Multimodal RAG
Retrieve and ground answers using text, images, tables, audio, and document layout.
Why text alone is insufficient
Important meaning may live in a chart, scanned signature, diagram, table layout, or audio timestamp rather than plain extracted text. Multimodal retrieval preserves several views of the same source so the system can answer the question using the appropriate evidence. The goal is not to make every asset textual; it is to retain enough structure for retrieval, reasoning, and verification.
Ingestion and representation
Extract text, render pages, detect tables, create captions or summaries, segment audio, and attach coordinates or timestamps. Store each representation with a stable document and asset identifier. Keep relationships between a page image, its OCR text, and its table cells. Version the extraction pipeline because changing OCR or layout detection can alter search results and citations.
{
"chunk_id": "doc-17-page-2-table-1",
"modality": "table",
"source": "report.pdf",
"page": 2,
"text": "Revenue increased 12 percent",
"access": "finance-team"
}Indexing strategies
Text embeddings support semantic search, while image or multimodal embeddings can find visual similarity. Keyword indexes remain useful for exact IDs, names, and numbers. Metadata filters can narrow by customer, date, permission, page, or media type. A practical system often searches several indexes, merges candidates, and reranks them rather than expecting one vector to represent every question equally well.
Retrieval and reranking
Classify the question by the evidence it likely needs, then retrieve text, images, tables, or audio segments accordingly. Reranking can use the original query and candidate content to improve relevance. Keep enough surrounding context to preserve meaning, but avoid filling the prompt with duplicate representations. Apply permission filters before the model sees any candidate.
Grounded generation
Tell the model which passages, regions, and timestamps are evidence and require it to distinguish supported facts from inference. For a chart question, provide the chart and nearby explanation; for a numeric question, provide the table cells and original page. If evidence conflicts or is missing, the answer should say so. Never let a polished response hide an unsupported transformation.
Citations and review
A useful citation identifies the source, page, region, table, or time range that supports a claim. Store enough provenance to render a preview or jump to the relevant location. Let reviewers compare the answer with the original asset, especially for OCR and chart interpretation. Citation presence alone is not grounding if the cited region does not actually support the statement.
Evaluation and operations
Evaluate retrieval recall, evidence precision, answer correctness, citation accuracy, permission isolation, and latency by modality. Include scanned documents, confusing layouts, visual-only facts, and questions that should be refused. Monitor extraction failures and index freshness. Multimodal RAG becomes dependable when its representations, retrieval decisions, and generated claims can each be inspected independently.
Worked example: searching a financial report
A financial report contains narrative paragraphs, tables, charts, and footnotes that should not be flattened into one text stream. Index each extracted unit with its page, coordinates, modality, and access policy. A question about revenue may need both a table cell and the paragraph that explains a restatement. Retrieval should return the evidence in a form the generator can cite, not merely a similarity score with no traceable source.
Code walkthrough
The JSON record preserves a chunk ID, modality, source, page, text, and access label. Add a document version and location coordinates, then filter by authorization before ranking results. For tables, store normalized cell values alongside a readable representation; for images, store an embedding reference and a caption. The final answer should carry source IDs so the UI can link back to the page or crop that supports it.
Trade-offs to measure
One shared embedding space simplifies search across text and images, while modality-specific indexes can preserve specialized signals. Captions make images searchable but may omit visual details; storing raw images improves evidence but costs more and raises access concerns. Hybrid lexical-plus-vector retrieval often helps exact figures and names. Compare recall of relevant evidence, citation correctness, latency, storage, and permission-filter failures.
Practical exercise
Index one public report containing prose, a table, and a chart. Create questions that require each modality and one that requires combining two pages. Evaluate whether the top five results include the supporting evidence and whether every result is authorized. Display an answer with page links and manually inspect any unsupported claim. Change chunking once and compare retrieval failures rather than only average similarity.
Sources and further reading
These primary or specialist references informed the concepts in this guide. Product details can change, so verify current documentation before implementation.