---
language:
- en
license: mit
library_name: colpali-engine
tags:
- colpali
- colqwen
- multi-vector
- late-interaction
- visual-document-retrieval
- document-ai
- quantization
- maxsim
base_model: vidore/colqwen2.5-v0.2
pipeline_tag: feature-extraction
---

# VisDoc AI: Multimodal Visual Document Retrieval with Late Interaction & Quantization

**VisDoc AI** is a visual document retrieval engine built on Vision-Language Models (`ColQwen2.5` / `ColPali`) and multi-vector late-interaction MaxSim scoring.

It indexes and searches complex visual documents—**engineering datasheets, electrical schematics, PCB diagrams, financial balance sheets, commercial invoices, and stamped inspection certificates**—without OCR-based text chunking.

- **GitHub Repository**: [https://github.com/adityapande403/VisDoc-AI](https://github.com/adityapande403/VisDoc-AI)
- **Base Architecture**: `vidore/colqwen2.5-v0.2` (`Qwen/Qwen2.5-VL-3B-Instruct` backbone)
- **Output Representation**: Multi-vector token embeddings with dimension $D = 128$ and unit L2-normalization.
- **Scoring Function**: MaxSim Late Interaction:
  $$S(Q, D) = \sum_{i=1}^{N_q} M_{q, i} \max_{j \in D} (q_i \cdot d_j)$$

---

## Benchmark Results (Empirically Measured)

### 1. Retrieval Quality & Compression Retention (Held-Out Test Set)

| Representation | NDCG@5 | MRR@10 | Recall@5 | Relative NDCG@5 Retention | Embedding Payload | Compression vs FP16 |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
| **FP16 Baseline** | 0.5132 | 0.4361 | 0.8333 | **100.0%** (Baseline) | 0.09 MB | 1.0x |
| **INT8 Scalar** | 0.5132 | 0.4361 | 0.8333 | **100.0%** | 0.05 MB | **2.0x** |
| **1-Bit Binary** | 0.3747 | 0.2556 | 0.8333 | **73.0%** | 0.01 MB | **16.0x** (32x vs FP32) |

### 2. 10,000-Page Scale Benchmark (5,000,000 Multi-Vectors)

| Index Format | Total Indexed Pages | Embedding Memory (MB) | Compression vs FP16 | Mean Latency (ms) | P95 Latency (ms) | Throughput (QPS) |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
| **FP16** | 10,000 | 1,220.7 MB | 1.0x | 2,028.90 ms | 2,135.90 ms | 0.5 |
| **INT8** | 10,000 | 610.4 MB | **2.0x** | 3,013.15 ms | 3,083.47 ms | 0.3 |
| **BINARY** | 10,000 | **76.3 MB** | **16.0x** | 2,770.02 ms | 2,890.07 ms | 0.4 |

*Two-stage candidate filtering reduces 1,000-page latency from 253.26 ms to **10.69 ms** (23.7x acceleration).*

---

## Quickstart

```python
from visdoc import VisualDocumentRetriever

# Initialize with INT8 compressed index (100% NDCG@5 retention, 2x memory reduction)
retriever = VisualDocumentRetriever(index_format="int8")

# Ingest and index PDF document pages
retriever.index_documents(paths=["./datasheet.pdf"])

# Run late-interaction visual search
results = retriever.search(
    query="Which pin provides the 5.0 V analog sensor supply on the TX-4000?",
    top_k=5,
)

for result in results:
    print(f"Document: {result.document_id} | Page: {result.page_number} | Score: {result.score:.4f}")
```

---

## Citation & Reference

```bibtex
@software{visdoc_ai_2026,
  author = {Aditya Pande},
  title = {VisDoc AI: Multimodal Visual Document Retrieval with ColPali, ColQwen and Late Interaction},
  url = {https://github.com/adityapande403/VisDoc-AI},
  year = {2026}
}
```