StudyDiary/.obsidian/plugins/smart-second-brain/main.js

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`)}async function _R(){return new Xf}let im;function IR(){const t="default"in wn?wn.default:wn;return new t({autoStart:!0,concurrency:1})}async function or(t,e){e===!0?await t():(typeof im>"u"&&(im=IR()),im.add(t))}let DR=class{setHandler(e){return this.setHandlers([e])}},j0=class{constructor(e,r,a,n,i,l,s,o){Object.defineProperty(this,"runId",{enumerable:!0,configurable:!0,writable:!0,value:e}),Object.defineProperty(this,"handlers",{enumerable:!0,configurable:!0,writable:!0,value:r}),Object.defineProperty(this,"inheritableHandlers",{enumerable:!0,configurable:!0,writable:!0,value:a}),Object.defineProperty(this,"tags",{enumerable:!0,configurable:!0,writable:!0,value:n}),Object.defineProperty(this,"inheritableTags",{enumerable:!0,configurable:!0,writable:!0,value:i}),Object.defineProperty(this,"metadata",{enumerable:!0,configurable:!0,writable:!0,value:l}),Object.defineProperty(this,"inheritableMetadata",{enumerable:!0,configurable:!0,writable:!0,value:s}),Object.defineProperty(this,"_parentRunId",{enumerable:!0,configurable:!0,writable:!0,value:o})}async handleText(e){await Promise.all(this.handlers.map(r=>or(async()=>{var a;try{await((a=r.handleText)==null?void 0:a.call(r,e,this.runId,this._parentRunId,this.tags))}catch(n){console.error(`Error in handler ${r.constructor.name}, handleText: ${n}`)}},r.awaitHandlers)))}},MR=class extends j0{getChild(e){const r=new bl(this.runId);return r.setHandlers(this.inheritableHandlers),r.addTags(this.inheritableTags),r.addMetadata(this.inheritableMetadata),e&&r.addTags([e],!1),r}async handleRetrieverEnd(e){await Promise.all(this.handlers.map(r=>or(async()=>{var a;if(!r.ignoreRetriever)try{await((a=r.handleRetrieverEnd)==null?void 0:a.call(r,e,this.runId,this._parentRunId,this.tags))}catch{console.error(`Error in handler ${r.constructor.name}, handleRetriever`)}},r.awaitHandlers)))}async handleRetrieverError(e){await Promise.all(this.handlers.map(r=>or(async()=>{var a;if(!r.ignoreRetriever)try{await((a=r.handleRetrieverError)==null?void 0:a.call(r,e,this.runId,this._parentRunId,this.tags))}catch(n){console.error(`Error in handler ${r.constructor.name}, handleRetrieverError: ${n}`)}},r.awaitHandlers)))}},O4=class extends j0{async handleLLMNewToken(e,r,a,n,i,l){await Promise.all(this.handlers.map(s=>or(async()=>{var o;if(!s.ignoreLLM)try{await((o=s.handleLLMNewToken)==null?void 0:o.call(s,e,r??{prompt:0,completion:0},this.runId,this._parentRunId,this.tags,l))}catch(d){console.error(`Error in handler ${s.constructor.name}, handleLLMNewToken: ${d}`)}},s.awaitHandlers)))}async handleLLMError(e){await Promise.all(this.handlers.map(r=>or(async()=>{var a;if(!r.ignoreLLM)try{await((a=r.handleLLMError)==null?void 0:a.call(r,e,this.runId,this._parentRunId,this.tags))}catch(n){console.error(`Error in handler ${r.constructor.name}, handleLLMError: ${n}`)}},r.awaitHandlers)))}async handleLLMEnd(e){await Promise.all(this.handlers.map(r=>or(async()=>{var a;if(!r.ignoreLLM)try{await((a=r.handleLLMEnd)==null?void 0:a.call(r,e,this.runId,this._parentRunId,this.tags))}catch(n){console.error(`Error in handler ${r.constructor.name}, handleLLMEnd: ${n}`)}},r.awaitHandlers)))}},xR=class extends j0{getChild(e){const r=new bl(this.runId);return r.setHandlers(this.inheritableHandlers),r.addTags(this.inheritableTags),r.addMetadata(this.inheritableMetadata),e&&r.addTags([e],!1),r}async handleChainError(e,r,a,n,i){await Promise.all(this.handlers.map(l=>or(async()=>{var s;if(!l.ignoreChain)try{await((s=l.handleChainError)==null?void 0:s.call(l,e,this.runId,this._parentRunId,this.tags,i))}catch(o){console.error(`Error in handler ${l.constructor.name}, handleChainError: ${o}`)}},l.awaitHandlers)))}async handleChainEnd(e,r,a,n,i){await Promise.all(this.handlers.map(l=>or(async()=>{var s;if(!l.ignoreChain)try{await((s=l.handleChainEnd)==null?void 0:s.call(l,e,this.runId,this._parentRunId,this.tags,i))}catch(o){console.error(`Error in handler ${l.constructor.name}, handleChainEnd: ${o}`)}},l.awaitHandlers)))}async handleAgentAction(e){await Promise.all(this.handlers.map(r=>or(async()=>{var a;if(!r.ignoreAgent)try{await
`)}mapCharactersToTokenIds(e,r,a){const n=[];r&&n.push(1),a&&(e=" "+e);const i=e.replaceAll(" ",this.vocabById[29871]),l=Array.from(i);for(let s=0;s<l.length;s++){const o=l[s];if(this.vocabByString.has(o))n.push(this.vocabByString.get(o));else{const d=this.utf8Encoder.encode(o);for(let u=0;u<d.length;u++){const c=this.vocabByString.get(z4(d[u]));n.push(c),c>=0||(console.log("Encountered unknown character "+o+" (partial UTF-8 byte "+d[u]+" + hex + "+z4(d[u])+")"),n[n.length-1]=0)}}}return n}encode(e,r=!0,a=!0,n=!1){let i=null;if(n&&(i=performance.now()),!this.vocabById||!this.vocabByString||!this.merges){console.log("Tokenizer not initialized properly!");return}if(e.length===0)return[];const l=this.mapCharactersToTokenIds(e,r,a),s=new PR((p,A)=>p.mergePrio<A.mergePrio),o=p=>{const A=this.getMergeIdentifierString(p.tokenId,p.next.tokenId),m=this.merges.get(A)+p.origPos/e.length;m&&(p.mergePrio=m,p.mergeToString=A.replace(" ",""),s.push(p))};let d={origPos:0,tokenId:l[0],prev:null,next:null},u=d;for(let p=1;p<l.length;p++){const A={origPos:p,tokenId:l[p],prev:u,next:null};u.next=A,o(u),u=A}for(;!s.isEmpty();){const p=s.pop();if(p.deleted||!p.next||p.next.deleted)continue;if(p.deleted=!0,p.next.deleted=!0,p.prev){const m=p.prev;m.deleted=!0;const h={origPos:m.origPos,tokenId:m.tokenId,prev:m.prev,next:m.next};p.prev=h,h.prev?h.prev.next=h:d=h}const A={origPos:p.origPos,tokenId:this.vocabByString.get(p.mergeToString),prev:p.prev,next:p.next.next};A.prev?(A.prev.next=A,A.prev,o(A.prev)):d=A,A.next&&(A.next.prev=A,o(A))}const c=[];for(let p=d;p!==null;p=p.next)c.push(p.tokenId);if(n){const p=performance.now();console.log("Tokenizer running time: "+(p-i)+" milliseconds")}return c}decode(e,r=!0,a=!0){const n=[],i=r?1:0;for(let d=i;d<e.length;d++){const u=e[d],c=this.vocabById[u];if(c.startsWith("<0x")&&c.endsWith(">")){const p=OR(c);n.push(p)}else this.utf8Encoder.encode(c).forEach(A=>n.push(A))}const l=new Uint8Array(n),o=this.utf8Decoder.decode(l).replaceAll(this.vocabById[29871]," ");return a?o.slice(1):o}defaultTests(e){function r(n,i){return n.length===i.length&&n.every(function(l,s){return l===i[s]})}function a(n,i){const l=e.encode(n,!0,!0,!0);if(!r(l,i))throw`Test failed. LLaMA Tokenizer Encoder returned unexpected result: expected tokenize(${n}) === ${i}, actual was: ${l}`;if(n!==e.decode(l))throw`Test failed. LLaMA Tokenizer Decoder returned unexpected result: expected decode(${l}) === ${n}, actual was: ${decode(l)}`}return a("grabbed",[1,2646,1327,287]),a(" grabbed",[1,29871,2646,1327,287]),a("           grabbed",[1,9651,2646,1327,287]),a(`
`,[1,29871,13]),a(` 
`,[1,259,13]),a("	tabs				out here",[1,29871,12,21175,12,12,12,12,449,1244]),a(`ax
####
boo`,[1,4853,13,4136,13,833,29877]),a("镇",[1,29871,30411]),a("🦙",[1,29871,243,162,169,156]),a("🦙Ꙋ",[1,29871,243,162,169,156,237,156,141]),a("Ꙋ🦙",[1,29871,237,156,141,243,162,169,156]),a('The llama (/ˈlɑːmə/; 🦙Spanish pronunciation: [ˈʎama]) (Lama glama) is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the Pre-Columbian era. Llamas are social animals and live with others as a herd. Their wool is soft and contains only a small amount of lanolin.[2] Llamas can learn simple tasks after a few repetitions. When using a pack, they can carry about 25 to 30% of their body weight for 8 to 13 km (5–8 miles).[3] The name llama (in the past also spelled "lama" or "glama") was adopted by European settlers from native Peruvians.[4] The ancestors of llamas are thought to have originated from the Great Plains of North America about 40 million years ago, and subsequently migrated to South America about three million years ago during the Great American Interchange. By the end of the last ice age (10,000–12,000 years ago), camelids were extinct in North America.[3] As of 2007, there were over seven million llamas and alpacas in South America and over 158,000 llamas and 100,000Ꙋ🦙 alpacas, descended from progenitors imported late in the 20th century, in the United States and Canada.[5] In Aymara mythology, llamas are important beings. The Heavenly Llama is said to drink water from the ocean and urinates as it rains.[6] According to Aymara eschatology, llamas will return to the water springs and lagoons where they come from at the end of time.[6]',[1,450,11148,3304,20374,30176,29880,30426,30215,29885,30184,29914,29936,29871,243,162,169,156,15495,728,11504,11173,362,29901,518,30176,31743,3304,2314,313,29931,3304,3144,3304,29897,338,263,21849,630,4275,3082,3949,295,333,29892,17644,1304,408,263,27654,322,4870,13019,491,1126,29872,273,4185,1973,1951,278,4721,29899,1625,3774,713,3152,29889,365,5288,294,526,5264,15006,322,5735,411,4045,408,263,902,29881,29889,11275,281,1507,338,4964,322,3743,871,263,2319,5253,310,10906,22878,7226,29906,29962,365,5288,294,508,5110,2560,9595,1156,263,2846,21159,2187,29889,1932,773,263,4870,29892,896,508,8677,1048,29871,29906,29945,304,29871,29941,29900,29995,310,1009,3573,7688,363,29871,29947,304,29871,29896,29941,2383,313,29945,29994,29947,7800,467,29961,29941,29962,450,1024,11148,3304,313,262,278,4940,884,805,14356,376,29880,3304,29908,470,376,3820,3304,1159,471,16356,491,7824,3604,9306,515,7531,25493,1403,550,7226,29946,29962,450,19525,943,310,11829,294,526,2714,304,505,3978,630,515,278,7027,13494,1144,310,4644,6813,1048,29871,29946,29900,7284,2440,8020,29892,322,17602,9725,630,304,4275,6813,1048,2211,7284,2440,8020,2645,278,7027,3082,4124,3167,29889,2648,278,1095,310,278,1833,14890,5046,313,29896,29900,29892,29900,29900,29900,29994,29896,29906,29892,29900,29900,29900,2440,8020,511,3949,295,4841,892,1294,5562,297,4644,6813,7226,29941,29962,1094,310,29871,29906,29900,29900,29955,29892,727,892,975,9881,7284,11829,294,322,394,29886,562,294,297,4275,6813,322,975,29871,29896,29945,29947,29892,29900,29900,29900,11829,294,322,29871,29896,29900,29900,29892,29900,29900,29900,237,156,141,243,162,169,156,394,29886,562,294,29892,5153,2760,515,410,1885,17259,19673,5683,297,278,29871,29906,29900,386,6462,29892,297,278,3303,3900,322,7400,7226,29945,29962,512,319,962,2518,22082,3002,29892,11829,294,526,4100,367,886,29889,450,22977,368,365,29880,3304,338,1497,304,13748,4094,515,278,23474,322,5065,262,1078,408,372,1153,1144,7226,29953,29962,7579,304,319,962,2518,831,13496,3002,29892,11829,294,674,736,304,278,4094,7689,886,322,301,4425,787,988,896,2041,515,472,278,1095,310,931,7226,29953,29962]),console.log("LLaMA Tokenizer tests passed successfully."),!0}runTests(e=this.defaultTests){e(this)}}const jR="PHVuaz4KPHM+Cjwvcz4KPDB4MDA+CjwweDAxPgo8MHgwMj4KPDB4MDM+CjwweDA0Pgo8MHgwNT4KPDB4MDY+CjwweDA3Pgo8MHgwOD4KPDB4MDk+CjwweDBBPgo8MHgwQj4KPDB4MEM+CjwweDBEPgo8MHgwRT4KPDB4MEY+CjwweDEwPgo8MHgxMT4KPDB4MTI+CjwweDEzPgo8MHgxND4KPDB4MTU+CjwweDE2Pgo8MHgxNz4KPD
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`).filter(Boolean).reduce((n,i)=>{const[l,s,...o]=i.split(" "),d=Number.parseInt(s,10);return o.forEach((u,c)=>n[u]=d+c),n},{});for(const[n,i]of Object.entries(a)){const l=a1.toByteArray(n);this.rankMap.set(l.join(","),i),this.textMap.set(i,l)}this.specialTokens={...e.special_tokens,...r},this.inverseSpecialTokens=Object.entries(this.specialTokens).reduce((n,[i,l])=>(n[l]=this.textEncoder.encode(i),n),{})}encode(e,r=[],a="all"){const n=new RegExp(this.patStr,"ug"),i=aA.specialTokenRegex(Object.keys(this.specialTokens)),l=[],s=new Set(r==="all"?Object.keys(this.specialTokens):r),o=new Set(a==="all"?Object.keys(this.specialTokens).filter(u=>!s.has(u)):a);if(o.size>0){const u=aA.specialTokenRegex([...o]),c=e.match(u);if(c!=null)throw new Error(`The text contains a special token that is not allowed: ${c[0]}`)}let d=0;for(;;){let u=null,c=d;for(;i.lastIndex=c,u=i.exec(e),!(u==null||s.has(u[0]));)c=u.index+1;const p=(u==null?void 0:u.index)??e.length;for(const m of e.substring(d,p).matchAll(n)){const h=this.textEncoder.encode(m[0]),C=this.rankMap.get(h.join(","));if(C!=null){l.push(C);continue}l.push(...NZ(h,this.rankMap))}if(u==null)break;let A=this.specialTokens[u[0]];l.push(A),d=u.index+u[0].length}return l}decode(e){const r=[];let a=0;for(let l=0;l<e.length;++l){const s=e[l],o=this.textMap.get(s)??this.inverseSpecialTokens[s];o!=null&&(r.push(o),a+=o.length)}const n=new Uint8Array(a);let i=0;for(const l of r)n.set(l,i),i+=l.length;return this.textDecoder.decode(n)}},TZ=aA;VZ(TZ,"specialTokenRegex",t=>new RegExp(t.map(e=>LZ(e)).join("|"),"g"));class kZ extends WZ{constructor(e){if(super(e),Object.defineProperty(this,"lc_namespace",{enumerable:!0,configurable:!0,writable:!0,value:["langchain","document_transformers","text_splitters"]}),Object.defineProperty(this,"chunkSize",{enumerable:!0,configurable:!0,writable:!0,value:1e3}),Object.defineProperty(this,"chunkOverlap",{enumerable:!0,configurable:!0,writable:!0,value:200}),Object.defineProperty(this,"keepSeparator",{enumerable:!0,configurable:!0,writable:!0,value:!1}),Object.defineProperty(this,"lengthFunction",{enumerable:!0,configurable:!0,writable:!0,value:void 0}),this.chunkSize=(e==null?void 0:e.chunkSize)??this.chunkSize,this.chunkOverlap=(e==null?void 0:e.chunkOverlap)??this.chunkOverlap,this.keepSeparator=(e==null?void 0:e.keepSeparator)??this.keepSeparator,this.lengthFunction=(e==null?void 0:e.lengthFunction)??(r=>r.length),this.chunkOverlap>=this.chunkSize)throw new Error("Cannot have chunkOverlap >= chunkSize")}async transformDocuments(e,r={}){return this.splitDocuments(e,r)}splitOnSeparator(e,r){let a;if(r)if(this.keepSeparator){const n=r.replace(/[/\-\\^$*+?.()|[\]{}]/g,"\\$&");a=e.split(new RegExp(`(?=${n})`))}else a=e.split(r);else a=e.split("");return a.filter(n=>n!=="")}async createDocuments(e,r=[],a={}){const n=r.length>0?r:[...Array(e.length)].map(()=>({})),{chunkHeader:i="",chunkOverlapHeader:l="(cont'd) ",appendChunkOverlapHeader:s=!1}=a,o=new Array;for(let d=0;d<e.length;d+=1){const u=e[d];let c=1,p=null,A=-1;for(const m of await this.splitText(u)){let h=i;const C=u.indexOf(m,A+1);if(p===null){const K=this.numberOfNewLines(u,0,C);c+=K}else{const K=A+await this.lengthFunction(p);if(K<C){const W=this.numberOfNewLines(u,K,C);c+=W}else if(K>C){const W=this.numberOfNewLines(u,C,K);c-=W}s&&(h+=l)}const w=this.numberOfNewLines(m),b=n[d].loc&&typeof n[d].loc=="object"?{...n[d].loc}:{};b.lines={from:c,to:c+w};const f={...n[d],loc:b};h+=m,o.push(new pG({pageContent:h,metadata:f})),c+=w,p=m,A=C}}return o}numberOfNewLines(e,r,a){return(e.slice(r,a).match(/\n/g)||[]).length}async splitDocuments(e,r={}){const a=e.filter(l=>l.pageContent!==void 0),n=a.map(l=>l.pageContent),i=a.map(l=>l.metadata);return this.createDocuments(n,i,r)}joinDocs(e,r){const a=e.join(r).trim();return a===""?null:a}async mergeSplits(e,r){const a=[],n=[];let i=0;for(const s of e){const o=await this.lengthFunction(s);if(i+o+n.length*r.length>this.chunkSize&&(i>this.chunkSize&&console.warn(`Created a chunk of size ${i}, +
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`," ",""]}),this.separators=(e==null?void 0:e.separators)??this.separators,this.keepSeparator=(e==null?void 0:e.keepSeparator)??!0}async _splitText(e,r){const a=[];let n=r[r.length-1],i;for(let d=0;d<r.length;d+=1){const u=r[d];if(u===""){n=u;break}if(e.includes(u)){n=u,i=r.slice(d+1);break}}const l=this.splitOnSeparator(e,n);let s=[];const o=this.keepSeparator?"":n;for(const d of l)if(await this.lengthFunction(d)<this.chunkSize)s.push(d);else{if(s.length){const u=await this.mergeSplits(s,o);a.push(...u),s=[]}if(!i)a.push(d);else{const u=await this._splitText(d,i);a.push(...u)}}if(s.length){const d=await this.mergeSplits(s,o);a.push(...d)}return a}async splitText(e){return this._splitText(e,this.separators)}static fromLanguage(e,r){return new Pc({...r,separators:Pc.getSeparatorsForLanguage(e)})}static getSeparatorsForLanguage(e){if(e==="cpp")return[`
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`,`
`," ",""];if(e==="php")return[`
function `,`
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do `,`
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`,`
`," ",""];if(e==="proto")return[`
message `,`
service `,`
enum `,`
option `,`
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`,`
`," ",""];if(e==="python")return[`
class `,`
def `,`
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`,`
`," ",""];if(e==="rst")return[`
===
`,`
---
`,`
***
`,`
.. `,`

