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Jina AI Embeddings Data Source

The Jina AI Embeddings connector enables you to generate embeddings from text using Jina AI's embedding models, allowing you to convert textual data into vector representations for use in search systems, recommendation engines, and AI-powered applications. This connector is particularly useful for applications that need to perform semantic search, build recommendation systems, analyze text similarity, or prepare data for vector databases. Follow the instructions below to create a new data flow that ingests data from a Jina AI Embeddings source in Nexla.
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Jina AI Embeddings

Create a New Data Flow​

  1. To create a new data flow, navigate to the Integrate section, and click the New Data Flow button. Then, select the desired flow type from the list, and click the Create button.

  2. Select the Jina AI Embeddings connector tile from the list of available connectors. Then, select the credential that will be used to connect to the Jina AI Embeddings API, and click Next; or, create a new Jina AI Embeddings credential for use in this flow.

  3. In Nexla, Jina AI Embeddings data sources can be created using pre-built endpoint templates, which expedite source setup for common Jina AI Embeddings API endpoints. Each template is designed specifically for the corresponding Jina AI Embeddings API endpoint, making source configuration easy and efficient.
    • To configure this source using a template, follow the instructions in Configure Using a Template.

    Jina AI Embeddings sources can also be configured manually, allowing you to ingest data from Jina AI Embeddings API endpoints not included in the pre-built templates or apply further customizations to exactly suit your needs.
    • To configure this source manually, follow the instructions in Configure Manually.

Configure Using a Template​

Nexla provides pre-built templates that can be used to rapidly configure data sources to ingest data from common Jina AI Embeddings API endpoints. Each template is designed specifically for the corresponding Jina AI Embeddings API endpoint, making data source setup easy and efficient.

Endpoint Settings​

  • Select the endpoint from which this source will fetch data from the Endpoint pulldown menu. Available endpoint templates are listed in the expandable boxes below. Click on an endpoint to see more information about it and how to configure your data source for this endpoint.

    Create Embeddings

    This endpoint converts text into embeddings using Jina AI's embedding models. Use this endpoint when you need to generate vector representations of text for semantic search, similarity matching, recommendation systems, or vector database storage.

    • Enter the input text data in the Input field. This should be a list of text objects in JSON format, where each object contains a text property with the text to be embedded. Example format: {"text": "A beautiful sunset over the beach"}, {"text": "Un beau coucher de soleil sur la plage"}. The default value provides sample text in English and French. You can include multiple text objects to generate embeddings for multiple texts in a single request.
    • Enter the model name in the Model field. This is the identifier for the Jina AI embedding model you want to use. Available models include jina-clip-v2, jina-embeddings-v2, jina-embeddings-v4, and other Jina AI embedding models. The default value is jina-clip-v2. Different models have different capabilities, such as multilingual support, embedding dimensions, and context lengths. Choose the model that best fits your use case.

    The Create Embeddings endpoint uses POST requests to send text data to the Jina AI embedding service. The endpoint returns vector embeddings for each input text, which can be used for semantic search, similarity calculations, or storage in vector databases. Embeddings are numerical representations that capture the semantic meaning of text, allowing you to find similar texts or perform semantic operations. For more information about the Create Embeddings endpoint and available models, refer to the Jina AI Embeddings API Documentation.

Endpoint Testing​

Once the selected endpoint template has been configured, Nexla can retrieve a sample of the data that will be fetched according to the current settings. This allows users to verify that the source is configured correctly before saving.

  • To test the current endpoint configuration, click the Test button to the right of the endpoint selection menu. Sample data will be fetched & displayed in the Endpoint Test Result panel on the right.

  • If the sample data is not as expected, review the selected endpoint and associated settings, and make any necessary adjustments. Then, click the Test button again, and check the sample data to ensure that the correct information is displayed.

Configure Manually​

Jina AI Embeddings data sources can be manually configured to ingest data from any valid Jina AI Embeddings API endpoint. Manual configuration provides maximum flexibility for accessing endpoints not covered by pre-built templates or when you need custom API configurations.

With manual configuration, you can also create more complex Jina AI Embeddings sources, such as sources that use chained API calls to fetch data from multiple endpoints or sources that require custom authentication headers or request parameters.

API Method​

  1. To manually configure this source, select the Advanced tab at the top of the configuration screen.

  2. Select the API method that will be used for calls to the Jina AI Embeddings API from the Method pulldown menu. The most common methods are:

    • GET: For retrieving data from the API
    • POST: For sending data to the API or triggering actions (most Jina AI Embeddings endpoints use POST for creating embeddings)

API Endpoint URL​

  1. Enter the URL of the Jina AI Embeddings API endpoint from which this source will fetch data in the Set API URL field. This should be the complete URL including the protocol (https://) and any required path parameters. Jina AI Embeddings API endpoints typically follow the pattern https://api.jina.ai/v1/embeddings.

Ensure the API endpoint URL is correct and accessible with your current credentials. You can test the endpoint using the Test button after configuring the URL. The URL should include the API version and the specific endpoint path.

Path to Data​

Optional

If only a subset of the data that will be returned by API endpoint is needed, you can designate the part(s) of the response that should be included in the Nexset(s) produced from this source by specifying the path to the relevant data within the response. This is particularly useful when API responses contain metadata, pagination information, or other data that you don't need for your analysis.

For example, when a request call is used to fetch embeddings, the API will typically return an array of embedding objects, along with metadata, in the response. By entering the path to the relevant data, you can configure Nexla to treat each element of the returned array as a record.

Path to Data is essential when API responses have nested structures. Without specifying the correct path, Nexla might not be able to properly parse and organize your data into usable records. For Jina AI Embeddings API responses, common paths include $.data[*] for embedding objects or $.embeddings[*] if the response uses a different structure.

  • To specify which data should be treated as relevant in responses from this source, enter the path to the relevant data in the Set Path to Data in Response field.

    • For responses in JSON format enter the JSON path that points to the object or array that should be treated as relevant data. JSON paths use dot notation (e.g., $.data[*] to access all elements in the data array).
    Path to Data Example:

    If the API response is in JSON format and includes a data array that contains embedding objects, the path to the response would be entered as $.data[*].

Autogenerate Path Suggestions​

Nexla can also autogenerate data path suggestions based on the response from the API endpoint. These suggested paths can be used as-is or modified to exactly suit your needs.

  • To use this feature, click the Test button next to the Set API URL field to fetch a sample response from the API endpoint. Suggested data paths generated based on the content & format of the response will be displayed in the Suggestions box below the Set Path to Data in Response field.

  • Click on a suggestion to automatically populate the Set Path to Data in Response field with the corresponding path. The populated path can be modified directly within the field if further customization is needed.

Metadata​

If metadata is included in the response but is located outside of the defined path to relevant data, you can configure Nexla to include this data as common metadata in each record. This is useful when you want to preserve important contextual information that applies to all records but isn't part of the main data array.

For example, when a request call is used to fetch embeddings, the API response will typically include an array of embedding objects along with metadata such as model information, usage statistics, or request IDs. In this case, if you have specified the path to the relevant data but metadata of interest is located in a different part of the response, you can specify a path to this metadata to include it with each record in the generated Nexset(s).

Metadata paths are particularly useful for preserving API response context like request IDs, timestamps, or usage statistics that apply to all records in the response.

  • To specify the location of metadata that should be included with each record, enter the path to the relevant metadata in the Path to Metadata in Response field.

    • For responses in JSON format, enter the JSON path to the object or array that contains the metadata.

Request Headers​

Optional

  • If Nexla should include any additional request headers in API calls to this source, enter the headers & corresponding values as comma-separated pairs in the Request Headers field (e.g., header1:value1,header2:value2). Additional headers are often required for API versioning, content type specifications, or custom authentication requirements.

    You do not need to include any headers already present in the credentials. Common headers like Authorization, Content-Type, and Accept are typically handled automatically by Nexla based on your credential configuration.

Endpoint Testing​

After configuring all settings for the selected endpoint, Nexla can retrieve a sample of the data that will be fetched according to the current configuration. This allows users to verify that the source is configured correctly before saving.

  • To test the current endpoint configuration, click the Test button to the right of the endpoint selection menu. Sample data will be fetched & displayed in the Endpoint Test Result panel on the right.

  • If the sample data is not as expected, review the selected endpoint and associated settings, and make any necessary adjustments. Then, click the Test button again, and check the sample data to ensure that the correct information is displayed.

Save & Activate the Source​

  1. Once all of the relevant steps in the above sections have been completed, click the Create button in the upper right corner of the screen to save and create the new Jina AI Embeddings data source. Nexla will now begin ingesting data from the configured endpoint and will organize any data that it finds into one or more Nexsets.