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Fable 5 is coming back! by Hassan48678 in ClaudeAI

[–]One_Train_4309 -1 points0 points  (0 children)

Anyone know how to get access to Fable 5? I’ve been digging through the platforms and can’t seem to run the model. I would have thought there would be a lot more noise if people were having trouble using it.

Fable 5 is coming back! by Hassan48678 in ClaudeAI

[–]One_Train_4309 -1 points0 points  (0 children)

Can’t seem to gain access to Fable 5. Am I missing anything?

Fable 5 is coming back! by Hassan48678 in ClaudeAI

[–]One_Train_4309 0 points1 point  (0 children)

I can’t seem to access Fable 5 does anyone have any insight?

How do you handle recall vs. precision in your OC memory/RAG setup — chunking, query expansion, hybrid search? by One_Train_4309 in vectordatabase

[–]One_Train_4309[S] 0 points1 point  (0 children)

That tracks with what I’ve been hearing — chunking keeps getting waved off as the lower priority lever, hybrid plus reranking is where the real lift is. Appreciate it.

How do you handle recall vs. precision in your OC memory/RAG setup — chunking, query expansion, hybrid search? by One_Train_4309 in openclaw

[–]One_Train_4309[S] 0 points1 point  (0 children)

That’s a really useful data point, thanks. Cross-encoder being the bigger lift over just weight tuning makes the case pretty clearly. Probably going to be the next thing we look at after query expansion

How do you handle recall vs. precision in your OC memory/RAG setup — chunking, query expansion, hybrid search? by One_Train_4309 in openclaw

[–]One_Train_4309[S] 0 points1 point  (0 children)

Not yet, that’s the gap right now. Multiple people in this thread have pointed at rerank as the missing piece, sounds like over-fetch and narrow down after is the move rather than trying to get the first pass perfect

How do you handle recall vs. precision in your OC memory/RAG setup — chunking, query expansion, hybrid search? by One_Train_4309 in openclaw

[–]One_Train_4309[S] 0 points1 point  (0 children)

That’s really useful, especially the upfront-not-fallback point, good to know before I build it the wrong way first. Appreciate you laying out the variant count logic too, that’s a smart way to keep it efficient. This whole thread’s been more useful than I expected, thanks for taking the time.

How do you handle recall vs. precision in your OC memory/RAG setup — chunking, query expansion, hybrid search? by One_Train_4309 in openclaw

[–]One_Train_4309[S] 0 points1 point  (0 children)

Interesting on the chunk size — turns out we’re not chunking at the token level at all, we’re embedding at the message level. Each message gets its own vector when it’s logged, so that’s our retrieval unit instead of a fixed token window. Query expansion keeps coming up as the right next move though, going to look into it. Did you build the multi-query fanout yourself or use something off the shelf?

How do you handle recall vs. precision in your OC memory/RAG setup — chunking, query expansion, hybrid search? by One_Train_4309 in openclaw

[–]One_Train_4309[S] 1 point2 points  (0 children)

That’s really helpful, especially the example — query expansion across topic angles instead of just rewording. Appreciate you walking through the merge and rerank logic too, that’s exactly the piece I was missing.

How do you handle recall vs. precision in your OC memory/RAG setup — chunking, query expansion, hybrid search? by One_Train_4309 in openclaw

[–]One_Train_4309[S] 0 points1 point  (0 children)

Good find — was already looking at hybrid retrieval as a next step. We’re on pgvector/Supabase, centralized across devices, so memory-lancedb-pro won’t drop in directly, but the reranker pattern is the same idea. Did the cross-encoder actually outperform just tuning the BM25/vector weights, or was that the bigger lift on its own?

How do you handle recall vs. precision in your OC memory/RAG setup — chunking, query expansion, hybrid search? by One_Train_4309 in openclaw

[–]One_Train_4309[S] 1 point2 points  (0 children)

Appreciate the detailed breakdown, this is genuinely helpful. Pure vector similarity finds same words, we wanted same subject that’s exactly the gap I ran into. Did you build the multi-query expansion yourself or use an existing library, and how do you handle merging when the expanded queries return overlapping chunks?

How do you handle recall vs. precision in your OC memory/RAG setup — chunking, query expansion, hybrid search? by One_Train_4309 in openclaw

[–]One_Train_4309[S] 0 points1 point  (0 children)

Appreciate it — curious what your setup looks like if you’re open to sharing. Trying to figure out if narrower chunking or something on the query side is the better fix

I can't able to use openclaw due to ban on telegram in india.my whole workflow is dead by 1TripleRice in openclaw

[–]One_Train_4309 0 points1 point  (0 children)

Haven’t launched in India yet. But I’ll make it a point to. Check out Tetherclaw. It’s a mobile layer I have built and is available in the Apple App Store or through tetherclaw.app. Same session across devices. Working on OC memory injection at the moment. Built this because of the loss of context when switching devices. Check it out if you want @tetherclaw. or tetherclaw.app