`,`
`," ",""];if(e==="ruby")return[`
def `,`
class `,`
if `,`
unless `,`
while `,`
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do `,`
begin `,`
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`,`
`," ",""];if(e==="rust")return[`
fn `,`
const `,`
let `,`
if `,`
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for `,`
loop `,`
match `,`
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`,`
`," ",""];if(e==="scala")return[`
class `,`
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def `,`
val `,`
var `,`
if `,`
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match `,`
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`,`
`," ",""];if(e==="swift")return[`
func `,`
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`,`
`," ",""];if(e==="markdown")return[`
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#### `,`
##### `,`
###### `,"```\n\n",`

***

`,`

---

`,`

___

`,`

`,`
`," ",""];if(e==="latex")return[`
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\\subsection{`,`
\\subsubsection{`,`
\\begin{enumerate}`,`
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\\begin{list}`,`
\\begin{quote}`,`
\\begin{quotation}`,`
\\begin{verse}`,`
\\begin{verbatim}`,`
\\begin{align}`,"$$","$",`

`,`
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pragma `,`
using `,`
contract `,`
interface `,`
library `,`
constructor `,`
type `,`
function `,`
event `,`
modifier `,`
error `,`
struct `,`
enum `,`
if `,`
for `,`
while `,`
do while `,`
assembly `,`

