### 1. Objective This document outlines a complete technical strategy and architecture for implementing an Agentic Retrieval-Augmented Generation (RAG) system. The final chosen technology stack comprises: - **Flowise** for no-code multimodal workflow orchestration. - **SigLIP (timm/ViT-B-16-SigLIP2-256)** for multimodal embeddings. - **Qdrant** for vector database management and semantic retrieval. - **DeepSeek 3.1** LLM for multimodal reasoning and text generation. - **n8n** for automation and orchestration of backend workflows. The system is optimized for local hardware: a desktop with AMD 12-core CPU, 64 GB RAM, and an Nvidia RTX 2080 Super GPU (8 GB VRAM), running Garuda Linux. ### 2. Data Profile - Total dataset size: ~5 TB - Data types: Text (DOCX, Markdown), Images (PNG, JPG), Videos, XLSX, PDFs, complex multimodal documents. ### 3. Embedding Strategy The selected embedding model is **timm/ViT-B-16-SigLIP2-256**, an open-source multimodal model optimized for cross-modal retrieval tasks. #### Embedding Process - **Textual data (DOCX, Markdown, XLSX):** Convert into plain text segments, embedding directly via SigLIP. - **Images:** Direct embedding with SigLIP image encoder. - **Videos:** Extract keyframes (1 every 10–30 seconds) and embed each frame with SigLIP image encoder. - **Complex documents (PDF, DOCX with visuals):** Render visually-rich pages as images and embed them via SigLIP, in addition to embedding the textual content separately. #### Advantages of SigLIP ViT-B-16-256 - Efficient 256-dimensional vector size reduces storage and computational overhead. - Superior cross-modal embedding quality, enabling effective multimodal search. - Optimized for local hardware, ensuring fast inference and low GPU VRAM consumption. ### 4. Vector Database: Qdrant #### Key Reasons for Choosing Qdrant - Highly efficient local performance via scalar quantization and disk-based vector storage. - Robust HNSW indexing, optimized for large-scale datasets (5TB). - Excellent Flowise integration. #### Qdrant Structure - Separate collections for different modalities (e.g., texts, images, videos). - Metadata-rich records, including original filenames, timestamps, modality type, and semantic labels. - Scalar quantization to optimize RAM usage and speed up searches. ### 5. Large Language Model (LLM): DeepSeek 3.1 #### Role in Architecture - Performs complex multimodal reasoning tasks based on retrieved content. - Provides generation of human-like, detailed answers and supports visual question-answering. #### Integration with Stack - Direct integration via Flowise pipelines. - Accepts context retrieved from Qdrant embeddings, providing insightful responses. ### 6. Workflow Automation: n8n #### Automation Capabilities - Data ingestion and embedding pipelines. - Scheduled embedding updates and Qdrant data synchronization. - Automated backup and maintenance tasks. #### Advantages - No-code approach aligning perfectly with Flowise. - Easy automation of repetitive workflows, simplifying operations significantly. ### 7. Workflow Orchestration: Flowise #### Role in System - Provides no-code orchestration for embedding processes, Qdrant integration, and DeepSeek LLM response workflows. - Visual management and intuitive pipeline creation. ### 8. Complete Hardware Setup - **CPU**: AMD 12-core - **RAM**: 64 GB - **GPU**: Nvidia RTX 2080 Super (8 GB VRAM) - **OS**: Garuda Linux #### Performance Expectations - Local inference of SigLIP embeddings within milliseconds. - Efficient batch embedding, leveraging GPU parallelism and CPU optimization. - Comfortable handling of large-scale multimodal dataset. ### 9. Comparison with Alternative Solutions - **OpenAI Embedding API:** Slightly better accuracy, but significantly higher cost, higher latency, lower privacy. - **Weaviate vs. Qdrant:** Qdrant provides better local performance, lower resource demands, simpler integration, and greater multimodal embedding efficiency. - **Higher Dimension Embeddings (384 or 512):** Higher dimensional embeddings provide marginal accuracy improvements at the expense of performance and scalability. The chosen 256-dimensional embeddings balance accuracy and efficiency optimally. ### 10. MacBook Pro M4 Performance Comparison - MacBook Pro M4 (48 GB Unified Memory) could perform embedding tasks faster and with higher batch throughput due to unified memory and efficiency of Apple Silicon. - For desktop workloads, current RTX 2080 Super is sufficient, though the M4 MacBook could be superior for portable embedding workflows. ### 11. Privacy and Compliance - Local-only setup ensures complete data privacy and full compliance with data protection regulations. - No external API calls or cloud dependencies, thus maintaining stringent data security. ### 12. Final Technical Stack The final technology stack of Flowise + SigLIP (timm/ViT-B-16-SigLIP2-256) + Qdrant + DeepSeek 3.1 + n8n is strongly recommended, providing optimal performance, scalability, ease-of-use, cost efficiency, and data privacy for the multimodal Agentic RAG system implementation. ---