Quick Start: Build a Working Vision RAG in 5 Minutes
Scraping financial reports using Tesseract or PyMuPDF is an absolute nightmare, especially when extracting data from complex documents. Numeric columns misalign. Merged cells break apart. Footnotes blend right into the main body text.
Vision RAG tackles this challenge from an entirely different angle. Instead of extracting raw text, the system directly renders the PDF page into an image. The model then embeds both visual tokens and textual context into a shared vector space. As a result, bar charts, multi-level tables, and spatial layouts remain completely intact.
Here is how to set it up quickly using byaldi — a lightweight Python wrapper around ColPali that drastically simplifies indexing and search.
Step 1: Set Up the Environment
You need Python 3.10+ and a GPU with at least 8GB of VRAM (such as an RTX 3060 or a Google Colab T4 instance):
# Install byaldi and image processing dependencies
pip install byaldi pdf2image torch torchvision --upgrade
# Required: install poppler to render PDFs to images (Ubuntu/Debian)
sudo apt-get update && sudo apt-get install -y poppler-utilsStep 2: Index Documents and Run a Search
Create a file named quick_vision_rag.py and test it with any financial report:
from byaldi import RAGMultiModalModel
# Load ColPali v1.2 checkpoint (based on PaliGemma-3B)
RAG = RAGMultiModalModel.from_pretrained("vidore/colpali-v1.2")
# 1. Index the PDF directly
print("Starting document indexing...")
RAG.index(
input_path="bao_cao_tai_chinh_q3.pdf", # Path to a file or folder containing multiple PDFs
index_name="financial_reports", # Index name stored on disk
store_collection_with_index=True, # Store image copies alongside index for quick preview
overwrite=True
)
# 2. Query tabular data using natural language
query = "What was the YoY growth percentage for cloud services revenue?"
results = RAG.search(query, k=3)
# 3. Retrieve the most relevant pages
for idx, res in enumerate(results):
print(f"Top {idx + 1} | Page: {res.page_num} | Similarity Score: {res.score:.4f}")No regex. No text chunking. The model pinpoints the exact page containing the data table you need.
Under the Hood: How ColPali and Byaldi Work
Traditional RAG pipelines typically follow this sequence:
PDF -> OCR/PyPDF -> Text Chunks -> Bi-Encoder Embedding -> Vector DB
The breaking point occurs right at step one. Raw text strips away line positions, font sizes, colors, and table dividers. In financial reports or technical specifications, losing the visual layout means losing critical context.
ColPali: Bringing Late Interaction to Document Images
ColPali merges the ColBERT late-interaction architecture with the PaliGemma-3B Vision-Language Model. Instead of compressing an entire page into a single dense vector, the model breaks the page image down into a fine-grained grid of visual patches:
- Image tokenization: Each page rendered at 448×448 resolution produces 1,024 visual tokens. Each token captures a local visual patch while maintaining full-page context.
- Query tokenization: The user query is tokenized into text tokens (typically 10 to 30 tokens).
- Late Interaction (MaxSim): Rather than calculating a simple cosine similarity between two single vectors, every query token identifies its highest-matching visual patch across the page. The sum of these maximum similarity scores forms the final relevance score.
This design excels at retrieving fine-grained details. Whether a figure is tucked away in the bottom-right cell of a complex table or written in 6pt font in a footnote, individual query tokens can reliably match its exact location.
Byaldi: Bridging Research into Production
Running raw ColPali from scratch is cumbersome. You must handle PDF rendering, manage millions of high-dimensional vectors, and write an optimized MaxSim scoring implementation. Byaldi (developed by Sean MacAvaney) completely abstracts this engineering layer. It integrates a C++/CUDA-optimized multi-vector search engine, allowing you to index and search documents in just two function calls.
Advanced: Pairing Byaldi with a VLM for End-to-End Q&A
Finding the right page is only retrieval. To generate a complete, contextual answer for the user, feed the retrieved page image into a VLM (such as GPT-4o-mini or Qwen2-VL-7B) to extract and interpret the values—or even enforce a structured output format:
import base64
from pdf2image import convert_from_path
from byaldi import RAGMultiModalModel
from openai import OpenAI
# 1. Reload the index from disk
RAG = RAGMultiModalModel.from_index(".byaldi/financial_reports")
query = "How did Q3 gross margin change compared to Q2?"
results = RAG.search(query, k=1)
top_hit = results[0]
# 2. Render the retrieved page to an image to send to the LLM
pages = convert_from_path("bao_cao_tai_chinh_q3.pdf", first_page=top_hit.page_num, last_page=top_hit.page_num)
image_path = f"page_{top_hit.page_num}.png"
pages[0].save(image_path, "PNG")
def encode_image_base64(path):
with open(path, "rb") as f:
return base64.b64encode(f.read()).decode("utf-8")
# 3. Send the page image to the VLM for analysis
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": f"Read the table in this image and answer concisely: {query}"},
{
"type": "image_url",
"image_url": {"url": f"data:image/png;base64,{encode_image_base64(image_path)}"}
}
]
}
]
)
print("\n--- RESULT ---")
print(response.choices[0].message.content)Production Optimization Strategies
Running smoothly in a Jupyter notebook does not guarantee an easy production deployment. Here are three critical factors to plan for before scaling up.
1. Managing RAM and Multi-Vector Storage Footprints
Every PDF page produces 1,024 vectors (128-dimensional float16), consuming roughly 256 KB of vector data per page. While that sounds negligible, it grows rapidly at scale:
- A 10,000-page corpus requires ~2.5 GB of RAM just to keep the index in memory.
- A 100,000-page corpus consumes 25 GB of RAM, causing hardware costs to escalate quickly.
- Solution: Utilize Binary Quantization or bfloat16 compression formats supported out-of-the-box by Byaldi. Once your collection exceeds 50,000 pages, transition to native late-interaction vector databases like Vespa or Qdrant instead of local file storage.
2. Calibrating DPI for PDF Rendering
PaliGemma normalizes input images to 448×448 pixels. However, the initial rendering resolution directly dictates whether characters remain legible to the model.
- DPI < 100: Small footnote typography becomes blurry, noticeably degrading Recall@k.
- DPI > 200: Provides no measurable gain in retrieval accuracy while tripling poppler conversion latency and overloading CPU threads.
- Recommendation: Standardize on
dpi=150. This provides the optimal balance between visual clarity and I/O throughput.
3. Implementing a Hybrid Search Pipeline: BM25 + Byaldi
ColPali is exceptionally proficient with spatial semantics and tabular structures. Nonetheless, vision-based models occasionally miss arbitrary alphanumeric strings like tax IDs, invoice serial numbers, or contract codes.
A robust production architecture should run both in parallel to optimize RAG accuracy:
- BM25 / Meilisearch: Accurately matches exact keyword identifiers (invoice codes, SKUs, unique names).
- Byaldi (ColPali): Captures overarching semantic context, flowchart logic, and structured data tables.
- Reranker: Retrieve the Top 10 from each retrieval leg, pass them through a cross-encoder to select the best 2-3 pages, and forward only those to the VLM API. This approach maximizes recall while dramatically reducing monthly vision token costs.
