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The JinaEmbedder class is used to embed text data into vectors using the Jina AI API. You can get started with Jina AI here. Get your API key.

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Usage

jina_embedder.py
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
from agno.knowledge.embedder.jina import JinaEmbedder

# Add embedding to database
embeddings = JinaEmbedder(id="jina-embeddings-v3").get_embedding("The quick brown fox jumps over the lazy dog.")
# Print the embeddings and their dimensions
print(f"Embeddings: {embeddings[:5]}")
print(f"Dimensions: {len(embeddings)}")

# Use an embedder in a knowledge base
knowledge = Knowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="jina_embeddings",
        embedder=JinaEmbedder(id="jina-embeddings-v3"),
    ),
    max_results=2,
)

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Advanced Usage

# Configure embedder with custom settings
embedder = JinaEmbedder(
    id="jina-embeddings-v3",
    dimensions=1024,
    embedding_type="float",
    late_chunking=True,
    batch_size=50,
    timeout=30.0
)

# Use async methods for better performance
import asyncio

async def embed_texts():
    embedder = JinaEmbedder()
    texts = ["First text", "Second text", "Third text"]
    
    # Get embeddings in batches
    embeddings, usage = await embedder.async_get_embeddings_batch_and_usage(texts)
    print(f"Generated {len(embeddings)} embeddings")
    print(f"Usage info: {usage[0]}")

# Run async example
asyncio.run(embed_texts())

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Params

ParameterTypeDefaultDescription
idstr"jina-embeddings-v3"The model ID to use for generating embeddings.
dimensionsint1024The number of dimensions for the embedding vectors.
embedding_typeLiteral["float", "base64", "int8"]"float"The format type of the returned embeddings.
late_chunkingboolFalseWhether to enable late chunking optimization.
userOptional[str]NoneUser identifier for tracking purposes. Optional.
api_keyOptional[str]JINA_API_KEY env varThe Jina AI API key. Can be set via environment variable.
base_urlstr"https://api.jina.ai/v1/embeddings"The base URL for the Jina API.
headersOptional[Dict[str, str]]NoneAdditional headers to include in API requests. Optional.
request_paramsOptional[Dict[str, Any]]NoneAdditional parameters to include in the API request. Optional.
timeoutOptional[float]NoneTimeout in seconds for API requests. Optional.
enable_batchboolFalseEnable batch processing to reduce API calls and avoid rate limits
batch_sizeint100Number of texts to process in each API call for batch operations.

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Features

  • Async Support: Full async/await support for better performance in concurrent applications
  • Batch Processing: Efficient batch processing of multiple texts with configurable batch size
  • Late Chunking: Support for Jina’s late chunking optimization technique
  • Flexible Output: Multiple embedding formats (float, base64, int8)
  • Usage Tracking: Get detailed usage information for API calls
  • Error Handling: Robust error handling with fallback mechanisms

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Developer Resources

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