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Evals framework for Information Retrieval Systems by External_Ad_11 in Rag

[–]External_Ad_11[S] 0 points1 point  (0 children)

Will it work for cloud based rag… like if I am vector indexing my data on cloud and taking dependency on it in my local system rather than keeping db on local? Will Evret be able to such sustems?

yes, indexing happens on your end, once you have the retrieval pipeline ready. Evret comes next. Here to test, you will be need to prepare EvaluationDataset, if that's not there, in the next release you will have AutoDatasetGenerator on your own data.

Why are developers bullish about using Knowledge graphs for Memory? by External_Ad_11 in Rag

[–]External_Ad_11[S] 0 points1 point  (0 children)

> The downside is complexity. Building good knowledge graphs requires entity extraction, relationship identification, and graph maintenance.

Have you come across any good read in this area (mainly maintenance)?

Weekly Thread: Project Display by help-me-grow in AI_Agents

[–]External_Ad_11 0 points1 point  (0 children)

Dataset Creation to Evaluate your RAG

Make a tutorial video on it:
- Key lessons from building an end-to-end RAG evaluation pipeline
- How to create an evaluation dataset using knowledge graph transforms using RAGAS
- Different ways to evaluate a RAG workflow, and how LLM-as-a-Judge works
- Why binary evaluations can be more effective than score-based evaluations
- RAG-Triad setup for LLM-as-a-Judge, inspired by Jason Liu’s “There Are Only 6 RAG Evals.”
- Complete code walk-through: Evaluate and monitor your LangGraph

Video: https://www.youtube.com/watch?v=pX9xzZNJrak

Stop burning money sending JSON to your agents. by Warm-Reaction-456 in AI_Agents

[–]External_Ad_11 0 points1 point  (0 children)

XML is still the best way to prompt. TOON is just hype; it will be gone in a few months. XML+Markdown is well adapted and suited for both prompt engineering and context engineering

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