Optimizing ColPali for Retrieval at Scale

qdrant-landing/content/blog/colpali-optimization.md

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+---
+title: "Optimizing ColPali for Retrieval at Scale, 13x Faster Results"
+draft: false
+slug: colpali-qdrant-optimization # Change this slug to your page slug if needed
+short_description:  Learn how we optimized ColPali, a document retrieval system, to be 13x faster for datasets of 20,000+ PDFs. Discover how pooling and reranking boost scalability while maintaining precision. # Change this
+description: ColPali combines text and visual contexts for precise document retrieval, but scaling posed challenges. We achieved 13x faster retrieval using pooling and reranking, reducing vector counts by ~30x while retaining near-original accuracy. Explore the open-source demo to optimize your retrieval workflows! # Change this
+
+preview_image: /blog/colpali-optimization/preview-2.png # Change this
+social_preview_image: /blog/colpali-optimization/preview-2.png # Optional image used for link previews
+# title_preview_image: /blog/Article-Image.png # Optional image used for blog post title
+# small_preview_image: /blog/colpali-optimization.png # Optional image used for small preview in the list of blog posts
+
+date: 2024-11-27T00:40:24-03:00
+author: Evgeniya Sukhodolskaya, Sabrina Aquino # Change this
+featured: yes # if true, this post will be featured on the blog page
+tags: # Change this, related by tags posts will be shown on the blog page
+  - colpali
+  - reranker
+  - optimization
+  - qdrant
+  - quantization
+  - multimodal
+
+
+---
+
+ColPali is a fascinating leap in document retrieval. Its precision in handling visually rich PDFs is phenomenal, but scaling it to handle real-world datasets comes with its share of computational challenges.
+
+Here's how we solved these challenges to make ColPali 13x faster without sacrificing the precision it’s known for.
+
+## The Scaling Dilemma
+
+ColPali generates **1,030 vectors for just one page of a PDF.** While this is manageable for small-scale tasks, in a real-world production setting where you may need to store hundreds od thousands of PDFs, the challenge of scaling becomes significant.
+
+Consider this scenario:
+
+- **Dataset Size:** 20,000 PDF pages.
+- **Number of Vectors:** Each page generates ~1,000 vectors of 128 dimensions.
+
+The total number of comparisons is calculated as:
+
+$$
+1,000 \cdot 1,000 \cdot 20,000 \cdot 128 = 2.56 \times 10^{16} \text{ comparisons!}
+$$
+
+
+
+That's trillions of comparisons needed to build the index. Even advanced indexing algorithms like **HNSW** struggle with this scale, as computational costs grow quadratically. 
+
+We turned to a hybrid optimization strategy combining **pooling** (to reduce computational overhead) and **reranking** (to preserve accuracy).
+
+Before we go any deeper, watch our [Webinar video](https://www.youtube.com/live/_h6SN1WwnLs?si=n8gwiIjJ5dnfucXC) for the full demo walkthrough.
+<iframe width="560" height="315" src="https://www.youtube.com/embed/_h6SN1WwnLs?si=HqJTcwDbdk4j_L0Y&amp;start=922" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+For those eager to explore, the [codebase is available here](https://github.com/qdrant/demo-colpali-optimized).
+
+
+## Two-Stage Retrieval Process
+
+### Pooling
+
+Pooling is well-known in machine learning as a way to compress data while keeping important information intact. For ColPali, we reduced ~1,030 vectors per page to just 38 vectors by pooling rows in the document's 32x32 grid.
+
+Max and mean pooling are the two most popular types, so we decided to test both approaches on the rows of the grid. Likewise, we could apply pooling on columns, which we plan to explore in the future.
+
+- **Mean Pooling:** Averages values across rows.
+- **Max Pooling:** Selects the maximum value for each feature.
+
+32 vectors represent the pooled rows, while the final 6 vectors encode contextual information derived from ColPali’s special tokens (e.g., <bos> for the beginning of the sequence, and task-specific instructions like “Describe the image”).
+
+For our experiments, we chose to preserve these 6 additional vectors.
+
+### The "ColPali as a Reranker" Experiment
+
+Pooling drastically reduces retrieval costs, but there’s a risk of losing fine-grained precision. To address this, we implemented a **two-stage retrieval system**, where embeddings generated with ColPali were max/mean pooled by grid rows to create lightweight vectors for the initial retrieval stage, followed by reranking with the original high-resolution embeddings:
+
+1. **Pooled Retrieval:** Quickly retrieves the top 200 candidates using lightweight pooled embeddings.
+2. **Full Reranking:** Refines these candidates using the original, high-resolution embeddings, delivering the final top 20 results.
+
+### Implementation
+
+We created a custom dataset with over 20,000 unique PDF pages by merging:
+
+- **ViDoRe Benchmark:** Designed for PDF documents retrieval evaluation.
+- **UFO Dataset:** Visually rich documents paired with synthetic queries.
+- **DocVQA Dataset:** A large set of document-derived Q&A pairs.
+
+Each document was processed into 32x32 grids, generating both full-resolution and pooled embeddings. 
+
+![](/blog/colpali-optimization/rows.png)
+
+These embeddings were stored in the **Qdrant vector database**:
+
+- **Full-Resolution Embeddings:** ~1,030 vectors per page.
+- **Pooled Embeddings:** Mean and max pooling variants.
+
+All embeddings were kept in RAM to avoid caching effect in experiments realted to the speed of retrieval.
+
+
+### Experiment Setup
+
+We evaluated retrieval quality using 1,000 random sampled queries and the retrieval process followed the two-stage approach:
+1. **Pooled embeddings** retrieved the top 200 candidates.
+2. **Full-resolution embeddings** reranked these candidates to produce the final top 20 results.
+
+To measure performance, we used:
+
+- **NDCG@20:** Measures ranking quality (how well the top results align with expectations).
+- **Recall@20:** Measures the overlap between this method and the original ColPali retrieval.
+
+## Results
+
+The experiments gave us some very promissing results:
+
+- **Speed:** Retrieval time improved **13x** compared to full-resolution embeddings alone.
+- **Accuracy:** Mean pooling preserved retrieval quality nearly identical to the original ColPali.
+
+### Metrics
+
+| Pooling Type | NDCG@20 | Recall@20 |
+|--------------|---------|-----------|
+| **Mean**     | 0.952   | 0.917     |
+| **Max**      | 0.759   | 0.656     |
+
+
+Mean pooling offered the ideal balance, combining speed and precision. Max pooling did not perform well enough to be considered viable since it sacrificed significant accuracy without delivering a meaningful speed advantage.
+
+## What’s Next?
+Future experiments could push these results even further:
+
+- Investigating column-wise pooling for additional compression.
+- Testing half-precision (float16) vectors to balance memory use and speed.
+- Skipping special multivectors during prefetch to streamline retrieval.
+- Combining quantization with oversampling for even faster search.
+
+
+### Try It Yourself
+
+Curious to see this in action? Explore the full codebase and experiment with ColPali optimizations:
+
+- **Demo Notebook:** [GitHub Repository](https://github.com/qdrant/demo-colpali-optimized)
+- **Webinar Walkthrough:** [Watch Here](https://www.youtube.com/live/_h6SN1WwnLs?si=n8gwiIjJ5dnfucXC)
+
+[Join the community](https://discord.com/invite/qdrant) and share your results!
+
+---