+ Xj) a0t$Rt^RIHt^RIt^RIt^RIt^RIt^RIt^RI t ^RI t ^RI H t ^RIHtHt^RIHtHtHt^RIHtHt^RIHt^RIHt^RIt]P8P;R R 4t]P8P;R R 4t]P8P;R R4t ]P8P;RR4t!]"!]P8P;RR44s#]P8P;RR 4t$]P8P;RR4PKR4t&]P8P;RR4t']P8P;RR4t(]P8P;RR4R8gt)RRlt*]+!]P8P;RR 44t,]+!]P8P;R!R"44t-]"!]P8P;R#R$44t.Rs/Rs0R%]1R&&R'R(lt2R)R*lt3R+R,lt4ERuR-R.llt5R/R0lt6R1R2lt7] R3R4l4t8]!R5]8R67t9]Pt!]94R7R8lt;!R9R:]4t<!R;R<]4t=!R=R>]4t>]9P;R?4R@4t?ERvRARBllt@ERwRCRDlltAREtBERxRFRGlltCRHRIltDRJRKltERLRMltFERyRORPlltG]9PRQ4]!RRR73RSRTll4tI]9PRU4]!RRR73RVRWll4tJ]9PRX4]!RRR73RYRZll4tK0R[kR\kR]kR^kR_kR`kRakRbkRckRdkRekRfkRgkRhkRikRjkRkkRlkRmkRnkRokRpkRqkRrkRskRtkRukRvkRwkRxkRykRzkR kR{kRkR|kR}kR~kRkRkRkRkRkRkRktLRRltMRRltNERzRRlltORRltPRRltQRRltRRRltSRRltT/RRbRRbRRbRRbRRbRRbRRbRRbRRbRRbRRbRRbRRbRRbRRbRRbRRb/RRbRRbRRbRRbRRbRRbRRbRRbRRbRRbRRbRRbRRbRRbRRbRRbRRbCtURRltVRRltWRRltXRRltY]9P;R4^ R^RNRRR]!RRR73RRll4tZ]9P;R4R^(^RNR]!RRR73RRll4t[!RR]4t\]9PR4]!RRR73RRll4t]]9P;R4RRN]!RRR73RRll4t^!RR]4t_]9PR4]!RRR73RRll4t`]9P;R4R]!RRR73RRll4ta0ER{mtbER|tc]P!R4te]P!R4tf]P!R]P4th]P!R]P4ti]P!R4tjRRltkRRltlRRltmERwRRlltn0ER}mto0ER~mtp0ERmtqERtrRRltsRRlttRtuRRltvRRltwRRltx]9P;R4R]!RRR73RRll4ty!RR]4tz]9PR4]!RRR73RRll4t{!RR]4t|]9PR4]!RRR73RRll4t}!RR]4t~]9PR4]!RRR73RRll4t]9PER4]!RRR73ERERll4t!ERER]4t]9PER4]!RRR73ERERll4t]9P;ER4R]!RRR73ERER ll4t!ER ER ]4t]9PER 4]!RRR73ER ERll4t!ERER]4t]9PER4]!RRR73ERERll4t!ERER]4t]9PER4]!RRR73ERERll4tERERlt!ERER]4t!ERER]4t]9PER4]!RRR73ER ER!ll4t!ER"ER#]4t]9PER$4]!RRR73ER%ER&ll4t]9PER'4]!RRR73ER(ER)ll4t!ER*ER+]4t]9PER,4]!RRR73ER-ER.ll4t!ER/ER0]4t]9PER14]!RRR73ER2ER3ll4t!ER4ER5]4t]9PER64]!RRR73ER7ER8ll4t]9P;ER94RER:^]!RRR73ER;ER<ll4t]9P;ER=4R^]!RRR73ER>ER?ll4t!ER@ERA]4t]9PERB4]!RRR73ERCERDll4t!EREERF]4t]9PERG4]!RRR73ERHERIll4t!ERJERK]4t]9PERL4]!RRR73ERMERNll4t]9P;ERO4R]!RRR73ERPERQll4t!ERRERS]4t]9PERT4]!RRR73ERUERVll4t!ERWERX]4t]9P;ERY4R]!RRR73ERZER[ll4t]9PER\4]!RRR73ER]ER^ll4t!ER_ER`]4t!ERaERb]4t]9PERc4]!RRR73ERdERell4t]9P;ERf4RRN]!RRR73ERgERhll4t]9PERi4]!RRR73ERjERkll4t]9PERl4]!RRR73ERmERnll4t]9PERo4]!RRR73ERpERqll4t]9P;ERr4R]!RRR73ERsERtll4tR#(u2Jarvis memory service — the shared brain (deploy on Railway). DUMB ON PURPOSE. There is NO model on this server. The intelligence lives in whatever model is already running on a device (Jarvis's Claude on Mac/Windows now, a local model on the phone later, future screenshot/transcribe daemons) — each one decides WHAT to remember and WHEN to recall, and structures the fact itself. This service only: • stores each memory as a node with a LOCAL embedding (FastEmbed, CPU — no LLM, no API), and • answers searches by MEANING (vector similarity) + returns CONNECTED facts (1 graph hop), so a vague query pulls back the linked context. Backed by FalkorDB (a graph DB with native vectors). Every device/daemon is a thin client that POSTs facts and GETs searches over HTTPS. Endpoints GET /health — liveness + memory/entity count + embed model + reflection POST /remember — {text, kind?, when?, importance?, raw?, source?, group?, links?, entities?} → {id, op, auto_linked, entities} (server-side dedup/supersede WRITE PIPELINE) GET /search?q=&k=&group=&hops=&as_of=&when_from=&when_to= — connected facts by meaning GET /memories?group=&limit=&offset= — newest-first list (HUD viewer) GET /graph?group= — whole graph {nodes,edges} (Memory+Entity+REL) POST /link — {from, to, type} POST /unlink — {from_id, to} cut the edge(s) between two nodes POST /backfill_links — {dry_run?,threshold?} auto-link all existing memories GET /entity?name= — everything mentioning an entity (facts + relations) GET /profile?name=&group= — fast RAW one-entity aggregate for a HUD card: entity + summary + read + quick contacts + relations + timeline + facts_by_kind + tasks + counts + has_composed (fuzzy resolver: difflib ratio ≥0.85 beats a substring hit) POST /dossier — {name, group?, force?} an LLM READS the /profile aggregate and COMPOSES an organized, adaptive card {header, sections[text|kv|chips|list]}; cached on the entity (24h / fact-count) → {composed, cached} POST /entity/update — {key, group?, etype?, name?} fix an entity's type / display name POST /summary — {name, text} upsert an entity's current-state summary POST /retire — {id} soft-retire a memory (stale but kept, for dedup) POST /attach_entities — {memory_id, entities} attach entities (backfill hook) POST /relation — {from, to, rtype} entity↔entity edge (resolved ends) POST /entities/merge — {keep_key, merge_keys} fold duplicate entities into one POST /entities/dedup_backfill — {dry_run} scan/auto-merge duplicate entities POST /reembed — {batch} re-embed every node with the current model GET /export — complete dump (feeds nightly backups) POST /reflection_ran — {status, note} record the reflection engine's last run GET /core_block · POST /core — Ahmed's core context block (singleton) POST /insight — {text,title,itype} reflection finding · GET /insights RECALL is SALIENCE-ranked: relevance × recency × confidence × reinforcement × importance (latest+firm+often-recalled facts surface first; nothing is deleted). Facts carry a `confidence` (0..1) and an `importance` (1..10, default 5). WRITE PIPELINE (Mem0-style, server-side): /remember embeds the text, pulls the top similar valid memories, and — if DEEPSEEK_API_KEY is set — asks ONE cheap DeepSeek call whether to ADD / UPDATE (supersede a specific memory) / NOOP (a duplicate), cleans a raw utterance into an atomic third-person fact, extracts the event date + entities + relations, and marks single-valued facts (srel) so a new value auto-retires the old. NO key ⇒ graceful degrade (cosine≥0.95 ⇒ noop, else plain add). An LLM failure NEVER loses a memory — it falls back to a plain add. BI-TEMPORAL: every memory carries valid_from (event/creation time) and valid_to (null while current, set on retire/supersede) alongside the legacy `valid` bool + invalid_at. /search?as_of= answers "what was true then". AUTO-LINKING (Layer 1): every write draws edges to the most similar existing memories (embedding + shared words) — no LLM. AUTOLINK=0 off. ENTITIES (Layer 2): entities resolve by exact key / alias / fuzzy (embedding + difflib) / DeepSeek adjudication into shared :Entity nodes, wired by (:Memory)-[:MENTIONS]->(:Entity) and (:Entity)-[:REL]->(:Entity). POST /supersede — {old_id, text, ...} mark old fact stale + add new POST /forget — {id} permanently delete a memory + its links Env: FALKOR_HOST/FALKOR_PORT, MEMORY_API_KEY (bearer the devices send), MEMORY_GROUP (default namespace), EMBED_MODEL, EMBED_DIM, DEEPSEEK_API_KEY (optional — enables the LLM write pipeline), WRITE_MODEL, DEEPSEEK_URL. ) annotationsN)asynccontextmanagerdatetimetimezone)FastAPIHeader HTTPException) BaseModelField)FalkorDB) TextEmbeddingMEMORY_API_KEY MEMORY_GROUPahmed MEMORY_GRAPHjarvis EMBED_MODELz;sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 EMBED_DIM384DEEPSEEK_API_KEY DEEPSEEK_URLzhttps://api.deepseek.com/ WRITE_MODELzdeepseek-v4-flash DOSSIER_MODELAUTOLINK10cV^8dQhRR/#returnfloat)formats""claude-voice/memory-service/app.py __annotate__r'{s!!e!c&R\V4, #)zFalkorDB's db.idx.vector.queryNodes YIELDs cosine DISTANCE (0 = identical, ~1 = unrelated), NOT similarity. Convert at every consumption site so all thresholds/scores in this file read as plain cosine similarity.?)r#)distances&r&_simr,{s x  r(AUTOLINK_THRESHOLDz0.60 INSIGHT_DEDUPz0.78 AUTOLINK_MAX5zTextEmbedding | None _embedderc V^8dQhRRRR/#)r!textstrr" list[float]r$)r%s"r&r'r's====r(c|\\PV.44Uu.uFp\V4NK up#uupiN)nextr1embedr#)r3xs& r&_embedr;s0"9??D6#:; <;E!H; << ":< ! =C   R  0 0A JJqvvx  1y!))bT1-11)<BBIr"))&(%%' ::g  1 0s >%E%% E5 cV^8dQhRR/#r r$)r%s"r&r'r's  U r(cVUu.uFp\V4NK ppVUu.uFp\V4NK ppT'd"T'd\T4\T48wdR#\R\ Y444p\ P !\RT444p\ P !