The Architecture: Unified Knowledge Retrieval

Retrieval-Augmented Generation (RAG) is the gold standard for connecting LLMs to private corporate data. This project showcases a production-grade RAG system built on Databricks, designed to process and query millions of documents from both structured and unstructured sources with sub-second latency.

Multi-Modal Ingestion Strategy

We start by ingesting data from every corner of the enterprise: SQL databases for structured records, PDF and Image stores for unstructured documents, and high-frequency real-time event streams. This comprehensive ingestion ensures the AI has a 360-degree view of your organization's knowledge.

Serverless Processing with Databricks

The heavy lifting of data preparation is handled by Databricks Workflows. We utilize Autoloader for effortless incremental file capture and Delta Lake notebooks for scalable ETL jobs, ensuring that new information is processed, cleaned, and updated in real-time without managing infrastructure.

Vector DB & Embedding Pipeline

Insight is only as good as the retrieval. We implement a custom chunking and embedding pipeline that transforms raw text into multi-dimensional vectors. These are stored in a high-performance Vector Database (Vector Search), enabling semantic relationship matching that far exceeds traditional keyword search.

Deployable Serving Layer

Accessing the knowledge is seamless. We serve LLaMA models (by Meta) through a dual-interface approach: Databricks Apps for technical users and a custom Streamlit frontend for business stakeholders, providing a natural language interface to the entire corporate knowledge base.

Orchestration & Governance

The entire flow is managed by LangChain, orchestrating document splitting and vector store interactions. Governance is baked in via Unity Catalog, while MLflow provides a complete audit trail of model versions, embeddings, and overall system performance.