> ## Documentation Index
> Fetch the complete documentation index at: https://simili.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Gemini Integration

> Google Gemini API integration details

# Gemini Integration

How Simili Bot uses Google Gemini for AI analysis.

## Services

Simili Bot uses Gemini for:

1. **Text Embeddings** - Convert text to vectors for semantic search
2. **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
```

**Use Cases:**

* 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](https://ai.google.dev/pricing) for current rates.

<Note>
  v0.1.0 used `text-embedding-004` (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.
</Note>

## 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

```yaml theme={null}
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
```

Simili Bot uses **exponential backoff retry** (typed errors) for transient failures.

## Performance

Typical latencies:

* 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`:

```yaml theme={null}
# Before (v0.1.0)
embedding:
  model: "text-embedding-004"
  dimensions: 768

# After (v0.2.0)
embedding:
  model: "gemini-embedding-001"
  dimensions: 3072
```

Then re-index your collection:

```bash theme={null}
simili index --repo owner/repo --since 2020-01-01
```

<Warning>
  The Qdrant collection dimension cannot be changed in-place. Delete and recreate the collection, or create a new one with a different name.
</Warning>

## Next steps

<CardGroup cols={2}>
  <Card title="Gemini configuration" href="/configuration/gemini">
    Setup Gemini for Simili Bot
  </Card>

  <Card title="OpenAI integration" href="/reference/integrations/openai">
    Use OpenAI as an alternative
  </Card>
</CardGroup>