Integrate TiDB Vector Search with Jina AI Embeddings API

This tutorial demonstrates how to use Jina AI to generate embeddings for text and image data, store them in TiDB vector storage, and perform semantic search.

Info

Currently, only the following product and regions support native SQL functions for integrating the Jina AI Embeddings API:

• TiDB Cloud Starter on AWS: Frankfurt (eu-central-1) and Singapore (ap-southeast-1)

Jina AI Embeddings

Jina AI provides high-performance, multimodal, and multilingual long-context embeddings for search, RAG, and agent applications.

Supported Models

Model Name	Dimensions	Max Input Tokens	Description
jina_ai/jina-embeddings-v4	2048	32,768	Multimodal, multilingual, text and image embeddings
jina_ai/jina-clip-v2	1024	8192	Multilingual multimodal embeddings for texts and images
jina_ai/jina-embeddings-v3	1024	8192	Multilingual, text and code embeddings

For a complete list of supported models and detailed specifications, see the Jina AI Embeddings Documentation.

Usage example

This example demonstrates creating a vector table, inserting documents, and performing similarity search using Jina AI embedding models.

Step 1: Connect to the database

PythonSQL

from pytidb import TiDBClient

tidb_client = TiDBClient.connect(
    host="{gateway-region}.prod.aws.tidbcloud.com",
    port=4000,
    username="{prefix}.root",
    password="{password}",
    database="{database}",
    ensure_db=True,
)

mysql -h {gateway-region}.prod.aws.tidbcloud.com \
    -P 4000 \
    -u {prefix}.root \
    -p{password} \
    -D {database}

Step 2: Configure the API key

Create your API key from the Jina AI Platform and bring your own key (BYOK) to use the embedding service.

PythonSQL

Configure the API key for the Jina AI embedding provider using the TiDB Client:

tidb_client.configure_embedding_provider(
    provider="jina_ai",
    api_key="{your-jina-api-key}",
)

Set the API key for the Jina AI embedding provider using SQL:

SET @@GLOBAL.TIDB_EXP_EMBED_JINA_AI_API_KEY = "{your-jina-api-key}";

Step 3: Create a vector table

Create a table with a vector field that uses the jina_ai/jina-embeddings-v4 model to generate 2048-dimensional vectors:

PythonSQL

from pytidb.schema import TableModel, Field
from pytidb.embeddings import EmbeddingFunction
from pytidb.datatype import TEXT

class Document(TableModel):
    __tablename__ = "sample_documents"
    id: int = Field(primary_key=True)
    content: str = Field(sa_type=TEXT)
    embedding: list[float] = EmbeddingFunction(
        model_name="jina_ai/jina-embeddings-v4"
    ).VectorField(source_field="content")

table = tidb_client.create_table(schema=Document, if_exists="overwrite")

CREATE TABLE sample_documents (
    `id`        INT PRIMARY KEY,
    `content`   TEXT,
    `embedding` VECTOR(2048) GENERATED ALWAYS AS (EMBED_TEXT(
        "jina_ai/jina-embeddings-v4",
        `content`
    )) STORED
);

Step 4: Insert data into the table

PythonSQL

Use the table.insert() or table.bulk_insert() API to add data:

documents = [
    Document(id=1, content="Java: Object-oriented language for cross-platform development."),
    Document(id=2, content="Java coffee: Bold Indonesian beans with low acidity."),
    Document(id=3, content="Java island: Densely populated, home to Jakarta."),
    Document(id=4, content="Java's syntax is used in Android apps."),
    Document(id=5, content="Dark roast Java beans enhance espresso blends."),
]
table.bulk_insert(documents)

Insert data using the INSERT INTO statement:

INSERT INTO sample_documents (id, content)
VALUES
    (1, "Java: Object-oriented language for cross-platform development."),
    (2, "Java coffee: Bold Indonesian beans with low acidity."),
    (3, "Java island: Densely populated, home to Jakarta."),
    (4, "Java's syntax is used in Android apps."),
    (5, "Dark roast Java beans enhance espresso blends.");

Step 5: Search for similar documents

PythonSQL

Use the table.search() API to perform vector search:

results = table.search("How to start learning Java programming?") \
    .limit(2) \
    .to_list()
print(results)

Use the VEC_EMBED_COSINE_DISTANCE function to perform vector search based on cosine distance metric:

SELECT
    `id`,
    `content`,
    VEC_EMBED_COSINE_DISTANCE(embedding, "How to start learning Java programming?") AS _distance
FROM sample_documents
ORDER BY _distance ASC
LIMIT 2;