\RT444pTR8XgTR8XdR#Y4T,, #uupiuupi \dR#i;i)c36"TFwrW,xK R#5ir7r$).0r:ys& r& _cosine..s * aee sc32"TF qV,xK R#5ir7r$rvr:s& r&rxry(a1uuac32"TF qV,xK R#5ir7r$)rvrws& r&rxryr|r})r# Exceptionlensumzipmathsqrt)abr:dotnanbs&& r&_cosiners !q!U1Xq ! !q!U1Xq ! AQ3q6) *A * *C 3(a(( )B 3(a(( )B SyB#I r'? " ! s+C1C'C1C,C1' C11 D?DcV^8dQhRR/#)r!r"Noner$)r%s"r&r'r's4P4PT4Pr(c B\\4s\\ R44pV'd&V\ 8wd\ R\ RV R2RR7Vs\\PPRR 4\\PPR R 44\PPR 4;'gRR 7pTP\4s\P!R\ R24\P!R4\P!R\ R24\P!R\ R24RFp\P!T4K \ R\ R\ R2RR7R# \dp\ RT 2RR7Rp?EL]Rp?ii;i \d9pR\#T4P%49d\ RT 2RR7Rp?LRp?ii;i \d:pR\#T4P%49d\ RT 2RR7Rp?EL'Rp?ii;i \d:pR\#T4P%49d\ RT 2RR7Rp?ELPRp?ii;i \d:pR\#T4P%49d\ RT 2RR7Rp?ELyRp?ii;i \dBpR\#T4P%49d\ RT 2RR7Rp?EKRp?EKRp?ii;i)zdimension probez[startup] EMBED_DIM z -> z (from model)Tflushz[startup] dim-probe note: N FALKOR_HOST localhost FALKOR_PORT6379FALKOR_PASSWORD)hostportpasswordzHCREATE VECTOR INDEX FOR (m:Memory) ON (m.embedding) OPTIONS {dimension: , similarityFunction: 'cosine'}alreadyz[startup] vector index note: z&CREATE INDEX FOR (e:Entity) ON (e.key)z[startup] entity index note: zICREATE VECTOR INDEX FOR (i:Insight) ON (i.embedding) OPTIONS {dimension: z[startup] insight index note: zHCREATE VECTOR INDEX FOR (e:Entity) ON (e.embedding) OPTIONS {dimension: z$[startup] entity vector index note: z[startup] range index note: zmemory service ready (z, dim ))z(CREATE INDEX FOR (m:Memory) ON (m.group)z-CREATE INDEX FOR (m:Memory) ON (m.created_at))r rr1rr;rprintrr osenvironrkrJ select_graph GRAPH_NAME_graphqueryr4lower)real_dimedb_stmts r& _init_graphrsk*I<v/01 I- ( 4zO  I rzz~~m[A2::>>-@A::>>*;<DD FB__Z (FC  $$-;.N P Q C =>D  $$-;.N P Q J  $$-;.N P Q C F LL C ";-vi[ B$OQ < *1#.d;;< C CFLLN * 1!5T BC C CFLLN * 1!5T BC D CFLLN * 21#6d CD J CFLLN * 82 !4 9j! --        s@-C-C -C(C(%C( CC C%$C%( C65C6cDV^8dQhRRRRRRRRRRRR R R R RR RRRRR/ #)r!r3r4rrrLrrembzlist[float] | Nonerr#rrrJsrelr"r$)r%s"r&r'r'Jsg#S C/,6(2>Ar(c V\P!4Pp Vf \V4p\ 4p \ P RRT RTRTRTRTRT RT;'gR R TR \V4R T;'gR R Ve \V4M^RT;'gT RT ;'gR / 4V #)a(Create a :Memory node. Bi-temporal: valid_from = the event date (`when`) if given, else creation time; valid_to is left UNSET (= null = current) and only set on retire/supersede. `srel` (subject|relation, lowercased) marks a single-valued fact so a newer value can deterministically supersede it.zCREATE (m:Memory {id:$id, text:$text, kind:$kind, source:$source, group:$grp, created_at:$now, refers_to:$when, valid:true, confidence:$conf, tone:$tone, importance:$imp, valid_from:$vf, srel:$srel}) SET m.embedding = vecf32($emb)idr3rrgrpr?rrrconfrimpvfr) uuiduuid4hexr;rBrrr#rJ) r3rrrrrrrrrmidr?s &&&&&&&&&& r&_createrJs **,  C {Tl *C LL 6 sFD&$& uc64::2uc z"FDJJB :#9Jq  fdjjb  2 3 Jr(c$V^8dQhRRRRRR/#)r!ids list[str] superseded_byrLr"rr$)r%s"r&r'r'cs! / /y / /t /r(cV'gR#\4p\PRRVRV/4V'd\PRRVRV/4R#R#)zBi-temporal retire: set valid=false + invalid_at + valid_to=now (and superseded_by when a replacement exists). Idempotent; skips already-stale.NzbUNWIND $ids AS rid MATCH (m:Memory {id:rid}) SET m.valid=false, m.invalid_at=$now, m.valid_to=$nowrr?DUNWIND $ids AS rid MATCH (m:Memory {id:rid}) SET m.superseded_by=$sbsb)rBrr)rrr?s&& r& _retire_nodesrcsT  *C LL @ UC " R C} - /r(aYou are the write layer of Ahmed's personal memory graph. Decide how to store one new incoming memory relative to what is already stored. Today's date is {today}. NEW INPUT (raw_utterance={raw}): {text} EXISTING SIMILAR MEMORIES (most similar first), "id: text": {similar} Choose ONE operation: - "add": genuinely new information -> store it. - "update": REPLACES/UPDATES a specific existing memory above (a changed fact or a new current value) -> put that memory's id in target_id. - "noop": an existing memory above ALREADY states this (a duplicate) -> put its id in target_id; nothing new is stored. Return ONLY a JSON object with EXACTLY these keys: {{"op":"add|update|noop", "target_id":"", "text":"", "when":"", "sro":{{"subject":"","relation":"","object":"","single_valued":true_or_false}} or null (single_valued=true only when the subject can hold ONE current value for this relation, e.g. lives-in, is-CEO-of, phone-number, so a new value supersedes), "entities":[{{"name":"","type":"person|company|project|place|product|amount|thing|concept"}}], "relations":[{{"from":"","to":"","rtype":""}}]}} JSON:c(V^8dQhRRRRRRRR/#) r!rr5rr4rrJr" list[dict]r$)r%s"r&r'r's. K K+ Kc Kc K* Kr(c 6\PRR\V^4^,^ ,RVRV/4PR\V^4pTUu.uF)pRT^,RT^,R\ T^,4/NK+ up# \d.u#i;iuupi) zuTop-n similar VALID same-group memories by embedding (empty on any error, e.g. before the vector index has any rows).zCALL db.idx.vector.queryNodes('Memory','embedding',$k,vecf32($emb)) YIELD node, score WHERE node.group=$grp AND node.valid=true RETURN node.id, node.text, scorekrrNrr3score)rrmaxrrr,)rrrrowsros&&& r&_similarrs|| /#a)a-"$eS E   'JzAq 3FJ JTT1Q41wQqT ;T JJ   JsA B/B BBc ,V^8dQhRRRRRRRRR R/#) r!pdict orig_textr4 orig_whenrL similar_idssetr"r$)r%s"r&r'r's1==$=3=:==$(=r(c\VPRR44P4P4pVR9dRpVPR4pVR9d\V4P4MRpV'd WS9dRpVR9d V'gRp\VPR4;'gR4P4;'gTpVPR4pVR9d\V4P4M T;'gRp\ VPR4\ 4'dVPR4MRpVPR 4;'g.U u.uFNp \ V \ 4'gK\V PR R44P4'gKLV NKP p p VPR 4;'g.U u.uFp \ V \ 4'gK\V PR R44P4'gKL\V PR R44P4'gK}V NK p p RTRT;'gRRVRVRVR V R V /#uup iuup i)uValidate/normalize the model's plan — never trust its shape or a hallucinated target_id; degrade update/noop-without-a-real-target to add.opadd target_idNrr3rsrorname relationsfromr)rupdatenoop)Nr)rr)r4rkrlr isinstancer) rrr r r tidr3rrrentsrorelss &&&& r& _norm_planrs QUU4  & & ( . . 0B **  %% C!3#c(.. C s%   quuV}"" # ) ) + 8 8yD 55=D $J 63t9?? Y=N=N$D$QUU5\488!%%,dCj)//R/ G/!!T" '*155+<'='C'C'E A/D Gk*00b0 .0!!T" '*155+<'='C'C'E AEE$O$**, A0D . "k3;;$fd 3 D+t ==  G .s*#I7,I71I7I<3,I<$,I<I<c 0V^8dQhRRRRRRRRRRR R /#) r!r3r4rrrLrrboolr"ztuple[dict, list[float]]r$)r%s"r&r'r's8s#Z7r(c\V4p\WS^4pRRRRRVRVRRR.R ./p\'dR PR V44;'gR p\ \ P \4\\V44P4VVR 74p \V \4'g \R4h\YY&U u0uF qR,kK up 4p W3#V'd7V^,R,R8d"RRRV^,R,RVRVRRR.R ./p W3#Tp W3#uup i \dTp Y3#i;i)uBuild the write PLAN. DeepSeek when keyed (fall back to plain add on any error — never lose a memory); cosine≥0.95 noop-dedup when not keyed.r rrNr3rrrr c3J"TFqR, RVR, 2xK R#5i)r: r3Nr$)rvss& r&rx_plan_memory..s!K7aT7)2ai[ 97s!#(none))todayrr3similarzplan not an objectrrffffff?r)r;rr_joinrq _WRITE_PROMPTr%rFr4rrrr ValueErrorrr) r3rrrrrr% plain_addsim_txtgotr!plans &&&&& r& _plan_memoryr.sl ,Cs1%Guk4vtj"k2?I| iiK7KK  !5!5hCS N$8$8$:"6"!"Cc4(( !566c/IA$/IJD 9 WQZ(D0fk71:d+;VTeT:r;L 9 90J D 9 s*D/A8D/ D*D/*D// EEc V^8dQhRRRR/#)r!rr4r"rr$)r%s"r&r'r's  s t r(cT;'g.Fp\T;'g/PRR44P4p\T;'g/PRR44P4p\T;'g/PRR44P4;'gRpV'gKV'gK\W4WQ4K R# \dKi;i)rrrrtyperN)r4rkrl _add_relationr)rrrofntnrts&& r&_apply_relationsr6s __"_ !''rvr* + 1 1 3 !''rtR( ) / / 1 !''rw 2 3 9 9 ; H Hy 2"" bb0   s C C)(C)FcDV^8dQhRRRRRRRRRRR RR R R R RRRRRR/ #)r!r-rrr4orig_embr5rrrrr#rrLrrJ no_retirerr"r$)r%s"r&r'r's~CQCQdCQsCQkCQCQCQ$'CQ5:CQBLCQCQ CQ-1CQr(c \VPR4;'g.4\T ;'g.4,p VR,p V R8XdWVPR4'd@\VR,W4p\VPR4V4RVR,RRR^RV/#VR,pVPR 4pW8XdTM \ V4pVPR 4pR p.p\ V\ 4'dVPR 4'd\VPR R44P4P4p\VPRR44P4P4pV'd]V'dUVR,V,p\PRRVRV/4PpTVUu.uF pV^,NK up, pV R8Xd>VPR4'd'VR,V9dVPVR,4V 'd.p\V4\WVWEVWgVVR7 pV'd5\PRRVRV/4\PRRVRV/4T ;'g.Fp\T;'g/PRR44P4pV'gK@\PRRTRTR\T;'g/PR R!44/4K \ 'd\#VVVV4M.p\VW4p\VPR4V4V'dRMR"pRVRVR\%V4RV/#uupi)#u$Execute a write plan: noop / add / update(+supersede) with bi-temporal retirement, single-valued (srel) supersession, entities and relations. `no_retire` (historical imports): keep the srel marker on the new node but NEVER retire existing memories — old data cannot supersede current truth.rr rrrr auto_linkedr3rrN single_valuedsubjectrrelation|zQMATCH (m:Memory) WHERE m.group=$grp AND m.valid=true AND m.srel=$srel RETURN m.idrrr)rrrrrrrrzkUNWIND $ids AS rid MATCH (a:Memory {id:$mid}),(b:Memory {id:rid}) CREATE (a)-[:LINK {type:'replaces'}]->(b)rrMMATCH (a:Memory {id:$a}),(b:Memory {id:$b}) CREATE (a)-[:LINK {type:$t}]->(b)rrtrRrr)listrk_attach_entitiesr6r;rrr4rlrrrrappendrrr _auto_linkr)r-rr8rrrrrr body_entities body_linksr9 all_entitiesr rr3rrrrretiredsubjrelrrorlkrautoop_outs&&&&&&&&&&&& r& _apply_planrOs,223d=;N;NB6OOL dB V|-- [ 1<G+.6d;'v}aD" " z /remember)defaultc V^8dQhRRRR/#)r!rmrrrLr$)r%s"r&r'r'4s8888J8r(cD\V4VPP4'g\RRR7hVP;'g\ pVPP4pVP e VP MRpVPf^M(\^\^ \VP444p\Y0P;'gRVPV\VP44wrgVP 'd!VP#R4R8wd R VR&RVR &\%YcYpP;'gRVP&;'gR VW@P(VVP*VP,\VP 4R 7 #) uSave one memory THROUGH the write pipeline (dedup/supersede/cleanup). Backward compatible: an old client POSTing just {text} still works — with no DEEPSEEK_API_KEY it's a plain add (plus cosine≥0.95 duplicate suppression).z empty textrN?rr rrrr) rrrrrrrFrGr9)rr3rlr rrrrrminrJr.rrrrrrkrOrrrr)rmrrr3rrr-rs&& r&rememberrV3s0  - 99??  LAA ** % % C 99?? D"oo94??tD&!C3r3t;O3P,QCT99#6#6 3!$((^-ID 488D>V3 T  [ t3YY-@-@&"kk55XS"&YY3%)]]tzz!