o >rojH@sddlmZddlZddlZddlZddlmZddlmZe dZ dd d Z Gd d d Z e dZdddZddddZdZdS)) annotationsN)Counter)Pathz[a-z0-9][a-z0-9'-]{1,}textstrreturn list[str]cCst|SN)TOKEN_REfindalllower)rr ,/root/openrights-assistant/openrights/rag.pytokenize src@sVeZdZdddZedd d Zedd dZd d!ddZd"ddZ ed#ddZ dS)$ TfidfIndexchunks list[dict]idfdict[str, float]cCs||_||_dSr )rr)selfrrr r r__init__s zTfidfIndex.__init__r 'TfidfIndex'csrt}|D] }|tt|dqtt|dfdd|D}|D] }||d||d<q'|||S)Nrcs,i|]\}}|td|ddqSr)mathlog).0term frequencytotalr r s,z$TfidfIndex.build..vector)rupdatesetrmaxlenitems_vector)clsrdocument_frequencychunkrr rrbuilds zTfidfIndex.buildrrcs4tt|}t|p dfdd|DS)Nrcs*i|]\}}|vr|||qSr r )rrcountrlengthr rr!$s*z&TfidfIndex._vector..)rrsumvaluesr')rrcountsr r.rr( s zTfidfIndex._vectorquestiontop_kintc s|||jttddDpd}g}|jD]0}|di}|ttdd|Dp4d}tfdd|D|}| ||fqddt |d d d d d|DS) Ncs|]}||VqdSr r rvaluer r r (z$TfidfIndex.search..rr"csr7r r r8r r rr:,r;c3s$|] \}}|d|VqdS)rN)get)rrr9queryr rr:-s"cSs"g|] \}}t|t|ddqS))score)dictround)rr@r+r r r /s"z%TfidfIndex.search..TcSs|dS)Nrr )itemr r r/z#TfidfIndex.search..)reversekey) r(rrsqrtr0r1rr<r'appendsorted) rr4r5 query_normresultsr+r" denominatorr@r r=rsearch&s   $$zTfidfIndex.searchpathrNonecCs6|jjddd|jtj|j|jddddddS)NT)parentsexist_ok)rrF) ensure_asciiutf-8encoding)parentmkdir write_textjsondumpsrr)rrPr r rsave1s&zTfidfIndex.savecCs$t|jdd}||d|dS)NrUrVrr)r[loads read_text)r)rPpayloadr r rload5szTfidfIndex.loadN)rrrr)rrrr)rrrrrr)r3)r4rr5r6rr)rPrrrQ)rPrrr) __name__ __module__ __qualname__r classmethodr, staticmethodr(rOr]rar r r rrs     rawhat when where which while about have does can the are from with without and get how been being same other their there they this that these those your not all any some more most much many into over under than then such only own just also need want should would could will shallr4cCsddt|DS)z0The words that carry what the question is about.cSs$g|]}t|dkr|tvr|qS)r?)r& GENERIC_WORDS)rtr r rrCDs$z!subject_words..)r)r4r r r subject_wordsBsrirrrtermsr6boolcst|}|sdS|dk}fdd|D}|r!t|t|kr!dS|s%dSt|fdddd}tt|tfd d |d |DS) a]True when the text is about what the question actually asked. Two callers, two standards, because the cost of being wrong differs. An answer must clear the strict test (terms=1): a confident explanation of the wrong subject is the worst thing this tool can do. It also refuses outright when any subject word is missing from the corpus, since a word the archive has never seen is the clearest possible signal that the archive does not cover the topic. That is what makes it decline on rent and eviction instead of answering with the nearest employment rule. Supporting passages use the looser test (terms=EVIDENCE_TERMS) and ignore unknown words, because a single rare word is often a figure of speech rather than the topic: "what should I know before getting a mortgage" contains *getting*, which appears nowhere in a corpus of statutes, and demanding it hid every relevant passage of the Truth in Lending Act. Measured on evals/coverage.json: 39/39 answers correct, 10/10 out-of-scope questions declined, and the law found for both mortgage questions. Trcsg|]}|vr|qSr r rwordrr rrCasz!is_on_subject..Fcs|Sr r )rmrnr rrEgrFzis_on_subject..)rHrGc3s|]}|vVqdSr r rl)presentr rr:ir;z is_on_subject..N)rir&rKr$rany)r4rrrjsubjectsstrictknownrankedr )rror is_on_subjectGs ru)rrrr)r4rrrr) r4rrrrrrjr6rrk) __future__rr[rre collectionsrpathlibrcompiler rr frozensetsplitrgriruEVIDENCE_TERMSr r r rs      /  '