Advanced Multimodal RAG Pipeline

Standard multimodal Retrieval-Augmented Generation (RAG) often fails to retrieve relevant images, tables, and figures because image summaries (captions) generated in isolation lack the surrounding textual context from the source document. This leads to poor retrieval for specific queries.

This advanced pipeline introduces two key modifications to solve this problem, resulting in more accurate and contextually relevant multimodal responses.

The Core Problem with Standard Multimodal RAG

The standard approach assumes that a summary generated solely from an image’s content is sufficient for retrieval. However, in complex documents like reports or research papers, an image’s relevance is defined by the narrative around it. For example, two visually similar tables about “working capital” might only be distinguishable by surrounding text that specifies one is for “farmers” and the other for “processors.” A standard image caption would miss this nuance.

Solution 1: Context-Aware Image Summaries (Ingestion)

Instead of generating a caption from the image alone, this method creates a richer summary by extracting the text immediately surrounding the image in the source document.

• Process: For each image, capture up to 200 characters of text before it and 200 characters after it.
• Result: This combined text becomes the image’s “summary” or “caption” for embedding. It naturally includes author-provided captions and the narrative context, ensuring the embedded representation is closely tied to its meaning in the document.

Solution 2: Text-Response-Guided Image Selection (Generation)

Instead of matching the user’s (often short) query directly against image embeddings, this method uses a two-step retrieval process.

1. Generate Text First: Retrieve the top text chunks relevant to the user’s query and use the LLM to generate the complete textual answer.
2. Retrieve Images for the Text Answer: Use the full, generated text response as a query to perform a similarity search against the context-aware image summaries.

This ensures that the selected images are relevant to the actual answer being provided, not just the initial, potentially ambiguous query.

High-Level Implementation

1. Parsing: Use a robust tool (e.g., Adobe PDF Extract API) to extract text, images, and their positions from documents.
2. Contextual Captioning: For each image, find its position in the document structure and concatenate the surrounding text to create its context-aware summary.
3. Embedding: Create embeddings for all text chunks and for the context-aware image summaries. Store them in a Vector Databases like FAISS.
4. Retrieval & Generation:
   • Retrieve text chunks based on the user query.
   • Generate the final text answer.
   • Use the text answer to retrieve the top N most relevant images.
   • (Optional) Generate a final, clean caption for the selected images before displaying them.