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* docs: add jina-embeddings-v3 integration guide * docs: fix model list table * docs: fix broken line in jina page * docs: fix model name in table Co-authored-by: CatStark <susana.guzman@jina.ai> * docs: update wording Co-authored-by: Anush <anushshetty90@gmail.com> * docs: update wording Co-authored-by: Anush <anushshetty90@gmail.com> * docs: update working Co-authored-by: Anush <anushshetty90@gmail.com> * docs: rename task_type as task * docs: set guide to example Co-authored-by: Anush <anushshetty90@gmail.com> * docs: improve wording Co-authored-by: Anush <anushshetty90@gmail.com> --------- Co-authored-by: CatStark <susana.guzman@jina.ai> Co-authored-by: Anush <anushshetty90@gmail.com>
99 lines
3.7 KiB
Markdown
99 lines
3.7 KiB
Markdown
---
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title: Jina Embeddings
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weight: 1900
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aliases:
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- /documentation/embeddings/jina-emebddngs/
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- ../integrations/jina-embeddings/
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---
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# Jina Embeddings
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Qdrant is compatible with [Jina AI](https://jina.ai/) embeddings. You can get a free trial key from [Jina Embeddings](https://jina.ai/embeddings/) to get embeddings.
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Qdrant users can receive a 10% discount on Jina AI APIs by using the code **QDRANT**.
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## Technical Summary
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| Model | Dimension | Language | MRL (matryoshka) | Context |
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|:----------------------:|:---------:|:---------:|:-----------:|:---------:|
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| jina-embeddings-v3 | 1024 | Multilingual (89 languages) | Yes | 8192 |
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| jina-embeddings-v2-base-en | 768 | English | No | 8192 |
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| jina-embeddings-v2-base-de | 768 | German & English | No | 8192 |
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| jina-embeddings-v2-base-es | 768 | Spanish & English | No | 8192 |
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| jina-embeddings-v2-base-zh | 768 | Chinese & English | No | 8192 |
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> Jina recommends using `jina-embeddings-v3` as it is the latest and most performant embedding model released by Jina AI.
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On top of the backbone, `jina-embeddings-v3` has been trained with 5 task-specific adapters for different embedding uses. Include `task` in your request to optimize your downstream application:
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+ **retrieval.query**: Used to encode user queries or questions in retrieval tasks.
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+ **retrieval.passage**: Used to encode large documents in retrieval tasks at indexing time.
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+ **classification**: Used to encode text for text classification tasks.
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+ **text-matching**: Used to encode text for similarity matching, such as measuring similarity between two sentences.
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+ **separation**: Used for clustering or reranking tasks.
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`jina-embeddings-v3` supports **Matryoshka Representation Learning**, allowing users to control the embedding dimension with minimal performance loss.
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Include `dimensions` in your request to select the desired dimension.
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By default, **dimensions** is set to 1024, and a number between 256 and 1024 is recommended.
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You can reference the table below for hints on dimension vs. performance:
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| Dimension | 32 | 64 | 128 | 256 | 512 | 768 | 1024 |
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|:----------------------:|:---------:|:---------:|:-----------:|:---------:|:----------:|:---------:|:---------:|
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| Average Retrieval Performance (nDCG@10) | 52.54 | 58.54 | 61.64 | 62.72 | 63.16 | 63.3 | 63.35 |
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## Example
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The code below demonstrate how to use `jina-embeddings-v3` together with Qdrant:
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```python
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import requests
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import qdrant_client
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from qdrant_client.models import Distance, VectorParams, Batch
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# Provide Jina API key and choose one of the available models.
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JINA_API_KEY = "jina_xxxxxxxxxxx"
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MODEL = "jina-embeddings-v3"
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DIMENSIONS = 1024 # Or choose your desired output vector dimensionality.
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TASK = 'retrieval.passage' # For indexing, or set to retrieval.query for quering
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# Get embeddings from the API
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url = "https://api.jina.ai/v1/embeddings"
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {JINA_API_KEY}",
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}
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data = {
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"input": ["Your text string goes here", "You can send multiple texts"],
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"model": MODEL,
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"dimensions": DIMENSIONS,
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"task": TASK,
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}
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response = requests.post(url, headers=headers, json=data)
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embeddings = [d["embedding"] for d in response.json()["data"]]
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# Index the embeddings into Qdrant
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client = qdrant_client.QdrantClient(":memory:")
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client.create_collection(
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collection_name="MyCollection",
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vectors_config=VectorParams(size= DIMENSIONS, distance=Distance.DOT),
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)
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qdrant_client.upsert(
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collection_name="MyCollection",
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points=Batch(
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ids=list(range(len(embeddings))),
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vectors=embeddings,
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),
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)
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```
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