implementation_rag laigit-dot · COMPLETED
LLMs are trained on a large but fixed corpus of data, limiting their ability to reason about private or recent information. Fine-tuning is one way to mitigate this, but is often not well-suited for factual recall and can be costly. Retrieval augmented generation (RAG) has emerged as a popular and powerful mechanism to expand an LLM's knowledge base
github.com/laigit-dot/implementation_rag · ★ 10 · Forks 0 · Size 6.7 MB
SUMMARY
Technologies 2
Scored 2
Observed 2
Practices 6
Evidence 2
Skips 0
COVERAGE
Analyzed 6 files · 23 commits · 0 API calls
TECHNOLOGIES & DEPTH
Markdown LANGUAGE Depth 70
1 files · PRODUCTION
RabbitMQ MESSAGE_BROKER Depth 65
2 files · CONFIGURATION
PRACTICES
documentation · observedautomated_tests · absentcontinuous_integration · absentcontainerization · absentlinting · absentformatting · absent
ACTIVITY & OWNERSHIP
First commit 2024-01-31
Last commit 2025-06-26
Active months 7
Commits 23