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qdrant-landing/content/documentation/advanced-tutorials/pdf-retrieval-at-scale.md
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@@ -20,9 +20,9 @@ Recent advancements in **Vision Large Language Models (VLLMs)**, such as [**ColP
## How VLLMs Work for PDF Retrieval
VLLMs like **ColPali** and **ColQwen** generate **multivector representations** for each PDF page; the representations are stored and indexed in a vector database. During the retrieval process, models dynamically create multivector representations for (textual) user queries, and precise retrieval -- matching between PDF pages and queries -- is achieved through [late-interaction mechanism](https://qdrant.tech/blog/qdrant-colpali/#how-colpali-works-under-the-hood).
VLLMs like **ColPali** and **ColQwen** generate **multivector representations** for each PDF page; the representations are stored and indexed in a vector database. During the retrieval process, models dynamically create multivector representations for (textual) user queries, and precise retrieval -- matching between PDF pages and queries -- is achieved through [late-interaction mechanism](/blog/qdrant-colpali/#how-colpali-works-under-the-hood).
<aside role="status"> Qdrant supports <a href="https://qdrant.tech/documentation/concepts/vectors/#multivectors">multivector representations</a>, making it well-suited for using embedding models such as ColPali, ColQwen, or <a href="https://qdrant.tech/documentation/fastembed/fastembed-colbert/">ColBERT</a></aside>
<aside role="status"> Qdrant supports <a href="/documentation/concepts/vectors/#multivectors">multivector representations</a>, making it well-suited for using embedding models such as ColPali, ColQwen, or <a href="/documentation/fastembed/fastembed-colbert/">ColBERT</a></aside>
## Challenges of Scaling VLLMs
@@ -32,10 +32,10 @@ The heavy multivector representations produced by VLLMs make PDF retrieval at sc
**ColPali** generates over **1,000 vectors per PDF page**, while its successor, **ColQwen**, generates slightly fewer — up to **768 vectors**, dynamically adjusted based on the image size. Typically, ColQwen produces **~700 vectors per page**.
To understand the impact, consider the construction of an [**HNSW index**](https://qdrant.tech/articles/what-is-a-vector-database/#1-indexing-hnsw-index-and-sending-data-to-qdrant), a common indexing algorithm for vector databases. Let's roughly estimate the number of comparisons needed to insert a new PDF page into the index.
To understand the impact, consider the construction of an [**HNSW index**](/articles/what-is-a-vector-database/#1-indexing-hnsw-index-and-sending-data-to-qdrant), a common indexing algorithm for vector databases. Let's roughly estimate the number of comparisons needed to insert a new PDF page into the index.
- **Vectors per page:** ~700 (ColQwen) or ~1,000 (ColPali)
- **[ef_construct](https://qdrant.tech/documentation/concepts/indexing/#vector-index):** 100 (default)
- **[ef_construct](/documentation/concepts/indexing/#vector-index):** 100 (default)
The number of comparisons required is:
@@ -67,7 +67,7 @@ We tested this approach with the ColPali model, mean pooling its multivectors by
- **Indexing time faster by an order of magnitude**
- **Retrieval quality comparable to the original model**
For details of this experiment refer to our [gitHub repository](https://github.com/qdrant/demo-colpali-optimized), [ColPali optimization blog post](https://qdrant.tech/blog/colpali-qdrant-optimization/) or [webinar "PDF Retrieval at Scale"](https://www.youtube.com/watch?v=_h6SN1WwnLs)
For details of this experiment refer to our [gitHub repository](https://github.com/qdrant/demo-colpali-optimized), [ColPali optimization blog post](/blog/colpali-qdrant-optimization/) or [webinar "PDF Retrieval at Scale"](https://www.youtube.com/watch?v=_h6SN1WwnLs)
## Goal of This Tutorial
@@ -95,7 +95,7 @@ from tqdm import tqdm
import uuid
```
To run these experiments, we’re using a **Qdrant cluster**. If you’re just getting started, you can set up a **free-tier cluster** for testing and exploration. Follow the instructions in the documentation ["How to Create a Free-Tier Qdrant Cluster"](https://qdrant.tech/documentation/cloud/create-cluster/?q=free+tier#free-clusters)
To run these experiments, we’re using a **Qdrant cluster**. If you’re just getting started, you can set up a **free-tier cluster** for testing and exploration. Follow the instructions in the documentation ["How to Create a Free-Tier Qdrant Cluster"](/documentation/cloud/create-cluster/?q=free+tier#free-clusters)
```python
client = QdrantClient(