grepai-embeddings-openai

Q: How do I install grepai-embeddings-openai?

Run npx skills add https://github.com/yoanbernabeu/grepai-skills --skill grepai-embeddings-openai in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/grepai-embeddings-openai/ (personal) or .claude/skills/grepai-embeddings-openai/ (project).

by yoanbernabeuAI & Agents

Configure OpenAI as embedding provider for GrepAI. Use this skill for high-quality cloud embeddings.

Installs: 553
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/yoanbernabeu/grepai-skills --skill grepai-embeddings-openai

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads grepai-embeddings-openai automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

GrepAI Embeddings with OpenAI

This skill covers using OpenAI's embedding API with GrepAI for high-quality, cloud-based embeddings.

When to Use This Skill

Need highest quality embeddings
Team environment with shared infrastructure
Don't want to manage local embedding server
Willing to trade privacy for quality/convenience

Considerations

Aspect	Details
✅ Quality	State-of-the-art embeddings
✅ Speed	Fast, no local compute needed
✅ Scalability	Handles any codebase size
⚠️ Privacy	Code sent to OpenAI servers
⚠️ Cost	Pay per token
⚠️ Internet	Requires connection

Prerequisites

OpenAI API key
Billing enabled on OpenAI account

Get your API key at: https://platform.openai.com/api-keys

Configuration

Basic Configuration

# .grepai/config.yaml
embedder:
  provider: openai
  model: text-embedding-3-small
  api_key: ${OPENAI_API_KEY}

Set the environment variable:

export OPENAI_API_KEY="sk-..."

With Parallel Processing

embedder:
  provider: openai
  model: text-embedding-3-small
  api_key: ${OPENAI_API_KEY}
  parallelism: 8  # Concurrent requests for speed

Direct API Key (Not Recommended)

embedder:
  provider: openai
  model: text-embedding-3-small
  api_key: sk-your-api-key-here  # Avoid committing secrets!

Warning: Never commit API keys to version control.

Available Models

text-embedding-3-small (Recommended)

Property	Value
Dimensions	1536
Price	$0.00002 / 1K tokens
Quality	Very high
Speed	Fast

Best for: Most use cases, good balance of cost/quality.

embedder:
  provider: openai
  model: text-embedding-3-small

text-embedding-3-large

Property	Value
Dimensions	3072
Price	$0.00013 / 1K tokens
Quality	Highest
Speed	Fast

Best for: Maximum accuracy, cost not a concern.

embedder:
  provider: openai
  model: text-embedding-3-large
  dimensions: 3072

Dimension Reduction

You can reduce dimensions to save storage:

embedder:
  provider: openai
  model: text-embedding-3-large
  dimensions: 1024  # Reduced from 3072

Model Comparison

Model	Dimensions	Cost/1K tokens	Quality
`text-embedding-3-small`	1536	$0.00002	⭐⭐⭐⭐
`text-embedding-3-large`	3072	$0.00013	⭐⭐⭐⭐⭐

Cost Estimation

Approximate costs per 1000 source files:

Codebase Size	Chunks	Small Model	Large Model
Small (100 files)	~500	$0.01	$0.06
Medium (1000 files)	~5,000	$0.10	$0.65
Large (10000 files)	~50,000	$1.00	$6.50

Note: Costs are one-time for initial indexing. Updates only re-embed changed files.

Optimizing for Speed

Parallel Requests

GrepAI v0.24.0+ supports adaptive rate limiting and parallel requests:

embedder:
  provider: openai
  model: text-embedding-3-small
  api_key: ${OPENAI_API_KEY}
  parallelism: 8  # Adjust based on your rate limit tier

Parallelism recommendations:

Tier 1 (Free): 1-2
Tier 2: 4-8
Tier 3+: 8-16

Batching

GrepAI automatically batches chunks for efficient API usage.

