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Related occupations SOC
Based on SOC occupation classification
name grepai-embeddings-openai description Configure OpenAI as embedding provider for GrepAI. Use this skill for high-quality cloud embeddings.
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
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
Direct API Key (Not Recommended) embedder:
provider: openai
model: text-embedding-3-small
api_key: sk-your-api-key-here
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
Model Comparison Model Dimensions Cost/1K tokens Quality text-embedding-3-small1536 $0.00002 ⭐⭐⭐⭐ text-embedding-3-large3072 $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
Parallelism recommendations:
Tier 1 (Free): 1-2Tier 2: 4-8Tier 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
export OPENAI_API_KEY="sk-..."
$env:OPENAI_API_KEY = "sk-..."
# Or permanently
[System.Environment]::SetEnvironmentVariable('OPENAI_API_KEY', 'sk-...', 'User')
Using .env Files Create .env in your project root:
Azure 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 keysAdd to .gitignore: Exclude .env filesRotate keys: Regularly rotate API keysMonitor usage: Check OpenAI dashboard for unexpected usageReview code: Ensure sensitive code isn't being indexed
Common Issues ❌ Problem: 401 Unauthorized
✅ Solution: Check API key is correct and environment variable is set:
❌ Problem: 429 Rate limit exceeded
✅ Solution: Reduce parallelism or upgrade OpenAI tier:
❌ 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:
❌ Problem: Privacy concerns
✅ Solution: Use Ollama for local embeddings instead
Migrating from Ollama to OpenAI embedder:
provider: openai
model: text-embedding-3-small
api_key: ${OPENAI_API_KEY}
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.
