accurate search synthesis engine.`
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- `LLM_MAX_TOKENS`: Default `200`.
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- `LLM_TEMPERATURE`: Default `0.2`.
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- `LLM_CONTEXT_DEEP_COUNT`: Results used as context with full snippets. Default `5`.
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- `LLM_CONTEXT_SHALLOW_COUNT`: Results with headlines only (additional breadth). Default `15`.
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- `LLM_TABS`: Tab whitelist, comma delimited. Default `general,science,it,news`.
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- `LLM_INTERACTIVE`: UI mode. Default `true` (interactive: copy, regenerate, follow up). Set to `false` for simple response only.
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- `LLM_QUESTION_MARK_REQUIRED`: Only trigger AI answers when the query contains `?`. Default `false`.
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## How It Works
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1. User performs initial search
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2. Results return server side
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3. `post_search` plugin hook fires
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4. Token-optimized context extracted from results
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5. UI/logic shell injected into the standard results answer object
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6. Client-side script calls custom endpoint with a signed token
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7. Ollama response renders token by token in the UI
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## Example
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### Docker Compose
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```yaml
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environment:
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- LLM_URL=http://ollama:11434/v1/chat/completions
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- LLM_MODEL=llama3.2
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```
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### Environment variables
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```
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LLM_URL=http://ollama:11434/v1/chat/completions
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LLM_MODEL=llama3.2
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```
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## Development
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```bash
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pip install flask flask-babel
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python tests/demo.py # UI demo at localhost:5000
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```
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