%doo!6  88r(z/linkc V^8dQhRRRR/#)r!rmrrrLr$)r%s"r&r'r'PstJr(c \V4\PRRVPRVPRVP ;'gR/4RR/#)r@rrrArrT)rrrrrrRrmrs&&r&linkrZOsL - LL , dllC#tyy/E/EIFH $<r(z /supersedec V^8dQhRRRR/#)r!rmrrrLr$)r%s"r&r'r'Zs))I)j)r(c D\V4VP;'g\pVPP 4p\ V4p\ Y0P;'gRVPVP;'gRY$VPe VPMRVPR7p\VP.VR7\PRRVRVP/4\ 'd \#WSWB4\%WPP&V4pR VR V/#) uA fact changed: mark the old one stale (kept for history — this is the 'temporal' bit) and add the new current one, linked to what it replaced.rrrT)rrr)rzUMATCH (a:Memory {id:$a}),(b:Memory {id:$b}) CREATE (a)-[:LINK {type:'replaces'}]->(b)rrrr)rrrr3rlr;rrrrrrrrrrrrErCr)rmrrr3rrrs&& r& superseder]Ys - ** % % C 99?? D ,C $ ++VTYY++))3040KT__QUyy "C4;;-s3 LL=sC-/x3c' C 4D #z4 ((r(whatrwhowherewhichaboutwiththeforandthatthishavehasdiddoeswaswereareyouyourhishershehimtheythemrintotellknowrVsirmemyiranofrisitdoc V^8dQhRRRR/#)r!r3r4r"r r$)r%s"r&r'r'vsSSr(c^RIpTPRT;'gRP44Uu0uFpV\9gKVkK up#uupi)uGContent words (names/terms) of a fact — used to spot shared entities.N [a-z0-9]{3,}r)refindallr_STOP)r3_rews& r&_significant_wordsrvsM{{?TZZR4F4F4HI I!~ AI  s A A c$V^8dQhRRRRRR/#)r!rr4rr"rr$)r%s"r&r'r'}s!CCDr(c\PRRVRV/4P^,^,p\V4^8#)zDMATCH (a:Memory {id:$a})-[l:LINK]-(b:Memory {id:$b}) RETURN count(l)rr)rrrrJ)rrrs&& r& _edge_existsr}sC N aa $*Q (() +A q6A:r(c8V^8dQhRRRRRRRRRRR R R R R R/#)r!new_idr4r3rr5r thresholdr# max_linksrJdry_runrr"rr$)r%s"r&r'r'sRCCsC#CKCCC36CC)3Cr(c Vf\MTpVf\MTp\V4p/p\P RRV^,^,RVRVRV/4P p V Fowrp \ T\T ;'gR4,4p \V 4R\V ^4,,pW9gWV ,^,8gKjW3W&Kq \V4R,pV'dR R PR \\ V4444,R ,pRVRV/\V4UUu/uF wppR V 2VbK uppCp\P VV4P Fnwr\ T\T ;'gR4,4p V ^8gK3RR\V ^4,,pW9gWV ,^,8gKiW3W&Kp \VP4RRR7p.pVF|wp wpp VV8dV#\ V4V8dV#\W 4'dK9V'g\P RRVRV /4VP!RV RV R\#V^4/4K~ V#uuppi)upLAYER 1 — connect a fact to the memories most like it, automatically. Candidates come from BOTH vector similarity (meaning) and shared significant words (so two notes that both name "Altimi" always link, even when the embeddings are only lukewarm). Best `max_links` above `threshold` get a :LINK edge of type 'similar' (skipping any pair already connected). No LLM.zCALL db.idx.vector.queryNodes('Memory','embedding',$k,vecf32($emb)) YIELD node, score WHERE node.group=$grp AND node.valid=true AND node.id <> $nid RETURN node.id, node.text, scorerrrnidrg{Gz?NNzKMATCH (m:Memory) WHERE m.group=$grp AND m.valid=true AND m.id <> $nid AND ( OR c3,"TF pRV 2xK R#5iztoLower(m.text) CONTAINS $wNr$rvr~s& r&rx_auto_link..7$5q9<$5z) RETURN m.id, m.textrgffffff?c"V^,^,#)r$)kvs&r&_auto_link..s Aqr(TkeyreversezTMATCH (a:Memory {id:$a}),(b:Memory {id:$b}) CREATE (a)-[:LINK {type:'similar'}]->(b)rrrr3r)r-r/rrrrrr,rUrBr'range enumeratesorteditemsrrDround)rr3rrrrrmy_wordscandvrowscidctextrsharedcombinedwordskwqr~rparamsrankedlinkedscs&&&&&&& r&rErEs'0&7"YI ) 1 yI!$'HD LL ? i!mb %eU     $  #EX 25;;B ??@;FA!66 ?hc15!)DI# N2 E ({{7$)#e*$5777' ' v?,5e,<=,>JC$6u{{$CCDF {$VQ"77?hc1&=!) 1DI?DJJL&94 HF F"[b% >  M v;) #  M  $ $  LL;fc3' )  tS&%%A,GH# M7>s6Jc V^8dQhRRRR/#)r!rr4r"r$)r%s"r&r'r's22s2s2r(cpRPT;'gRP4P44#)z=Canonical key for an entity so the same name reuses one node. r)r'rsplit)rs&r& _norm_entityrs) 88TZZR&&(..0 11r(c V^8dQhRRRR/#)r!r!r4r"r$)r%s"r&r'r's77c7c7r(cRP\T;'gRP4P444#)uOrder-independent token string — 'al temimi' and 'temimi al' compare equal under difflib, so a reversed name still resolves to the same entity.rr)r'rrr)r!s&r&_sorted_tokensrs. 88FAGG??,2245 66r(c(V^8dQhRRRRRRRR/#)r! entity_keyr4aliasrr"rr$)r%s"r&r'r's(  c # c d r(c FV'dW8XdR#\PRRVRV/4PpV'dV^,^,MR;'g.pW9dR#\PRRVRVR\V4V.,/4R# \dR#i;i)zAdd `alias` (a canonical key) to an entity's alias list if new. Read-modify -write in Python (FalkorDB has no APOC list helpers); None-tolerant.Nz6MATCH (e:Entity {key:$k, group:$grp}) RETURN e.aliasesrrz7MATCH (e:Entity {key:$k, group:$grp}) SET e.aliases=$alal)rrrrBr)rrrrcurs&&& r& _append_aliasrs E'  || D *eU +--7Z "tAwqzt22 <  E *eUD$s)ug2E F H    s"+BB B"-B B B c(V^8dQhRRRRRRRR/#)r!rr4etyperr"rLr$)r%s"r&r'r's.FF#FcF#F*Fr(c\V4pV'gR#T;'gRP4P4;'gRp\P RRVRV/4P pV'dV^,^,#\ V4p/p\P RR^ RVRVR V/4P Fp\V^,4Wg^,&K \P R RVR V/4P p\V4p RR R rp VFwrp\P!RT \T;'gT 44P4pT;'g.F<p\V\P!RV \V44P44pK> \VPV R 4V4pVV 8gKT TTrp K V 'dV R 8dV R 8d\WV4V #V 'dV R8d\ 'dRV,R,V,R,V ,R,p\#V^R7p\%V\&4'd/\)VPR44'd\WV4V #\P RRVRVRVR VR\+4RV/4V# \dELi;i \dLKi;i)udENTITY RESOLUTION — map an extracted name to ONE canonical :Entity, so 'Al Temimi' / 'Al-Tamimi' / a reversed spelling all land on the same node. Flow: (1) exact canonical-key OR alias match (any type) → reuse it. (2) else fuzzy over SAME-etype same-group entities, scored max(name-embedding cosine, difflib token ratio over name+aliases): ≥0.92 → attach + record the incoming spelling as an alias; 0.80–0.92 AND a DeepSeek key → ONE yes/no adjudication → attach+alias or create; (3) else create a fresh entity (with a name embedding, so the next lookalike can find it). Returns the canonical key.NthingzeMATCH (e:Entity {group:$grp}) WHERE e.key=$key OR $key IN coalesce(e.aliases,[]) RETURN e.key LIMIT 1rrzCALL db.idx.vector.queryNodes('Entity','embedding',$k,vecf32($emb)) YIELD node, score WHERE node.group=$grp AND node.etype=$et RETURN node.key, scorerretzHMATCH (e:Entity {group:$grp, etype:$et}) RETURN e.key, e.name, e.aliasesrtgq= ףp?333333?皙?zAre these the same z? A: "z" B: "z5". Reply ONLY JSON {"same": true} or {"same": false}.)rIsamezMERGE (e:Entity {key:$key, group:$grp}) ON CREATE SET e.name=$name, e.etype=$et, e.created_at=$now, e.aliases=[] SET e.embedding = vecf32($emb)rr?)rrlrrrrr;r,rrdifflibSequenceMatcherratiorrkrr_rqrrrrB)rrrrrremb_simrocandsqtokbest_key best_scorebest_tokckeycnamecaliasestokrrrHr,s&&& r&_resolve_entityrs t C    g $ $ & , , . 9 9'E << !&uc : <=GJ  Awqz ,CG  %"eS%e < >?Ij IA !1JGaDM I LL *,15$+F HHR   $ D%)3(H!&X%%dD.$2O((- ..b.Ac722dN1-//4uw8C!T3' - ?-12s(H("'J$&8s?hU+J$&<< +e37#$&/02:;>KKF!B7C#t$$cggfo)>)>hU3 LL 6 UE64u  E3 ( ) JO    <   s&A J4 A(:Entity). Returns how many were attached.rrrRrzWMATCH (m:Memory {id:$mid}),(e:Entity {key:$key, group:$grp}) MERGE (m)-[:MENTIONS]->(e)rrr)r4rkrlrrrrr)rrrrrrrrs&&& r&rCrC8s A ^^^AGG==,-335 t9q= agg2]]6734::<BBD d51   ) FE3u 5 7 Q Hr( located_at located_inbased_inbased_atis_atis_inatinlocationlives_inworks_at works_for employed_at employed_by employee_ofworks_inownsowner_offounded founder_ofrunsceo_of client_of customer_of partner_of partners_with works_withcollaborates_with colleague_ofpart_of member_of belongs_tor related_tocV^8dQhRR/#r=r$)r%s"r&r'r'cs33s3r(c\P!RR\T;'gR4P4P 44PR4p\ P Y;'gR4#)z[\s\-]+_rr)rsubr4rlr _RTYPE_CANONrk)r5rs& r& _norm_rtyper csT &&S#bhhB-"5"5"7"="="? @ F Fs KC   C!1!1 22r(c ,V^8dQhRRRRRRRRRR/#)r! from_namer4to_namer1rr"rr$)r%s"r&r'r'hs/  S 3 s 3 4 r(c\VRV4p\VRV4pV'dV'dWE8XdR#\PRRVRVRVR\V4R\ 4/4R#) a(:Entity)-[:REL {rtype, created_at}]->(:Entity). BOTH ends go through the resolver first, so relations attach to the same canonical nodes as mentions; rtype is canonicalized so phrasing variants share one edge type. Self-relations (both names resolving to one entity) are skipped.rNzMATCH (a:Entity {key:$fk, group:$grp}),(b:Entity {key:$tk, group:$grp}) MERGE (a)-[r:REL {rtype:$rt}]->(b) ON CREATE SET r.created_at=$nowfktkrr5r?)rrrr rB)r r r1rrrs&&&& r&r2r2hs` GU 3B '5 1B R28 LL M r4UE4U1C   r(c V^8dQhRRRR/#)r!frr"r#r$)r%s"r&r'r'xs444%4r(c ^RIHpHpVPRR4RVPR^4,,pRpVPR4;'gRP4;'gVPR4;'gRpV'djVP VP 4VP V4, P4R , pR R \VR4)R , ,,pR VPRR4R, ,pVPR4;'g^pR\P!^\\V4^4,4,p VPR4p V f^MT p R\V 4^, ,p W4,V,V ,V ,# \dLi;i)uRank facts by relevance + keyword hits + recency + confidence + reinforcement + importance — the Generative-Agents recency×importance× relevance idea, plus a use-based boost. Recency anchors on the EVENT date (refers_to) when present, else creation time. Nothing is ever dropped.rrrtg333333? keyword_hits refers_tor created_atg@g?g@g^@g?rrT? access_count皙?rg?) rrrkrlr?r@ fromisoformat total_secondsrrrlogrJ) r_dt_tzbaserecanchorage_daysrac reinforcerimp_terms & r& _saliencer%xsi : 55# na(@!@ @D CeeK &&B - - / N NAEE,4G4M4M2F  (3+<+?C !%% d+c1 2D ~  # #!Bdhhq3s2w?233I %% C{!Cs3x!|$H : y (8 33   sA(F99 GGc 0V^8dQhRRRRRRRRRRR R/#) r!rr4 include_staleras_ofrL when_fromwhen_tor"r$)r%s"r&r'r'sACC#CdC:C(C3=CBECr(c .pV'd8VPRV RV R24VPRV RV RV RV R2 4MV'gVPV R24V'g V'dER V R V R V R 2pV'dVPV R 24V'dVPV R24V'd RRPV4,R,#R#)uiWHERE-fragment (leading ' AND …') scoping a node alias by validity + the temporal window. `as_of` selects bi-temporally (what was true then, tolerant of old nodes missing valid_from/valid_to via coalesce); otherwise the legacy `valid` bool. when_from/when_to filter the EVENT date (refers_to else created_at). All values are parameters — never interpolated.z coalesce(z .valid_from,z.created_at)<=$as_ofz (coalesce(z .valid_to,z!.invalid_at) IS NULL OR coalesce(z.invalid_at)>$as_of)z .valid=truez (CASE WHEN z.refers_to<>'' THEN z.refers_to ELSE z.created_at END)z >=$when_fromz <=$when_toz AND r)rDr')rr'r(r)r*partsr s&&&&& r&_search_filterr-s E  y|E7:NOP z%1 'E72F H I wk*+Gw&:5'B 02  LLF8<0 1  LLF8:. /49Ggll5) )C /BsBr(z/searchc<V^8dQhRRRRRRRRRR R RR RR RR R/ #)r!qr4rrJrrLhopsr'rr(r)r*rr$)r%s"r&r'r's]v,v,cv,cv,zv,v,v,.8v,v,1;v,$v,r(c b a!