Building a Knowledge Base with RAG and Vector Stores
Explore how to build a retrieval-augmented generation (RAG) system in n8n to provide AI agents with long-term memory using vector stores. Learn to ingest data, convert it into embeddings, and enable efficient semantic searches to detect duplicate issues and improve workflow intelligence.
In our last lesson, our Triage Agent made a monumental leap. By connecting it to an LLM, we gave it a “brain.” It can now analyze the content of a GitHub issue and make an intelligent judgment about its priority. For Alex, this means the agent is now a router that is also an analyst.
However, the agent’s knowledge is still ephemeral. It processes one issue at a time and has no memory of past issues. A common and time-consuming problem in any large project is the creation of duplicate issues. Currently, our agent can’t help with this. It would triage two identical bug reports as two separate tasks, leading to wasted developer effort as two engineers unknowingly investigate the same problem.
In this lesson, we will give our agent a long-term memory and build a retrieval-augmented generation (RAG) system to create a custom knowledge base of past issues. By the end, our agent will be able to query this knowledge base to find and flag potential duplicates.
Retrieval-augmented generation (RAG)
To make our agent knowledgeable, we need to ground its responses in our own data. The state-of-the-art pattern for this is retrieval-augmented generation (RAG).