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${t.stack}`:""):typeof t=="string"?t:`${t}`}_addChildRun(t,e){t.child_runs.push(e)}async _startTrace(t){var a;const e=K7(t.start_time,t.id),r={...t};if(r.parent_run_id!==void 0){const n=this.runMap.get(r.parent_run_id);n&&(this._addChildRun(n,r),n.child_execution_order=Math.max(n.child_execution_order,r.child_execution_order),r.trace_id=n.trace_id,n.dotted_order!==void 0&&(r.dotted_order=[n.dotted_order,e].join(".")))}else r.trace_id=r.id,r.dotted_order=e;this.runMap.set(r.id,r),await((a=this.onRunCreate)==null?void 0:a.call(this,r))}async _endTrace(t){var r;const e=t.parent_run_id!==void 0&&this.runMap.get(t.parent_run_id);e?e.child_execution_order=Math.max(e.child_execution_order,t.child_execution_order):await this.persistRun(t),this.runMap.delete(t.id),await((r=this.onRunUpdate)==null?void 0:r.call(this,t))}_getExecutionOrder(t){const e=t!==void 0&&this.runMap.get(t);return e?e.child_execution_order+1:1}async handleLLMStart(t,e,r,a,n,i,l,s){var p;const o=this._getExecutionOrder(a),d=Date.now(),u=l?{...n,metadata:l}:n,c={id:r,name:s??t.id[t.id.length-1],parent_run_id:a,start_time:d,serialized:t,events:[{name:"start",time:new Date(d).toISOString()}],inputs:{prompts:e},execution_order:o,child_runs:[],child_execution_order:o,run_type:"llm",extra:u??{},tags:i||[]};return await this._startTrace(c),await((p=this.onLLMStart)==null?void 0:p.call(this,c)),c}async handleChatModelStart(t,e,r,a,n,i,l,s){var p;const o=this._getExecutionOrder(a),d=Date.now(),u=l?{...n,metadata:l}:n,c={id:r,name:s??t.id[t.id.length-1],parent_run_id:a,start_time:d,serialized:t,events:[{name:"start",time:new Date(d).toISOString()}],inputs:{messages:e},execution_order:o,child_runs:[],child_execution_order:o,run_type:"llm",extra:u??{},tags:i||[]};return await this._startTrace(c),await((p=this.onLLMStart)==null?void 0:p.call(this,c)),c}async handleLLMEnd(t,e){var a;const r=this.runMap.get(e);if(!r||(r==null?void 0:r.run_type)!=="llm")throw new Error("No LLM run to end.");return r.end_time=Date.now(),r.outputs=t,r.events.push({name:"end",time:new Date(r.end_time).toISOString()}),await((a=this.onLLMEnd)==null?void 0:a.call(this,r)),await this._endTrace(r),r}async handleLLMError(t,e){var a;const r=this.runMap.get(e);if(!r||(r==null?void 0:r.run_type)!=="llm")throw new Error("No LLM run to end.");return r.end_time=Date.now(),r.error=this.stringifyError(t),r.events.push({name:"error",time:new Date(r.end_time).toISOString()}),await((a=this.onLLMError)==null?void 0:a.call(this,r)),await this._endTrace(r),r}async handleChainStart(t,e,r,a,n,i,l,s){var c;const o=this._getExecutionOrder(a),d=Date.now(),u={id:r,name:s??t.id[t.id.length-1],parent_run_id:a,start_time:d,serialized:t,events:[{name:"start",time:new Date(d).toISOString()}],inputs:e,execution_order:o,child_execution_order:o,run_type:l??"chain",child_runs:[],extra:i?{metadata:i}:{},tags:n||[]};return await this._startTrace(u),await((c=this.onChainStart)==null?void 0:c.call(this,u)),u}async handleChainEnd(t,e,r,a,n){var l;const i=this.runMap.get(e);if(!i)throw new Error("No chain run to end.");return i.end_time=Date.now(),i.outputs=fm(t,"output"),i.events.push({name:"end",time:new Date(i.end_time).toISOString()}),(n==null?void 0:n.inputs)!==void 0&&(i.inputs=fm(n.inputs,"input")),await((l=this.onChainEnd)==null?void 0:l.call(this,i)),await this._endTrace(i),i}async handleChainError(t,e,r,a,n){var l;const i=this.runMap.get(e);if(!i)throw new Error("No chain run to end.");return i.end_time=Date.now(),i.error=this.stringifyError(t),i.events.push({name:"error",time:new Date(i.end_time).toISOString()}),(n==null?void 0:n.inputs)!==void 0&&(i.inputs=fm(n.inputs,"input")),await((l=this.onChainError)==null?void 0:l.call(this,i)),await this._endTrace(i),i}async handleToolStart(t,e,r,a,n,i,l){var u;const s=this._getExecutionOrder(a),o=Date.now(),d={id:r,name:l??t.id[t.id.length-1],parent_run_id:a,start_time:o,serialized:t,events:[{name:"start",time:new Date(o).toISOString()}],inputs:{input:e},execution_order:s,child_execution_order:s,run_type:"tool",child_runs:[],extra:i?{metadata:i}:{},tags:n||[]};return await this._s
`).map(s=>JSON.parse(s))}async*listDatasets({limit:e=100,offset:r=0,datasetIds:a,datasetName:n,datasetNameContains:i}={}){const l="/datasets",s=new URLSearchParams({limit:e.toString(),offset:r.toString()});if(a!==void 0)for(const o of a)s.append("id",o);n!==void 0&&s.append("name",n),i!==void 0&&s.append("name_contains",i);for await(const o of this._getPaginated(l,s))yield*o}async deleteDataset({datasetId:e,datasetName:r}){let a="/datasets",n=e;if(e!==void 0&&r!==void 0)throw new Error("Must provide either datasetName or datasetId, not both");if(r!==void 0&&(n=(await this.readDataset({datasetName:r})).id),n!==void 0)Et(n),a+=`/${n}`;else throw new Error("Must provide datasetName or datasetId");const i=await this.caller.call(fetch,this.apiUrl+a,{method:"DELETE",headers:this.headers,signal:AbortSignal.timeout(this.timeout_ms)});if(!i.ok)throw new Error(`Failed to delete ${a}: ${i.status} ${i.statusText}`);await i.json()}async createExample(e,r,{datasetId:a,datasetName:n,createdAt:i,exampleId:l}){let s=a;if(s===void 0&&n===void 0)throw new Error("Must provide either datasetName or datasetId");if(s!==void 0&&n!==void 0)throw new Error("Must provide either datasetName or datasetId, not both");s===void 0&&(s=(await this.readDataset({datasetName:n})).id);const o=i||new Date,d={dataset_id:s,inputs:e,outputs:r,created_at:o==null?void 0:o.toISOString(),id:l},u=await this.caller.call(fetch,`${this.apiUrl}/examples`,{method:"POST",headers:{...this.headers,"Content-Type":"application/json"},body:JSON.stringify(d),signal:AbortSignal.timeout(this.timeout_ms)});if(!u.ok)throw new Error(`Failed to create example: ${u.status} ${u.statusText}`);return await u.json()}async createExamples(e){const{inputs:r,outputs:a,sourceRunIds:n,exampleIds:i,datasetId:l,datasetName:s}=e;let o=l;if(o===void 0&&s===void 0)throw new Error("Must provide either datasetName or datasetId");if(o!==void 0&&s!==void 0)throw new Error("Must provide either datasetName or datasetId, not both");o===void 0&&(o=(await this.readDataset({datasetName:s})).id);const d=r.map((p,A)=>({dataset_id:o,inputs:p,outputs:a?a[A]:void 0,id:i?i[A]:void 0,source_run_id:n?n[A]:void 0})),u=await this.caller.call(fetch,`${this.apiUrl}/examples/bulk`,{method:"POST",headers:{...this.headers,"Content-Type":"application/json"},body:JSON.stringify(d),signal:AbortSignal.timeout(this.timeout_ms)});if(!u.ok)throw new Error(`Failed to create examples: ${u.status} ${u.statusText}`);return await u.json()}async createLLMExample(e,r,a){return this.createExample({input:e},{output:r},a)}async createChatExample(e,r,a){const n=e.map(l=>jw(l)?qw(l):l),i=jw(r)?qw(r):r;return this.createExample({input:n},{output:i},a)}async readExample(e){Et(e);const r=`/examples/${e}`;return await this._get(r)}async*listExamples({datasetId:e,datasetName:r,exampleIds:a,asOf:n,inlineS3Urls:i}={}){let l;if(e!==void 0&&r!==void 0)throw new Error("Must provide either datasetName or datasetId, not both");if(e!==void 0)l=e;else if(r!==void 0)l=(await this.readDataset({datasetName:r})).id;else throw new Error("Must provide a datasetName or datasetId");const s=new URLSearchParams({dataset:l}),o=n?typeof n=="string"?n:n==null?void 0:n.toISOString():void 0;o&&s.append("as_of",o);const d=i??!0;if(s.append("inline_s3_urls",d.toString()),a!==void 0)for(const u of a)s.append("id",u);for await(const u of this._getPaginated("/examples",s))yield*u}async deleteExample(e){Et(e);const r=`/examples/${e}`,a=await this.caller.call(fetch,this.apiUrl+r,{method:"DELETE",headers:this.headers,signal:AbortSignal.timeout(this.timeout_ms)});if(!a.ok)throw new Error(`Failed to delete ${r}: ${a.status} ${a.statusText}`);await a.json()}async updateExample(e,r){Et(e);const a=await this.caller.call(fetch,`${this.apiUrl}/examples/${e}`,{method:"PATCH",headers:{...this.headers,"Content-Type":"application/json"},body:JSON.stringify(r),signal:AbortSignal.timeout(this.timeout_ms)});if(!a.ok)throw new Error(`Failed to update example ${e}: ${a.status} ${a.statusText}`);return await a.json()}async evaluateRun(e,r,{sourceInfo:a,loadChildRuns:n,referenceExample:i}={loadCh
