Shivang Bhatnagar

What an agent can do with the cache-rag-gateway corpus

Shivb237/cache-rag-gateway: Layered LLM caches (exact, semantic, retrieval, prompt) behind a RAG gateway, plus an eval harness measuring cost, latency and wrong…

• Look up how cache-rag-gateway implements aws-fargate, benchmarking, caching, with answers cited to file and line.
• Reuse a worked example: the commit “Add charts, story card and dev.to cover, drawn from the logged runs” (3 files · TypeScript).
• Answer from cache-rag-gateway's real TypeScript source instead of memory (AI, prompts and agents).

• Where does cache-rag-gateway handle “Add charts, story card and dev.to cover, drawn from the logged runs”?
• How does cache-rag-gateway implement aws-fargate?
• What does scripts do in cache-rag-gateway?

Search the GitHub repository Shivb237/cache-rag-gateway with XERJ (https://hub.xerj.org/corpus/shivb237--cache-rag-gateway).
Run these in a shell, in order, and check each result before the next:
1. xerj --help  (success: it prints usage). If xerj is missing: curl -fsSL https://xerj.org/get | sh
2. git clone https://github.com/Shivb237/cache-rag-gateway cache-rag-gateway && git -C cache-rag-gateway checkout 6d96d93acc59a93236e7f7c838d0d4ced5e7f3c6
3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data
4. xerj autoindex ./cache-rag-gateway --url http://127.0.0.1:9200 --prefix cache-rag-gateway --progress plain
5. xerj autoindex map --url http://127.0.0.1:9200
Then answer my question from the indexed files and cite the ax_path of every hit. Query syntax: https://xerj.org/llms.txt
Licence: MIT. Keep the copyright notice and credit the authors.

corpus shivb237--cache-rag-gateway · AI, prompts and agents
Related corpora: xobotyi--cc-foundry-ai-helpers-skill-engineering, autorag, Brxerq/claude-cockpit, CarlesCanos/RAGen
AI search and retrieval with XERJ · Case study: reference coding for agents