Multimodal RAG with Images and Tables
Extend RAG beyond plain text to retrieve and reason over images, charts, and structured tables.
Multimodal RAG with Images and Tables is a free LangChain / RAG / Vector DBs lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the LangChain / RAG / Vector DBs learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Beyond Plain Text
Real documents contain images, charts, and tables. Multimodal RAG indexes and retrieves these non-text elements so the model can answer questions that depend on them.
What Counts as Multimodal
Multimodal sources include scanned pages, diagrams, screenshots, photos, and spreadsheet-style tables embedded in PDFs or web pages.
- Images
- Charts and figures
- Tables
Strategy 1: Describe Then Embed
Use a vision model to generate a text description of each image, then embed the description with your normal text embeddings. Retrieval stays text-based.
caption = vision_model.describe(image)
store.add_texts([caption], metadatas=[{"image": image_id}])Strategy 2: Multimodal Embeddings
Models like CLIP embed images and text into the same vector space, so a text query can directly match an image without a caption step.
Handling Tables
Tables lose meaning when flattened. Preserve structure by converting each table to Markdown or HTML before chunking so rows and headers stay linked.
table_md = "| Year | Revenue |\n|---|---|\n| 2024 | 10M |\n| 2025 | 12M |"
store.add_texts([table_md], metadatas=[{"type": "table"}])Summarizing Large Tables
For wide or long tables, store both a natural-language summary (for retrieval) and the raw table (for the answer), linking them by id.
Routing by Modality
At query time, detect what the question needs. A request about a chart should retrieve image elements; a numeric lookup should target tables.
Passing Images to the LLM
Multimodal LLMs accept images directly in the prompt. After retrieving the relevant image, include it alongside the question for grounded reasoning.
messages = [{"role": "user", "content": [
{"type": "text", "text": "What trend does this chart show?"},
{"type": "image_url", "image_url": {"url": img_url}},
]}]Citing Visual Sources
Track which image or table produced an answer in metadata, so you can show the user the exact figure or table the model relied on.
Cost and Latency
Vision calls and image embeddings cost more than text. Cache captions, downscale images, and only invoke vision when the query truly needs it.
Putting It Together
Extract images and tables during loading, index them via captions or multimodal embeddings, route queries by modality, and feed the right element to a multimodal LLM.
Quick Check
Test your understanding of multimodal RAG.
Recap
You extended RAG to multiple modalities:
- Describe-then-embed or multimodal embeddings for images
- Preserve table structure as Markdown
- Route queries by modality
- Feed images to a multimodal LLM and cite visual sources
Frequently asked questions
Is the “Multimodal RAG with Images and Tables” lesson free?
Yes — the full text of “Multimodal RAG with Images and Tables” is free to read here on the web, and the LangChain / RAG / Vector DBs course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the LangChain / RAG / Vector DBs course, upgrade to CoddyKit PRO.
What will I learn in “Multimodal RAG with Images and Tables”?
Extend RAG beyond plain text to retrieve and reason over images, charts, and structured tables. You practise LangChain / RAG / Vector DBs with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start LangChain / RAG / Vector DBs?
No prior experience is required. LangChain / RAG / Vector DBs on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Multimodal RAG with Images and Tables” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this LangChain / RAG / Vector DBs lesson?
Yes. Every LangChain / RAG / Vector DBs lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- RAG for Code Generation and Assistance
- Building Real-time RAG Systems
- Emerging Trends and Research in RAG
- Multimodal RAG with Images and Tables