`)}var GQ,fB,jc=be({"node_modules/@langchain/core/dist/utils/fast-json-patch/src/helpers.js"(){GQ=Object.prototype.hasOwnProperty,fB=class extends Error{constructor(t,e,r,a,n){super(B2(t,{name:e,index:r,operation:a,tree:n})),Object.defineProperty(this,"name",{enumerable:!0,configurable:!0,writable:!0,value:e}),Object.defineProperty(this,"index",{enumerable:!0,configurable:!0,writable:!0,value:r}),Object.defineProperty(this,"operation",{enumerable:!0,configurable:!0,writable:!0,value:a}),Object.defineProperty(this,"tree",{enumerable:!0,configurable:!0,writable:!0,value:n}),Object.setPrototypeOf(this,new.target.prototype),this.message=B2(t,{name:e,index:r,operation:a,tree:n})}}}}),SQ={};iu(SQ,{JsonPatchError:()=>St,_areEquals:()=>Ud,applyOperation:()=>hl,applyPatch:()=>sd,applyReducer:()=>L7,deepClone:()=>XQ,getValueByPointer:()=>qc,validate:()=>ZQ,validator:()=>$c});function qc(t,e){if(e=="")return t;var r={op:"_get",path:e};return hl(t,r),r.value}function hl(t,e,r=!1,a=!0,n=!0,i=0){if(r&&(typeof r=="function"?r(e,0,t,e.path):$c(e,0)),e.path===""){let l={newDocument:t};if(e.op==="add")return l.newDocument=e.value,l;if(e.op==="replace")return l.newDocument=e.value,l.removed=t,l;if(e.op==="move"||e.op==="copy")return l.newDocument=qc(t,e.from),e.op==="move"&&(l.removed=t),l;if(e.op==="test"){if(l.test=Ud(t,e.value),l.test===!1)throw new St("Test operation failed","TEST_OPERATION_FAILED",i,e,t);return l.newDocument=t,l}else{if(e.op==="remove")return l.removed=t,l.newDocument=null,l;if(e.op==="_get")return e.value=t,l;if(r)throw new St("Operation `op` property is not one of operations defined in RFC-6902","OPERATION_OP_INVALID",i,e,t);return l}}else{a||(t=zr(t));const s=(e.path||"").split("/");let o=t,d=1,u=s.length,c,p,A;for(typeof r=="function"?A=r:A=$c;;){if(p=s[d],p&&p.indexOf("~")!=-1&&(p=EQ(p)),n&&(p=="__proto__"||p=="prototype"&&d>0&&s[d-1]=="constructor"))throw new TypeError("JSON-Patch: modifying `__proto__` or `constructor/prototype` prop is banned for security reasons, if this was on purpose, please set `banPrototypeModifications` flag false and pass it to this function. More info in fast-json-patch README");if(r&&c===void 0&&(o[p]===void 0?c=s.slice(0,d).join("/"):d==u-1&&(c=e.path),c!==void 0&&A(e,0,t,c)),d++,Array.isArray(o)){if(p==="-")p=o.length;else{if(r&&!wA(p))throw new St("Expected an unsigned base-10 integer value, making the new referenced value the array element with the zero-based index","OPERATION_PATH_ILLEGAL_ARRAY_INDEX",i,e,t);wA(p)&&(p=~~p)}if(d>=u){if(r&&e.op==="add"&&p>o.length)throw new St("The specified index MUST NOT be greater than the number of elements in the array","OPERATION_VALUE_OUT_OF_BOUNDS",i,e,t);const m=HQ[e.op].call(e,o,p,t);if(m.test===!1)throw new St("Test operation failed","TEST_OPERATION_FAILED",i,e,t);return m}}else if(d>=u){const m=$i[e.op].call(e,o,p,t);if(m.test===!1)throw new St("Test operation failed","TEST_OPERATION_FAILED",i,e,t);return m}if(o=o[p],r&&d<u&&(!o||typeof o!="object"))throw new St("Cannot perform operation at the desired path","OPERATION_PATH_UNRESOLVABLE",i,e,t)}}}function sd(t,e,r,a=!0,n=!0){if(r&&!Array.isArray(e))throw new St("Patch sequence must be an array","SEQUENCE_NOT_AN_ARRAY");a||(t=zr(t));const i=new Array(e.length);for(let l=0,s=e.length;l<s;l++)i[l]=hl(t,e[l],r,!0,n,l),t=i[l].newDocument;return i.newDocument=t,i}function L7(t,e,r){const a=hl(t,e);if(a.test===!1)throw new St("Test operation failed","TEST_OPERATION_FAILED",r,e,t);return a.newDocument}function $c(t,e,r,a){if(typeof t!="object"||t===null||Array.isArray(t))throw new St("Operation is not an object","OPERATION_NOT_AN_OBJECT",e,t,r);if($i[t.op]){if(typeof t.path!="string")throw new St("Operation `path` property is not a string","OPERATION_PATH_INVALID",e,t,r);if(t.path.indexOf("/")!==0&&t.path.length>0)throw new St('Operation `path` property must start with "/"',"OPERATION_PATH_INVALID",e,t,r);if((t.op==="move"||t.op==="copy")&&typeof t.from!="string")throw new St("Operation `from` property is not present (applicable in `move` and `copy` operations)","OPERATION_FROM_REQUIRED",e,t,r
Instead got an unsupported type.`)}var Zm,Ot,Co,W2,v2,Bi,od,kQ,F2,yA,y2,Ma=be({"node_modules/@langchain/core/dist/runnables/base.js"(){Zm=Gr(hB(),1),uu(),I7(),lu(),pu(),zs(),s1(),D7(),M7(),NQ(),Ot=class extends Fl{constructor(){super(...arguments),Object.defineProperty(this,"lc_runnable",{enumerable:!0,configurable:!0,writable:!0,value:!0}),Object.defineProperty(this,"name",{enumerable:!0,configurable:!0,writable:!0,value:void 0})}getName(t){const e=this.name??this.constructor.lc_name()??this.constructor.name;return t?`${e}${t}`:e}bind(t){return new Co({bound:this,kwargs:t,config:{}})}map(){return new W2({bound:this})}withRetry(t){return new v2({bound:this,kwargs:{},config:{},maxAttemptNumber:t==null?void 0:t.stopAfterAttempt,...t})}withConfig(t){return new Co({bound:this,config:t,kwargs:{}})}withFallbacks(t){return new F2({runnable:this,fallbacks:t.fallbacks})}_getOptionsList(t,e=0){if(Array.isArray(t)){if(t.length!==e)throw new Error(`Passed "options" must be an array with the same length as the inputs, but got ${t.length} options for ${e} inputs`);return t.map(lr)}return Array.from({length:e},()=>lr(t))}async batch(t,e,r){var s;const a=this._getOptionsList(e??{},t.length),n=((s=a[0])==null?void 0:s.maxConcurrency)??(r==null?void 0:r.maxConcurrency),i=new su({maxConcurrency:n,onFailedAttempt:o=>{throw o}}),l=t.map((o,d)=>i.call(async()=>{try{return await this.invoke(o,a[d])}catch(u){if(r!=null&&r.returnExceptions)return u;throw u}}));return Promise.all(l)}async*_streamIterator(t,e){yield this.invoke(t,e)}async stream(t,e){const r=new tl(this._streamIterator(t,lr(e)));return await r.setup,jn.fromAsyncGenerator(r)}_separateRunnableConfigFromCallOptions(t={}){const e=lr({callbacks:t.callbacks,tags:t.tags,metadata:t.metadata,runName:t.runName,configurable:t.configurable,recursionLimit:t.recursionLimit,maxConcurrency:t.maxConcurrency}),r={...t};return delete r.callbacks,delete r.tags,delete r.metadata,delete r.runName,delete r.configurable,delete r.recursionLimit,delete r.maxConcurrency,[e,r]}async _callWithConfig(t,e,r){const a=lr(r),n=await Ni(a),i=await(n==null?void 0:n.handleChainStart(this.toJSON(),pr(e,"input"),void 0,a==null?void 0:a.runType,void 0,void 0,(a==null?void 0:a.runName)??this.getName()));let l;try{l=await t.call(this,e,a,i)}catch(s){throw await(i==null?void 0:i.handleChainError(s)),s}return await(i==null?void 0:i.handleChainEnd(pr(l,"output"))),l}async _batchWithConfig(t,e,r,a){const n=this._getOptionsList(r??{},e.length),i=await Promise.all(n.map(Ni)),l=await Promise.all(i.map((o,d)=>o==null?void 0:o.handleChainStart(this.toJSON(),pr(e[d],"input"),void 0,n[d].runType,void 0,void 0,n[d].runName??this.getName())));let s;try{s=await t.call(this,e,n,l,a)}catch(o){throw await Promise.all(l.map(d=>d==null?void 0:d.handleChainError(o))),o}return await Promise.all(l.map(o=>o==null?void 0:o.handleChainEnd(pr(s,"output")))),s}async*_transformStreamWithConfig(t,e,r){let a,n=!0,i,l=!0;const s=lr(r),o=await Ni(s);async function*d(){for await(const c of t){if(n)if(a===void 0)a=c;else try{a=el(a,c)}catch{a=void 0,n=!1}yield c}}let u;try{const c=await U7(e.bind(this),d(),async()=>o==null?void 0:o.handleChainStart(this.toJSON(),{input:""},void 0,s==null?void 0:s.runType,void 0,void 0,(s==null?void 0:s.runName)??this.getName()),s);u=c.setup;const p=h=>h.name==="log_stream_tracer",A=u==null?void 0:u.handlers.find(p);let m=c.output;A!==void 0&&u!==void 0&&(m=await A.tapOutputIterable(u.runId,c.output));for await(const h of m)if(yield h,l)if(i===void 0)i=h;else try{i=el(i,h)}catch{i=void 0,l=!1}}catch(c){throw await(u==null?void 0:u.handleChainError(c,void 0,void 0,void 0,{inputs:pr(a,"input")})),c}await(u==null?void 0:u.handleChainEnd(i??{},void 0,void 0,void 0,{inputs:pr(a,"input")}))}pipe(t){return new Bi({first:this,last:Pi(t)})}pick(t){return this.pipe(new y2(t))}assign(t){return this.pipe(new yA(new od({steps:t})))}async*transform(t,e){let r;for await(const a of t)r===void 0?r=a:r=el(r,a);yield*this._streamIterator(r,lr(e))}async*streamLog(t,e,r){const a=new WA({...r,autoClose:!1,_schemaFormat:"original"}),n=lr(e);yie
                         should be one of ${a}`)}try{const a=r.reduce((n,i)=>(n[i]="foo",n),{});Array.isArray(t)?t.forEach(n=>{if(n.type==="text")qn(n.text,e,a);else if(n.type==="image_url")if(typeof n.image_url=="string")qn(n.image_url,e,a);else{const i=n.image_url.url;qn(i,e,a)}else throw new Error(`Invalid message template received. ${JSON.stringify(n,null,2)}`)}):qn(t,e,a)}catch(a){throw new Error(`Invalid prompt schema: ${a.message}`)}}}}),EA={};iu(EA,{PromptTemplate:()=>La});var La,_d=be({"node_modules/@langchain/core/dist/prompts/prompt.js"(){u1(),cu(),La=class _o extends QB{static lc_name(){return"PromptTemplate"}constructor(e){if(super(e),Object.defineProperty(this,"template",{enumerable:!0,configurable:!0,writable:!0,value:void 0}),Object.defineProperty(this,"templateFormat",{enumerable:!0,configurable:!0,writable:!0,value:"f-string"}),Object.defineProperty(this,"validateTemplate",{enumerable:!0,configurable:!0,writable:!0,value:!0}),Object.assign(this,e),this.validateTemplate){let r=this.inputVariables;this.partialVariables&&(r=r.concat(Object.keys(this.partialVariables))),t0(this.template,this.templateFormat,r)}}_getPromptType(){return"prompt"}async format(e){const r=await this.mergePartialAndUserVariables(e);return qn(this.template,this.templateFormat,r)}static fromExamples(e,r,a,n=`

