Building a Secure RAG Chatbot with Gemini 1.5 &pgvector

The Problem with Standard Large Language Models

Standard LLM models are trained on public datasets and lack context about your specific business operations. If you ask them private questions, they might hallucinate answers. Retrieval-Augmented Generation (RAG) fixes this by supplying matching context from a secure private database before generating replies.

Step 1: Chunking the Knowledge Base

Large documents must be divided into smaller paragraphs or 'chunks' of text. We recommend chunk sizes of 500 to 1000 characters with a 10% overlap to preserve semantic context across chunk boundaries.

Step 2: Creating Embeddings

We convert each text chunk into a high-dimensional vector (embedding) using Google's embedding-001 model or OpenAI's text-embedding-3-small. These embeddings are stored inside a PostgreSQL database with the pgvector extension activated.

Step 3: Query Routing & LLM Call

When a user asks a question, we convert their query into an embedding, perform a cosine similarity search in the vector database to retrieve the top 3 matching chunks, and pass those chunks as context along with the user's prompt to Gemini 1.5 Flash.

Summary and Security Best Practices

Ensure you encrypt your PostgreSQL database at rest, enforce rate limits on your API endpoints, and establish user authentication token blocks to protect knowledge vectors from public reads.