^RIp \V4T;'g\p \V4p \ RWEWg4p \ RWEWg4p /pV'dW^R&V'dWnR&V'dW~R&\ P RV ,R,R \V^4^,^ ,R V R V /VC4PR\V^4p.p\4pVFo!VPS!^,4TPR S!^,R S!^,RS!^,RS!^,RS!^,R\\S!^,4^4RS!^,e\S!^,4MRRS!^,e S!^,M^RS!^,;'g^RR/ 4K V PRVP!44Uu.uFpV\"9gKVNK upR,pV'EdRV ,R,RP%R\'\)V4444,R,R,pR V /VC\+V4UUu/uF wppR V 2VbK uppCp\ P VV4PFo!S!^,V9dK\-V!3R!lV44pTPR S!^,R S!^,RS!^,RS!^,RS!^,R\R"R#V,,^4RS!^,e\S!^,4MRRS!^,e S!^,M^RS!^,;'g^R$VRR/ 4VPS!^,4K VFp\/V4VR%&K VP1R&R'R(7VUu.uF pVR ,NK ppV'd$\ P R)R*VR+\34/4V^8Ed~V'EduVUu/uFpVR ,VR%,bK pp\ R,WEWg4p\ P R-V,R.,R*V/VC4Pp/pVF\o!S!^,S!^,ppVV9dKR/VP7VR04,p VV9gV VV,^,8gKUV S!3VV&K^ VP94Fwpwp o!VPV4TPR TR S!^,RS!^,RS!^,RS!^,RS!^,e\S!^,4MRR1S!^,RR'R\V ^4R%V / 4K VP1R2R'R(7VFpVP;R%R4K R3VR\V^4^,/#uupiuuppiuupi \4dELi;iuupi)4uHYBRID recall: vector similarity (meaning) UNIONed with keyword matches (names/terms). A long detailed memory has a blurry embedding and can lose a short vague one on pure vector search, so we also surface any fact whose text literally contains the query's significant words — that's how "Ziyad" or "Journey Joy" reliably comes back. TEMPORAL: `as_of=` answers "what was true THEN" (bi-temporal — valid_from <= as_of AND (valid_to null OR > as_of)); `when_from`/`when_to` window the EVENT date (refers_to else created_at). Old clients omit all three and get today's valid facts, exactly as before. Primary hits are REINFORCED (access_count++, last_accessed=now) and their 1-hop linked context is folded in, scored at 0.6× the parent's salience, capped at k+5 total.Nnodemr(r)r*zkCALL db.idx.vector.queryNodes('Memory','embedding',$k,vecf32($emb)) YIELD node, score WHERE node.group=$grpzRETURN node.id, node.text, node.kind, node.refers_to, node.created_at, score, node.confidence, node.importance, node.access_countrrrrrrrrrrrTrrrFrrz#MATCH (m:Memory) WHERE m.group=$grpzAND (rc3,"TF pRV 2xK R#5irr$rs& r&rxsearch..s%O.sQvYr(TrztUNWIND $ids AS rid MATCH (m:Memory {id:rid}) SET m.access_count = coalesce(m.access_count,0)+1, m.last_accessed=$nowrr?rz7MATCH (m:Memory)-[l:LINK]-(n:Memory) WHERE m.id IN $idszRRETURN m.id, n.id, n.text, n.kind, n.refers_to, n.created_at, n.confidence, l.typerrtrZcVR,#r;r$r<s&r&rr=s6r(facts)rrrr;r-rrrrr rrDrr,r#rrrr'rrrrr%sortrBrrkrpop)"r/rrr0r'r(r)r*rrrrfnodefmtparamsrr?seenrrkwr~rhitsrprim_ids sal_by_idr3neighbestparentrrros"&&&&&&&&& @r&searchrMs" -  =C )C 6= LE ]9 FBG  ( $  << 249 :   c!Qi!mb %eS      Z Q ,D E 5D  1 dAaD&!A$!!1Q4qtuT!A$Z3"1Q43CE!A$K"AaD,)  *D 9  fd U>C1IM* ++G >,(    8s0: V  V VV0"V'V, V)(V)z /memoriesc 0V^8dQhRRRRRRRRRRR R/#) r!rrLlimitrJoffsetr'rrrr$)r%s"r&r'r'%sAQQJQcQQ Q0:Q&Qr(cz\V4T;'g\pV'dRMRpV'dRMRpRVR\V^4R\^\V^44/p V'dWIR&\P RV,V,R ,V 4P p \P RV,R ,RV/4P ^,^,p R T R V U u.uFNp R V ^,RV ^,RV ^,RV ^,RV ^,RV ^,RV ^,;'gR/NKP up /#uup i)uList memories newest-first — powers the HUD memory VIEWER (browse & curate, not search). `kind` filters (e.g. feedback/preference) so the reflection engine can pull just Ahmed's stated preferences to respect.rAND m.valid=true zAND m.kind=$kind rofflimr$MATCH (m:Memory) WHERE m.group=$grp zxRETURN m.id, m.text, m.kind, m.refers_to, m.created_at, m.source, m.tone ORDER BY m.created_at DESC SKIP $off LIMIT $limzRETURN count(m)totalrrrrrrr)rrrrUrrr) rrOrPr'rrrvalidkfrrrVros &&&&&& r&rr$sG -  =CB%8E $ "BS%VQSCsO,.F v <<.6; : :     LL.69JJ    j $$% 'E UJJN)PJNQ qtVQqT61Q4ad qtXqtVQqTZZR AJN)P QQ)Ps A D8 ,D8 c0]tRtRt$R]R&RtR]R&RtR#)Forgeti@r4rNrLrr$rrrrrrrr$r(r&rZrZ@ GE:r(rZz/forgetc V^8dQhRRRR/#)r!rmrZrrLr$)r%s"r&r'r'Fs    r(c\V4VPP4'g\RRR7h\P RRVPP4/4RR/#)uPermanently delete a memory (and its links) — Ahmed removed a WRONG fact from the HUD viewer. Unlike /supersede (which keeps the old node as history), this is gone for good; use it only for genuinely bad/incorrect memories.rSzno idrz)MATCH (m:Memory {id:$id}) DETACH DELETE mrrT)rrrlr rrrYs&&r&forgetr_EsS  - 77==??G<< LL< (* $<r(z/graphc$V^8dQhRRRRRR/#)r!rrLr'rrr$)r%s"r&r'r'Ss"),),),4),#),r(c \V4T;'g\pV'dRMRp\PRV,R,RV/4Pp\PRV'dRMR,R,RV/4Pp\PR RV/4Pp\PR V'dRMR,R ,RV/4Pp\PR RV/4Pp VU u.uF)p R V ^,RV ^,RV ^,RV ^,/NK+ p p T VU u.uF:p R RV ^,,RV ^,RRV ^,;'gR,RR/NK< up , p VU u.uF*p RV ^,RV ^,RV ^,;'gR/NK, p p T VU u.uF p RV ^,RRV ^,,RR/NK" up , p T V U u.uF?p RRV ^,,RRV ^,,RRV ^,;'gR,/NKA up , p RV RV /#uup iuup iuup iuup iuup i)uThe WHOLE memory graph for a group — :Memory nodes + :LINK edges, PLUS Layer-2 :Entity nodes and (:Memory)-[:MENTIONS]->(:Entity) edges — for the HUD's graph view. Ahmed walks/zooms this to see what's connected to what.rrRrUz)RETURN m.id, m.text, m.kind, m.created_atrzJMATCH (a:Memory)-[l:LINK]->(b:Memory) WHERE a.group=$grp AND b.group=$grp z"AND a.valid=true AND b.valid=true zRETURN a.id, b.id, l.typezAMATCH (e:Entity) WHERE e.group=$grp RETURN e.key, e.name, e.etypezMMATCH (m:Memory)-[:MENTIONS]->(e:Entity) WHERE m.group=$grp AND e.group=$grp zRETURN m.id, e.keyzeMATCH (a:Entity)-[r:REL]->(b:Entity) WHERE a.group=$grp AND b.group=$grp RETURN a.key, b.key, r.rtyperr3rrzent:zentity:rrrrRrmentionszrel:nodesedgesrrrrr) rr'rrrWnrowserowsentrowsmentrowsrelrowsrorcrds &&& r&graphrkRs  -  =CB%8E LL.63 46;S\ CCM:  LL /2$H J $ $',S\  34>:  llK   j || /"5 7   %cl ,-7J  ll '). 67Aj  AaD&!A$!lAaD I    !Vad]FAaDyAaDOOG4lBH  E adD!A$!0A0A B   !!qtT6AaD=&*E!!E >EG>Ev!}dFQqTMv1!2!235>EGGE UGU ++   !Gs0/I /I:I'I ?I &I%6I*9I*c:]tRtRt$R]R&R]R&RtR]R&RtR#) Unlinkir4rrNrLrr$r[r$r(r&rmrms L GE:r(rmz/unlinkc V^8dQhRRRR/#)r!rmrmrrLr$)r%s"r&r'r' r(c|\V4\PRRVPRVP/4RR/#)uCut the connection(s) between two memories (either direction) — Ahmed disconnected two nodes in the graph view. The nodes stay; only the edge goes.z=MATCH (a:Memory {id:$a})-[l:LINK]-(b:Memory {id:$b}) DELETE lrrrT)rrrrrrYs&&r&unlinkrqs; - LLG dllC)+ $<r(z/entityc$V^8dQhRRRRRR/#r!rr4rrLrr$)r%s"r&r'r's"#-#-#-Z#-$#-r(c\V4T;'g\p\V4pV'gR./#\P RRVRV/4P p/pVFZwrxrrrTP TRTRTRT RT ;'gRR T ;'gRR .R ./4pVR ,PR V R V RV/4K\ VP4Fwr.pRFwpp\P RV,R,RVRV/4P FDpTPRV^,RV^,;'gRR\V^,4RV/4KF K VVR &K R\VP44/#)uEverything hanging off one entity — the 'pull up Al Temimi' view. Matches by canonical key with light fuzziness (substring either way).raMATCH (e:Entity)<-[:MENTIONS]-(m:Memory) WHERE e.group=$grp AND m.valid=true AND (e.key=$key OR e.key CONTAINS $key OR $key CONTAINS e.key OR $key IN coalesce(e.aliases,[])) RETURN e.key, e.name, e.etype, e.summary, e.read, m.id, m.text, m.created_at ORDER BY e.name, m.created_atrrrrRsummaryrrjr?rrrrz%MATCH (a:Entity {key:$k, group:$grp})z*(b:Entity) RETURN b.name, b.etype, r.rtyperotherotyperr1 direction)outz -[r:REL]->)rz <-[r:REL]-) rrrrrr setdefaultrDrr rBvalues)rrrrrrrekeyenamerruereadrmtextmcreatedrrrxarrowrrs&&& r&entityrs  -  =C t C B << 5 UC  "#-* DDH@UU OOD5$vu#,gmmVU[[b#*B R#A B ' 4fe\8LM EI ::< M Iull7%?C22$s+-.8Z8 WbeWbe6F6Fw$k"Q%&8()56 8!N+  T[[]+ ,,r(z(?\"')]+u(?:(?:SAR|SR|USD|AED|\$|€|£|﷼|ريال|ر\.?\s?س)\s?\d[\d,]*(?:\.\d+)?)|(?:\d[\d,]*(?:\.\d+)?\s?(?:SAR|SR|USD|AED|riyals?|ريال|درهم|ر\.?\s?س))u(?<=[.!?؟])\sc V^8dQhRRRR/#)r!r3r4r"r$)r%s"r&r'r'sDD#D#Dr(cT;'gRP4pV'd-\PV^R7^,P4#R#)zJFirst sentence of a summary (for a relation's other_summary). "" if empty.r)maxsplit)rl _SENT_SPLITr)r3rAs& r&_first_sentencersA A:;;  Q  +A . 4 4 6CCr(c V^8dQhRRRR/#)r!rr4r"r$)r%s"r&r'r's  3 3 r(cT;'gRPR4pVPR4'dRVR,,pV#)zINormalize a matched Saudi mobile to the local 05XXXXXXXX form for dedupe.r+966r:NN)lstrip startswith)rds& r& _norm_phoners9 bA||E !B%K Hr(cV^8dQhRR/#)r!r"rBr$)r%s"r&r'r's44D4r(cL\\PRV444#)zOrder-preserving dedupe.c38"TFq'gK VxK R#5ir7r$r{s& r&rx_dedupe..s2Aaas  )rBrfromkeys)seqs&r&_dedupers  222 33r(c$V^8dQhRRRRRR/#)r!textsrB near_termsrr"rr$)r%s"r&r'r's& @ @D @k @T @r(c Ja ....3wr#rET;'g.Uu.uF qf'gK \V4^8gKVNK" upo RV 3RllpVEFpT;'gRpV'gKVP4p \PV4FYp V!WP 4V P 44'gK0VP \V P^444K[ \PV4F^p V!WP 4V P 44'gK0VP V P^4P44K` T\PV4U u.uFqPR4NK up , pT\PV4U u.uF"p RPV P44NK$ up , pEK R\!V4R\!V4R\!V4R \!V4/#uupiuup iuup i) uRegex-harvest contacts/amounts from an entity's fact texts (None-tolerant). `near_terms` (the entity's name + aliases, lowercased): when given, a PHONE or EMAIL only counts if it appears within ~100 chars of one of the terms in that fact — a shared fact mentioning two people must not donate one person's email to the other's card (Elie's email showed up on Modern Intelligent Solution's dossier). urls/amounts stay loose — they're rarely personal.c(V^8dQhRRRRRRRR/#)r!s_lowr4startrJendr"rr$)r%s"r&r'_harvest..