
Grepai Embeddings Openai
- 510 installs
- 18 repo stars
- Updated February 1, 2026
- yoanbernabeu/grepai-skills
grepai-embeddings-openai is a Claude Code skill that configures OpenAI as GrepAI's embedding provider so developers get cloud-quality semantic vectors without running a local embed server.
About
grepai-embeddings-openai is a configuration skill for GrepAI that routes embedding generation through OpenAI's API instead of a self-hosted model. Developers use it when they want state-of-the-art vector quality, fast indexing without local GPU compute, and scalable search across large repositories. The skill documents trade-offs—privacy versus convenience—and when a shared team setup beats local inference. Reach for grepai-embeddings-openai when GrepAI is already in your workflow but local embeddings are too slow, too weak, or too costly to maintain.
- OpenAI embedder block for .grepai/config.yaml with env-based API key
- Models such as text-embedding-3-small with optional parallelism for concurrent requests
- Documents quality, speed, scalability vs privacy, cost, and internet dependency
- Team-friendly shared infrastructure without local embedding compute
- Prerequisites: OpenAI API key and billing-enabled account
Grepai Embeddings Openai by the numbers
- 510 all-time installs (skills.sh)
- +4 installs in the week ending Jul 28, 2026 (Skillselion tracking)
- Ranked #1,753 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Security screen: MEDIUM risk (skills.sh audit)
- Data as of Jul 28, 2026 (Skillselion catalog sync)
npx skills add https://github.com/yoanbernabeu/grepai-skills --skill grepai-embeddings-openaiAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 510 |
|---|---|
| repo stars | ★ 18 |
| Security audit | 3 / 3 scanners passed |
| Last updated | February 1, 2026 |
| Repository | yoanbernabeu/grepai-skills ↗ |
How do you configure GrepAI with OpenAI embeddings?
Point GrepAI at OpenAI embeddings when you want cloud-quality vectors without running a local embed server.
Who is it for?
Developers running GrepAI semantic search who prefer managed OpenAI embeddings over operating a local embed server.
Skip if: Teams that require fully offline or air-gapped embedding pipelines where API keys and outbound network calls are prohibited.
When should I use this skill?
A developer asks to point GrepAI at OpenAI, switch from local to cloud embeddings, or improve semantic search vector quality.
What you get
GrepAI config pointing at OpenAI embeddings, cloud vector index, and documented quality-versus-privacy trade-offs.
- GrepAI OpenAI embedding configuration
- Provider trade-off notes
Files
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
1. OpenAI API key 2. 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 speedDirect 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-smalltext-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: 3072Dimension Reduction
You can reduce dimensions to save storage:
embedder:
provider: openai
model: text-embedding-3-large
dimensions: 1024 # Reduced from 3072Model 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 tierParallelism 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:
.envAzure OpenAI
For Azure-hosted OpenAI:
embedder:
provider: openai
model: your-deployment-name
api_key: ${AZURE_OPENAI_API_KEY}
endpoint: https://your-resource.openai.azure.comSecurity Best Practices
1. Use environment variables: Never hardcode API keys 2. Add to .gitignore: Exclude .env files 3. Rotate keys: Regularly rotate API keys 4. Monitor usage: Check OpenAI dashboard for unexpected usage 5. 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-smallinstead 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
1. Update configuration:
embedder:
provider: openai
model: text-embedding-3-small
api_key: ${OPENAI_API_KEY}2. Delete existing index:
rm .grepai/index.gob3. Re-index:
grepai watchImportant: 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.Related skills
How it compares
Choose grepai-embeddings-openai over local GrepAI embed setups when vector quality and zero local ops matter more than keeping all code embeddings on-premises.
FAQ
When should GrepAI use OpenAI embeddings?
grepai-embeddings-openai recommends OpenAI when developers need highest-quality vectors, fast indexing without local compute, and scalable search across large codebases. The skill notes this trades privacy for convenience versus self-hosted embed servers.
Does grepai-embeddings-openai require a local embed server?
grepai-embeddings-openai configures cloud-based OpenAI embeddings so GrepAI does not need a local embedding server. Indexing runs through the OpenAI API, removing local GPU or CPU embed infrastructure.
Is Grepai Embeddings Openai safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.