Multimodal RAG Document Search

Document Ingestion & Multimodal Parsing

• Complex PDF Processing: Writing extraction pipelines using tools like Unstructured, LlamaParse, or PyMuPDF to split documents into distinct modalities (text, tables, and images).
• Visual Layout Analysis: Implementing layout detection models (like LayoutLM or YOLO-based document parsers) to understand the structure of pages, ensuring that multi-column texts, headers, and footers are not jumbled.
• Image & Chart Captioning: Feeding extracted charts, graphs, and images into Vision-Language Models (like LLaVA, GPT-4o, or Gemini) to generate text summaries describing what the graphic represents.
• Table Extraction: Using OCR tools or table transformers to convert complex financial or technical tables into clean markdown or semi-structured JSON text.

Vector Embedding & Multimodal Indexing

• Multi-Vector Strategy Setup: Structuring a database where text chunks, table summaries, and image descriptions are converted into dense vector representations.
• Multimodal Embedding Generation: Using specialized models (like CLIP, BridgeTower, or OpenAI/Cohere multimodal embeddings) to project text and visual data into the same vector space, allowing a text query to match an image directly.
• Vector Database Engineering: Storing and indexing these embeddings in vector databases (like Pinecone, Milvus, Qdrant, or ChromaDB) for lightning-fast mathematical similarity searches.

Advanced Retrieval Pipelines

• Hybrid Search Implementation: Combining semantic vector search with traditional keyword search (BM25) to catch specific acronyms, item codes, or precise numeric metrics.
• Reranking Optimisation: Incorporating cross-encoder models (like Cohere Rerank or BGE-Reranker) to re-evaluate the top retrieved document chunks and bubbles up only the most hyper-relevant context.
• Metadata Filtering: Setting up structured tags (e.g., date, document type, author, section) to allow users to narrow down the search space prior to execution.

Generation & System Evaluation

• Context Augmentation & Prompting: Designing the system prompt that feeds the retrieved text, markdown tables, and original raw image crops directly into a Multimodal LLM to generate the final answer.
• RAG Triad Evaluation: Running framework assessments using Ragas or TruLens to score the system on:
  • Faithfulness: Is the model making up facts, or is it strictly adhering to the context?
  • Answer Relevance: Does the generated output actually answer the user's initial question?
  • Context Precision: Did the retrieval system pick up the exact right pages?
• UI/UX Interface Construction: Building a clean, interactive user interface using Streamlit or Gradio where users can upload a PDF, type a question, and visually see the exact text block or chart the AI used to source its answer.