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eduardtomasek
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grepai-embeddings-openai

Configure OpenAI as the embedding provider for GrepAI semantic code search.

Updated May 12, 2026
Search Tags:#semantic search#embeddings#configuration#code search#api key#openai#grepai
One-click install
npx skills add https://github.com/eduardtomasek/codex-dev-stack --skill grepai-embeddings-openai-eduardtomasek
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: grepai-embeddings-openai
Source: https://github.com/eduardtomasek/codex-dev-stack/tree/main/skills/.agents/grepai-embeddings-openai
Command: npx skills add https://github.com/eduardtomasek/codex-dev-stack --skill grepai-embeddings-openai-eduardtomasek

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Setting up cloud-based embeddings for GrepAI requires choosing the right OpenAI model, managing API keys securely, tuning parallelism against rate limits, and estimating indexing costs, all of which are easy to get wrong. ## Core Features & Use Cases - Provider Configuration: Set up GrepAI's embedder with OpenAI models like text-embedding-3-small or text-embedding-3-large, including dimension reduction and Azure OpenAI endpoints. - Performance & Cost Tuning: Adjust parallelism based on your OpenAI rate limit tier and estimate indexing costs per codebase size. - Troubleshooting & Migration: Resolve 401/429 errors, secure API keys via environment variables, and migrate an existing index from Ollama to OpenAI. - Use Case: A team wants high-quality semantic code search without running a local embedding server, so they configure GrepAI with their OpenAI API key and re-index their repository. ## Quick Start Configure GrepAI to use OpenAI embeddings with the text-embedding-3-small model and my OPENAI_API_KEY environment variable, then re-index the project.

Frequently Asked Questions about grepai-embeddings-openai

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I configure OpenAI embeddings in GrepAI?▼

Set the embedder provider to openai in .grepai/config.yaml, choose a model like text-embedding-3-small, and reference your key as ${OPENAI_API_KEY}. Export the OPENAI_API_KEY environment variable before running GrepAI.

Which OpenAI embedding model should I use for code search?▼

text-embedding-3-small is recommended for most cases, offering 1536 dimensions at $0.00002 per 1K tokens. Use text-embedding-3-large with 3072 dimensions when maximum accuracy matters more than cost.

Can I use Azure OpenAI with GrepAI embeddings?▼

Yes, set the provider to openai, use your Azure deployment name as the model, provide AZURE_OPENAI_API_KEY, and specify your Azure endpoint URL in the embedder configuration.

Why am I getting 429 rate limit errors with OpenAI embeddings?▼

Rate limit errors occur when parallelism exceeds your OpenAI account tier's requests-per-minute allowance. Lower the parallelism value in the embedder config, for example to 2, or upgrade your OpenAI tier.

How do I migrate GrepAI from Ollama to OpenAI embeddings?▼

Update the embedder config to the openai provider, delete the existing .grepai/index.gob file, and re-index with grepai watch. Embeddings from different models or providers cannot be mixed in one index.

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