Gemini Integration
How Simili Bot uses Google Gemini for AI analysis.
Services
Simili Bot uses Gemini for:
- Text Embeddings - Convert text to vectors for semantic search
- LLM Analysis - AI reasoning, duplicate detection, routing, and triage
Embeddings
gemini-embedding-001
Default embedding model in v0.2.0:
Input: Text string
Output: 3072-dimensional vector
Speed: 100-500ms per request
- Convert issue text to vector for similarity search
- Generate embeddings for all issues during bulk indexing
- Embed PR content (title + body + changed files) for PR duplicate detection
Cost: Refer to the Google AI pricing page for current rates.
v0.1.0 used gemini-embedding-001 (768 dimensions). v0.2.0 uses gemini-embedding-001 (3072 dimensions). If you’re migrating from v0.1.0, you must re-index your collection after updating.
LLM analysis
Default LLM model: gemini-2.5-flash
1. Duplicate detection
Input: Current issue + similar issues
Task: Determine if duplicates
Output: Boolean + confidence (0.0-1.0) + reasoning
Speed: 2-5 seconds
2. Quality assessment
Input: Issue title + body
Task: Evaluate description quality
Output: Score (0-100) + suggestions
Speed: 1-3 seconds
3. Issue routing
Input: Issue + repository descriptions
Task: Determine correct repository
Output: Target repo name + reasoning
Speed: 2-5 seconds
4. Auto triage
Input: Issue content + available labels
Task: Suggest appropriate labels
Output: Labels + confidence scores
Speed: 1-2 seconds
Models
| Model | Type | Default |
|---|
gemini-embedding-001 | Embeddings | Yes |
gemini-2.5-flash | LLM | Yes |
gemini-2.0-flash-lite | LLM | No (previous default) |
Configuration
embedding:
provider: "gemini"
api_key: "${GEMINI_API_KEY}"
model: "gemini-embedding-001"
dimensions: 3072
llm:
provider: "gemini"
api_key: "${GEMINI_API_KEY}"
model: "gemini-2.5-flash"
API quotas
Free Tier:
- Embeddings: 50 requests/minute
- LLM Calls: 15 requests/minute
- Generous monthly limits
Paid:
- Pay-as-you-go
- Higher rate limits available
Error handling
Graceful degradation if Gemini is unavailable:
If embeddings fail:
→ Step skipped
→ No similarity search
→ Continue with other analysis
If LLM fails:
→ That analysis step skipped
→ Other steps continue
→ Error logged
- Embedding request: 200-500ms
- LLM analysis: 2-5 seconds
- Batch embedding: 500ms-2s
Bottleneck is usually external API calls.
Migration from v0.1.0
If upgrading from v0.1.0, update your simili.yaml:
# Before (v0.1.0)
embedding:
model: "text-embedding-004"
dimensions: 768
# After (v0.2.0)
embedding:
model: "gemini-embedding-001"
dimensions: 3072
simili index --repo owner/repo --since 2020-01-01
The Qdrant collection dimension cannot be changed in-place. Delete and recreate the collection, or create a new one with a different name.
Next steps
Gemini configuration
Setup Gemini for Simili Bot
OpenAI integration
Use OpenAI as an alternative
