!iEdZddlmZddlZddlZddlmZmZddlmZm Z ddl m Z ddl m Z mZeGdd ZeGd d ZeGd d Ze dZdZGddZy)u~ core/evolution/meta_architect.py Story 8.03: MetaArchitect — Scar-Driven Structural Analysis Reads Qdrant L3 scars and Postgres L4 sagas, identifies structural bottlenecks, and determines if a code-level fix or prompt-level fix is warranted. VERIFICATION_STAMP Story: 8.03 Verified By: parallel-builder Verified At: 2026-02-25 Tests: 8/8 (BB1–BB4, WB1–WB4) Coverage: 100% ) annotationsN) dataclassfield)datetimetimezone)Path)AnyOptionalc^eZdZUdZded<ded<eeZded<eeZded <y ) BottleneckzIRepresents a recurring failure pattern identified across scars and sagas.str descriptionint frequencydefault_factoryz list[str]affected_saga_idsscar_idsN) __name__ __module__ __qualname____doc____annotations__rlistrr6/mnt/e/genesis-system/core/evolution/meta_architect.pyr r s/SN#(#>y>5Hi5rr c0eZdZUdZded<ded<ded<y) FixProposalz7A proposed structural fix for an identified bottleneck.r target_file change_type rationaleN)rrrrrrrrrr)sANrrcXeZdZUdZeeZded<eeZded<dZ ded <y ) ArchitectureAnalysisu Complete result of a MetaArchitect analysis run. scope: "epistemic" — prompt-level fix sufficient, no PR needed. "ontological" — code-level fix required; must raise a PR. rlist[Bottleneck] bottleneckslist[FixProposal]recommended_fixes epistemicr scopeN) rrrrrrr&rr(r*rrrr$r$2s4%*$$?K!?+0+F(FE3rr$zA/mnt/e/genesis-system/data/observability/meta_architect_log.jsonlg333333?ceZdZdZ d ddZdddZddZddZddZddZ dd Z e dd Z dd Z dd Z dd Ze ddZddZy) MetaArchitectu Scar-driven structural analyser. Queries: • Qdrant L3 — semantic scar vectors (past failure memories) • Postgres L4 — swarm_sagas with status='PARTIAL_FAIL' Then clusters recurring failure patterns, determines whether they are prompt-fixable (epistemic) or require a code PR (ontological), and writes the analysis to a JSONL log. All external I/O is dependency-injected so every method is fully mockable in tests — zero real network calls are ever required. Nc>||_||_|xst|_yN) qdrant_client pg_connection_DEFAULT_LOG_PATHlog_path)selfr/r0r2s r__init__zMetaArchitect.__init__\s! +*&;*; rc|j|}|j|}|j|}|j||}|j |}|j ||}t |||}|j|||S)aH Run a full scar-driven architecture analysis. Parameters ---------- lookback_days: How many days back to scan for failed sagas and scars. Returns ------- ArchitectureAnalysis Populated with bottlenecks, fix proposals, and a scope label. )r&r(r*) _query_scars _query_sagas_cluster_scars_identify_bottlenecks_determine_scope_generate_proposalsr$ _write_log) r3 lookback_daysscarssagasclustersr&r* proposalsanalysiss ranalyzezMetaArchitect.analyzejs!!-0!!-0&&u-005A %%k2,,[%@ '#'  -0rc R|jgS |jjdddd}g}|D]`}|jt|j dd|j dij dd|j d gd b|S#t $rgcYSwxYw) u Retrieve recent scar records from Qdrant L3. The Qdrant client is expected to expose a ``scroll`` method that returns a list of point dicts, each with at least: • "id" — scar identifier • "payload" — dict with "description" and "vector" keys When ``qdrant_client`` is None, returns an empty list. r>iT)collection_namelimit with_payload with_vectorsidpayloadrvector)rIrrL)r/scrollappendr get Exception)r3r=resultsr>points rr6zMetaArchitect._query_scarss    %I ((// '!! 0G E   eiib12#(99Y#;#?#? r#R#ii"5  L I sBB B&%B&c8|jgS d|d}|jj}|j||j}g}|D]1}|j t |d|dxsd|dxsdd3|S#t $rgcYSwxYw)a Retrieve PARTIAL_FAIL sagas from Postgres L4. The pg_connection is expected to behave like a psycopg2 connection with a ``cursor()`` context manager. SQL executed: SELECT id, description, error_trace FROM swarm_sagas WHERE status = 'PARTIAL_FAIL' AND created_at > NOW() - INTERVAL '{lookback_days} days' When ``pg_connection`` is None, returns an empty list. zvSELECT id, description, error_trace FROM swarm_sagas WHERE status = 'PARTIAL_FAIL' AND created_at > NOW() - INTERVAL 'z days'rrJ)rIr error_trace)r0cursorexecutefetchallrNr rP)r3r=sqlrWrowsr?rows rr7zMetaArchitect._query_sagass    %I 67D_FL  ''..0F NN3 ??