Multimodal and Structured Data RAG

Multimodal Indexing

Images, charts and slides follow the same pattern: a multimodal embedding model converts each one to a vector, and it becomes retrievable next to text chunks.

Storage Options

• One vector per image - Simplest; retrieve then caption.
• Image plus caption chunk - Merge visual and text signals.
• Late interaction models - Higher quality with more compute.

Structured Data: Text-to-SQL

For tabular data, retrieval can pick a schema and the model writes SQL instead of answering directly.

prompt = (
    "Given the schema, write PostgreSQL.
"
    f"Schema: {schema}
Question: {question}"
)
# sql = call_your_llm(prompt)
# rows = run_sql(sql)

Always run generated SQL read-only and with tight permissions, and validate the output table matches the schema.

Key Points

• Multimodal embeddings make images searchable.
• Combine visual vectors with captions for better recall.
• Retrieve schemas, then let the model generate SQL.
• Guard generated SQL strictly.