jdZddlZddlmZmZddlmZmZmZm Z ddl m Z ddl m Z ejeZhdZdZd d d Zd Zd edefdZdedefdZdedededefdZdedefdZy)aADeciding whether a visitor's activity is worth a suggestion. Deterministic rules and a database lookup, deliberately. This runs on every page view of every visitor, so it must answer in tens of milliseconds; a model call would take seconds and would spend the tenant's ai_tokens quota -- the same budget the chatbot itself needs -- on visitors who never open the widget. Neighbours come from the pairing graph (`servable_neighbours`), which is built offline and already carries a real relationship (`pair_type`, `pair_score`) rather than "same category". `related_products` (the `related_keys` column) is the fallback for a tenant who has never run pairing -- it keeps that tenant no worse off than before the graph existed, but it is not the primary source. Nothing reachable from `decide()` calls a model. The `search` branch matches on text (`match_products`, a plain ILIKE lookup) rather than on meaning -- it is deliberately not `search_products`, which embeds the query and spends ai_tokens quota. That embedding search is fine inside the chat tool, where a model call is already in the loop and the visitor is waiting on a reply anyway; it is not fine here, where the caller is an unauthenticated widget firing on every page view. Do not swap this back to `search_products` to "improve" match quality -- that would reintroduce a model call, and therefore cost and latency, into a path that must stay a cheap lookup. There is no cooldown in this version. Every qualifying event returns a recommendation, which means a visitor moving through several pages is suggested something on each one. N) parse_qslurlparse) find_by_urlmatch_productsrelated_productsto_card)get_tool_settings)servable_neighbours>dwellsearch page_view add_to_cart cart_abandoncheckout_startz*Still deciding? You might also like these.z Here's what matched your search.)r r )qsqueryr keywordkurlreturnc ttt|jd}t D](}|j |s||jcSy#t$rYywxYw)z=The search term a search results page carries in its own URL.F)keep_blank_values)dictrrr Exception_SEARCH_PARAMSgetstrip)rparamsnames C/var/www/html/strategist-ai/app/services/pairing/recommendations.pysearch_query_from_urlr$6ski 3 3uMN( ::d $<%%' '(  s)A A)(A)reasonc d|dS)NF) recommendr%)r%s r#_nothingr)Bs & 11 tenant_ideventpagecp t|d}|jdds tdSt||jdxsd}|s tdS|dk(r'|jd xsd tkrtd S|d k7r td S|d k(r:t |jdxsd}|s td St ||d }n!t||dd }|st||d }|Dcgc]'}|jd|jdk7r|)c}dd}|s tdSdtj|td|Dcgc] }t|c}dScc}wcc}w#t$r2} tjd|d| dtdcYd} ~ Sd} ~ wwxYw)z4Whether to suggest anything, and what. Never raises.featuresproduct_recommendationT feature_offrr page_unknownr dwell_secondsrno_rule_matchedr )limit product_keyNno_match)r'messageproductsz#Recommendation decision failed for z: )exc_info)r rr)rDWELL_THRESHOLD_SECONDSr$rr rMESSAGES _attributedrloggererror) r+r,r-r/currentr candidatesrothersexs r#deciderGFs+$$Y/ ;||4d;M* *i%)>B?N+ + G ).Q2II 122 h -. . H )$((5/*?R@E 122' 5BJ -Y 8N346J-iJ (IUU=)W[[-GGIIK!MJ' '!#<<x/@A5;<[^<> > I= $ :9+RtLW[ \ ##$s^*E:+E:+E:E:/E:4E:9,E0%E:8!E:E5+E:0 E:: F5'F0*F50F5rowcvt|}|jdr|d|d<|jd|d<|S)zto_card is shared with the chat path and must not learn about pairing, so the attribution is attached here. Phase 5 needs it to work out which pair types earn their place. pair_type pair_score)rr)rHcards r#r?r?vs@ 3rstrr$rr)rGr?r(r*r#rWs6,``><   8 $A :0 ? s s 2S2T2-$c-$#-$T-$d-$`Tdr*