Building a Knowledge Base with RAG and Vector Stores
Learn how to build a retrieval-augmented generation (RAG) system using Vector Store and Embeddings nodes to create a custom knowledge base for the AI Agent.
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).
RAG is an AI architecture that enhances an LLM’s capabilities by connecting it to an external, up-to-date knowledge source. An LLM’s built-in knowledge is like a massive library it was trained on months or even years ago. It’s vast, but it knows nothing about your specific project, your team’s private codebase, or the bug that was filed yesterday. RAG is like giving the LLM real-time, indexed access to your project’s README.md, your internal wiki, or, in our case, a database of all recent GitHub issues. The LLM can “read” this relevant information just before answering a question.
The core components we will use to build our RAG system are:
Vector store: A specialized database where we’ll store our knowledge.
Embeddings: The process of converting our text into a numerical format (vectors) that the database can understand.
Retriever: The component the AI Agent uses to search the vector store for relevant information.
Why not just feed everything to the LLM?
As a developer, your first thought might be: “Why build a complex RAG system? Why can’t I just fetch all my past GitHub issues and stuff them into the prompt?” The answer lies in the fundamental limitations of current LLMs.
The context window limitation: Every LLM ...