Rate Limits

OpenAI has rate limits based on your account tier:

Tier	RPM	TPM
Free	3	150,000
Tier 1	500	1,000,000
Tier 2	5,000	5,000,000

GrepAI handles rate limiting automatically with adaptive backoff.

Environment Variables

Setting the API Key

macOS/Linux:

# In ~/.bashrc, ~/.zshrc, or ~/.profile
export OPENAI_API_KEY="sk-..."

Windows (PowerShell):

$env:OPENAI_API_KEY = "sk-..."
# Or permanently
[System.Environment]::SetEnvironmentVariable('OPENAI_API_KEY', 'sk-...', 'User')

Using .env Files

Create .env in your project root:

OPENAI_API_KEY=sk-...

Add to .gitignore:

.env

Azure OpenAI

For Azure-hosted OpenAI:

embedder:
  provider: openai
  model: your-deployment-name
  api_key: ${AZURE_OPENAI_API_KEY}
  endpoint: https://your-resource.openai.azure.com

Security Best Practices

Use environment variables: Never hardcode API keys
Add to .gitignore: Exclude .env files
Rotate keys: Regularly rotate API keys
Monitor usage: Check OpenAI dashboard for unexpected usage
Review code: Ensure sensitive code isn't being indexed

Common Issues

❌ Problem: 401 Unauthorized ✅ Solution: Check API key is correct and environment variable is set:

echo $OPENAI_API_KEY

❌ Problem: 429 Rate limit exceeded ✅ Solution: Reduce parallelism or upgrade OpenAI tier:

embedder:
  parallelism: 2  # Lower value

❌ Problem: High costs ✅ Solutions:

Use text-embedding-3-small instead of large
Reduce dimension size
Add more ignore patterns to reduce indexed files

❌ Problem: Slow indexing ✅ Solution: Increase parallelism:

embedder:
  parallelism: 8

❌ Problem: Privacy concerns ✅ Solution: Use Ollama for local embeddings instead

Migrating from Ollama to OpenAI

Update configuration:

embedder:
  provider: openai
  model: text-embedding-3-small
  api_key: ${OPENAI_API_KEY}

Delete existing index:

rm .grepai/index.gob

Re-index:

grepai watch

Important: You cannot mix embeddings from different models/providers.

Output Format

Successful OpenAI configuration:

✅ OpenAI Embedding Provider Configured

   Provider: OpenAI
   Model: text-embedding-3-small
   Dimensions: 1536
   Parallelism: 4
   API Key: sk-...xxxx (from environment)

   Estimated cost for this codebase:
   - Files: 245
   - Chunks: ~1,200
   - Cost: ~$0.02

   Note: Code will be sent to OpenAI servers.

FAQ

What does the grepai-embeddings-openai skill do?

Configure OpenAI as embedding provider for GrepAI. Use this skill for high-quality cloud embeddings.

How do I install grepai-embeddings-openai?

Run npx skills add https://github.com/yoanbernabeu/grepai-skills --skill grepai-embeddings-openai in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/grepai-embeddings-openai/ (personal) or .claude/skills/grepai-embeddings-openai/ (project).

Which AI agents can use grepai-embeddings-openai?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is grepai-embeddings-openai safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains grepai-embeddings-openai?

It is published by yoanbernabeu in yoanbernabeu/grepai-skills. It has 553 recorded installs.

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grepai-embeddings-openai

Install

Install

Describe the task

Review the result

SKILL.md

GrepAI Embeddings with OpenAI

When to Use This Skill

Considerations

Prerequisites

Configuration

Basic Configuration

With Parallel Processing

Direct API Key (Not Recommended)

Available Models

text-embedding-3-small (Recommended)

text-embedding-3-large

Dimension Reduction

Model Comparison

Cost Estimation

Optimizing for Speed

Parallel Requests

Batching

Rate Limits

Environment Variables

Setting the API Key

Using .env Files

Azure OpenAI

Security Best Practices

Common Issues

Migrating from Ollama to OpenAI

Output Format

FAQ

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