A step-by-step guide to building a LegalTech RAG agent using DigitalOcean's managed services without operating your own vector database. The tutorial covers uploading case files to DigitalOcean Spaces, indexing them with Knowledge Bases (backed by managed OpenSearch), exposing retrieval via MCP at a dedicated endpoint, and wiring everything into a FastAPI service deployed on App Platform. Serverless Inference (Claude Sonnet or Llama) handles answer generation on a pay-per-token basis. The guide includes scripts for each step, troubleshooting tables, cost estimates, and a GitHub repo with sample fictional LegalTech case files.
Table of contents
IntroductionKey takeawaysWhen to use Knowledge Bases + MCP and when not toPrerequisitesWhat is RAG?What you will buildThe six steps at a glanceStep 0: Configure your environment fileStep 1: Upload case files to a Spaces bucketStep 2: Create a Knowledge Base via APIStep 3: Enable MCP integration and test retrievalStep 4: Build the FastAPI RAG serviceStep 5: Point the agent at a Serverless Inference modelStep 6: Deploy the FastAPI service to App PlatformCost sketch for a solo founderWhen things go wrongCleanup (so lab spend stops)FAQsConclusionWhat to read next818 Impressions