---
title: "JinaTextEmbedder"
id: jinatextembedder
slug: "/jinatextembedder"
description: "This component transforms a string into a vector that captures its semantics using a Jina Embeddings model. When you perform embedding retrieval, you use this component to transform your query into a vector. Then, the embedding Retriever looks for similar or relevant documents."
---

# JinaTextEmbedder

This component transforms a string into a vector that captures its semantics using a Jina Embeddings model. When you perform embedding retrieval, you use this component to transform your query into a vector. Then, the embedding Retriever looks for similar or relevant documents.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before an embedding [Retriever](../retrievers.mdx)  in a query/RAG pipeline |
| **Mandatory init variables** | `api_key`: The Jina API key. Can be set with `JINA_API_KEY` env var. |
| **Mandatory run variables** | `text`: A string |
| **Output variables** | `embedding`: A list of float numbers  <br /> <br />`meta`: A dictionary of metadata |
| **API reference** | [Jina](/reference/integrations-jina) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/jina |
| **Package name** | `jina-haystack` |

</div>

## Overview

`JinaTextEmbedder` embeds a simple string (such as a query) into a vector. For embedding lists of documents, use the use the [`JinaDocumentEmbedder`](jinadocumentembedder.mdx), which enriches the document with the computed embedding, also known as vector. To see the list of compatible Jina Embeddings models, head to Jina AI’s [website](https://jina.ai/embeddings/). The default model for `JinaTextEmbedder` is `jina-embeddings-v2-base-en`.

To start using this integration with Haystack, install the package with:

```shell
pip install jina-haystack
```

The component uses a `JINA_API_KEY` environment variable by default. Otherwise, you can pass an API key at initialization with `api_key`:

```python
embedder = JinaTextEmbedder(api_key=Secret.from_token("<your-api-key>"))
```

To get a Jina Embeddings API key, head to https://jina.ai/embeddings/.

## Usage

### On its own

Here is how you can use the component on its own:

```python
from haystack_integrations.components.embedders.jina import JinaTextEmbedder

text_to_embed = "I love pizza!"

text_embedder = JinaTextEmbedder(api_key=Secret.from_token("<your-api-key>"))

print(text_embedder.run(text_to_embed))

# {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
# 'meta': {'model': 'text-embedding-ada-002-v2',
# 'usage': {'prompt_tokens': 4, 'total_tokens': 4}}}
```

:::info
We recommend setting JINA_API_KEY as an environment variable instead of setting it as a parameter.
:::

### In a pipeline

```python
from haystack import Document
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.jina import JinaDocumentEmbedder
from haystack_integrations.components.embedders.jina import JinaTextEmbedder
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [
    Document(content="My name is Wolfgang and I live in Berlin"),
    Document(content="I saw a black horse running"),
    Document(content="Germany has many big cities"),
]

document_embedder = JinaDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"))
documents_with_embeddings = document_embedder.run(documents)["documents"]
document_store.write_documents(documents_with_embeddings)

query_pipeline = Pipeline()
query_pipeline.add_component(
    "text_embedder",
    JinaTextEmbedder(api_key=Secret.from_token("<your-api-key>")),
)
query_pipeline.add_component(
    "retriever",
    InMemoryEmbeddingRetriever(document_store=document_store),
)
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "Who lives in Berlin?"

result = query_pipeline.run({"text_embedder": {"text": query}})

print(result["retriever"]["documents"][0])

# Document(id=..., mimetype: 'text/plain',
# text: 'My name is Wolfgang and I live in Berlin')
```

## Additional References

🧑‍🍳 Cookbook: [Using the Jina-embeddings-v2-base-en model in a Haystack RAG pipeline for legal document analysis](https://haystack.deepset.ai/cookbook/jina-embeddings-v2-legal-analysis-rag)
