beer-bears: Week 1

content/docs/2025/beer-bears/_index.md

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+---
+bookCollapseSection: true
+title: "Beer Bears"
+---

content/docs/2025/beer-bears/week1.md

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+---
+title: "Week #1"
+---
+
+# Week #1
+
+## Project description
+
+### Project name: Scaffold
+
+**Code repository**:
+ - [Course Repository](https://github.com/IU-Capstone-Project-2025/scaffold)
+ - [Our Repository](https://github.com/Beer-Bears/scaffold)
+
+Scaffold is a temporary structure used to support a work crew and materials to aid in the construction, maintenance and repair of buildings
+
+Our Scaffold is a code management system designed to translate and maintain source code as a graph in a graph database,
+enabling seamless context injection for large language models (LLMs). And helps AI agents in construction, maintenance and repair of your project.
+
+### **Team Members**
+
+| Team Member     | Telegram Alias   | Email Address           | Track         | Responsibilities                          |
+| :-------------- | :--------------- | :---------------------- | :------------ | :---------------------------------------- |
+| Melnikov Sergei        | @peplxx        | [email protected]        | Project Owner       | Team Management, RAG Algorithms |
+| Razmakhov Serhei      | @onemoreslacker       | [email protected]         | Developer      | Languages parsers, AT Generation |
+| Prosvirkin Dmitry | @dmitry5567          | [email protected]          | Developer        | Vector, Graph Database Management |
+| Mashenkov Timofei  | @mashfeii       | [email protected]      | Developer  |  Context Fethcing Algotihm |
+| Glazov Sergei      | @pushkin404          | [email protected]       |  QA        | QA Research, MCP Analysis|
+
+## Brainstorming
+
+### Ideas during brainstorming
+
+1  Graph-based code context platform for LLMs 
+Translate source code into a graph database (AST, function/class relations) to serve as rich structured context for AI agents. Enables scalable, accurate retrieval of relevant information for code generation and QA.
+
+2  AI codebase companion (Scaffold CLI) 
+CLI tool integrated into developer workflows that allows querying, summarizing, or modifying the codebase using LLMs with graph-backed context.
+
+3  LLM-aware refactoring assistant 
+Leverages code graphs and embeddings to propose or automate safe refactoring operations (rename symbols, split/merge functions, remove dead code).
+
+### Brief market research / problem validation
+
+Idea 1: Graph-based code context platform for LLMs
+Problem: Modern LLMs operate on tokenized text and lack awareness of the structural and semantic organization of real-world codebases. Existing solutions (e.g., embedding chunks into a vector DB) do not capture hierarchical or reference-based relationships well.
+
+Existing solutions: Tools like Sourcegraph Cody, Codeium, and GitHub Copilot use text embeddings but struggle with large-scale project structure and maintaining long-term context.
+
+Validation: Research from OpenAI, Meta, and others highlights the importance of hierarchical and symbolic context in improving AI performance on large-scale code reasoning tasks. Graph-based representations are also used in tools like CodeQL for similar reasons.
+
+Idea 2: LLM-aware refactoring assistant
+Problem: Refactoring at scale (e.g., renaming a core service method used in hundreds of files) is high-risk and hard to reason about, especially across language boundaries.
+
+Existing solutions: IDEs like IntelliJ or VSCode offer local static analysis refactors, but not AI-assisted reasoning or graph-level semantic refactoring.
+
+Validation: Enterprise engineering teams report significant friction in large-scale refactoring, especially when team members are unfamiliar with legacy code or there’s poor documentation. GitHub Copilot lacks this structured reasoning.
+
+## Basic requirements
+
+Parse code into AST and build code graphs
+
+Store in a graph DB (e.g., Neo4j) and vector DB (e.g., Qdrant)
+
+Extract structural/code entity relationships (calls, imports, etc.)
+
+Provide API/CLI for context queries
+
+Support incremental updates (e.g., Git hooks or file watchers)
+
+Enable context injection into LLMs (RAG)
+
+Basic testing and validation tools
+
+### Target users and their primary needs
+
+Developers	Understand and refactor code faster using AI and graph context
+AI Engineers	Provide structured context to LLMs for better accuracy
+Tech Writers	Auto-generate or update documentation from code structure
+QA Engineers	Understand dependencies and test impact of code changes
+
+### User stories
+
+As a developer, I want to find all references to a function to safely rename it.
+
+As an AI engineer, I want structured code context to improve RAG results.
+
+As a tech writer, I want to auto-generate docs from code relationships.
+
+As a QA engineer, I want to trace service dependencies for better test coverage.
+
+### Initial scope
+
+Python code parser → graph + vector DB
+
+Neo4j + Qdrant integration
+
+Basic API/CLI for context lookup
+
+LLM context injection (early RAG prototype)
+
+CLI tool for developers
+
+Basic graph update system (e.g., file watcher)
+
+
+## Tech-stack
+
+Python – Widely used in AI and tooling; ideal for building parsers, integrating LLMs, and rapid prototyping.
+
+Neo4j – Purpose-built graph database optimized for modeling and querying complex code relationships.
+
+VectorDB (e.g., Qdrant) – Enables high-performance semantic search over embedded code/document chunks.
+
+Docker – Provides consistent, containerized environments for development, testing, and deployment.
+
+LLM Chain (e.g., LangChain) – Modular framework for orchestrating Retrieval-Augmented Generation pipelines.
+
+# Weekly commitments
+
+## Individual contribution of each participant
+
+Melnikov Sergei
+ - brainstorming,
+ - [READme and structure of repository](https://github.com/Beer-Bears/scaffold/pull/2),
+ - [Excalidraw Scheme](https://excalidraw.com/#json=8DxFWGT66eisYHShw5etd,FTPXATItDi33jkpVtVr54A),
+ - informtaion research
+ - 
+Razmakhov Sergey
+ - brainstorming,
+ - [Excalidraw Scheme](https://excalidraw.com/#json=8DxFWGT66eisYHShw5etd,FTPXATItDi33jkpVtVr54A),
+ - repository
+ - 
+Prosvirkin Dmitry
+ - brainstorming,
+ - writing report,
+ - [Excalidraw Scheme](https://excalidraw.com/#json=8DxFWGT66eisYHShw5etd,FTPXATItDi33jkpVtVr54A),
+ - [Google Docs description](https://docs.google.com/document/d/1K4CPKvia2kNnlKm9MNFnxmQRqHM1KS_lJMJzueEnQVE/edit?usp=sharing) 
+
+Mashenkov Timofei
+ - brainstorming,
+ - [Excalidraw Scheme](https://excalidraw.com/#json=8DxFWGT66eisYHShw5etd,FTPXATItDi33jkpVtVr54A),
+
+Glazov Sergei
+ - brainstorming,
+ - informtaion research,
+ - [Excalidraw Scheme](https://excalidraw.com/#json=8DxFWGT66eisYHShw5etd,FTPXATItDi33jkpVtVr54A),
+
+
+## Confirmation of the code's operability
+
+We confirm that the code in the main branch:
+- [ ] In working condition.
+- [ ] Run via docker-compose (or another alternative described in the `README.md`).