# Ollama AI Answers Plugin for SearXNG **Based on [ai-answers-searxng](https://github.com/cra88y/ai-answers-searxng) by [cra88y](https://github.com/cra88y)** A SearXNG plugin that generates local AI overviews powered by Ollama, using search results as RAG context. ## Features: - Inline numbered citations - Interactive mode - Continue summary, ask follow-ups, copy, or regenerate - Overview of ranked results with prompts based on detected query intent: - `How To` `Technical` `Factual` `Comparison` `Opinion` `Current` `Local` `Geneal` - Internally called RAG for follow-ups - Native network integration via `searx.network` - Stateless conversation presistence/shareability via URL hash - Ollama model selector - Feeds fetched results to Ollama without slowing down SearXNG results - Real-time streaming via Valkey (No waiting for a completed response) - TF-IDF result ranking before being sent to Ollama - Smart chunking - Pages are split into 512-token segments and highest-scoring chunk per page used for context - Conversation memory - 30-minute cross-search conversation history via Valkey for follow-up questions - Markdown support - Intent emoji badge showing what intent prompt was used ## Install 1. Download the plugin: ### Main repo (Gitea) ```bash curl -o ollama_answers.py https://git.tysstech.com/TySS-Dev/ollama-ai-answers-searxng/raw/branch/main/ollama_answers.py ``` ### Mirror repo (Github): ```bash curl -o ollama_answers.py https://raw.githubusercontent.com/TySP-Dev/ollama-ai-answers-searxng/main/ollama_answers.py ``` 3. Copy to your SearXNG plugins directory: ```bash cp ollama_answers.py path_to/searxng/plugins/ollama_answers.py ``` 4. Add the volume mount to your `docker-compose.yml` under the searxng service: ```yaml volumes: - ./plugins/ollama_answers.py:/usr/local/searxng/searx/plugins/ollama_answers.py:Z ``` 5. Add environment variables to `docker-compose.yml`: ```yaml environment: - LLM_URL=http://ollama:11434/v1/chat/completions - LLM_MODEL=qwen3.5:9b - VALKEY_HOST=searxng-valkey ``` 6. Add to `settings.yml` plugins section: ```yaml plugins: searx.plugins.ollama_answers.SXNGPlugin: active: true ``` 7. Restart SearXNG: ```bash docker compose up -d --force-recreate core ``` ## Configuration Configure via environment variables. | Variable | Default | Description | |---|---|---| | `LLM_URL` | `http://ollama:11434/v1/chat/completions` | Ollama endpoint | | `LLM_MODEL` | `qwen3.5:9b` | Default model | | `LLM_MAX_TOKENS` | `200` | Max response tokens | | `LLM_TEMPERATURE` | `0.2` | Response temperature | | `LLM_TABS` | `general,science,it,news` | Tabs to show AI overview on | | `LLM_QUESTION_MARK_REQUIRED` | `false` | Only trigger on queries with `?` | | `LLM_INTERACTIVE` | `true` | Show copy/regen/follow-up UI | | `LLM_SYSTEM_PROMPT` | *(built-in)* | Override the system prompt | | `LLM_CONTEXT_DEEP_COUNT` | `5` | Full-content results to fetch | | `LLM_CONTEXT_SHALLOW_COUNT` | `15` | Headline-only results | | `VALKEY_HOST` | `searxng-valkey` | Valkey container hostname | | `VALKEY_PORT` | `6379` | Valkey port | ## 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 ## Architecture ``` ┌─────────────────────────────────────────────────────┐ │ Browser │ │ POST /ai-stream → GET /ai-status/{id} (poll 150ms) │ └────────────────┬────────────────────────────────────┘ │ ┌────────────────▼────────────────────────────────────┐ │ SearXNG + Plugin │ │ │ │ post_search() │ │ → _enrich_results() ← ThreadPoolExecutor │ │ → _fetch_page_text() × 5 parallel │ │ → _chunk_text() + _tfidf_score() │ │ → rerank by score │ │ → _assemble_context() │ │ → inject AI Overview HTML + JS │ │ │ │ /ai-stream │ │ → validate token │ │ → _detect_intent() → select system prompt │ │ → _load_conversation() from Valkey │ │ → launch stream_to_valkey() thread │ │ → return {job_id} immediately │ │ │ │ stream_to_valkey() [background thread] │ │ → Ollama stream=True │ │ → RPUSH tokens to Valkey │ │ → RPUSH __DONE__ when complete │ │ │ │ /ai-status/{job_id} │ │ → LRANGE chunks from offset │ │ → return {chunks, done} │ └────────────────┬────────────────────────────────────┘ │ ┌────────────────▼────────────────────────────────────┐ │ Valkey │ │ ai:job:{id}:chunks (list, TTL 120s) │ │ ai:job:{id}:status (string, TTL 120s) │ │ ai:conv:{session} (JSON, TTL 1800s) │ └─────────────────────────────────────────────────────┘ ``` ## Docker Compose Example ```yaml services: searxng: environment: - LLM_URL=http://ollama:11434/v1/chat/completions - LLM_MODEL=qwen3.5:9b - VALKEY_HOST=searxng-valkey 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. ```yaml environment: - LLM_URL=https://ollama.example.com/v1/chat/completions - LLM_API_KEY=your-bearer-token ``` ## Project Structure ``` ollama-ai-answers-searxng/ ├── ollama_answers.py # single plugin file — all logic here ├── README.md ├── requirements.txt # flask, flask-babel (for local dev only) └── tests/ └── dev.py # local dev server ``` ## 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 ```bash pip install flask flask-babel certifi ``` ### Run ```bash python tests/dev.py ``` Then open [http://127.0.0.1:5000/](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 server reads the same variables as the plugin: ```bash 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.