Skip to content

rishabh15b/DevRecall

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

10 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

🧠 DevRecall

💡 Rediscover the “why” behind engineering decisions — across pull requests, internal docs, and issues.

DevRecall is a local-first AI memory assistant for engineering teams. It ingests your GitHub PRs, issues, and internal docs to let you semantically query project history and get context-rich answers — powered by local LLMs and vector search.


🚀 Features

  • 🔁 Ingest GitHub PRs and Issues on demand
  • 📄 Upload internal docs (.txt, .md, .docx)
  • 🧠 Semantic search across all sources (Docs, PRs, Issues)
  • 🔍 Smart chunking of long text for high-relevance matching
  • 🎛 Filter by source type and file format
  • 🔐 Token-based GitHub access for private repositories
  • Runs 100% locally — no external API costs or data leaks

🛠 Tech Stack

  • Frontend: Streamlit
  • Embeddings: SentenceTransformers (MiniLM)
  • Vector Store: ChromaDB (local)
  • Chunking: LangChain
  • Ingestion: GitHub API + local file parser
  • LLM (Optional): Ollama + llama3 (for summarization)

📁 Folder Structure

Devrecall/ 
├── data_docs/ # Place .md, .txt, .docx files here 
├── scripts/ 
│ ├── ingest_github.py 
│ ├── ingest_markdown.py 
│ └── ingest_github_issues.py 
│ ├── ingest_devrecall.py 
├── streamlit_app.py # Main Streamlit UI 
├── requirements.txt └── README.md 

  • Scripts folder is optional now.

📦 Installation

# Clone this repo
git clone https://github.com/your-username/Devrecall.git
cd Devrecall

# Setup environment (recommended: conda)
conda create -n Devrecall python=3.10 -y
conda activate Devrecall

# Install dependencies
pip install -r requirements.txt

🛠️ How It Works

  • Ingests GitHub PRs, issues, and local docs (.md, .txt, .docx)
  • Embeds them using sentence-transformers (MiniLM)
  • Stores them in ChromaDB vector database
  • Enables semantic querying via Streamlit UI

ℹ️ All document and GitHub ingestion is handled inside the app — no need to run separate ingestion scripts manually.

📦 Setup Instructions

# 1. Install required dependencies
pip install -r requirements.txt

# 2. (Optional) Start your local LLM
ollama run llama3

# 3. Launch the app
streamlit run streamlit_app.py

💡 Example Queries

  • “Why did we migrate to PostgreSQL?”
  • “What decision was made in issue #45?”
  • “Where is the reasoning behind the auth flow rewrite?”
  • “What were the alternatives to Firebase?”

🌱 Planned Enhancements

  • 🤖 LLM-powered summaries and Q&A using local Ollama
  • 🧭 Interactive document explorer with full-chunk linking
  • 🔄 GitHub PR timeline view with decision threads
  • 🧩 Multi-user project mode with per-user storage
  • 🌐 Qdrant migration for scalable vector search
  • 📚 Exportable search insights and summaries (PDF, Markdown)

🙌 Contributing

  • Feel free to fork and customize!
  • Ideas, issues, or pull requests are welcome.

📜 License

  • MIT License — free to use, modify, and share.

👨‍💻 Author

  • Developed by Rishabh Balaiwar. Feel free to reach out for questions or collaboration opportunities!

📞 Contact

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors