| BR1 |
Allow users to efficiently search and understand code within Git repositories. |
| BR2 |
Improve developer productivity by facilitating code search/understanding workflows. |
| FR1.1 |
As a user, I can specify a Git repository URL to inspect its code. |
| FR1.2 |
As a user, I can search for code using keywords or natural language queries. |
| FR1.3 |
As a user, I can view the search results with code snippets and links to the original files in the repository. |
| FR1.4 |
As a user, I can filter search results by programming language. |
| FR1.5 |
As a user, I can view the context around a code snippet in the search results. |
| FR1.6 |
As a user, I can ask code-related questions via chat, and the chat history is preserved. |
| FR2.1 |
As a developer, I need the system to fetch and clone Git repositories from provided URLs. |
| FR2.2 |
As a developer, I need the system to index the code of the fetched repositories, to generate fast responses. |
| FR2.3 |
As a developer, I need the system to use a vector database to store code embeddings for semantic search. |
| FR2.4 |
As a developer, I need the system to integrate with an LLM to process natural language queries. |
| NFR1 |
The system will index code for targeted repositories within 10 seconds on the specified hardware. |
| NFR2 |
The system should achieve a System Usability Scale (SUS) score of 70+ based on at least 5 target users. |
| NFR3 |
The system should sanitize user search query inputs to prevent Cross-Site Scripting (XSS) attacks. |
| NFR4 |
A 2D visualization tool will display code embeddings to help analyze and improve indexing and search. |
| IR1 |
The system should be implemented in Scala, following functional programming principles. |
| IR2 |
The system should use Qdrant as the vector database for code embeddings. |
| IR3 |
The system should integrate with Ollama for LLM functionalities. |
| IR4 |
The system should follow a layered architecture approach, ensuring better modularity. |