Project Title: Engineering an Automated Intelligence Pipeline (RAG Workflow) The Challenge: In a world drowning in data, the problem isn't having information; it's accessing it instantly. Whether it's managing massive video libraries or technical documentation, most valuable assets stay buried in static folders. As a CS student and Video Editor, I saw an opportunity to bridge the gap between "stored data" and "active intelligence." I wanted to build a system that doesn't just store files but understands them.

The Solution: I engineered a sophisticated Retrieval-Augmented Generation (RAG) pipeline using n8n for orchestration. This isn't just a simple link-up; it’s a multi-layered architectural approach to data automation:

Data Ingestion: Automatically triggering on Google Drive updates to ensure the AI's "brain" is always current.

Vector Processing: Implementing a Recursive Character Text Splitter to ensure data chunks maintain semantic meaning.

Memory & Storage: Utilizing Pinecone Vector Store for high-speed similarity searches, allowing for near-instant retrieval of relevant context.

Model Integration: Leveraging the power of Google Gemini as the reasoning engine, supported by OpenAI Embeddings for high-precision vectorization.

The Intersection of Post-Production & Code: Being a 4th-semester BSCS student, I focused heavily on the logic of the workflow—ensuring the data indexing was optimized for performance. However, my background as a Video Editor gives me a unique perspective on "flow" and "sequencing." Just as a good edit makes a story seamless, a good automation makes a technical process invisible. I believe that an automation is only as good as its usability; it should be as smooth as a perfectly timed transition.

Why This Matters for Your Business: This workflow effectively creates a "Custom Brain" for your organization. Imagine chatting with your entire library of scripts, project logs, and SOPs as if you were talking to an expert teammate. By automating this pipeline, I eliminate the need for manual searching, allowing creative teams to focus on the edit and the story while the AI handles the information retrieval.

Technical Stack Used:

Logic: n8n Workflow Automation

LLMs: Google Gemini & OpenAI

Database: Pinecone (Vector Search)

Storage: Google Drive API

Theory: RAG (Retrieval-Augmented Generation) & Semantic Search

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