grepai-embeddings-ollama_skill

Overview

This skill configures Ollama as the embedding provider for GrepAI to enable fully local, private embedding generation for semantic code search. It guides choosing models, configuring endpoints, and optimizing performance so code never leaves your machine. Use it to run fast, offline embeddings with control over model selection and resource trade-offs.

How this skill works

The skill instructs GrepAI to call an Ollama server endpoint for embedding requests and shows how to set provider, model, endpoint, and optional dimensions in .grepai/config.yaml. It explains pulling models into Ollama, verifying the server, testing embeddings, and keeping models loaded for reduced latency. It also covers running Ollama as a background service or on a remote host.

When to use it

• You need fully private, on-premise embeddings for code search.
• You want to avoid API costs and network latency.
• You need to choose or tune an embedding model for a specific codebase.
• You want to run Ollama locally or on a dedicated server and connect GrepAI to it.
• You need to optimize memory, GPU usage, and model persistence.

Best practices

• Start with nomic-embed-text for a good balance of speed and quality.
• Run Ollama as a background service (systemd, launchd, or nohup) to keep models available.
• Match embedding dimensions across models; re-index when switching models.
• Use GPU acceleration where available and monitor with ollama ps.
• Prefer smaller models on limited RAM or use a remote Ollama server for heavy workloads.

Example use cases

• Local semantic code search across private repositories without exposing code to external APIs.
• Running GrepAI in an air-gapped environment with offline embedding generation.
• Using a high-quality model (bge-m3) for large, complex codebases where accuracy matters.
• Running Ollama on a powerful remote server and pointing developer machines to that endpoint.
• Automating re-indexing after model updates to keep search results consistent.