This skill covers using OpenAI's embedding API with GrepAI for high-quality, cloud-based embeddings.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiongrepai-embeddings-openaiExecute the skills CLI command in your project's root directory to begin installation:
Fetches grepai-embeddings-openai from yoanbernabeu/grepai-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate grepai-embeddings-openai. Access via /grepai-embeddings-openai in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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This skill covers using OpenAI's embedding API with GrepAI for high-quality, cloud-based embeddings.
| 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 |
Get your API key at: https://platform.openai.com/api-keys
# .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-..."
embedder:
provider: openai
model: text-embedding-3-small
api_key: ${OPENAI_API_KEY}
parallelism: 8 # Concurrent requests for speed
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.
| 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
| 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
You can reduce dimensions to save storage:
embedder:
provider: openai
model: text-embedding-3-large
dimensions: 1024 # Reduced from 3072
| Model | Dimensions | Cost/1K tokens | Quality |
|---|---|---|---|
text-embedding-3-small |
1536 | $0.00002 | ⭐⭐⭐⭐ |
text-embedding-3-large |
3072 | $0.00013 | ⭐⭐⭐⭐⭐ |
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.
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:
GrepAI automatically batches chunks for efficient API usage.
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.
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')
Create .env in your project root:
OPENAI_API_KEY=sk-...
Add to .gitignore:
.env
For Azure-hosted OpenAI:
embedder:
provider: openai
model: your-deployment-name
api_key: ${AZURE_OPENAI_API_KEY}
endpoint: https://your-resource.openai.azure.com
.env files❌ 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:
text-embedding-3-small instead of large❌ Problem: Slow indexing ✅ Solution: Increase parallelism:
embedder:
parallelism: 8
❌ Problem: Privacy concerns ✅ Solution: Use Ollama for local embeddings instead
embedder:
provider: openai
model: text-embedding-3-small
api_key: ${OPENAI_API_KEY}
rm .grepai/index.gob
grepai watch
Important: You cannot mix embeddings from different models/providers.
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.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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We added grepai-embeddings-openai from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added grepai-embeddings-openai from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
grepai-embeddings-openai has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: grepai-embeddings-openai is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in grepai-embeddings-openai — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in grepai-embeddings-openai — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
grepai-embeddings-openai reduced setup friction for our internal harness; good balance of opinion and flexibility.
grepai-embeddings-openai reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: grepai-embeddings-openai is focused, and the summary matches what you get after install.
grepai-embeddings-openai is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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