Strategies for managing context and indexing in repositories with millions of lines of code.
Tackling large codebases or massive monorepos requires smart context management. Loading millions of lines of code directly into the LLM's prompt window would cause overflow errors. CoderX resolves this by using Partial Indexing, Semantic Vector Search, and Focus Mode to target only relevant subsets of your project.
When you open a large codebase, CoderX processes it using a multi-layered search pipeline:
flowchart LR
A[User Request] --> B{Semantic Query}
B -->|Query Embeddings| C[Local Vector SQLite]
B -->|Lookup References| D[AST Symbol Map]
C -->|Fetch Diffs| E[Context Window]
D -->|Fetch Imports| ECoderX indexes files in the background to build semantic embedding segments:
If you are working in a large monorepo, you can activate Focus Mode to restrict the search boundaries:
Navigate to the Codebase Indexer tab in your sidebar.
Choose a specific folder path (e.g. /packages/auth-service/) to define your boundary.
The agent will restrict semantic searches, symbol lookups, and file modifications strictly inside this path boundary, saving token context space.
[!TIP] To maintain high performance and low indexing memory usage in large codebases, follow these optimization guidelines:
Ensure node modules, logs, build folders, and third-party vendor directories are added to .m0xignore. Excluding vendor/ or node_modules/ can reduce indexing time by up to 90%.
.m0xrulesCreate directory-specific rules. For example, place one .m0xrules in the backend Go folder and another in the frontend Next.js folder. CoderX will inject only the rules matching the files the agent is currently modifying.
If you have multiple independent projects, open each project folder separately in the editor rather than opening the parent folder containing all of them. This keeps the index clean and limits vector search scopes.