`,i=""){const l=[i,...e,r].join(n);return new _o({inputVariables:a,template:l})}static fromTemplate(e,{templateFormat:r="f-string",...a}={}){const n=new Set;return DQ(e,r).forEach(i=>{i.type==="variable"&&n.add(i.name)}),new _o({inputVariables:[...n],templateFormat:r,template:e,...a})}async partial(e){const r=this.inputVariables.filter(i=>!(i in e)),a={...this.partialVariables??{},...e},n={...this,inputVariables:r,partialVariables:a};return new _o(n)}serialize(){if(this.outputParser!==void 0)throw new Error("Cannot serialize a prompt template with an output parser");return{_type:this._getPromptType(),input_variables:this.inputVariables,template:this.template,template_format:this.templateFormat}}static async deserialize(e){if(!e.template)throw new Error("Prompt template must have a template");return new _o({inputVariables:e.input_variables,template:e.template,templateFormat:e.template_format})}}}}),MQ=be({"node_modules/@langchain/core/dist/prompts/image.js"(){l1(),mu(),cu()}}),xQ,WB=be({"node_modules/@langchain/core/dist/prompts/chat.js"(){ta(),l1(),Ma(),u1(),mu(),_d(),MQ(),cu(),xQ=class extends vB{constructor(t){super(t)}async format(t){return(await this.formatPromptValue(t)).toString()}async formatPromptValue(t){const e=await this.formatMessages(t);return new AB(e)}}}}),JQ={};iu(JQ,{FewShotChatMessagePromptTemplate:()=>PQ,FewShotPromptTemplate:()=>OQ});var OQ,PQ,zQ=be({"node_modules/@langchain/core/dist/prompts/few_shot.js"(){u1(),cu(),_d(),WB(),OQ=class GA extends QB{constructor(e){if(super(e),Object.defineProperty(this,"lc_serializable",{enumerable:!0,configurable:!0,writable:!0,value:!1}),Object.defineProperty(this,"examples",{enumerable:!0,configurable:!0,writable:!0,value:void 0}),Object.defineProperty(this,"exampleSelector",{enumerable:!0,configurable:!0,writable:!0,value:void 0}),Object.defineProperty(this,"examplePrompt",{enumerable:!0,configurable:!0,writable:!0,value:void 0}),Object.defineProperty(this,"suffix",{enumerable:!0,configurable:!0,writable:!0,value:""}),Object.defineProperty(this,"exampleSeparator",{enumerable:!0,configurable:!0,writable:!0,value:`

