Write the problem hypothesis
For [user], the agent loses or retrieves [information] incorrectly across [scope], causing [wrong action or repeated work].
You can name one user, one situation and one measurable consequence without proposing a feature.[FEAT]: RAG with Hybrid Search
Evidence observed in Mintplex-Labs/anything-llm, a Research project.
10 comments14 positive reactions376 days openProject Radar 98
enhancementfeature request
Start free validation sprint4 guided steps · private notes · cloud sync
SOURCE EVIDENCE
Read original GitHub Issue ↗Start with what users actually said
Reporter context: What would you like to see? I’d like to see support for RAG using hybrid search that combines both keyword-based and semantic vector search. This would allow AnythingLLM to retrieve more relevant context from documents by leveraging both traditional search techniques and modern embedding-based similarity.Excerpted from the public Issue. Read the complete thread before interpreting it.
Interview five affected users
- Which information must persist?
- For how long and within what scope?
- Show the last wrong or missing recall.
- What data must never cross boundaries?
- How do you correct memory today?
Run the smallest experiment
Test one constrained memory scope with a small real dataset and a written expected-recall benchmark.
The system retrieves the right evidence across repeated sessions without leaking or inventing context.Make a build decision
- Build: repeated pain and active commitment
- Narrow: pain is real but the audience or job differs
- Stop: weak frequency or no behavioral proof
Why this brief exists
Information has value only when it changes action. This page turns one public signal into a bounded validation exercise. It is a research aid, not proof of demand, investment advice or a product recommendation.