Build a MultiModal RAG PDF Q&A Chatbot

Standard text-only RAG pipelines fail when critical information is stored in charts, tables, diagrams, or images within PDF documents. A multimodal retrieval-augmented generation architecture bridges this gap by embedding both text and visual assets into a shared semantic space.

Pipeline Architecture

[Input PDF] ──► [PyMuPDF Extraction] ──┬──► [Text Chunks]   ──► [Cohere embed-v4.0] ──┐
                                       │                                              │
                                       └──► [Extracted Images] ──► [Cohere embed-v4.0] ──┼──► [ChromaDB Vector Store]
                                                                                      │             │
[User Query] ────────────────────────► [Cohere Embedding] ───────────────────────────┘             │
                                                                                                    ▼
                                                                                      [Top-k Text + Image Retrieval]
                                                                                                    │
                                                                                                    ▼
                                                                                      [LangChain LCEL Prompt Chain]
                                                                                                    │
                                                                                                    ▼
                                                                                      [Gemini 2.5 Flash Generation]

Key Workflow Stages

1. Extraction: PyMuPDF parses the source PDF, separating structural text streams and raster images into distinct artifacts.

2. Unified Multimodal Embeddings: Extracted text and Base64-encoded images are passed to Cohere's embed-v4.0 model. This maps both modalities into a shared vector space, allowing a text query to retrieve relevant images and vice versa.

3. Vector Indexing: ChromaDB stores and indexes the multimodal vectors alongside metadata tags indicating whether a node represents text or visual data.

4. Retrieval & Context Formatting: When a user asks a question, ChromaDB executes a similarity search to return the most relevant text chunks and images.

5. Reasoning & Answer Synthesis: LangChain Expression Language (LCEL) chains format the retrieved text and visual payloads into structured inputs for Google Gemini 2.5 Flash, generating answers grounded in both textual data and visual figures.

Technology Stack Overview