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${Tp(t)}`);return{...R,id:W,type:K,function:{...y,name:L,arguments:v}}})}}:{...c,message:{...h,content:p,role:C},finish_reason:o,index:d,logprobs:u}}),created:a,model:n,object:"chat.completion",...i?{system_fingerprint:i}:{}}}function Tp(t){return JSON.stringify(t)}var Vb=class Ac extends v9{static fromReadableStream(e){const r=new Ac;return r._run(()=>r._fromReadableStream(e)),r}static runFunctions(e,r,a){const n=new Ac,i={...a,headers:{...a==null?void 0:a.headers,"X-Stainless-Helper-Method":"runFunctions"}};return n._run(()=>n._runFunctions(e,r,i)),n}static runTools(e,r,a){const n=new Ac,i={...a,headers:{...a==null?void 0:a.headers,"X-Stainless-Helper-Method":"runTools"}};return n._run(()=>n._runTools(e,r,i)),n}},F9=class extends yt{runFunctions(t,e){return t.stream?Vb.runFunctions(this._client.chat.completions,t,e):Xb.runFunctions(this._client.chat.completions,t,e)}runTools(t,e){return t.stream?Vb.runTools(this._client.chat.completions,t,e):Xb.runTools(this._client.chat.completions,t,e)}stream(t,e){return v9.createChatCompletion(this._client.chat.completions,t,e)}},b0=class extends yt{constructor(){super(...arguments),this.completions=new F9(this._client)}};(function(t){t.Completions=F9})(b0||(b0={}));var K0=class extends yt{retrieve(t,e,r,a){return this._client.get(`/threads/${t}/messages/${e}/files/${r}`,{...a,headers:{"OpenAI-Beta":"assistants=v1",...a==null?void 0:a.headers}})}list(t,e,r={},a){return _a(r)?this.list(t,e,{},r):this._client.getAPIList(`/threads/${t}/messages/${e}/files`,aC,{query:r,...a,headers:{"OpenAI-Beta":"assistants=v1",...a==null?void 0:a.headers}})}},aC=class extends vn{};(function(t){t.MessageFilesPage=aC})(K0||(K0={}));var f0=class extends yt{constructor(){super(...arguments),this.files=new K0(this._client)}create(t,e,r){return this._client.post(`/threads/${t}/messages`,{body:e,...r,headers:{"OpenAI-Beta":"assistants=v1",...r==null?void 0:r.headers}})}retrieve(t,e,r){return this._client.get(`/threads/${t}/messages/${e}`,{...r,headers:{"OpenAI-Beta":"assistants=v1",...r==null?void 0:r.headers}})}update(t,e,r,a){return this._client.post(`/threads/${t}/messages/${e}`,{body:r,...a,headers:{"OpenAI-Beta":"assistants=v1",...a==null?void 0:a.headers}})}list(t,e={},r){return _a(e)?this.list(t,{},e):this._client.getAPIList(`/threads/${t}/messages`,nC,{query:e,...r,headers:{"OpenAI-Beta":"assistants=v1",...r==null?void 0:r.headers}})}},nC=class extends vn{};(function(t){t.ThreadMessagesPage=nC,t.Files=K0,t.MessageFilesPage=aC})(f0||(f0={}));var Q0=class extends yt{retrieve(t,e,r,a){return this._client.get(`/threads/${t}/runs/${e}/steps/${r}`,{...a,headers:{"OpenAI-Beta":"assistants=v1",...a==null?void 0:a.headers}})}list(t,e,r={},a){return _a(r)?this.list(t,e,{},r):this._client.getAPIList(`/threads/${t}/runs/${e}/steps`,iC,{query:r,...a,headers:{"OpenAI-Beta":"assistants=v1",...a==null?void 0:a.headers}})}},iC=class extends vn{};(function(t){t.RunStepsPage=iC})(Q0||(Q0={}));var W0=class extends yt{constructor(){super(...arguments),this.steps=new Q0(this._client)}create(t,e,r){return this._client.post(`/threads/${t}/runs`,{body:e,...r,headers:{"OpenAI-Beta":"assistants=v1",...r==null?void 0:r.headers}})}retrieve(t,e,r){return this._client.get(`/threads/${t}/runs/${e}`,{...r,headers:{"OpenAI-Beta":"assistants=v1",...r==null?void 0:r.headers}})}update(t,e,r,a){return this._client.post(`/threads/${t}/runs/${e}`,{body:r,...a,headers:{"OpenAI-Beta":"assistants=v1",...a==null?void 0:a.headers}})}list(t,e={},r){return _a(e)?this.list(t,{},e):this._client.getAPIList(`/threads/${t}/runs`,lC,{query:e,...r,headers:{"OpenAI-Beta":"assistants=v1",...r==null?void 0:r.headers}})}cancel(t,e,r){return this._client.post(`/threads/${t}/runs/${e}/cancel`,{...r,headers:{"OpenAI-Beta":"assistants=v1",...r==null?void 0:r.headers}})}submitToolOutputs(t,e,r,a){return this._client.post(`/threads/${t}/runs/${e}/submit_tool_outputs`,{body:r,...a,headers:{"OpenAI-Beta":"assistants=v1",...a==null?void 0:a.headers}})}},lC=class extends vn{};(function(t){t.RunsPage=lC,t.Steps=Q0,t.RunStepsPage=iC})(W0||(W0={}));var v0=class extends

This is disabled by default, as it risks exposing your secret API credentials to attackers.
If you understand the risks and have appropriate mitigations in place,
you can set the \`dangerouslyAllowBrowser\` option to \`true\`, e.g.,

new OpenAI({ apiKey, dangerouslyAllowBrowser: true });

https://help.openai.com/en/articles/5112595-best-practices-for-api-key-safety
`);super({baseURL:i.baseURL,timeout:i.timeout??6e5,httpAgent:i.httpAgent,maxRetries:i.maxRetries,fetch:i.fetch}),this.completions=new y0(this),this.chat=new c0(this),this.embeddings=new R0(this),this.files=new E0(this),this.images=new Z0(this),this.audio=new h0(this),this.moderations=new H0(this),this.models=new X0(this),this.fineTuning=new S0(this),this.beta=new F0(this),this._options=i,this.apiKey=r,this.organization=a}defaultQuery(){return this._options.defaultQuery}defaultHeaders(e){return{...super.defaultHeaders(e),"OpenAI-Organization":this.organization,...this._options.defaultHeaders}}authHeaders(e){return{Authorization:`Bearer ${this.apiKey}`}}};y9=qt;qt.OpenAI=y9;qt.OpenAIError=it;qt.APIError=Nr;qt.APIConnectionError=Au;qt.APIConnectionTimeoutError=A1;qt.APIUserAbortError=Ci;qt.NotFoundError=PB;qt.ConflictError=zB;qt.RateLimitError=qB;qt.BadRequestError=xB;qt.AuthenticationError=JB;qt.InternalServerError=$B;qt.PermissionDeniedError=OB;qt.UnprocessableEntityError=jB;var{OpenAIError:GD,APIError:SD,APIConnectionError:ZD,APIConnectionTimeoutError:BV,APIUserAbortError:CV,NotFoundError:XD,ConflictError:HD,RateLimitError:VD,BadRequestError:YD,AuthenticationError:ND,InternalServerError:LD,PermissionDeniedError:TD,UnprocessableEntityError:kD}=u9;(function(t){t.toFile=m9,t.fileFromPath=qW,t.Page=eC,t.CursorPage=vn,t.Completions=y0,t.Chat=c0,t.Embeddings=R0,t.Files=E0,t.FileObjectsPage=sC,t.Images=Z0,t.Audio=h0,t.Moderations=H0,t.Models=X0,t.ModelsPage=uC,t.FineTuning=S0,t.Beta=F0})(qt||(qt={}));d1();function R9(t,e,r,a){a!=null&&a.errorMessages&&r&&(t.errorMessage={...t.errorMessage,[e]:r})}function gt(t,e,r,a,n){t[e]=r,R9(t,e,a,n)}var Yb={name:void 0,$refStrategy:"root",basePath:["#"],effectStrategy:"input",pipeStrategy:"all",dateStrategy:"string",mapStrategy:"entries",definitionPath:"definitions",target:"jsonSchema7",strictUnions:!1,definitions:{},errorMessages:!1,markdownDescription:!1,patternStrategy:"escape",emailStrategy:"format:email"},wV=t=>typeof t=="string"?{...Yb,name:t}:{...Yb,...t},xd;(function(t){t.assertEqual=n=>n;function e(n){}t.assertIs=e;function r(n){throw new Error}t.assertNever=r,t.arrayToEnum=n=>{const i={};for(const l of n)i[l]=l;return i},t.getValidEnumValues=n=>{const i=t.objectKeys(n).filter(s=>typeof n[n[s]]!="number"),l={};for(const s of i)l[s]=n[s];return t.objectValues(l)},t.objectValues=n=>t.objectKeys(n).map(function(i){return n[i]}),t.objectKeys=typeof Object.keys=="function"?n=>Object.keys(n):n=>{const i=[];for(const l in n)Object.prototype.hasOwnProperty.call(n,l)&&i.push(l);return i},t.find=(n,i)=>{for(const l of n)if(i(l))return l},t.isInteger=typeof Number.isInteger=="function"?n=>Number.isInteger(n):n=>typeof n=="number"&&isFinite(n)&&Math.floor(n)===n;function a(n,i=" | "){return n.map(l=>typeof l=="string"?`'${l}'`:l).join(i)}t.joinValues=a,t.jsonStringifyReplacer=(n,i)=>typeof i=="bigint"?i.toString():i})(xd||(xd={}));var Nb;(function(t){t.mergeShapes=(e,r)=>({...e,...r})})(Nb||(Nb={}));xd.arrayToEnum(["string","nan","number","integer","float","boolean","date","bigint","symbol","function","undefined","null","array","object","unknown","promise","void","never","map","set"]);xd.arrayToEnum(["invalid_type","invalid_literal","custom","invalid_union","invalid_union_discriminator","invalid_enum_value","unrecognized_keys","invalid_arguments","invalid_return_type","invalid_date","invalid_string","too_small","too_big","invalid_intersection_types","not_multiple_of","not_finite"]);var Lb=class extends Error{constructor(t){super(),this.issues=[],this.addIssue=r=>{this.issues=[...this.issues,r]},this.addIssues=(r=[])=>{this.issues=[...this.issues,...r]};const e=new.target.prototype;Object.setPrototypeOf?Object.setPrototypeOf(this,e):this.__proto__=e,this.name="ZodError",this.issues=t}get errors(){return this.issues}format(t){const e=t||function(n){return n.message},r={_errors:[]},a=n=>{for(const i of n.issues)if(i.code==="invalid_union")i.unionErrors.map(a);else if(i.code==="invalid_return_type")a(i.returnTypeError);else if(i.code==="invalid_arguments")a(i.argumentsError);else if(i.path.lengt
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`);case"array":return t.items?`${Y0(t.items,e)}[]`:"any[]";default:return""}}function sY(t){return t.role!=="system"&&t.role!=="assistant"&&t.role!=="user"&&t.role!=="function"&&t.role!=="tool"&&console.warn(`Unknown message role: ${t.role}`),t.role}function V9(t){const e=t._getType();switch(e){case"system":return"system";case"ai":return"assistant";case"human":return"user";case"function":return"function";case"tool":return"tool";case"generic":{if(!kd.isInstance(t))throw new Error("Invalid generic chat message");return sY(t)}default:throw new Error(`Unknown message type: ${e}`)}}function oY(t){switch(t.role){case"assistant":return new i1(t.content||"",{function_call:t.function_call,tool_calls:t.tool_calls});default:return new kd(t.content||"",t.role??"unknown")}}function dY(t,e){const r=t.role??e,a=t.content??"";let n;return t.function_call?n={function_call:t.function_call}:t.tool_calls?n={tool_calls:t.tool_calls}:n={},r==="user"?new nA({content:a}):r==="assistant"?new ti({content:a,additional_kwargs:n}):r==="system"?new iA({content:a}):r==="function"?new lA({content:a,additional_kwargs:n,name:t.name}):r==="tool"?new AQ({content:a,additional_kwargs:n,tool_call_id:t.tool_call_id}):new sA({content:a,role:r})}function _b(t){return t.map(e=>({role:V9(e),content:e.content,name:e.name,function_call:e.additional_kwargs.function_call,tool_calls:e.additional_kwargs.tool_calls,tool_call_id:e.tool_call_id}))}var uY=class extends DW{static lc_name(){return"ChatOpenAI"}get callKeys(){return[...super.callKeys,"options","function_call","functions","tools","tool_choice","promptIndex","response_format","seed"]}get lc_secrets(){return{openAIApiKey:"OPENAI_API_KEY",azureOpenAIApiKey:"AZURE_OPENAI_API_KEY",organization:"OPENAI_ORGANIZATION"}}get lc_aliases(){return{modelName:"model",openAIApiKey:"openai_api_key",azureOpenAIApiVersion:"azure_openai_api_version",azureOpenAIApiKey:"azure_openai_api_key",azureOpenAIApiInstanceName:"azure_openai_api_instance_name",azureOpenAIApiDeploymentName:"azure_openai_api_deployment_name"}}constructor(t,e){var r,a,n,i,l,s,o,d;if(super(t??{}),Object.defineProperty(this,"lc_serializable",{enumerable:!0,configurable:!0,writable:!0,value:!0}),Object.defineProperty(this,"temperature",{enumerable:!0,configurable:!0,writable:!0,value:1}),Object.defineProperty(this,"topP",{enumerable:!0,configurable:!0,writable:!0,value:1}),Object.defineProperty(this,"frequencyPenalty",{enumerable:!0,configurable:!0,writable:!0,value:0}),Object.defineProperty(this,"presencePenalty",{enumerable:!0,configurable:!0,writable:!0,value:0}),Object.defineProperty(this,"n",{enumerable:!0,configurable:!0,writable:!0,value:1}),Object.defineProperty(this,"logitBias",{enumerable:!0,configurable:!0,writable:!0,value:void 0}),Object.defineProperty(this,"modelName",{enumerable:!0,configurable:!0,writable:!0,value:"gpt-3.5-turbo"}),Object.defineProperty(this,"modelKwargs",{enumerable:!0,configurable:!0,writable:!0,value:void 0}),Object.defineProperty(this,"stop",{enumerable:!0,configurable:!0,writable:!0,value:void 0}),Object.defineProperty(this,"user",{enumerable:!0,configurable:!0,writable:!0,value:void 0}),Object.defineProperty(this,"timeout",{enumerable:!0,configurable:!0,writable:!0,value:void 0}),Object.defineProperty(this,"streaming",{enumerable:!0,configurable:!0,writable:!0,value:!1}),Object.defineProperty(this,"maxTokens",{enumerable:!0,configurable:!0,writable:!0,value:void 0}),Object.defineProperty(this,"logprobs",{enumerable:!0,configurable:!0,writable:!0,value:void 0}),Object.defineProperty(this,"topLogprobs",{enumerable:!0,configurable:!0,writable:!0,value:void 0}),Object.defineProperty(this,"openAIApiKey",{enumerable:!0,configurable:!0,writable:!0,value:void 0}),Object.defineProperty(this,"azureOpenAIApiVersion",{enumerable:!0,configurable:!0,writable:!0,value:void 0}),Object.defineProperty(this,"azureOpenAIApiKey",{enumerable:!0,configurable:!0,writable:!0,value:void 0}),Object.defineProperty(this,"azureOpenAIApiInstanceName",{enumerable:!0,configurable:!0,writable:!0,value:void 0}),Object.defineProperty(this,"azureOpenAIApiDeployme
<conversation>
{chatHistory}
</conversation>
Titel:`,initialReduce:`Fasse meine Notes (durch XML-Tags begrenzt) zusammen, sodass die Frage "{query}" im nachhinein noch ausführlich beantwortet werden könnte.
Fasse nur die Notes zusammen die zur beantwortung der Frage beitragen könnten und überspringe die anderen ohne diese weiter zu erwähnen.
Achte darauf, für jede zusammengefassten Note den Wikilink (format: [[<Note Path><# Header1><## Header2>...]]) zur Note als Referenz anzugeben.
Bitte behalte die markdown formatiertung der Notes bei.
<notes>
{content}
</notes>
Zusammenfassung:`,reduce:`Fasse meine Notes (durch XML-Tags begrenzt) zusammen, sodass die Frage "{query}" im nachhinein noch ausführlich beantwortet werden könnte.
Achte darauf, die markdown und wikilink formatierung in den Notes beizubehalten.
<notes>
{content}
</notes>
Zusammenfassung:`,rag:`Als mein Assistent, bitte antworte basierend auf meiner Frage und der chathistory, wobei du ausschließlich auf mein vorhandenes Wissen (durch XML-Tags begrenzt) aus Obsidian zurückgreifst.
Achte darauf, die Markdown-Formatierung zu verwenden und deiner Antwort, die im Wissen hinterlegten Wikilinks (wikilink format: [[<Note Path>]]) als Referenz hinzu zufügen. 
<knowledge>
{context}
</knowledge>
<chathistory>
{chatHistory}
</chathistory>
<query>
{query}
</query>
Antwort:`,conversation:`Antworte als mein Assistent auf meine Frage basierend auf der chathistory (durch XML-Tags begrenzt).
<chathistory>
{chatHistory}
</chathistory>
<query>
{query}
</query>
Antwort:`},en:{initialAssistantMessage:"Hello, I am your assistant. How can I help you?",createTitle:`Create a very short title as a summary of the following conversation (enclosed by XML tags), so that it can be used directly as a filename. The title may only consist of letters and spaces.
<conversation>
{chatHistory}
</conversation>
Title:`,initialReduce:`Summarize my notes (enclosed by XML tags) so that the question "{query}" could still be answered in detail afterwards.
Only summarize the notes that could contribute to answering the question and skip the others without mentioning them further.
Make sure to include the wikilink (format: [[<Note Path><# Header1><## Header2>...]]) to the note as a reference for each summarized note.
Please keep the markdown formatting of the notes.
<notes>
{content}
</notes>
Summary:`,reduce:`Summarize my notes (enclosed by XML tags) so that the question "{query}" could still be answered in detail afterwards.
Make sure to keep the markdown and wikilink formatting (format: [[<Note>]]) in the notes.
<notes>
{content}
</notes>
Summary:`,rag:`As my assistant, please respond to my query, using only my existing knowledge (enclosed by XML tags) from Obsidian.
Make sure to use Markdown formatting and add the wikilinks (format: [[<Note>]]) from the knowledge as a reference to your answer.
<knowledge>
{context}
</knowledge>
<chathistory>
{chatHistory}
</chathistory>
<query>
{query}
</query>
Response:`,conversation:`Respond to my query as my assistant based on the chathistory.
<chathistory>
{chatHistory}
</chathistory>
<query>
{query}
</query>
Response:`},fr:{initialAssistantMessage:"Bonjour, je suis votre assistant. Comment puis-je vous aider?",createTitle:`Créez un titre très court comme résumé de la conversation suivante (encadrée par des balises XML), de sorte qu'il puisse être utilisé directement comme nom de fichier. Le titre ne peut contenir que des lettres et des espaces.
<conversation>
{chatHistory}
</conversation>
Titre:`,initialReduce:`Résumez mes notes (encadrées par des balises XML) de manière à ce que la question "{query}" puisse encore être répondue en détail par la suite.
Résumez uniquement les notes qui pourraient contribuer à répondre à la question et ignorez les autres sans les mentionner davantage.
Assurez-vous d'inclure le wikilink (format: [[<Note Path><# Header1><## Header2>...]]) à la note comme référence pour chaque note résumée.
<notes>
{content}
</notes>
Résumé:`,reduce:`Résumez mes notes (encadrées par des balises XML) de manière à ce que la question "{query}" puisse encore être répondue en détail par la suite.
Veillez à conserver la mise en forme markdown et les wikilinks (format: [[<Note>]]) dans les notes.
<notes>
{content}
</notes>
Résumé:`,rag:`En tant qu'assistant, veuillez répondre à ma question en utilisant uniquement mes connaissances existantes (encadrées par des balises XML) provenant d'Obsidian.
Assurez-vous d'utiliser la mise en forme Markdown et d'ajouter les wikilinks (format: [[<Note>]]) des connaissances comme référence à votre réponse.
<knowledge>
{context}
</knowledge>
<chathistory>
{chatHistory}
</chathistory>
<query>
{query}
</query>
Réponse:`,conversation:`Répondez à ma question en tant qu'assistant basé sur l'historique du chat.
<chathistory>
{chatHistory}
</chathistory>
<query>
{query}
</query>
Réponse:`}},QY=Object.keys(wa);async function wl(t,e){return L9(t)?await t.lcModel.getNumTokens(e):(Yf.encode(e)||[]).length}function WY(t,e,r){return Bi.from([{query:n=>n.userQuery,chatHistory:n=>n.chatHistory,context:Bi.from([n=>n.userQuery,t.withConfig({runName:"Retrieving"}).pipe(FY(e,r)).withConfig({runName:"PPDocs"}).pipe(yY(e,r))]).withConfig({runName:"Retrieving Notes"})},RY(e,r),e.lcModel,new g1]).withConfig({runName:"RAG Chat Pipe"})}function vY(t,e){return Bi.from([{query:a=>a.userQuery,chatHistory:a=>a.chatHistory},La.fromTemplate(wa[e.lang].conversation),t.lcModel,new g1]).withConfig({runName:"Normal Chat Pipe"})}function FY(t,e){return async r=>{const a=(await La.fromTemplate(wa[e.lang].reduce).formatPromptValue({query:e.userQuery,content:""})).toString(),n=t.contextWindow-await wl(t,a);zt.debug("Retrieved Docs",r);const i={};for(const o of r)i[o.metadata.filepath]||(i[o.metadata.filepath]=[]),i[o.metadata.filepath].push(o);const l=[];for(const o in i){i[o].sort((A,m)=>A.metadata.order-m.metadata.order);let d=[],u=[""];for(const A of i[o]){let m="";for(let h=0;h<A.metadata.header.length;h++)A.metadata.header[h]!==u[h]&&(m+=A.metadata.header[h]+`
`);u=A.metadata.header,d.push(m+A.pageContent)}const c=await T9(d,A=>wl(t,A),n),p=c.length>1;c.forEach((A,m)=>{let h="";h+=`<note>
`,h+="Wikilink: [["+o.replace(".md","")+"]]"+(p?" Part "+(m+1):"")+`
`,h+=A.join(`