$DE  c!f+#&q6z/MetaArchitect._cluster_scars..sI  quuXu 6 W3quuXr?R;SVW;W W sAA)any_cluster_by_cosine_cluster_by_text)r3r> has_vectorss rr8zMetaArchitect._cluster_scarssLI   **51 1((/ /rcdgt|z}g}t|D]\}}||r |g}d||<|jdg}t|dzt|D]P}||r ||jdg} |j || t k\s8|j ||d||<R|j ||S)uJGreedy cosine-based clustering (O(n²) — acceptable for ≤1 000 scars).FTrLrT)ra enumeraterOrange_cosine_similarity_CLUSTER_THRESHOLDrN) r3r>assignedr@iscar_iclustervec_ijvec_js rrfz MetaArchitect._cluster_by_cosines7SZ'%'"5) %IAv{hGHQKJJx,E1q5#e*- 'A;a Xr2**5%8bucketsscarkeys rrgzMetaArchitect._cluster_by_textsz *, 5D((="-335;;=S$PRASC   sB ' . .t 4 5GNN$%%rc|r|rt|t|k7rytdt||D}td|Ddz}td|Ddz}|dk(s|dk(ry|||zz S)z Compute cosine similarity between two equal-length float vectors. Returns 0.0 on zero-length or mismatched inputs. gc3,K|] \}}||zywr.r)rbabs rrdz3MetaArchitect._cosine_similarity..s6DAq!a%6sc3&K|] }||z ywr.r)rbrs rrdz3MetaArchitect._cosine_similarity..)aAE)g?c3&K|] }||z ywr.r)rbrs rrdz3MetaArchitect._cosine_similarity..rr)rasumzip)vec_avec_bdotmag_amag_bs rrlz MetaArchitect._cosine_similaritys{ ESZ3u:%=6Cu$566)5))S0)5))S0 C<5C<eem$$rc  g}|D]-}|s|Dcgc]}|d }}|djdd}|j}g} |D]} | jdxs| jdxsdj |jD cgc]} t| dk\s| } } | r)t fd| Dr| j | d| r| j | d|j t |t|ttj| | 0|Scc}wcc} w) u Map scar clusters to Bottleneck instances, cross-referencing sagas. A cluster becomes a Bottleneck when: • It contains ≥1 scar, AND • Either the cluster has frequency > 1 OR at least one saga description matches the cluster's description theme. All clusters are surfaced as bottlenecks (frequency ≥ 1) so that even singleton failures are visible for review. rIrrzunknown failure patternrVrJc3&K|]}|v ywr.r)rbw saga_descs rrdz6MetaArchitect._identify_bottlenecks..Bs ?Ai ?rrrr) rOrwsplitrarerNr rdictfromkeys)r3r@r?r&rqrcrr desc_lowerrsagarwordsrs @rr9z#MetaArchitect._identify_bottlenecks!sB )+  G)01A$1H1!!*..8QRK%**,J+-  9!XXm4U8OUSU\\^ $.$4$4$6Fq#a&A+FFS ? ??%,,T$Z8%,,T$Z8 9    +!'l&*4==9J+K&L%  + <52Gs D9D>'D>c |syhd}hd}|D]z}|jj t j}t d|D}|xst fd|D}t fd|D}|sw|rzyy)u Classify the required fix scope. "epistemic" — all bottlenecks are prompt-fixable: - description mentions "prompt", "instruction", "context", or - no .py file appears in any FixProposal target (pre-proposal stage) "ontological" — at least one bottleneck's description references code artefacts (.py files) OR the word "code", "module", "function", "class", "import", or "refactor". This is intentionally conservative: when in doubt, escalate to "ontological" so that structural regressions are not silently swept under prompt changes. r)>codeclassimportmodulefunctionrefactorimplementation.py> system promptpromptcontext instructionc3$K|]}d|v ywrNr)rbtokens rrdz1MetaArchitect._determine_scope..psAeunAsc32K|]}|dk7s |vywrrrbkwrs rrdz1MetaArchitect._determine_scope..qs0pbdfjodoz1A0ps  c3&K|]}|v ywr.rrs rrdz1MetaArchitect._determine_scope..rsNRj 0Nr ontological)rrwsetrre) r3r&ontological_keywordsepistemic_keywords bottlenecktokens has_py_filehas_ontological has_epistemicrs @rr:zMetaArchitect._determine_scopeSs  S% %J#//557J))+,FA&AAK)pS0pL`0p-pON;MNNM}$ %rc ^g}|D]}|jj}|dk(rM|j|}|sd}|jt |dd|j d|jo|jt ddd |j d|j|S) u Generate FixProposal instances for each bottleneck. For "epistemic" scope → proposals target prompt/config files. For "ontological" scope → proposals target Python source files. rz core/evolution/meta_architect.pyruRecurring bottleneck (×z): r r!r"zprompts/system_prompt.md prompt_updateuPrompt-fixable bottleneck (×)rrw_extract_py_filerNrr)r3r&r*rArrr s rr;z!MetaArchitect._generate_proposalsys(* % J#//557J %"33J? ""DK  $/$.6z7K7K6LC)5568    $>$3;Jrrlist[list[dict]])r list[float]rrrfloat)r@rr?rrr%)r&r%rr )r&r%r*r rr')rr rr )rBr$r=rrr)rrrrr4rCr6r7r8rfrg staticmethodrlr9r:r;rr<rrrr,r,Ls ""!#' <<<! <  <F@%V02, & % %0"00  0d$L-%--  -^  */rr,)r __future__rros dataclassesrrrrpathlibrtypingr r r rr$r1rmr,rrrrs # ('  66 6       &\]V/V/r