cra88y/pc
111 lines
3.1 KiB
Markdown
111 lines
3.1 KiB
Markdown
# 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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A SearXNG plugin that generates AI answers using search results as RAG context. Supports 8+ LLM providers.
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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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- provider detection based on 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 the environment variables:
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### Required
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- `LLM_PROVIDER`: openrouter, openai, ollama, localai, lmstudio, gemini, azure, or huggingface
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- `LLM_KEY`: Provider API key (optional for local providers: ollama, localai, lmstudio)
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### Optional
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- `LLM_MODEL`: Model identifier. Defaults vary. Recommended: 10-30B dense or 5-15B MoE activated.
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- `LLM_URL`: Overrides endpoint URL for any provider preset.
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- `LLM_MAX_TOKENS`: Default `500`.
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- `LLM_TEMPERATURE`: Default `0.2`.
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- `LLM_CONTEXT_DEEP_COUNT`: results 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 delimiter. Default `general,science,it,news`.
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- `LLM_INTERACTIVE`: UI mode. Default is `true` (interactive: copy, regenerate, follow up). Set to `false` for simple response only mode.
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- `LLM_QUESTION_MARK_REQUIRED`: Only trigger AI answers when the query contains `?`. Default `false`.
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- `LLM_OLLAMA_UNLOAD_AFTER`: Unload Ollama model after each response. Default `false`.
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## How It Works
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1 user initial search
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2 results return server side
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3 `post_search` plugin hook entry
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4 token optimized context extracted
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5 inject the ui/logic "shell" into standard results answer object
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6 client side script calls custom endpoint with signed token
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7 LLM response streams back token by token
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## Examples
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### OpenRouter
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```
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LLM_PROVIDER=openrouter
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LLM_KEY=sk-or-xxx
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LLM_MODEL=google/gemma-3-27b-it:free
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```
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### Ollama (Local)
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```
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LLM_PROVIDER=ollama
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LLM_KEY=ollama
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LLM_MODEL=llama3.2
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```
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### LocalAI
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```
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LLM_PROVIDER=localai
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LLM_KEY=your-key
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LLM_MODEL=gpt-4
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LLM_URL=http://localai.lan:8080/v1/chat/completions
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```
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### Gemini
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```
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LLM_PROVIDER=gemini
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LLM_KEY=AIzaSy...
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LLM_MODEL=gemma-3-27b-it
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```
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### Azure
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```
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LLM_PROVIDER=azure
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LLM_KEY=your-api-key
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LLM_URL=https://your-resource.openai.azure.com/openai/deployments/your-deployment/chat/completions?api-version=2024-02-01
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```
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### Hugging Face
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```
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LLM_PROVIDER=huggingface
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LLM_KEY=hf_xxx
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LLM_MODEL=meta-llama/Meta-Llama-3-8B-Instruct
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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 # Interactive demo at localhost:5000
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python tests/test.py # One-shot test suite
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```
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