Great write-up on building a production-quality RAG system.
I recently built something similar and thought it might be useful to share another implementation for comparison. The latest version of my project is rag-foundry-universal, which aims to be “universal” in the sense that it can ingest a variety of document types, including entire Python repositories, and then allow you to query them.
It expects that you already have Ollama installed and Docker available (Docker Desktop works well). Once running, it can index a repo and let you ask questions about the codebase. It’s intentionally not agentic—the focus was on keeping the architecture simple and reliable for retrieval and question answering over code.
It does not do other languages such as java as yet .
For comparison, this implementation by Priya includes additional observability and evaluation tooling (for example using Opik), which is an interesting direction for testing and tracing RAG pipelines:
Thanks for contributing to this amazing piece!
Amazing augmentation for the good 😊
Great write-up on building a production-quality RAG system.
I recently built something similar and thought it might be useful to share another implementation for comparison. The latest version of my project is rag-foundry-universal, which aims to be “universal” in the sense that it can ingest a variety of document types, including entire Python repositories, and then allow you to query them.
It expects that you already have Ollama installed and Docker available (Docker Desktop works well). Once running, it can index a repo and let you ask questions about the codebase. It’s intentionally not agentic—the focus was on keeping the architecture simple and reliable for retrieval and question answering over code.
It does not do other languages such as java as yet .
Repo: https://github.com/sankar-ramamoorthy/rag-foundry-universal
For comparison, this implementation by Priya includes additional observability and evaluation tooling (for example using Opik), which is an interesting direction for testing and tracing RAG pipelines:
https://github.com/CalvHobbes/rag-101
Curious how others are approaching evaluation and observability as RAG systems move toward more production-oriented setups