__annotate__s(,,S,,3,4,r(c<aS'gR#\^V^d, 4V^d,rCWVo\;QJdV3RlS4F 'gK R# R#!V3RlS44#)Tc3,<"TF qS9xK R#5ir7r$)rvrAwins& r&rx*_harvest.._near..s+U8UsF)rany)rrrlohirtermss&&& @r&_near_harvest.._nearsQQ $cCiBrls+U+ss+s+s+U+++r(rz .,;:)]}'"rphonesemailsurlsamounts)rr _PHONE_REfinditerrrrDrr _EMAIL_RE_URL_RErrstrip _AMOUNT_REr'rr) rrrrrrrArr!rr3rs && @r&_harvestrs%'BN!FD#))r) @)1aQCFaKQQ) @E,, GG  ##A&AUGGIquuw// k!''!*56'##A&AUGGIquuw// aggaj..01' 1CD1CA,'1CDD1C1CA1FG1FACHHQWWY'1FGG gfox GDM9gg.> @@- A(EGs H HH8H2(H cV^8dQhRR/#r=r$)r%s"r&r'r's33c3r(c@Ve\V4P4#R#)zCCoerce any model-supplied scalar to a trimmed string ('' for None).r)r4rl)r:s&r&_svrs]3q6<<>22r(c$V^8dQhRRRRRR/#)r!cacher nfacts_nowrJr"rr$)r%s"r&r'r's!))$)C)D)r(cT;'g/pVPR4'gR#VPR4pVe\V4\V48wdR#\VPR44pV'gR#\P!\ P 4\P!V4, P4p^Tu;8*;'d \8#u# \dR#i;i)zA stored dossier is usable when it exists, was built for the SAME fact count (so a new fact invalidates it), and is < 24h old. None-tolerant.raFnfactsr) rkrJrrr?rr@rrr_DOSSIER_MAX_AGE_S)rrrrages&& r&_dossier_freshrs KKRE 99V   (AyCFc*o- UYYt_ B ||HLL)''+,-:]_   ( (( (( (( s;A C"" C10C1u0You are the dossier composer for Ahmed's personal memory. Ahmed Al-Rajeh runs Reval Studio, a Dammam digital agency; he also works at Alrugaib Furniture. Today is {today}. You are given EVERYTHING the memory graph knows about ONE entity. READ all of it and COMPOSE a clean, organized profile card for Ahmed. NEVER paste raw memory text and NEVER dump checkpoint blobs — REWRITE every line into short, scannable prose or key/value rows. Arabic fragments may stay in Arabic. Invent nothing. ENTITY: "{name}" (stored memory type: {etype}) FACTS (newest first) — "date · kind · text": {facts} RELATIONS (this entity → other, and how they connect): {relations} QUICK CONTACTS harvested from the facts: {quick} OPEN TASKS naming this entity: {tasks} STEP 1 — Decide what this entity IS TO AHMED: client / outreach partner / employee-intern / colleague / lead / supplier / friend / his own product / project / company / concept — and WHICH context connects them (Reval Studio, Alrugaib, a named campaign, etc.). STEP 2 — Choose 4-7 sections that FIT THAT relationship. Do NOT force one template: - a client -> the deal, what Reval does for them, THEIR clients/leads, money owed/paid; - an employee/intern -> role, what they're working on now, performance notes; - an outreach partner/lead -> status, last touch, next follow-up, money discussed; - a concept/project -> what it is, current status, next steps; - a supplier -> what they supply, terms, how to reach them. Surface MONEY, DATES, PROMISES and FOLLOW-UPS prominently. OMIT any section with nothing real to say. Max 4-7 sections. STEP 3 — Write a one-line role_line for the header (e.g. "Your outreach client — Modern Intelligent Solution, pays Reval 3,500 SAR") and 1-3 short lowercase badges (e.g. ["client","paying","active"]). OUTPUT — return ONLY this JSON object, nothing else. Use EXACTLY these four section "type" values; any other type is dropped: {{"header": {{"role_line": "", "badges": ["<=3 short tags>"]}}, "sections": [ {{"title": "...", "type": "text", "body": ""}}, {{"title": "...", "type": "kv", "rows": [{{"k": "Deal", "v": "3,500 SAR/mo"}}]}}, {{"title": "...", "type": "chips", "chips": [{{"kind": "phone|email|link|map|whatsapp", "label": "...", "value": "..."}}]}}, {{"title": "...", "type": "list", "rows": [{{"icon": "person|company|event|task|money|note", "title": "...", "sub": "", "date": "", "entity": ""}}]}} ]}} CHIP values MUST be actionable: phone -> "tel:+9665XXXXXXXX" whatsapp -> "https://wa.me/9665XXXXXXXX" email -> "mailto:name@example.com" map -> "https://maps.apple.com/?q=" link -> the raw URL A list row with "entity" set becomes a tap-through to that entity's card — set it for the people/companies connected to this one. EXAMPLE (shape only — copy the STRUCTURE, never this content): {{"header": {{"role_line": "Your outreach client — Modern Intelligent Solution, pays Reval 3,500 SAR", "badges": ["client","paying","active"]}}, "sections": [ {{"title": "The deal", "type": "kv", "rows": [{{"k": "Pays Reval", "v": "3,500 SAR/mo"}}, {{"k": "Service", "v": "Lead-gen + ads"}}, {{"k": "Since", "v": "2026-05"}}]}}, {{"title": "Contact", "type": "chips", "chips": [{{"kind": "whatsapp", "label": "WhatsApp Faisal", "value": "https://wa.me/966501234567"}}, {{"kind": "map", "label": "Riyadh office", "value": "https://maps.apple.com/?q=Riyadh"}}]}}, {{"title": "Their leads", "type": "list", "rows": [{{"icon": "company", "title": "Bin Dawood", "sub": "warm lead handed off", "date": "2026-06-20", "entity": "Bin Dawood"}}]}}, {{"title": "Follow-ups", "type": "text", "body": "Promised a campaign report by 2026-07-18; Ahmed still owes them the June numbers."}} ]}} JSON:c V^8dQhRRRR/#)r!ctxrr"r4r$)r%s"r&r'r'rs==t==r(c VPR4;'g/pVPR4;'g.pVPR4;'g.pVPR4;'g/pVPR4;'g.pRPRVR,44;'gR pRPR V44;'gR pRUu.uFPpVPV4'gKV R 2R PVPV4;'g.4,NKR p pR PV 4;'gR p RPRV44;'gR p \P\ 4VPR4;'gRVPR4;'gRVWzV R7#uupi)zRender the composition prompt from a /profile ctx (facts, relations, quick contacts, tasks). Includes a compact JSON example (in _DOSSIER_PROMPT).chosenr?rquicktasksrc 3"TF[pRVPR4;'gR RVPR4;'gR RVPR4;'gR 2xK] R#5i)- rE?u · rrrNrk)rvrs& r&rx(_build_dossier_prompt..zs`6*4Q aeeFm""s $Dv)@)@&(A 55=  B  !*4sA%A%A% A%:NPNr#c 3Z"TFpRVPR4;'gR RVPR4R8XdRMR RVPR4;'gR R VPR 4;'gR R 2 VPR4'd RVR, 2MR ,xK R#5i)rr1rrrxrzz->z<-rvrz (rwrr other_summaryr Nr)rvros& r&rxr}s A QUU7^ ( (y )55%.4D 9 55>  R 155>#<#(>R/"# $B H HsB+6B+B+3B+B+r , z; c3"TFNpRVPR4;'gR 2VPR4'dRVR, R2MR,xKP R#5i)rr3rduez (due rNr)rvrAs& r&rxrsYA QUU6] b !"#$55<\VPR 44p V 'dVPRVRR R V /4KKVR 8Xd.p VPR 4;'g.Fp \ V 4^ 8dMv\V \4'gK-\V PR 44\V PR44rV 'g V 'gKrV PR V RV /4K V 'dVPRVRR R V /4EKEKVR8XEd.pVPR4;'g.Fp\ V4^8dM\V\4'gK-\VPR44P 4p\VPR44pV\9g V'gKTPRTRTR\VPR44;'gT/4K V'dVPRVRRRV/4EKEKVR8XgEK.p VPR 4;'g.EFp \ V 4^ 8dM\V \4'gK.\V PR44pV'gKR\V PR44P 4pT PRV\9dTMRRTR\V PR44;'gRR\V PR44;'gRR\V PR44;'gR/4EK V 'gEKVPRVRRR V /4EK V'gR#RVRV/#uupi)aValidate/normalize the model's output to the strict widget schema the Swift renderer knows. Drops unknown section types + chip kinds, coerces icons, caps sections=8 / rows=12 / chips=8. Returns None when nothing usable survives (so the caller falls back to the raw /profile card).Nheaderbadges:NrN role_linesectionsrRrr3rmrrrvchipsrvaluelabelrBiconnoterrEr) rrrkrrr _WIDGET_TYPESrD _CHIP_KINDS _LIST_ICONS)objhdrrrrrzsectyprrmrrorrrcrrrtitlers& r&_normalize_dossierrs c4 )#''(*;TBB#''( C"wwx066B6 B63q6fc!f6 B2 FF3sww{34h GFC #))r) s8q= #t$$ #''&/"((* m # CGGG$% &=swwv'D GUFFFDIJ D[Dggfo+++t9?!!T**155:AEE#J1KKaa 01, GUFD&$GH G^Eggg&,,",u:?!!T**155=)//1AEE'N+{*% fdGU%s155>':'C'CeEF- GUFGWeLM F]Dggfo+++t9?!!T**QUU7^,155=)//1 VT[-@Tf$f"Ce $5$=$=#Sv%7%?%?4%s155?';'C'Ct EF,t GUFFFDIJs*t  fj# ..Cs .S&S&c$V^8dQhRRRRRR/#)r!rr4rr"ztuple[dict, dict]r$)r%s"r&r'r's'll#lCl,=lr(ca;aa?a@\V4o?RRRRRRRR.R.R .R ./R R ^R ^RR//pRRR^R//pS?'gW#3#\PRRV/4PpV'gW#3#RV?3Rllp^R^R^R^R/pRo;\ V4p.pVEF[p V ^,V ^,;'g.rV!W4p \ P !RT\ V ^,;'gT 44P4p V F<p\V \ P !RV\ V44P44p K> V ^8Xd V S;8dKTPRT RV ^,;'gT RV ^,;'gRRT RV ^,;'gRRV ^,;'gRRV ^,;'gRR \V ^,;'g^4R!\Wl,V 4/ 4EK^ V'gW#3#VPR"R#7V^,pVR,o>VR$,Uu.uF pVR,NK pp\ VR,4o=\VR,4o3R&llpVUu.uFpV!V4'gKVNK ppVUu.uF pVR,NK pp\PR'R(VRV/4PpVU u0uF q^,kK pp \VR,P4.VR,Uu.uFqP4NK up,VUu.uF*pVR,S>8wgKVR,P4NK, up,4pVUu.uFp\V4^8gKVNK upR),pV'EdkR*P!R+\#\V4444pRV/\%V4UUu/uF wppR,V 2VbK uppCpVUu.uF;p\&P(!R-\&P*!V4,R.,4NK= pp\PR/V,R0,V4PFp V ^,;'gRP4o@V ^,V9gK4\,;QJdV@3R1lV4F 'gK R2M R3M!V@3R1lV44'gKvVP/V ^,4VPV 4K ..pp/pVFwp p!p"p#p$p%T#;'gRP14;'g T$;'gRp&T";'gR4p'TPR5T R6T&R7T'R4T!;'gRR8T%;'gR/4V'\29d2VP5V'.4PR5T R4T!;'gRR6V&/4V'RV9gKV&'gKVPV&4K VPR9R2R:7VP74Fp(V(PR;R2R:7K \9VU)u.uF p)V)R4,NK up)VR,P4.VR,Uu.uFqP4NK up,R<7p*V'd \V4MRp+\V4p,\;V4p-VR=,pR>p.\PR?RS>RV/4Pp/V/U u.uF2p V.!V ^,V ^,V ^,V ^,V ^,4NK4 p0p V0U u0uFqRC,P4kK p3p \PRDR(VRV/4PF[p V ^,;'gRP4V39gK+V0PV.!V ^,V ^,REV ^,R@44K] \VR,P4.