Prepare multimodal data
Extract text passages from PDFs and crop or annotate screenshot regions; store source identifiers so retrieved results can be traced back to their file and page.
01 / THE IDEA
Multimodal RAG in practice.
Multimodal retrieval-augmented generation (RAG) finds context from different input modalities (text, images, documents) and uses that grounded context to support generation. Important practical issues include consistent embedding spaces across modalities, indexing and recall at K, latency for real-time queries, and checking that retrieved context actually supports the final answer.
Real-world use case · Proposed extension
A proposed application: answer questions about PDF reports and screenshots by retrieving relevant document passages and image regions, then prompting a language model with those grounded snippets. For healthcare, finance or security uses, a human reviewer must remain in charge and the system should not be used for unreviewed clinical, financial or security decisions.
02 / THE BUILD
How to build a multimodal rag for pdfs and screenshots project
step by step.
Create consistent embeddings
Embed text and image regions with a shared or aligned model (for example CLIP or cross-modal encoders) and normalise vectors before indexing.
Index and retrieve
Build a vector index (FAISS, Annoy) and measure recall at K and grounding: whether retrieved passages actually contain the answer.
Assemble RAG prompt and evaluate
Concatenate top retrieved contexts into a prompt for a generative model and evaluate with retrieval-aware metrics plus human usefulness checks.
03 / SOURCE SNAPSHOT
What the multimodal rag for pdfs and screenshots starter actually does.
The supplied starter generates synthetic text and image embeddings with a NumPy random generator, concatenates them into combined multimodal vectors, creates a noisy query from the first item, computes cosine similarity to every combined vector, and prints the top-3 match indices and their scores.
SOURCE EXCERPTPython
text_embeddings = rng.normal(size=(5, 8))
image_embeddings = rng.normal(size=(5, 8))
combined_embeddings = np.hstack([text_embeddings, image_embeddings])
query_text_embedding = text_embeddings[0] + rng.normal(0, 0.05, 8)
query_image_embedding = image_embeddings[0] + rng.normal(0, 0.05, 8)
query = np.hstack([query_text_embedding, query_image_embedding]).reshape(1, -1)Important limitation
The code uses randomly generated NumPy arrays as embeddings and does not load PDFs, screenshots, real CLIP/encoder models, a vector index, or any generative model. It demonstrates nearest-neighbour similarity on synthetic vectors but does not implement retrieval augmentation, document chunking, modality-specific preprocessing, or grounding checks.
04 / BEYOND THE CLASSROOM
Where a multimodal rag for pdfs and screenshots project could go next.
PROPOSED BUSINESS CONCEPT
Pilot: RAG for document Q&A
A proposed extension: replace the synthetic embeddings with CLIP or dedicated OCR+text encoders on real PDFs and screenshots, index passages with FAISS, and prompt a language model with the top-k retrieved passages to answer user questions.
WHO IT COULD SERVETeams needing searchable document QA
FIRST MVPIndex OCR-extracted PDF passages and screenshot regions, retrieve top-k results for a query, and return the retrieved sources alongside model-generated answers for human review.
KEY RISKMissing or incorrect OCR, modality misalignment or hallucinated generation can produce misleading answers; always keep a human reviewer in the loop for sensitive decisions and log retrieved sources for audit.
This is a potential application pathway, not a delivered product. The supplied starter does not implement this service or validate it with real users.
05 / WHAT YOU TAKE AWAY
What a multimodal rag for pdfs and screenshots project puts on your resume.
- Explain the end-to-end multimodal RAG workflow from extraction to generation and review.
- Create and normalise embeddings for text and image inputs and measure cosine similarity.
- Build a simple nearest-neighbour retrieval baseline and measure recall at K on a small dataset.
- Describe production concerns: indexing, latency, grounding checks and human-in-the-loop validation.
Want to build this with mentors and a lab?
AI Engineer Course Networkers Home runs this topic inside its AI Engineer Course, with placement support and hands-on labs.
06 / QUESTIONS STUDENTS ASK
Multimodal RAG for PDFs and Screenshots project FAQs.
- Is this suitable as a final year project for CSE students?
- Yes — it’s an advanced final year project idea that combines multimodal embedding, indexing and RAG components; expect substantial research, engineering and evaluation work.
- What skills and tools will I need to complete it?
- Familiarity with Python, PyTorch, Hugging Face models, embedding techniques (e.g., CLIP, text encoders), vector indexes like FAISS, and basics of information retrieval and evaluation metrics.
- How should I evaluate progress and how long will it take?
- Measure embedding alignment across modalities, recall@K, latency and answer grounding; a prototype with real models and indexing can take 6–12 weeks depending on scope and optimisation.