gemini-embed

# Gemini Embeddings API

Generate text embeddings using Google Gemini API via REST.

## Prerequisites

- Environment variable `GOOGLE_API_KEY` must be set
- API endpoint: `https://generativelanguage.googleapis.com/v1beta`
- Model: `gemini-embedding-001`

## Workflow

### Phase 1: Determine Embedding Type

- **Single Embedding**: For one text input
- **Batch Embedding**: For multiple texts (more efficient)

### Phase 2: Configure Task Type (Optional)

Choose based on use case:
- `RETRIEVAL_QUERY`: For search queries
- `RETRIEVAL_DOCUMENT`: For documents to be searched
- `SEMANTIC_SIMILARITY`: For comparing text similarity
- `CLASSIFICATION`: For text classification
- `CLUSTERING`: For grouping similar texts

### Phase 3: Execute API Call

---

## 1. Single Text Embedding

### Basic Embedding

```bash
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "models/gemini-embedding-001",
      "content": {
        "parts": [{"text": "Hello world"}]
      }
    }'
```

### With Task Type

```bash
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "models/gemini-embedding-001",
      "content": {
        "parts": [{"text": "What is machine learning?"}]
      },
      "task_type": "RETRIEVAL_QUERY"
    }'
```

### With Output Dimensionality Control

Truncate embeddings to a smaller size for efficiency:

```bash
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "models/gemini-embedding-001",
      "content": {
        "parts": [{"text": "Hello world"}]
      },
      "output_dimensionality": 256
    }'
```

---

## 2. Batch Embedding

Process multiple texts in a single API call: