JinaTextEmbedder

Embed strings, such as a user query, using Jina AI models.

Basic Information

• Type: haystack_integrations.components.embedders.jina.text_embedder.JinaTextEmbedder
• Components it can connect with:
  • EmbeddingRetriever: JinaTextEmbedder sends the embedded text to EmbeddingRetriever to be used for semantic search.
  • Input: JinaTextEmbedder can receive a string to embed from the Input component.

Inputs

Parameter	Type	Default	Description
text	str		The string to embed.

Outputs

Parameter	Type	Default	Description
embedding	List[float]		A dictionary with following keys: - embedding: The embedding of the input string. - meta: A dictionary with metadata including the model name and usage statistics.
meta	Dict[str, Any]		A dictionary with following keys: - embedding: The embedding of the input string. - meta: A dictionary with metadata including the model name and usage statistics.

Overview

JinaTextEmbedder embeds a single text string (typically a query) using Jina AI models. Use this component in query pipelines to convert user queries into embeddings for semantic search.

For embedding documents in indexes, use JinaDocumentEmbedder instead.

Embedding Models in Query Pipelines and Indexes

The embedding model you use to embed documents in your indexing pipeline must be the same as the embedding model you use to embed the query in your query pipeline.

This means the embedders for your indexing and query pipelines must match. For example, if you use CohereDocumentEmbedder to embed your documents, you should use CohereTextEmbedder with the same model to embed your queries.

Authorization

Create a secret with your Jina API key. Type JINA_API_KEY as the secret key. For detailed instructions on creating secrets, see Create Secrets.

Get your API key from Jina AI.

Usage Example

This example shows a query pipeline that embeds a user query using Jina and retrieves relevant documents.

components:
  JinaTextEmbedder:
    type: haystack_integrations.components.embedders.jina.text_embedder.JinaTextEmbedder
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - JINA_API_KEY
        strict: false
      model: jina-embeddings-v3
      task: retrieval.query
  InMemoryEmbeddingRetriever:
    type: haystack.components.retrievers.in_memory.embedding_retriever.InMemoryEmbeddingRetriever
    init_parameters:
      document_store:
        type: haystack.document_stores.in_memory.document_store.InMemoryDocumentStore
        init_parameters:
          bm25_tokenization_regex: (?u)\b\w\w+\b
          bm25_algorithm: BM25L
          bm25_parameters:
          embedding_similarity_function: dot_product
          index: 'default'
          async_executor:
      top_k: 5

connections:
- sender: JinaTextEmbedder.embedding
  receiver: InMemoryEmbeddingRetriever.query_embedding

max_runs_per_component: 100

metadata: {}

inputs:
  query:
  - JinaTextEmbedder.text
  - InMemoryEmbeddingRetriever.query

outputs:
  documents: InMemoryEmbeddingRetriever.documents

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
api_key	Secret	Secret.from_env_var('JINA_API_KEY')	The Jina API key. It can be explicitly provided or automatically read from the environment variable JINA_API_KEY (recommended).
model	str	jina-embeddings-v3	The name of the Jina model to use. Check the list of available models on Jina documentation.
prefix	str		A string to add to the beginning of each text.
suffix	str		A string to add to the end of each text.
task	Optional[str]	None	The downstream task for which the embeddings will be used. The model will return the optimized embeddings for that task. Check the list of available tasks on Jina documentation.
dimensions	Optional[int]	None	Number of desired dimension. Smaller dimensions are easier to store and retrieve, with minimal performance impact thanks to MRL.
late_chunking	Optional[bool]	None	A boolean to enable or disable late chunking. Apply the late chunking technique to leverage the model's long-context capabilities for generating contextual chunk embeddings. The support of task and late_chunking parameters is only available for jina-embeddings-v3.

Run Method Parameters

These are the parameters you can configure for the component's run() method. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

Parameter	Type	Default	Description
text	str		The string to embed.