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docs: Misc. fixes jina-embeddings.md (#1312)

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Signed-off-by: Anush008 <anushshetty90@gmail.com>
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Anush
2024-11-21 17:41:45 +05:30
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qdrant-landing/content/documentation/embeddings/jina-embeddings.md
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@@ -2,8 +2,8 @@
title: Jina Embeddings
weight: 1900
aliases:
- /documentation/embeddings/jina-emebddngs/
- ../integrations/jina-embeddings/
- /documentation/embeddings/jina-embeddings/
- /documentation/integrations/jina-embeddings/
---
# Jina Embeddings
@@ -18,10 +18,10 @@ Qdrant users can receive a 10% discount on Jina AI APIs by using the code **QDRA
|:----------------------:|:---------:|:---------:|:-----------:|:---------:|
| **jina-clip-v2** | **1024** | **Multilingual (100+, focus on 30)** | **Yes** | **Text/Image** |
| jina-embeddings-v3 | 1024 | Multilingual (89 languages) | Yes | 8192 |
| jina-embeddings-v2-base-en | 768 | English | No | 8192 |
| jina-embeddings-v2-base-de | 768 | German & English | No | 8192 |
| jina-embeddings-v2-base-es | 768 | Spanish & English | No | 8192 |
| jina-embeddings-v2-base-zh | 768 | Chinese & English | No | 8192 |
| jina-embeddings-v2-base-en | 768 | English | No | 8192 |
| jina-embeddings-v2-base-de | 768 | German & English | No | 8192 |
| jina-embeddings-v2-base-es | 768 | Spanish & English | No | 8192 |
| jina-embeddings-v2-base-zh | 768 | Chinese & English | No | 8192 |
> Jina recommends using `jina-embeddings-v3` for text-only tasks and `jina-clip-v2` for multimodal tasks or when enhanced visual retrieval is required.
@@ -38,19 +38,17 @@ Include `dimensions` in your request to select the desired dimension.
By default, **dimensions** is set to 1024, and a number between 256 and 1024 is recommended.
You can reference the table below for hints on dimension vs. performance:
| Dimension | 32 | 64 | 128 | 256 | 512 | 768 | 1024 |
| Dimension | 32 | 64 | 128 | 256 | 512 | 768 | 1024 |
|:----------------------:|:---------:|:---------:|:-----------:|:---------:|:----------:|:---------:|:---------:|
| Average Retrieval Performance (nDCG@10) | 52.54 | 58.54 | 61.64 | 62.72 | 63.16 | 63.3 | 63.35 |
| Average Retrieval Performance (nDCG@10) | 52.54 | 58.54 | 61.64 | 62.72 | 63.16 | 63.3 | 63.35 |
`jina-embeddings-v3` supports [Late Chunking](https://jina.ai/news/late-chunking-in-long-context-embedding-models/), the technique to leverage the model's long-context capabilities for generating contextual chunk embeddings. Include `late_chunking=True` in your request to enable contextual chunked representation. When set to true, Jina AI API will concatenate all sentences in the input field and feed them as a single string to the model. Internally, the model embeds this long concatenated string and then performs late chunking, returning a list of embeddings that matches the size of the input list.
`jina-embeddings-v3` supports [Late Chunking](https://jina.ai/news/late-chunking-in-long-context-embedding-models/), the technique to leverage the model's long-context capabilities for generating contextual chunk embeddings. Include `late_chunking=True` in your request to enable contextual chunked representation. When set to true, Jina AI API will concatenate all sentences in the input field and feed them as a single string to the model. Internally, the model embeds this long concatenated string and then performs late chunking, returning a list of embeddings that matches the size of the input list.
## Example
### Jina Embeddings v3
The code below demonstrate how to use `jina-embeddings-v3` together with Qdrant:
The code below demonstrates how to use `jina-embeddings-v3` with Qdrant:
```python
import requests
@@ -62,7 +60,7 @@ from qdrant_client.models import Distance, VectorParams, Batch
JINA_API_KEY = "jina_xxxxxxxxxxx"
MODEL = "jina-embeddings-v3"
DIMENSIONS = 1024 # Or choose your desired output vector dimensionality.
TASK = 'retrieval.passage' # For indexing, or set to retrieval.query for quering
TASK = 'retrieval.passage' # For indexing, or set to retrieval.query for querying
# Get embeddings from the API
url = "https://api.jina.ai/v1/embeddings"
@@ -105,6 +103,7 @@ qdrant_client.upsert(
### Jina CLIP v2
The code below demonstrates how to use `jina-clip-v2` with Qdrant:
```python
import requests
from qdrant_client import QdrantClient
@@ -195,4 +194,4 @@ search_results = client.query_points(
for result in search_results:
print(f"ID: {result.id}, Score: {result.score}")
```
```