\;VR,4,4p4V4Uu.uFp\V4^8gKVNK p4p.p5V4'dR*P!RF\#\V44444pRV/\%V44UUu/uF wppRGV 2VbK uppCp6\PRHV,RI,V64PU u.uF*p R5V ^,RJV ^,RKV ^,;'gR/NK, p5p /p7\PRLRMS>RV/4Pp8V8'd3RNV8^,^,ROV8^,^,RPV8^,^,/p7RRS>RTR,RTR,RTR,RTR,/RTR,RTR,RT*R R T,R \T04RT+//p9T0'dT0T9R &T'dTT9RQ&T'dTT9RR&T5'dT5T9RS&T'dTT9RT&RS>RUTR T-RT,R T0RT*RST5RT7/p:T9T:3#uupiuupiuupiuup iuupiuupiuupiuuppiuupiuup)iuupiuup i \<d.p0RWFvwp1p2\PRAT2,RB,RS>RT/4PF:p T0PT.!T ^,T ^,T ^,T ^,T144K< Kx ELi;iuup i \<dELi;iuupiuuppiuup i \<dELi;i)XuShared aggregation behind BOTH /profile and /dossier. Resolves the entity (fuzzy-tolerant), then gathers timeline, durable facts, quick contacts, relations, co-mentions and open tasks into the /profile response dict. Also returns a `ctx` carrying the chosen key, the UNCAPPED facts, and the entity's dossier cache props — so /dossier can compose + cache without re-querying. entity=None (and ctx["ekey"]=None) when nothing resolves.rNrurrjrrrrrcountsr?r last_touchr} facts_totalrzMATCH (e:Entity) WHERE e.group=$grp OPTIONAL MATCH (e)<-[:MENTIONS]-(m:Memory {valid:true}) RETURN e.key, e.name, e.etype, e.aliases, e.created_at, e.summary, e.read, count(m) AS mentionsrcV^8dQhRR/#)r!r"rJr$)r%s"r&r'%_gather_profile..__annotate__scr(c`<VS8Xd^#ST;'g.9d^#SV9gVS9d^#^#)r$)ckaliasesrs&&r&_tier_gather_profile.._tiers1 9 7==b ! "9c r(r*g?rtg333333?rrrrrrrbrcVVR,)VR,)VR,P43#)rrbrr8)rs&r&r!_gather_profile..s"qzkAjM>1V9??;LMr()r:rNNcV^8dQhRR/#)r!r"rr$)r%s"r&r'r&sPPtPr(c<VR,pVS8XgVS9gSV9dR#VS9gS\VR,49dR#\P!RS\VR,44P 4S8#)rTrNr)r rrrr)rr RATIO_FLOORchosen_aliases chosen_tokr}s& r&_variant!_gather_profile.._variant&sp uX :ttrz  43q|+<#<&& *nQvY799>KP Pr(zMATCH (m:Memory)-[:MENTIONS]->(e:Entity) WHERE e.group=$grp AND e.key IN $keys AND m.valid=true RETURN DISTINCT m.id, m.text, m.kind, m.refers_to, m.created_at, m.tonekeysrrc3,"TF pRV 2xK R#5i)ztoLower(m.text) CONTAINS $sNr$rs& r&rx"_gather_profile..As=$;q9<$;rr!(?._relks4WbmmGRY9NP Pr(zMATCH (e:Entity {key:$ekey, group:$grp})-[r:REL]-(b:Entity) RETURN b.name, b.etype, r.rtype, b.summary, CASE WHEN startNode(r).key=$ekey THEN 'out' ELSE 'in' END ORDER BY b.namerzz(MATCH (a:Entity {key:$ekey, group:$grp})z5(b:Entity) RETURN b.name, b.etype, r.rtype, b.summaryrvakMATCH (e:Entity)<-[:MENTIONS]-(m:Memory {valid:true})-[:MENTIONS]->(o:Entity) WHERE e.group=$grp AND e.key IN $keys AND NOT o.key IN $keys AND o.group=$grp AND o.etype <> 'amount' AND NOT (e)-[:NOT_RELATED]-(o) WITH o, count(DISTINCT m) AS n WHERE n >= 2 OR (o.summary IS NOT NULL AND o.summary <> '') RETURN o.name, o.etype, o.summary, n ORDER BY n DESC LIMIT 12zmentioned-withc3,"TF pRV 2xK R#5i)ztoLower(t.text) CONTAINS $tNr$rs& r&rxr rrrAz6MATCH (t:Task) WHERE t.grp=$grp AND t.done=false AND (z) RETURN t.id, t.text, t.duer3rz[MATCH (e:Entity {key:$k, group:$grp}) RETURN e.dossier_json, e.dossier_at, e.dossier_nfactsrrarrtimeline facts_by_kindr also_matchedr)event checkpointry)rrrrrrrrrrDrJr@r rrrr'rrrcompileescaperrrl_DURABLE_KINDSr{r|rrBr)Arrempty empty_ctx cand_rowsr _TIER_SCORErrrorrtierrrrrrrvariantsall_keysfrowsseen_ids sweep_termsrAcondr~sparamsboundr event_datesrrrmkindrrrrErlstrrrr facts_allrrrowsrrxrhave_relrrrDrcrowresprrrrr}rrsA&& @@@@@@r&_gather_profiler8s  t C tYFB xXr62y"M KL"E GE}a"=I   '*/ 67Aj  c1c1dAs3KK $ D E dQqTZZRGR!'' $qtzzr2449EG Aw66dN1- //4uw8E 19,  eR1Wadoog,! 1VQqTZZR #adiia.s;#4e< > ?  JJMJN 1XF %=D',Ry1y!AfIIyL1 v/J *+NPP!05aHQK5H0"*+(Q%(H+ LL R 5#&  ()3  $$e!eH$     vi7H#I7H!GGI7H#II&. Ch!E(d2B 1V9?? h C DEK*9kSVq[11k9"=K{{{=$)#k*:$;==#Q;9O!P9OAAaS'1*9O!PQ &'%Q.1=@PPQ% ' H # #$+-.8Z8AQ4::2$$&Ct8#+I5+I+I5+I(I(I QqT" Q8kHM:?6UE9j$R&&(>>Z-=-=2sFD&$"fdjjbB C > !  $ $T2 . 5 5sFEKKR> @ * *tt   t $;@ MM)4M8##% ($7& 2Aaii2!'!5!5!7 8+1)+<=+ ?E&1[!bJe*KXI}HPJ  T5# & ( )3  BGGAT!A$!adAaD!A$7 G-66Iq'   "IH6  '5#. 01;  ;A! !!#83  adAaD2BAaD%!PQ ;" VF^))+,tF94E/FF GE -1Q1QQE - E {{7$)#e*$577#K59I!J9IAAaS'1*9I!JK ,,M<=)j)*)Q!fadE1Q4<<4@) *E  || D $s #%&0Z  T!WQZtAwqztAwqz+E 5$vF7OYy8I !57 6)$ffVngu7Kc)n-  D%[#Z -_ W +^ 467I +{Iw E7E +C 9W2&1+%$J C9"Q '23=&H J M Iu\\:UBF==T5#.01; ;  adAaD!A$!i!HI ;!NJ7    . "K*"    s+g/3g4g4g9g> h .h h /h h h5Ah0h&h" (h,,8h'$h,*j< 6kk4k k"k/k6'kk++k#2k#'h,,B j98j9 kk# k21k2z/profilec$V^8dQhRRRRRR/#rsr$)r%s"r&r'r's"#j%r(c\V4T;'g\p\W4wrE\VP R4;'g/VP R^44VR&V#)uFULL dossier of one entity for a HUD card — the FAST quick-render the HUD depends on, so this response SHAPE is stable. Resolves the name fuzzily (exact key → alias → substring → difflib token-sort ratio; a full-name ratio ≥0.85 beats a short substring hit), picks the best match (rest → also_matched), and returns {entity, summary, read, quick, relations, timeline, facts_by_kind, tasks, counts, also_matched}. Every section is None-tolerant and empty optional sections are omitted, so any etype renders gracefully. `has_composed` (NEW, additive) is true when a fresh LLM-composed dossier is cached and instantly available from POST /dossier.rr has_composed)rrr8rrk)rrrrr7rs&&& r&profiler<s[ -  =C*ID)#'''*:*@*@b*-''-*CED Kr(c>]tRtRt$R]R&RtR]R&RtR]R &R tR#) Dossierir4rNrLrFrforcer$)rrrrrrr?rr$r(r&r>r>s IE:E;r(r>z/dossierc V^8dQhRRRR/#)r!rmr>rrLr$)r%s"r&r'r's.3.3'.3*.3r(c \V4VP;'g\p\VPV4wr4VP R4pV'gRRRR/#\ VP R^44pVP R4;'g/pVP'g4\Wv4'd#R\P!VR,4RR /#\\V4R \R R 7p\V4p T fRRRR/#\(P+RRTRTR\P,!T 4R\/4RT/4RT RR/# \dLi;i \dKp \!R T: R\#T 4P$ R\'T 4R, 2R R7RRRR/uRp ? #Rp ? ii;i \dL|i;i)uCOMPOSED dossier — an LLM READS the raw /profile aggregate and returns an organized, ADAPTIVE profile card {header{role_line,badges}, sections[]} whose sections fit what the entity IS TO AHMED (client vs intern vs concept …). The four widget types are text / kv / chips / list (validated hard in code). CACHE: the composed JSON is stored on the entity node (dossier_json/at/nfacts). A cache hit — SAME fact count, < 24h old, and `force` not set — returns instantly {composed, cached:true}. Otherwise ONE DeepSeek call (DOSSIER_MODEL, temp 0.3) composes it; ANY LLM failure (or no key) returns {composed:null} so the caller falls back to the raw /profile card. On success the result is validated, cached, and returned {composed, cached:false}.r}composedNcachedFrrraTig333333?)rIrKrMzdossier compose FAILED for r :NNrzbMATCH (e:Entity {key:$k, group:$grp}) SET e.dossier_json=$j, e.dossier_at=$at, e.dossier_nfacts=$nrrjrr)rrrr8rrkrJr?rrarirrqrrrrrRrr4rrrbrB) rmrr_resprr}rrr,rBrs && r&dossierrGs - ** % % C C0JE 776?D D(E22 * +F GGG  " "E :::.77  5= 98TJ J3237D#0cC%c* D(E22   K $sCH)= 8:sF , - (E 22-    3 +D82a!!""SVD\N4;? AD(E223    sB7 E 'E 1JJL L M"t  yy!2!2 L!yy(t   8499T? J   ,,F c5# *  $s #SVAY2 3c!fQi 11,. 11r(c:]tRtRt$R]R&R]R&RtR]R&RtR#) SummaryiFr4rr3NrLrr$r[r$r(r&rRrRFs I IE:r(rRz/summaryc V^8dQhRRRR/#r!rmrRrrLr$)r%s"r&r'r'Msgjr(c\V4VP;'g\p\VP4pV'd!VP P 4'g\RRR7h\PRRVRVRVPP 4RVP P 4R \4/4R R /#) uUpsert a current-state SUMMARY onto an entity — the reflection engine's rolling 'profile' so 'pull up Al Temimi' returns the gist, not just raw facts.rSname and text requiredrzMERGE (e:Entity {key:$key, group:$grp}) ON CREATE SET e.name=$name, e.etype='thing', e.created_at=$now SET e.summary=$text, e.summary_at=$nowrrrr3r?rT rrrrrr3rlr rrrBrmrrrs&& r& summary_setrYLs - ** % % C tyy !C diioo''4LMM LL 1 UC): "E8: 7 8 $<r(z/readc V^8dQhRRRR/#rTr$)r%s"r&r'r'_s7:r(c\V4VP;'g\p\VP4pV'd!VP P 4'g\RRR7h\PRRVRVRVPP 4RVP P 4R \4/4R R /#) uTHEORY OF MIND — upsert a psychological 'read' onto a person/company: what they seem to want, how they decide, the state of the relationship, and how Ahmed should approach them. Models THEM, for Ahmed. (Reuses the Summary shape: {name, text}.)rSrVrzMERGE (e:Entity {key:$key, group:$grp}) ON CREATE SET e.name=$name, e.etype='thing', e.created_at=$now SET e.read=$text, e.read_at=$nowrrrr3r?rTrWrXs&& r&read_setr\^s  - ** % % C tyy !C diioo''4LMM LL + UC): "E8: 7 8 $<r(c0]tRtRt$R]R&RtR]R&RtR#)Valuesirr4r3NrLrr$r[r$r(r&r^r^r IE:r(r^z/valuesc V^8dQhRRRR/#)r!rmr^rrLr$)r%s"r&r'r'xs  V J r(c &\V4VP;'g\pVPP 4'g\ RRR7h\ PRRVRVPP 4R\4/4RR /#) uVALUE MODEL — upsert Ahmed's north-star: his priorities, what he chases, what he avoids. A singleton profile the reflection engine and the brain consult so advice fits HIM, not generic best-practice.rSz text requiredrzOMERGE (p:Profile {id:'values', group:$grp}) SET p.text=$text, p.updated_at=$nowrr3r?rT) rrrr3rlr rrrBrmrrs&& r& values_setrcwsv  - ** % % C 99??  