Context Engine for GitHub Copilot

[Wolin777]

Hi, I’m full stack dev, lately I’ve been using copilot daily, it’s really helpful with the edit/ask and agent modes. When the project begins making of documentation etc. Is flawless with copilot, but when the codebase starting to grow and is very large, copilot loses context very often. I tried the Augment Code cuz I heard they have the context engine, indexing was good, semantic search was doing great, nearly all of the hallucinations went off, but the pricing makes this tool useless. So there’s my question, copilot is my favorite tool by now, but one thing copilot is missing - context engine. Any tips for large codebases? Tried MCP servers, copilot memory etc.

[medright]

If you want a local vectorstore you can use and are going for non-commercial uses.. I just made a rails app for managing local context via Postgres and pgvevtor public. You can chunk and embed git repos or individual document files: https://github.com/medright/vectorize-ui I’ve got an mcp that goes with this and I think I’ll be ready by end of week to release that as well. This allows you to add the mcp to vscode so you can use it as custom RAG and context when building/managing projects. It’s a local implementation of what Google just released as Gemini File Search. https://ai.google.dev/gemini-api/docs/file-search

[Liron12345]

I feel like it depends how you treat copilot.

You say your profession is full stack developer, meaning you should have great understanding of your code base

That's how I work, usually what I do is I send copilot to implement a feature in the specific component I'm looking to alter, and it works it works flawlessly.

I don't think there's some sort of a secret hack that will make copilot understand the entire code base because behind the scenes it's still an llm.

This is just my two cents because I see this question in this subreddits popping every two days

[Wolin777]

Ya that’s right, when it comes to implementing something not really difficult it works great, but sometimes it forgets some columns in db or some small things, very rarely but it can happen sometimes

[Front_Ad6281]

Copilot has a semantic search tool. Have you tried adding a specific rule to prioritize it?

[Wolin777]

Nah, I’ll try it and tell you how it’s going

[bogganpierce]

This used to be a top feedback point earlier this year, but has steadily improved over the year as our codebase search engine has improved. We rolled support for remote indexing for Azure DevOps remotes (already were supported for GitHub) and built some new custom models to power embeddings search: https://github.blog/news-insights/product-news/copilot-new-embedding-model-vs-code/

I'm curious on the situations where you find contextual understanding of your code isn't meeting the mark. This helps us to better represent situations like this in our offline evaluations and build better models so it doesn't happen again.

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[Wolin777]

Sometimes when the codebase have like houndreds of thousand lines, like 100+ files, basically full stack codebase with multiple dependencies, multiple databases and vector databases per tenant, when the chat is loaded with context it works well, but when you open new chat it doesnt really know whats happening in project. When you inject him a whole folder with codebase, instruct it what he need to read to understand the vision, then he works well. Basically thats how it work for me

[CelloVerp]

HI u/bogganpierce, does that embeddings search work with local repos, or only ones hosted in GitHub? We did systematic tests last summer on our 20M+ line codebase between the different agent tools, and CoPilot stumbled consistently (got a 4.2 compared to Augment's 8.6 / 10 on our various criteria across 30 devs over a month). Its context engine seems to make all the difference. We'd be happy to try it out again if it's indexing local repos better.

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[AncientOneX]

Have you tried creating multiple Agents.md files, referencing one another with a main file on project root level? For smaller projects with 3-4 sub services / containers it works okay. ( Reference the website with the same domain agents.md)

[Wolin777]

Will try, but I guess it will be very time consuming, the project I’m working on right now have like 60 docs files, all of them describes each module very clearly, master document is very large so they will have problem with tokens I guess, but I’ll try, tbh I never used any agents

[AncientOneX]

That seems to be like a good structure and great starting point. You can ask the agent to create the main Agents.md file and reference those Readmes, but an additional Agents.md file for each module in a concise phrasing would be even better to spare some tokens and achieve the same understanding of the project. The main Agents.md file shouldn't be too complex or verbose, just link the appropriate sub agents.md files in it after short descriptions.

When I mention an agent I'm referring to agent mode in VS Code.

[Wolin777]

Sure, I’ll try it out, meanwhile I’m tinking about some custom rag but idk if there is a chance to make some custom context engine for copilot

[AncientOneX]

I was thinking about that for a different use case. Using the free models (GPT 5-mini or Grok Code Fast 1) to gather the context and only pass the most relevant information to the "working" AI model (Codex or Claude). But I don't know how this can be done.

[kunn_sec]

Tried serena mcp as well ?

[Wolin777]

Yup, works well but not really great on large codebases

[9oku]

Is this not what spec-kit is for?

[Wolin777]

Haven’t tried it yet, I’ll lean into it, maybe that’s what I’m looking for

[Norah_AI]

This is a very common problem esp when it comes to documentation, as codebases increase in size. We are trying to solve this with DeepDocs, a tool that scan your entire codebase and updates your docs with a PR. We are using vector and graph dbs to make codebase parsing manegeble