---

name: gemini-embed description: Generate text embeddings using Google Gemini API for RAG, semantic similarity, classification, and clustering tasks. Invoke when user wants to embed text, create embeddings, or convert text to vectors with Gemini. trigger_keywords:

• gemini embed
• gemini embedding
• gemini embeddings
• embed with gemini
• text to vector
• gemini vector

---

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

Copycurl "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

Copycurl "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:

Copycurl "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:

Copycurl "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:batchEmbedContents?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "requests": [
        {
          "model": "models/gemini-embedding-001",
          "content": {"parts": [{"text": "What is the meaning of life?"}]}
        },
        {
          "model": "models/gemini-embedding-001",
          "content": {"parts": [{"text": "How does the brain work?"}]}
        },
        {
          "model": "models/gemini-embedding-001",
          "content": {"parts": [{"text": "What is quantum computing?"}]}
        }
      ]
    }'

Batch with Task Type

Copycurl "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:batchEmbedContents?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "requests": [
        {
          "model": "models/gemini-embedding-001",
          "content": {"parts": [{"text": "Document about AI"}]},
          "task_type": "RETRIEVAL_DOCUMENT"
        },
        {
          "model": "models/gemini-embedding-001",
          "content": {"parts": [{"text": "Document about ML"}]},
          "task_type": "RETRIEVAL_DOCUMENT"
        }
      ]
    }'

---

Response Structure

Single Embedding Response

Copy{
  "embedding": {
    "values": [
      -0.02342152,
      0.01676572,
      0.009261323,
      ...
    ]
  }
}

Batch Embedding Response

Copy{
  "embeddings": [
    {
      "values": [-0.022374554, -0.004560777, ...]
    },
    {
      "values": [-0.007975887, -0.02141119, ...]
    },
    {
      "values": [-0.0047850125, 0.008764064, ...]
    }
  ]
}

---

Task Types Reference

---

Common Use Cases

RAG (Retrieval-Augmented Generation)

1. Embed documents with RETRIEVAL_DOCUMENT
2. Store embeddings in vector database
3. Embed user query with RETRIEVAL_QUERY
4. Find similar documents by vector similarity

Semantic Search

Copy# Embed the query
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": "How to train a neural network?"}]},
      "task_type": "RETRIEVAL_QUERY"
    }'

Text Similarity Comparison

Copycurl "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:batchEmbedContents?key=$GOOGLE_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "requests": [
        {
          "model": "models/gemini-embedding-001",
          "content": {"parts": [{"text": "The cat sat on the mat"}]},
          "task_type": "SEMANTIC_SIMILARITY"
        },
        {
          "model": "models/gemini-embedding-001",
          "content": {"parts": [{"text": "A feline rested on the rug"}]},
          "task_type": "SEMANTIC_SIMILARITY"
        }
      ]
    }'

Then compute cosine similarity between the two embedding vectors.

---

Best Practices

1. Use batch embedding: More efficient for multiple texts
2. Set task_type: Improves embedding quality for specific use cases
3. Reduce dimensionality: Use output_dimensionality for smaller, faster embeddings when full precision isn't needed
4. Match task types: Use same task_type for queries and documents in RAG
5. Normalize vectors: Cosine similarity works best with normalized vectors