ODD LL . VTYY__.xzBD $<r(c V^8dQhRRRR/#r!rrLrr$)r%s"r&r'r's F Fj F( Fr(c\V4T;'g\p\PRRV/4PpV'gRRRR/#RV^,^,;'gRRV^,^,;'gR/#)zGMATCH (p:Profile {id:'values', group:$grp}) RETURN p.text, p.updated_atrr3r updated_atrerrrrs&& r& values_getris| -  =C <<Q    j  L"-- DGAJ$$"lDGAJ4D4D" EEr(c0]tRtRt$R]R&RtR]R&RtR#)Retireir4rNrLrr$r[r$r(r&rkrkr\r(rkz/retirec V^8dQhRRRR/#)r!rmrkrrLr$)r%s"r&r'r'ror(c\V4\PRRVPP 4R\ 4/4RR/#)uSoft-retire a memory — mark it stale (hidden from recall) but KEEP it as history, unlike /forget which deletes. Used by the consolidation pass to fold away near-duplicate facts without losing anything.z>MATCH (m:Memory {id:$id}) SET m.valid=false, m.invalid_at=$nowrr?rT)rrrrrlrBrYs&&r&retirerns=  - LLQ  ;= $<r(c:]tRtRt$R]R&R]R&RtR]R&R tR#) AttachEntitiesir4 memory_idrrNrLrr$r[r$r(r&rprpsNE:r(rpz/attach_entitiesc V^8dQhRRRR/#)r!rmrprrLr$)r%s"r&r'r's^&0r(c\V4VP;'g\p\VPVP V4pRV/#)uBackfill hook — attach extracted entities to an EXISTING memory (used by the engine-side entity backfill over the memories saved before Layer 2).attached)rrrrCrqr)rmrrrs&& r&attach_entities_eprus=  - ** % % C(:Entity). Both the `from` and `to` names run through the resolver, so the edge attaches to the same canonical nodes that mentions do.rrSzfrom and to requiredrrrT) rrrrxrlrr r2r1)rmrrr3r4s&& r& relation_addr{s  - ** % % C ++   " " $B ''--R   B R4JKK"$**11 37 $<r(c(V^8dQhRRRRRRRR/#)r!keepr4mkrr"rr$)r%s"r&r'r's(::S:c:#:$:r(cV'dW8XdR#\PRRVRV/4Pp\PRRVRV/4PpV'd V'gR#\PRRVRVRV/4\4p\PRRVRV/4PpVFDwrxp WpV39dK\PR RTR TRTR T;'gR R T ;'gT/4KF \PRRVRV/4Pp V FDwrxp WpV39dK\PRRTR TRTR T;'gR R T ;'gT/4KF V^,^,;'g.p V^,^,;'g.p \P \ V 4\ V 4,V.,4U u.uF qV8wgK V NK pp V^,^,;'gRV^,^,;'gRppV^,^,;'gRV^,^,;'gRpp\V4\V48dTMTp\V4\V48dTMTp\PRRVRVRVRVRV/4\PRRVRV/4R#uup i)uFold entity `mk` into `keep`: re-point MENTIONS + REL edges, union aliases, keep the richer (longer) summary/read, then delete `mk`. No APOC — REL edges are collected in Python and recreated so their rtype/created_at survive.FzGMATCH (e:Entity {key:$k, group:$g}) RETURN e.aliases, e.summary, e.readrrzMATCH (m:Memory)-[r:MENTIONS]->(:Entity {key:$mk, group:$g}) MATCH (k:Entity {key:$keep, group:$g}) MERGE (m)-[:MENTIONS]->(k) DELETE rr~r}z[MATCH (:Entity {key:$mk, group:$g})-[r:REL]->(b:Entity) RETURN b.key, r.rtype, r.created_atzMATCH (k:Entity {key:$keep, group:$g}),(b:Entity {key:$bk, group:$g}) MERGE (k)-[nr:REL {rtype:$rt}]->(b) ON CREATE SET nr.created_at=$catbkr5rcatz[MATCH (b:Entity)-[r:REL]->(:Entity {key:$mk, group:$g}) RETURN b.key, r.rtype, r.created_atzMATCH (b:Entity {key:$bk, group:$g}),(k:Entity {key:$keep, group:$g}) MERGE (b)-[nr:REL {rtype:$rt}]->(k) ON CREATE SET nr.created_at=$catrzTMATCH (k:Entity {key:$keep, group:$g}) SET k.aliases=$al, k.summary=$sum, k.read=$rdrrrdz4MATCH (e:Entity {key:$mk, group:$g}) DETACH DELETE eT)rrrrBrrrBr)r}r~rkrowmrowr?outsbkeyr5rinskalmalrunionksumkreadmsummreadrurjs&&& r& _merge_oners  << .03T3/F HHR   << .03Re/D FFPj  t LL . r64e, . *C << .04b#u/E GGQz  # ":    S T4sE4y CJJ3  ! ,, .04b#u/E GGQz # ":    S T4sE4y CJJ3  ! q'!*  C q'!*  C d3i$s)&;rd&BC QC1DyQQCE Qq'!*""DGAJ$4$4"%Dq'!*""DGAJ$4$4"%D$i3t9,d$GJ#e*,5%D LL 8 sE4wdKM LLGC')  Rs K &K c:]tRtRt$R]R&R]R&RtR]R&RtR#) EntityUnrelatei r4rrNrLrr$r[r$r(r&rr s F FE:r(rc:]tRtRt$R]R&R]R&RtR]R&R tR#) EntityAliasesir4rrBrNrLrr$r[r$r(r&rrs H ME:r(rz/entity/aliasesc V^8dQhRRRR/#)r!rmrrrLr$)r%s"r&r'r's444",4r(c \V4VP;'g\p\VP4p\ VP ;'g.Uu.uF3p\V4'gK\V4V8wgK(\V4NK5 up4p\PRRVRVRV/4PpV'g\RRR7hRR R VR V/#uupi) zREPAIR/curation: REPLACE an entity's alias list outright (dedup, lowered, self-name removed). Used to strip pollution after the corrupt-vector alias auto-attach incident; aliases regrow organically through resolution.zDMATCH (e:Entity {key:$k, group:$grp}) SET e.aliases=$al RETURN e.keyrrrrLrMrrTrr) rrrrrrrrrrr )rmrrrrcleanress&& r&entity_aliasesrs  - ** % % CTXXA t||/A/Ar/AB/A$Q%,8Oq,@%\!_/AB CE ,, aT59 ;;E: 4FGG $q)U 33BsC0 CCc0]tRtRt$R]R&RtR]R&RtR#) EntityDetachi(r4rNrLrr$r[r$r(r&rr(s HE:r(rz/entity/detachc V^8dQhRRRR/#)r!rmrrrLr$)r%s"r&r'r'.s.. .!+.r(c a \V4VP;'g\p\VP4p\ P RRVRV/4PpV'gRRR^/#V^,^,;'gTP4pV^,^,;'g.Uu.uFqfP4NK pp\V.V,4Uu.uFp\V4^8gKVNK p pV Uu.uF;p\P!R\P!V4,R,4NK= p pVU a u.uFNo \;QJdV 3R lV 4F 'gK RM R M!V 3R lV 44'dKES ^,NKP p p V 'd\ P R R V RVRV/4RRR\V 4/#uupiuupiuupiuup i) zREPAIR: remove MENTIONS edges from an entity to memories whose text does NOT actually contain the entity's name or an alias (word-boundary). Used to undo bad merges that hijacked another entity's facts. Returns the count.zeMATCH (e:Entity {key:$k, group:$grp})<-[r:MENTIONS]-(m:Memory) RETURN m.id, m.text, e.name, e.aliasesrrrTdetachedr r c3<"TF4qPS^,;'gRP44xK6 R#5ir7)rMr)rvrros& r&rx entity_detach..As.Dehh! 11344es??FzcUNWIND $ids AS mid MATCH (e:Entity {key:$k, group:$grp})<-[r:MENTIONS]-(m:Memory {id:mid}) DELETE rr)rrrrrrrrrrrrr r!r) rmrrrrrrrrArr/robads && ` r& entity_detachr-s  - ** % % CTXXA << 1365#2F HHR   dJ** GAJOO! " " $D#'71:#3#3#35#3Qwwy#3G5 01 A11SVq[QQ1E A ZZ*RYYq\9._resolveWsE|| 8Ca "#-* "tAwqz+t+r(rLrMzVMATCH (a:Entity {key:$a, group:$grp})-[r:REL]-(b:Entity {key:$b, group:$grp}) DELETE rrrrzMATCH (a:Entity {key:$a, group:$grp}),(b:Entity {key:$b, group:$grp}) MERGE (a)-[x:NOT_RELATED]->(b) ON CREATE SET x.created_at=$nowr?rT) rrrrrrr rrrB)rmrkakbrrarbrs&& @r&entity_unrelaterJs - ** % % C $&& !<#7 R284OPP,b\8B< R284FGG LL "%r3E3!?A LL I b#r5#uhj9; $Rb ))r(c:]tRtRt$R]R&R]R&RtR]R&R tR#) EntityMergeikr4keep_keyr merge_keysNrLrr$r[r$r(r&rrksME:r(rz/entities/mergec V^8dQhRRRR/#)r!rmrrrLr$)r%s"r&r'r'rs888",8r(cr\V4VP;'g\p\VP4pV'g\ RRR7h.pVP ;'g.FEp\V4pV'gKWc8wgK \W6V4'gK4VPV4KG RRRVRV/#)uManually fold one or more duplicate entities into a canonical one — moves every MENTIONS + REL edge, unions aliases, and keeps the richer profile.rSzkeep_key requiredrrTr}merged) rrrrrr rrrD)rmrrr}rrr~s&& r&entities_mergerqs  - ** % % C  &D 4GHH F%%2% #  2"*Dc!:!: MM" & $h 77r(c4]tRtRt$RtR]R&RtR]R&RtR#) DedupBackfilliTrrNrLrr$)rrrrrrrrr$r(r&rrsGTE:r(rz/entities/dedup_backfillc V^8dQhRRRR/#)r!rmrrrLr$)r%s"r&r'r's#@H@H-@H+5@Hr(c\V4VP;'g\p\P RRV/4P pVUu.uFNpRV^,RV^,;'g V^,RV^,;'gRRV^,;'g./NKP ppVUu/uFqfR,\ VR,4bK pp/p\P RRV/4P F(p\V^,;'g^4W^,&K* /p VF+pV PVR,.4PV4K- .p V P4EFwr\\V 44EFp \V ^,\V 44EF{pW,W,pp\WR,,VVR,,4p\VR,4p\VR,4p\P !R VV4P#4p\%VR,4\%VR,4,Fjp\'V\P !R V\V44P#4\P !R V\V44P#44pKl \'VV4pVR 8gEK2V PVR,VR,\)\+V4^4\)\+V4^434EK~ EK EK V P-R R R 7VP.'d5RR R\V 4RV UUUUu.uFwppppRVRVRVRV/NK upppp/#\14p..ppV FwppppVV9gVV9dKVR8dyVR8drVP3V^4VP3V^48dVV3MVV3wpp\5VVV4'd+VP7V4VPRVRVRV/4KKVPRVRVRVRV/4K RRRVRV/#uupiuupiuuppppi)u9Scan same-type entities for lookalike pairs (max of name-embedding cosine and difflib token ratio). `dry_run` returns every candidate pair (≥0.80) with its score; a real run AUTO-MERGES the confident ones (≥0.95, keeping whichever has more mentions) and returns the 0.80–0.95 band as `ambiguous` for review.zFMATCH (e:Entity {group:$grp}) RETURN e.key, e.name, e.etype, e.aliasesrrrrrrzXMATCH (e:Entity {group:$grp}) OPTIONAL MATCH (e)<-[r:MENTIONS]-() RETURN e.key, count(r)NrcV^,#)r!r$)rs&r&r)entities_dedup_backfill..sQqTr(Trr candidatespairsrrrrr&gffffff?r}rF ambiguous)rrrrrrr;rJr{rDrrrrrrrrrBrrr#r@rr rkrr)rmrrrrorrembsmcby_typerr group_entsr~rErrratokbtokrrrr!rAgonerrr}drops&& r&entities_dedup_backfillrs - ** % % C <<P    j .2 3-1AaD&!A$,,!A$!A$//' !  $-1  315 6AeHfQvY' 'D 6B \\ %',cl44>J?qtyyq>Q4?G 1W:r*11!4 E$]]_s:'A1q5#j/2!}jm1!$x.$qx.A%ai0%ai0--dD$?EEGq|,tAiL/AAABc7#:#:dN2$6$88=// $r(:<4J0 |||4s5z,13,1jaAq37Aua@,134 4DBIF 1a 9T  9c#%66!Q<266!Q<#?!QaVJD$$c** vtXtWaHI+   c1c1gq%C D uh Y GGk 3 6>3s$ Q +Q Q Q &"QQcL]tRtRt$R]R&RtR]R&RtR]R&R tR]R &R tR #) Insightir4r3rrLr observationitypeNrr$) rrrrrrrrrr$r(r&rrs% IE:%E:%E:r(rz/insightc V^8dQhRRRR/#)r!rmrrrLr$)r%s"r&r'r's((g(j(r(c\V4VPP4pV'g\RRR7hVP;'g\ p\ V4p\PRRVRVRR\, /4PpV'd%\V^,^,4^8dRR R R /#\P!4Pp\PR R TRTRTP ;'gRRTP";'gRRTR\%4RT/4R TRR/# \dLi;i)rSz empty insightrzCALL db.idx.vector.queryNodes('Insight','embedding',3,vecf32($e)) YIELD node, score WHERE node.group=$grp AND score<$thr RETURN count(node)rrthrr*createdFreason duplicatezCREATE (i:Insight {id:$id, text:$text, title:$title, itype:$itype, group:$grp, created_at:$now, surfaced:false}) SET i.embedding = vecf32($emb)rr3rrrrr?rT)rr3rlr rrr;rrr.rrJrrrrrrrB)rmrr3rrdupiids&& r& insight_addrs1 - 99?? D ODD ** % % C ,C  ll !#&UC#(# *=#?@AK 3s1vay>A%uh < < **,  C LL ) sFD'4::+;+; 4::..  E3 ( ) #y$ ''    s!AD77 EEz /insightsTc(V^8dQhRRRRRRRR/#)r!rrLunsurfaced_onlyrrOrJrr$)r%s"r&r'r's0 M M MT M M!