Multimodal RAG: documents also contain images
Multimodal RAG prepares image information for retrieval.
What you will learn
- Explain: different modalities, different errors
- Apply the idea in an example: Multimodal RAG: documents also contain images
- Recognize limitations and verify the exercise outcome
Multimodal RAG prepares image information for retrieval. Check whether the pipeline uses text descriptions, multimodal embeddings or another representation.
These steps refer to features identified in the application code. This material has not been validated on a live instance; check the documentation and the options in your version.
How it works, step by step
Different modalities, different errors
A multimodal model can process multiple input types, but exact support depends on the model and integration. OCR extracts image text; vision may describe objects or structure. Audio can be transcribed or generated, while video adds temporal order. A blurred label or cropped unit can completely change the conclusion. Preserve the original and the information’s location. In RAG, an image may become indexed descriptive text without native multimodal embeddings. File acceptance does not imply correct content understanding.
RAG accesses evidence, it does not retrain
RAG means retrieval-augmented generation: retrieval supplies relevant passages before the model answers. Ingestion prepares documents, while query execution searches the index and builds context. An embedding model represents text numerically; the response model writes the answer. Rerankers, vision, graph and router models have different roles when needed. RAG helps with private or changing information but does not guarantee truth. Use an authorized API for balances, exact inventory or permissions; an old document is not a reliable source for current state.
Good documents come before good answers
Prepare readable text, clear headings, tables with headers and sources without unnecessary duplicates. Chunking divides a document into separately retrievable passages; overlap preserves continuity between neighbors. A short chunk can lose conditions, while a large one introduces noise. Metadata preserves source, position and version, plus access scope where enforced by the implementation. Upload stores the file; indexing makes it searchable. When a source changes, check that old chunks disappear and new ones are retrieved before declaring the knowledge base current.
Detailed lab
Check the image representation
A synthetic image may contain “P-12 · 12 V”. Compare extraction with the original: part name, number and unit must remain correct. For cropped or unreadable photographs, the answer should not fill assumed characters.
For tables and diagrams, retain relationships between row, header and legend. If the vision description drops an important limitation, vector search cannot recover missing information. Check ingestion before optimizing retrieval. Consult current configuration for supported files and models.
The visual map
Follow the solid arrows for the main flow. Dashed blue arrows supply data or context; dashed pink arrows show feedback or returning results. Colors and shapes distinguish models, stores, decisions and outputs. Visual descriptions and source references are indexed alongside text. The response model is optional. Retrieval returns evidence. If a response model is enabled, it also composes an answer; retrieved evidence still needs checking. On smaller screens, scroll horizontally to follow the entire diagram.
A complete example
A manual includes a diagram and 12 V label. Vision can extract a searchable description. The local template queries vector_search; upload does not prove native image-to-image retrieval.
Try it yourself
- Choose Multimodal and compatible vision; index a synthetic image and inspect its description before answering.
- Record the input, source and expected outcome before running the experiment. Use only the fictional data in the example.
- Follow the diagram stages. At every step record what information is received and produced; do not confuse intermediate output with the final outcome.
- Repeat after removing necessary information or making the input ambiguous. Check whether the system clarifies, stops or invents an answer.
- Compare with the explained solution. Keep the configuration, date, result and an explanation for differences. Change one thing and retest.
An explained solution
The answer uses correctly extracted text or descriptions and flags insufficient readability. A successful exercise lets you show the connection between input, stages and outcome. When information is missing, a cautious answer is more useful than invented details. Compare more than style: check conditions, sources and operations too.
When it helps and what can go wrong
Do not infer dimensions or compatibility from ambiguous images. Choose this approach when it improves a measured need. Keep a simple baseline and compare outcomes using identical inputs. One successful example does not establish reliability in every situation.
Check your understanding
Does image upload prove native multimodal embeddings?
No. The pipeline may be vision → text description → text embedding.
Does RAG change LLM parameters?
No. It supplies request-time context; parameter changes belong to training.
What outcome should this exercise produce?
The answer uses correctly extracted text or descriptions and flags insufficient readability.
Words to remember
- OCR: Extracting text from an image.
- Retrieval: Finding relevant information in accessible sources.
- Chunk: A document passage that can be retrieved separately.
Sources and your next step
To prepare: R04 — Rerank RAG: better evidence first