Skill: twinsgeeks/fleet-embeddings (version 1.1.1)

--- name: fleet-embeddings description: Embeddings with nomic-embed-text, mxbai-embed, and snowflake-arctic-embed across your device fleet. Fleet-routed via Ollama for RAG, semantic search, and vector similarity. Batch embed thousands of documents across nodes instead of bottlenecking on one machine. Use when the user needs to create embeddings, build a knowledge base, or set up semantic search. version: 1.0.1 homepage: https://github.com/geeks-accelerator/ollama-herd metadata: {"openclaw":{"emoji":"search","requires":{"anyBins":["curl","wget"],"optionalBins":["python3","pip"]},"configPaths":["~/.fleet-manager/latency.db","~/.fleet-manager/logs/herd.jsonl"],"os":["darwin","linux","windows"]}} --- # Fleet Embeddings You're helping someone generate embeddings — converting text into vectors for semantic search, RAG pipelines, duplicate detection, or recommendation systems. Instead of hitting one Ollama instance, the fleet distributes embedding requests across all available nodes automatically. ## Why fleet embeddings matter Building a RAG knowledge base means embedding thousands of document chunks. On a single machine, embedding 10,000 chunks takes significant time and blocks LLM inference. With fleet routing, embedding requests spread across nodes — the machine that's least busy handles each batch, and LLM inference continues uninterrupted on other nodes. Same Ollama embedding models you already know. Same API. Just faster because the fleet parallelizes it. ## Get started ```bash pip install ollama-herd herd # start the router (port 11435) herd-node # start on each device ollama pull nomic-embed-text # pull an embedding model ``` No feature toggle needed — embeddings route through Ollama automatically. Package: [ollama-herd](https://pypi.org/project/ollama-herd/) | Repo: [github.com/geeks-accelerator/ollama-herd](https://github.com/geeks-accelerator/ollama-herd) ## Generate embeddings ### Ollama format (curl) ```bash curl http://localhost:11435/api/embeddings -d '{ "model": "nomic-embed-text", "prompt": "The fleet manages all inference routing" }' ``` ### OpenAI SDK (Python) ```python from openai import OpenAI client = OpenAI(base_url="http://localhost:11435/v1", [REDACTED_SECRET]") response = client.embeddings.create( model="nomic-embed-text", input="The fleet manages all inference routing", ) vector = response.data[0].embedding print(f"Dimensions: {len(vector)}") ``` ### Python (httpx) ```python import httpx def embed(text, model="nomic-embed-text"): resp = httpx.post( "http://localhost:11435/api/embeddings", json={"model": model, "prompt": text}, timeout=30.0, ) resp.raise_for_status() return resp.json()["embedding"] vector = embed("search query here") ``` ### Batch embedding for RAG ```python import httpx def embed_batch(texts, model="nomic-embed-text"): """Embed a list of texts. Fleet distributes across nodes.""" vectors = [] for text in texts: resp = httpx.post( "http://localhost:11435/api/embeddings", json={"model": model, "prompt": text}, timeout=30.0, ) resp.raise_for_status() vectors.append(resp.json()["embedding"]) return vectors # Embed document chunks for RAG chunks = [ "Introduction to fleet management...", "The scoring engine uses 7 signals...", "Context protection prevents model reloads...", ] vectors = embed_batch(chunks) print(f"Embedded {len(vectors)} chunks, {len(vectors[0])} dimensions each") ``` ### Available embedding models Check what's available: ```bash curl -s http://localhost:11435/api/tags | python3 -c " import json, sys for m in json.load(sys.stdin)['models']: if 'embed' in m['name'].lower() or 'nomic' in m['name'].lower(): print(f' {m[\"name\"]}')" ``` Common models: `nomic-embed-text`, `mxbai-embed-large`, `all-minilm`, `snowflake-arctic-embed`. Pull a model if needed: ```bash curl -X POST http://localhost:11435/dashboard/api/pull \ -H "Content-Type: application/json" \ -d '{"model": "nomic-embed-text", "node_id": "your-node-id"}' ``` ### Usage analytics Tag embedding requests to track per-project usage: ```python resp = httpx.post( "http://localhost:11435/api/embeddings", json={ "model": "nomic-embed-text", "prompt": text, "metadata": {"tags": ["my-rag-pipeline", "indexing"]}, }, ) ``` ## Also available on this fleet ### LLM inference ```bash curl http://localhost:11435/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{"model":"gpt-oss:120b","messages":[{"role":"user","content":"Hello"}]}' ``` Drop-in OpenAI SDK compatible. 7-signal scoring routes to the optimal node. ### Image generation ```bash curl -o image.png http://localhost:11435/api/generate-image \ -H "Content-Type: application/json" \ -d '{"model":"z-image-turbo","prompt":"a sunset","width":1024,"height":1024,"steps":4}' ``` Requires `FLEET_IMAGE_GENERATION=true`. Uses mflux (MLX-native Flux). ### Speech-to-text ```bash curl -s http://localhost:11435/api/transcribe \ -F "audio=@recording.wav" | python3 -m json.tool ``` Requires `FLEET_TRANSCRIPTION=true`. Uses Qwen3-ASR. ## Monitoring ```bash # Fleet health and model recommendations curl -s http://localhost:11435/dashboard/api/health | python3 -m json.tool # Per-app usage (see which projects use the most tokens) curl -s http://localhost:11435/dashboard/api/apps | python3 -m json.tool ``` Dashboard at `http://localhost:11435/dashboard` — embedding requests flow through the same queues as LLM requests. ## Full documentation [Agent Setup Guide](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/guides/agent-setup-guide.md) — complete reference for all 4 model types. [Request Tagging Guide](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/guides/request-tagging-analytics.md) — tag requests for per-project analytics. ## Guardrails - Never delete or modify files in `~/.fleet-manager/`. - Never pull or delete models without user confirmation. - If embedding model not available, suggest: `ollama pull nomic-embed-text`. - If router not running, suggest: `herd` or `uv run herd`.


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