+ Mr(ch\V4T;'g\pV'dRMRp\PRV,R,RVR\ ^\ V^d44/4P pRVUu.uF2pRV^,RV^,R V^,R V^,R V^,/NK4 up/#uupi) zAND i.surfaced=false rz%MATCH (i:Insight) WHERE i.group=$grp zYRETURN i.id, i.text, i.title, i.itype, i.created_at ORDER BY i.created_at DESC LIMIT $limrrTinsightsrr3rrr)rrrrrrUr)rrrOrrr-rros&&&& r& insights_listrs -  =C&5 "2D <</$6 0 0 UC3uc?34  67Aj  FJLFJ!fadGQqT!1Q4qt=FJL MMLs38B/z/insights/searchc(V^8dQhRRRRRRRR/#)r!r/r4rrLrrJrr$)r%s"r&r'r' s)))))s)):))))#-))r(c^RIp\V4T;'g\p\V4p\ 4p.p\ P RR\V^4^,^,RVRV/4PF|p V ^,V9gKVPV ^,4VPRV ^,RV ^,RV ^,R V ^,R \\V ^,4^4/4K~ VPR VP44U u.uFp V \ 9gKV NK up R ,p V 'dR RP#R\%\'V 4444,R,p RV/\)V 4U U u/uF wrRV 2V bK up p Cp\ P W4PFbp V ^,V9gKVPV ^,4VPRV ^,RV ^,RV ^,R V ^,R R/4Kd RVR\V^4/# \dELPi;iuup iuup p i \dL;i;i)uUnsurfaced insights RELEVANT to a query — used to raise a reflection contextually (only when Ahmed asks about something related), never on a timer.NzCALL db.idx.vector.queryNodes('Insight','embedding',$k,vecf32($emb)) YIELD node, score WHERE node.group=$grp AND node.surfaced=false AND score < 0.5 RETURN node.id, node.text, node.title, node.itype, scorerrrrr3rrrrrz?MATCH (i:Insight) WHERE i.group=$grp AND i.surfaced=false AND (rc3,"TF pRV 2xK R#5i)ztoLower(i.text) CONTAINS $wNr$)rvrs& r&rx"insights_search..% rrz') RETURN i.id, i.text, i.title, i.ityperrr)rrrr;r rrrrrrDrr,rrrrr'rrr)r/rrrrrrrErzrorrrrrs&&&& r&insights_searchr sI  -  =C )CDC   s1ay1}q0%& ' (2z 2A t41 D!A$!gqt#QqT7E$qt*a4HJK2 OQWWY? ?1Q? " $E P{{7$)#e*$577799J %8H I8H1QC!8H IJ \\#.99Q4t#HHQqTNJJadFAaD'1Q4 '1w =>: JSAY (('     !J   sDA H8 A-H8 I *I  I"-IAI8 II I#"I#c0]tRtRt$R]R&RtR]R&RtR#) InsightSeeni4 r4rNrLrr$r[r$r(r&rr4 r\r(rz /insight/seenc V^8dQhRRRR/#)r!rmrrrLr$)r%s"r&r'r': s{ *r(cd\V4\PRRVP/4RR/#)z.MATCH (i:Insight {id:$id}) SET i.surfaced=truerrTrrrrrYs&&r& insight_seenr9 s0 - LLA" $<r(cP]tRtRt$RtR]R&RtR]R&RtR]R &RtR ]R &R t R#) BackfilliB NrrrrFrrrLrr$) rrrrrrrrrrr$r(r&rrB s*"I|" Iz GTE:r(rz/backfill_linksc V^8dQhRRRR/#)r!rmrrrLr$)r%s"r&r'r'J sKKK",Kr(c  \V4VP;'g\pVPe VPM\pVP ;'g\ p\PRRV/4Pp\4p.pVFwr\T ;'gR4p \Y;'gRWW4VPR7Fp \WR,34p W9dKVPV 4\!V4^8gKATP#RT ;'gRR,RV R ,;'gRR,R V R ,/4K K R \!V4R \!V4R VRVPRV/#)uLAYER 1 backfill — run auto-linking over EVERY existing memory once, so today's loose cloud gets wired up. `dry_run` reports what it WOULD link (with samples) without touching the graph, so the threshold can be tuned first.zHMATCH (m:Memory) WHERE m.group=$grp AND m.valid=true RETURN m.id, m.textrr)rrrrrr3rrc linked_pairsrrsamples)rrrrr-rr/rrrr r;rEr frozensetrrrD) rmrrrmxrcrrrntextrrLpairs && r&backfill_linksrI sO  - ** % % C NN6$.. @ Q !8  H LL-eWHTFC((1{2RT U $eY & ""Q   J HA . 5'qc:$GG/ Hs)G #G GG H%#1H  H%z/exportc V^8dQhRRRR/#rer$)r%s"r&r'r' s5<5<*5<$5V / #uupiuupiuupiuupiuupiuupiuupiuupiuupiuupi)?uCOMPLETE dump of a group for nightly backups — memories (INCLUDING stale), entities, links, mentions, relations, insights, tasks, profile, core, meta.zMATCH (m:Memory {group:$grp}) RETURN m.id, m.text, m.kind, m.source, m.created_at, m.refers_to, m.valid, m.valid_from, m.valid_to, m.invalid_at, m.superseded_by, m.confidence, m.importance, m.srel, m.tone, m.access_countrrr3rrrrrW valid_fromvalid_to invalid_atrrrrrrzgMATCH (e:Entity {group:$grp}) RETURN e.key, e.name, e.etype, e.aliases, e.summary, e.read, e.created_atrrrrrurrjzYMATCH (a:Memory {group:$grp})-[l:LINK]->(b:Memory {group:$grp}) RETURN a.id, b.id, l.typerrrRzUMATCH (m:Memory {group:$grp})-[:MENTIONS]->(e:Entity {group:$grp}) RETURN m.id, e.keymemoryrziMATCH (a:Entity {group:$grp})-[r:REL]->(b:Entity {group:$grp}) RETURN a.key, b.key, r.rtype, r.created_atr1z^MATCH (i:Insight {group:$grp}) RETURN i.id, i.text, i.title, i.itype, i.created_at, i.surfacedrrsurfacedzUMATCH (t:Task {grp:$grp}) RETURN t.id, t.text, t.due, t.done, t.created_at, t.done_atrdonedone_atz@MATCH (p:Profile {group:$grp}) RETURN p.id, p.text, p.updated_atrgz7MATCH (c:Core {group:$grp}) RETURN c.text, c.updated_atzEMATCH (x:Meta {group:$grp}) RETURN x.id, x.last_run, x.status, x.notelast_runstatusrr exported_atrrrrbrrrr<coremeta)rrrrrrB)rrrrorrrrbrrrr<r r s&& r&exportr  s  -  =C   !$)#, 01; ; < ;A KqtKVQqTK61Q4K1Q4KqtK%0!A$K8?1KqtK%/1K7CQqTK!!B%K*6quK?KAbEK2 K!'" K0>quzz K ;  <  *,13<99CDEDA!fadGQqT9adjjbAaDJJB! L!A$PD E @F|| $&+S\@33=:@> ?@>!fadD!A$! 5@>E ?;A,, $cl;,,6J;78;7Q1Q41Q40;7H8!,, 57A=1qtVQqT7AaD'1Q4qtZ17A= > =CLL "$)3<=11;=< == =" " "sWB,P=-P=0Q QQ2Q)&Q5Q 8/Q AQ4AQ&Q 'Q%*/Q*cB]tRtRt$RtR]R&RtR]R&RtR]R&R tR#) ReflectionRani rrLrrrNrr$) rrrrrrrrrr$r(r&r r  s FJD*E:r(r z/reflection_ranc V^8dQhRRRR/#)r!rmr rrLr$)r%s"r&r'r' s ) ) )", )r(c \V4VP;'g\p\4p\P RRTRTRVP ;'gRRVP;'gR/4RRR V/#) zThe reflection engine calls this after each run so /health can report when it last executed (stored on a :Meta {id:'reflection'} singleton per group).z\MERGE (x:Meta {id:'reflection', group:$grp}) SET x.last_run=$now, x.status=$st, x.note=$noterr?strrrTr)rrrrBrrrr)rmrrr?s&& r&reflection_ranr ss  - ** % % C *C LL : UCt{{':':d b "# $ C ((r(c0]tRtRt$R]R&RtR]R&RtR#)Corei r4r3NrLrr$r[r$r(r&rr r_r(rz /core_blockc V^8dQhRRRR/#rer$)r%s"r&r'r' s F Fj F( Fr(c\V4T;'g\p\PRRV/4PpV'gRRRR/#RV^,^,;'gRRV^,^,;'gR/#)uAhmed's always-on core context block (a :Core singleton per group) — the identity/preamble every brain loads before recalling anything else.zBMATCH (c:Core {id:'core', group:$grp}) RETURN c.text, c.updated_atrr3rrgrerhs&& r& core_blockr s|  -  =C <<L    j  L"-- DGAJ$$"lDGAJ4D4D" EEr(z/corec V^8dQhRRRR/#)r!rmrrrLr$)r%s"r&r'r' s4 r(c \V4VP;'g\p\P RRTRVP ;'gRR\ 4/4RR/#)z.Upsert Ahmed's core context block (singleton).zJMERGE (c:Core {id:'core', group:$grp}) SET c.text=$text, c.updated_at=$nowrr3rr?rT)rrrrrr3rBrbs&& r&core_setr sW - ** % % C LL . VTYY__"eXZ@B $<r(cL]tRtRt$R]R&RtR]R&RtR]R&RtR]R &R tR#) Taski r4r3NrLrrrrr$) rrrrrrrrrr$r(r&rr s% ICE:!FJ!r(rc0]tRtRt$R]R&RtR]R&RtR#)TaskIdi$ r4rNrLrr$r[r$r(r&rr$ r\r(rz/taskc V^8dQhRRRR/#)r!rmrrrLr$)r%s"r&r'r'* s  4  r(c\V4VPP4'g\RRR7h\P !4P p\PRRTRVPP4RVP;'gRRVP;'gR R VP;'g\R \4/4RV/#) rSz empty taskrznCREATE (t:Task {id:$id, text:$text, due:$due, done:false, source:$src, grp:$grp, created_at:$now, done_at:''})rr3rrsrcrrr?)rr3rlr rrrrrrrrrrB)rmrrs&& r&task_addr!) s - 99??  LAA **,  C LL ? sFDIIOO-udhhnn"  ''x 0K0Km    #;r(z/tasksc$V^8dQhRRRRRR/#)r!rrL include_donerrr$)r%s"r&r'r'9 s' @ @Z @d @' @r(c\V4T;'g\p\PRV'dRMR,R,RV/4PpRVUu.uFXpRV^,RV^,RV^,;'gR R \ V^,4R V^,R V^,;'gR /NKZ up/#uupi) z MATCH (t:Task) WHERE t.grp=$grp rzAND t.done=false z`RETURN t.id, t.text, t.due, t.done, t.created_at, t.done_at ORDER BY t.done, t.due, t.created_atrrrr3rNrrr)rrrrrr)rr#rrrros&&& r& task_listr%8 s -  =C <<*2#6 8 / /    !j  9=?9=AQqT61Q4! tAaDz<1 !A$,,$09=? @@?s'B=-B=1B=z /task/donec V^8dQhRRRR/#r!rmrrrLr$)r%s"r&r'r'I sF:r(cx\V4\PRRVPR\ 4/4RR/#)z7MATCH (t:Task {id:$id}) SET t.done=true, t.done_at=$nowrr?rT)rrrrrBrYs&&r& task_doner)H s5 - LLJ 35 $<r(z /task/reopenc V^8dQhRRRR/#r'r$)r%s"r&r'r'Q sfZr(cd\V4\PRRVP/4RR/#)z6MATCH (t:Task {id:$id}) SET t.done=false, t.done_at=''rrTrrYs&&r& task_reopenr,P s. - LLI" $<r(z /task/deletec V^8dQhRRRR/#r'r$)r%s"r&r'r'Y sfZr(cd\V4\PRRVP/4RR/#)z MATCH (t:Task {id:$id}) DELETE trrTrrYs&&r& task_deleter/X s+ - LL3dDGG_E $<r(z/reminders/duec V^8dQhRRRR/#rer$)r%s"r&r'r'` sLLL!+Lr(c L\V4T;'g\p\4p\P RRVRV/4P pVF"p\P RRV^,/4K$ RVUu.uF pRV^,RV^,RV^,/NK" up/#uupi)uqTasks whose reminder time has passed and aren't done/fired — the scheduler polls this to know what to announce.zMATCH (t:Task) WHERE t.grp=$grp AND t.done=false AND t.due<>'' AND t.due<=$now AND coalesce(t.fired,false)=false RETURN t.id, t.text, t.duerr?z(MATCH (t:Task {id:$id}) SET t.fired=truerrr3)rrrBrrr)rrrr?rros&& r& reminders_duer2_ s  -  =C *C << %',c5#&> @AK   ?$!N TJTT1Q41uad;TJ KKJs7&B!)i Nr)NrTNNr7)r)F)NNF>placeramountpersoncompanyconceptproduct) preferencer-pattern synthesis convention>rrBr3r>maprZemailphonewhatsapp>rtaskrmoneyr6r7iQ)__conditional_annotations____doc__ __future__rrrarrrurllib.requestrdr contextlibrrrfastapirrr pydanticr r falkordbr fastembedr rrrkrrrrrJrr_rrgrrrr,r#r-r.r/rr1rr;rBrFrqrrrrmountrrrrrrrr(rrr.r6rOpostrVrZr]rrrrErrrrrCrr r2r%r-rMrrZr_rkrmrqrrNr"r rrIrrrrrrrrrrrrrrrrr8r<r>rGrIrPrRrYr\r^rcrirkrnrprurwr{rrrrrrrrrrrrrrrrrrrrrr r rrrrrrr!r%r)r,r/r2)rDs@r&rPsJV#  *'22%# **..)2 . ~w7 ZZ^^NH 5 jjnnPR  {E2 3 zz~~0"5 (B C J J3 Ojjnn],?@  0CD ::>>*c *c 1!2::>>*>GHbjjnn_f=> 2::>>.#67 "& &=298  4PnOi8 N $y $"!9!  --02 / /. D K K=48 CQCQL+9?9M886'171E,;A$;O))* M M M M M M' M6 M5 M  M M M% M'- M/4 M6; M=C MEJ M  M M M% M', M.3 M5: ME MGL M   M  M  M #  M%(  M*.  M04  M6:  M<@  MBF  MHL  MCCZ2 7  &FR 0, ,l ZZYDD jj*+ D  4 @ @L0 ; E 3 )(F R=>G/Tl^+/(.t(<&i *7=d7K.3.3b9 .4T.B%1%1Pi *;A$;O"'8>t8L&Y )9?9M   #'+1$+? F F FY )5;D5IY  39$3Gy+-3D-A   :zY I  /5d/C444$9  .4T.B...8 06t0D***@)  /5d/C888"I  $%8>t8L@H@H&@HPi*;A$;O(((<&*D!.4T.B M M M 04q06t0D))))))X) /-3D-Ay /5d/CKKKBi*7=d7K7"7"7"t#'-d';5<5<5 @ @ @,8>t8L.:@:N.:@:N  &*.4T.BLLLr(