Retrieval-Augmented Generation (RAG)
Purpose
[stub: retrieval-augmented-generation]
Metadata
| Author | Amit Singh |
| Scope | agentic-ai-projects-and-mastery |
Local graph
Related notes
7.1 Connecting Agents to Grafana
Wiring an agent's tool layer to Grafana's HTTP API and Prometheus datasource — authentication, the metrics query surface, and the error handling an agent needs when a query fails mid-investigation.
3. Build an Agent with Memory
Hand-rolling short-term and long-term memory for an agent — SQLite-backed storage for conversation history and investigation history across sessions.
4.1 Building an Operational Knowledge Base
Turning runbooks, playbooks, architecture documents, incident reports, and best practices into a RAG corpus an investigation agent can actually retrieve from.
1.1 Setting Up the Development Environment
Python project setup, virtual environments, and installing the OpenAI SDK, LangChain, and LangGraph so the rest of the book's code samples run without friction.