How It Works
From repo to answers in minutes
RepoMind indexes your codebase into a private vector store, then uses retrieval-augmented generation to answer questions grounded in your actual code.
Indexing flow
Your repository goes through five stages to become a searchable, private vector index.
Connect
Authorize read-only GitHub access. We never write to your code or store credentials beyond the OAuth token.
Filter
We detect relevant source files and skip binaries, lock files, and generated assets so only meaningful code is processed.
Chunk
Files are split into semantically meaningful chunks - functions, classes, and logical blocks - to preserve context.
Embed
Each chunk is converted into a vector embedding that captures its meaning, not just keywords.
Store
Embeddings are stored in an isolated, per-repo vector index. Your data is never shared across users or repos.
Query flow
Ask a question in plain English and get a grounded, cited answer in seconds.
Embed your question
Your natural-language question is converted into the same embedding space as your code.
Vector search
We find the most relevant code chunks by semantic similarity - not keyword matching.
Grounded answer
An LLM generates an answer using only the retrieved code as context. Every claim is backed by file-path citations.
Built for trust
Transparency and accuracy are core to how RepoMind works.
Citations on every answer
Every response includes the exact file paths and code chunks that informed it, so you can verify in seconds.
"I don't know" when uncertain
If the answer isn't in your indexed code, RepoMind says so - instead of hallucinating an answer.
No training on your code
Your code is used for retrieval only. It is never used to train or fine-tune any model.