A vector-based retrieval architecture designed to eliminate LLM hallucinations by grounding AI responses in verifiable business data using n8n and Supabase.
Standard LLMs hallucinate when asked about private business data. Relying on "prompt engineering" alone resulted in 15-20% error rates for domain-specific queries, creating a liability for automated support.
Engineered a Retrieval-Augmented Generation (RAG) pipeline that intercepts user queries, converts them to vector embeddings, retrieves the top 3 matching documentation chunks, and feeds strictly factual context to GPT-4.
Achieved 99.9% factual accuracy with <800ms latency. The system now powers autonomous internal knowledge agents that refuse to answer if the data does not exist in the source of truth.
The architecture begins with a Python-based ingestion script that acts as the "Gatekeeper." It parses raw PDF, Notion, and Text documents, cleaning the noise before splitting the content into semantic chunks of 500 tokens.
These chunks are processed through OpenAI's text-embedding-3-small model, converting human language into high-dimensional vector arrays that represent the meaning rather than just the keywords.
We utilized Supabase with pgvector (migrated from Pinecone) to store these embeddings. This allows us to perform "Nearest Neighbor" searches directly within our Postgres database, keeping our stack unified and strictly typed.
When a query arrives, an n8n workflow executes a cosine similarity search, retrieving only the context that is mathematically relevant to the user's intent.
The final step is the "Synthesis." The retrieved context is injected into a strict system prompt for GPT-4. The prompt logic enforces a rule: "Answer ONLY using the provided context. If the answer is not found, state that you do not know."
This creates a closed-loop system where the AI acts as a reasoning engine, not a creative writer, effectively solving the hallucination problem for business-critical operations.