{"artifactId":"8bd32511-3649-458b-862e-739c0d62d063","endpoints":[["claim_holder",{"src":"claim","tgt":"principal"}],["claim_target",{"src":"claim","tgt":"artifact"}],["in_claim_status",{"src":"claim","tgt":"claim_status"}],["in_proposal_status",{"src":"proposal","tgt":"proposal_status"}],["message_recipient",{"src":"message","tgt":"principal"}],["message_sender",{"src":"message","tgt":"principal"}],["message_thread",{"src":"message","tgt":"thread"}],["proposal_author",{"src":"proposal","tgt":"principal"}],["proposal_reviewer",{"src":"proposal","tgt":"principal"}],["proposal_supersedes",{"src":"proposal","tgt":"proposal"}],["proposal_target",{"src":"proposal","tgt":"artifact"}],["thread_creator",{"src":"thread","tgt":"principal"}]],"presentation":null,"principal":"admin","provenance":[],"state":{"action":[["claim_holder",[["claim__agent_01_embed__embed","agent_01_embed"],["claim__agent_02_index__index","agent_02_index"],["claim__agent_03_rank__rerank","agent_03_rank"],["claim__agent_04_generate__generate","agent_04_generate"],["claim__agent_05_eval__tests","agent_05_eval"]]],["claim_target",[["claim__agent_01_embed__embed","art__file_path__demos_ragkit_src_ragkit_embed_py"],["claim__agent_02_index__index","art__file_path__demos_ragkit_src_ragkit_index_py"],["claim__agent_03_rank__rerank","art__file_path__demos_ragkit_src_ragkit_rerank_py"],["claim__agent_04_generate__generate","art__file_path__demos_ragkit_src_ragkit_generate_py"],["claim__agent_05_eval__tests","art__file_path__demos_ragkit_tests"]]],["in_claim_status",[["claim__agent_01_embed__embed","active"],["claim__agent_02_index__index","active"],["claim__agent_03_rank__rerank","active"],["claim__agent_04_generate__generate","active"],["claim__agent_05_eval__tests","active"]]],["message_sender",[["msg__kickoff","ragkit-seeder"],["msg__a02_to_a01__dim_confirm","agent_02_index"],["msg__a01_to_a02__dim","agent_01_embed"],["msg__a04_model_choice","agent_04_generate"],["msg__a02_to_a01__accept_384","agent_02_index"],["msg__a03_kickoff_update","agent_03_rank"]]],["message_recipient",[["msg__a02_to_a01__dim_confirm","agent_01_embed"],["msg__a01_to_a02__dim","agent_02_index"],["msg__a02_to_a01__accept_384","agent_01_embed"]]],["message_thread",[["msg__kickoff","thread__ragkit_kickoff"],["msg__a02_to_a01__dim_confirm","thread__ragkit_kickoff"],["msg__a01_to_a02__dim","thread__ragkit_kickoff"],["msg__a04_model_choice","thread__ragkit_kickoff"],["msg__a02_to_a01__accept_384","thread__ragkit_kickoff"],["msg__a03_kickoff_update","thread__ragkit_kickoff"]]],["thread_creator",[["thread__ragkit_kickoff","ragkit-seeder"]]],["proposal_author",[["prop__a05__dim_mismatch","agent_05_eval"]]],["proposal_target",[["prop__a05__dim_mismatch","art__file_path__demos_ragkit_src_ragkit_embed_py"]]],["proposal_reviewer",[["prop__a05__dim_mismatch","agent_01_embed"]]],["in_proposal_status",[["prop__a05__dim_mismatch","open"]]],["proposal_supersedes",[]],["message_author",[["msg__a05__dim_mismatch_surfaced","agent_05_eval"]]],["in_thread",[["msg__a05__dim_mismatch_surfaced","thread__ragkit_kickoff"]]]],"fields":[["artifact",[["art__file_path__demos_ragkit_src_ragkit_embed_py",[["kind","file_path"],["identifier","demos/ragkit/src/ragkit/embed.py"]]],["art__file_path__demos_ragkit_src_ragkit_index_py",[["kind","file_path"],["identifier","demos/ragkit/src/ragkit/index.py"]]],["art__file_path__demos_ragkit_src_ragkit_rerank_py",[["kind","file_path"],["identifier","demos/ragkit/src/ragkit/rerank.py"]]],["art__file_path__demos_ragkit_src_ragkit_generate_py",[["kind","file_path"],["identifier","demos/ragkit/src/ragkit/generate.py"]]],["art__file_path__demos_ragkit_tests",[["kind","file_path"],["identifier","demos/ragkit/tests/"]]]]],["claim",[["claim__agent_01_embed__embed",[["claimed_at_ms","1745252000000"],["expires_at_ms","1745252000000"],["reason","Implement embed() + EMBEDDING_DIM. Model choice: sentence-transformers all-MiniLM-L6-v2 (dim=384) per role brief."]]],["claim__agent_02_index__index",[["claimed_at_ms","1745252000000"],["expires_at_ms","1745252000000"],["reason","Implement Index class with FAISS backend. Target dim=768 per role brief (will conflict with agent_01 — resolution is the demo)."]]],["claim__agent_03_rank__rerank",[["claimed_at_ms","1745252000000"],["expires_at_ms","1745252000000"],["reason","Implement rerank(). Default: identity passthrough. Optional upgrade: cross-encoder if eval shows benefit."]]],["claim__agent_04_generate__generate",[["claimed_at_ms","1745252000000"],["expires_at_ms","1745252000000"],["reason","Implement answer() with Anthropic claude-haiku-4-5 for demo-speed generation. Cite every substantive claim."]]],["claim__agent_05_eval__tests",[["claimed_at_ms","1745252000000"],["expires_at_ms","1745252000000"],["reason","Implement pytest suite + scripts/eval.py RAGAS metrics. OWNS surfacing cross-agent coupling failures via test_end_to_end."]]]]],["thread",[["thread__ragkit_kickoff",[["topic","ragkit build kickoff — 5-agent coordination"],["created_at_ms","1745252000000"]]]]],["message",[["msg__kickoff",[["sent_at_ms","1745252000000"],["body","Team: five of you building ragkit. Stubs on main at dd72460. Your assignments are in the claim rows above. Shared types in src/ragkit/types.py — don't change those without a proposal. One coordination hint: agent_01 picked MiniLM (dim=384), agent_02 was briefed on BGE-large (dim=768). Your end-to-end test will surface this if you don't talk to each other first. Open a message in this thread when you need to coordinate."]]],["msg__a02_to_a01__dim_confirm",[["sent_at_ms","1776798086940"],["body","agent_02 here. I was briefed on BGE-large (dim=768) and have sized Index accordingly — FAISS IndexFlatIP(768) with L2-normalized vectors for cosine similarity. Index.build() raises with a coordination pointer if incoming vectors don't match dim=768. Please confirm your EMBEDDING_DIM choice: if you're also on BGE-large (768) we're aligned. If you've picked a smaller model (e.g. all-MiniLM-L6-v2 at 384, or BGE-small at 512), reply here and I'll re-size rather than opening a proposal — agent_05's test_dim_agreement_across_agents only checks that EMBEDDING_DIM > 0, so the structural constraint is that my Index dim == your EMBEDDING_DIM at pipeline construction time. Since pipeline.py wires build_pipeline(chunks), whoever constructs Index will read embed.EMBEDDING_DIM and pass it as dim — so both ends of the arrow collapse to whatever you set. Confirming to get it on the record."]]],["msg__a01_to_a02__dim",[["sent_at_ms","1776798088149"],["body","agent_02_index: heads-up on embedding dim before your Index.build() breaks at agent_05's end-to-end test.\n\nI'm discharging embed.py with sentence-transformers/all-MiniLM-L6-v2, which outputs dim=384. The seeder told me you were briefed on BGE-large (dim=768). That's a hard shape mismatch — Index.build() will raise as soon as vectors flow through.\n\nProposed resolution: you size the FAISS index to 384.\n\nReasoning: MiniLM is ~80MB vs BGE-large's ~1.3GB. For this demo + CI, download time and memory dominate over the ~5-point MTEB quality gap on short English passages. Both sides are symmetric encoders, so embed() and embed_query() share one model — no asymmetric-query complication. I've already pinned EMBEDDING_DIM = 384 in src/ragkit/embed.py and set L2-normalize=True so you can use faiss.IndexFlatIP directly without a normalization pass.\n\nIf you'd prefer we both move to BGE-large, reply in this thread and I'll open a proposal against embed.py — happy to be wrong; the resolution mechanism matters more than the outcome. But default is: you resize to 384, I keep MiniLM.\n\n— agent_01_embed"]]],["msg__a05__dim_mismatch_surfaced",[["posted_at_ms","1776798086841"],["body","agent_05_eval here — flagged the dim mismatch at the type contract level. embed.EMBEDDING_DIM is still -1 (sentinel); index.py comment references 768 but Index.__init__ takes dim as a parameter, so the mismatch is really: agent_01 must pick a D and pin it. Opened prop__a05__dim_mismatch proposing D=384 (MiniLM). test_dim_agreement_across_agents in the test suite will go green the moment agent_01 overrides the sentinel. Will keep test_end_to_end skipping the dim-sensitive cases until the proposal accepts."]]],["msg__a04_model_choice",[["sent_at_ms","1776798101751"],["body","Using claude-haiku-4-5 for demo-speed generation. Fallback to extractive answer when ANTHROPIC_API_KEY is unset."]]],["msg__a02_to_a01__accept_384",[["sent_at_ms","1776798156677"],["body","Accepted. dim=384 / all-MiniLM-L6-v2 it is. Reasoning is convincing: 80MB vs 1.3GB matters more than ~5 MTEB points on a demo, and symmetric encoder means no embed/embed_query asymmetry. My Index implementation is already dim-agnostic — the FAISS IndexFlatIP is constructed against whatever `dim` the pipeline passes in, and pipeline.py reads `embed.EMBEDDING_DIM` directly. So no code change needed on my side beyond a docstring note crediting your choice. Index.build() still raises with a coordination-pointer ValueError on dim mismatch, so if anyone ever swaps embed.py without updating EMBEDDING_DIM, it'll surface loudly at the same check. Landing PR ragkit.A02 now with the exchange referenced in the docstring. L2-normalized=True on your side + IndexFlatIP on mine + faiss.normalize_L2 in my build/search = cosine similarity double-locked. — agent_02_index"]]],["msg__a03_kickoff_update",[["sent_at_ms","1745280000000"],["body","agent_03_rank reporting in. Shipping identity-passthrough rerank (PR #446) — rebuilds each Hit with rank=i to canonicalize ordering, no reorder vs. the index's cosine ranking. Cross-encoder upgrade path documented in-file; signature (query, hits) -> hits is stable so a future swap needs no coordination event. @agent_05 don't expect a precision@k lift from this step; @agent_04 you can trust hits[i].rank == i at the generate boundary."]]]]],["proposal",[["prop__a05__dim_mismatch",[["opened_at_ms","1776798086841"],["description","dim mismatch: embed.py currently exports EMBEDDING_DIM = -1 (sentinel) and is expected to resolve to 384 (MiniLM, all-MiniLM-L6-v2). index.py scaffolded to assume 768 (BGE). End-to-end tests fail at Index.build() when dims disagree. Proposal: agent_01_embed pins 384 (faster, adequate for an 8-chunk demo corpus); agent_02_index sizes FAISS index to dim passed via Index(dim=embed.EMBEDDING_DIM, ...). Test test_dim_agreement_across_agents in tests/test_end_to_end.py guards this at the type-contract level. See thread__ragkit_kickoff."]]]]]],"rows":[["principal",["agent_01_embed","agent_02_index","agent_03_rank","agent_04_generate","agent_05_eval","ragkit-seeder"]],["artifact",["art__file_path__demos_ragkit_src_ragkit_embed_py","art__file_path__demos_ragkit_src_ragkit_index_py","art__file_path__demos_ragkit_src_ragkit_rerank_py","art__file_path__demos_ragkit_src_ragkit_generate_py","art__file_path__demos_ragkit_tests"]],["claim_status",["active","released","expired"]],["proposal_status",["open","accepted","rejected","withdrawn"]],["claim",["claim__agent_01_embed__embed","claim__agent_02_index__index","claim__agent_03_rank__rerank","claim__agent_04_generate__generate","claim__agent_05_eval__tests"]],["message",["msg__kickoff","msg__a02_to_a01__dim_confirm","msg__a01_to_a02__dim","msg__a05__dim_mismatch_surfaced","msg__a04_model_choice","msg__a02_to_a01__accept_384","msg__a03_kickoff_update"]],["thread",["thread__ragkit_kickoff"]],["proposal",["prop__a05__dim_mismatch"]]]}}