AI / Merge Mind: The Self Learning AI Code Review Bot Interview Questions
Explain the internal working of Merge Mind's vector-based codebase comparison?
flowchart LR
A[Source Files] --> B[Embedding Model]
B --> C[Vectors stored in Qdrant]
D[New MR Code] --> E[Embedding Model]
E --> F[Similarity Search in Qdrant]
C --> F
F --> G[Top Matches Retrieved]
G --> H[Passed to GPT-4 as Context]
Vector-based comparison works by converting code into embeddings, numerical vectors that place semantically similar code close together in a high-dimensional space, and then searching that space for neighbors.
- During Initial and Continuous Learning, existing code is converted into embeddings and stored in Qdrant along with metadata about where it came from
- When a new merge request arrives, the changed code goes through the same embedding process
- Qdrant performs a similarity search to find the closest existing embeddings, effectively the most similar code the team has written before
- Those matches, along with framework rules and anti-pattern references, are assembled into context and handed to GPT-4
This is what allows Merge Mind's feedback to reference the team's own established patterns rather than generic advice pulled from GPT-4's general training alone.
What does Qdrant's similarity search return?
What is ultimately handed to GPT-4 as context?
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