`),h+=`
</note>`,l.push(h)})}const s=await wl(t,l.join(`

`))>n;return zt.debug("Postprocessed Docs",l),{notes:l,needsReduce:s}}}function yY(t,e){return async(r,a)=>{if(!r.needsReduce)return r.notes.join(`

`);const n=a==null?void 0:a.config;let i=r.notes,l=0;const s=(await La.fromTemplate(wa[e.lang].reduce).formatPromptValue({query:e.userQuery,content:""})).toString(),o=t.contextWindow-await wl(t,s);do{n&&(n.runName=`Reduce ${l+1}`);const d=await T9(i,c=>wl(t,c),o),u=Bi.from([{content:new OA,query:()=>e.userQuery},l===0?La.fromTemplate(wa[e.lang].initialReduce):La.fromTemplate(wa[e.lang].reduce),t.lcModel,new g1]);i=await Promise.all(d.map(c=>u.invoke(c.join(`

`)))),zt.debug("Reduced Docs",i),l+=1}while(await wl(t,i.join(`

`))>o);return i.join(`

`)}}async function T9(t,e,r){const a=[];let n=[];for(const i of t)if(n.push(i),await e(n.join(`

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@langchain/core/dist/utils/fast-json-patch/src/helpers.js:
  (*!
   * https://github.com/Starcounter-Jack/JSON-Patch
   * (c) 2017-2022 Joachim Wester
   * MIT licensed
   *)

@langchain/core/dist/utils/fast-json-patch/src/duplex.js:
  (*!
   * https://github.com/Starcounter-Jack/JSON-Patch
   * (c) 2013-2021 Joachim Wester
   * MIT license
   *)

web-streams-polyfill/dist/ponyfill.mjs:
  (**
   * @license
   * web-streams-polyfill v4.0.0-beta.3
   * Copyright 2021 Mattias Buelens, Diwank Singh Tomer and other contributors.
   * This code is released under the MIT license.
   * SPDX-License-Identifier: MIT
   *)

formdata-node/lib/esm/blobHelpers.js:
  (*! Based on fetch-blob. MIT License. Jimmy Wärting <https://jimmy.warting.se/opensource> & David Frank *)

formdata-node/lib/esm/Blob.js:
  (*! Based on fetch-blob. MIT License. Jimmy Wärting <https://jimmy.warting.se/opensource> & David Frank *)

humanize-ms/index.js:
  (*!
   * humanize-ms - index.js
   * Copyright(c) 2014 dead_horse <dead_horse@qq.com>
   * MIT Licensed
   *)

web-streams-polyfill/dist/ponyfill.es2018.js:
  (**
   * @license
   * web-streams-polyfill v3.3.3
   * Copyright 2024 Mattias Buelens, Diwank Singh Tomer and other contributors.
   * This code is released under the MIT license.
   * SPDX-License-Identifier: MIT
   *)

node-domexception/index.js:
  (*! node-domexception. MIT License. Jimmy Wärting <https://jimmy.warting.se/opensource> *)

@langchain/core/dist/utils/js-sha1/hash.js:
  (*
   * [js-sha1]{@link https://github.com/emn178/js-sha1}
   *
   * @version 0.6.0
   * @author Chen, Yi-Cyuan [emn178@gmail.com]
   * @copyright Chen, Yi-Cyuan 2014-2017
   * @license MIT
   *)
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Please retry initialization...`}},OT={creativity:"Adjusts the level of originality in the model's outputs to suit your preference.",similarity:"How similar the retrieved documents should be to the user query."},PT={general:NT,ribbon:LT,cmd:TT,modal:kT,init_third_party_modal:UT,notice:_T,chat:IT,onboarding:DT,incognito_toggle:MT,settings:xT,quick_settings:JT,tooltip:OT},zT={delete:"Eliminar",install:"Instalar"},jT={chat:"Abrir Chat S2B"},qT={chat:"Abrir Chat",pull_model:"Obtener Modelo Ollama",remove_model:"Eliminar Modelo Ollama"},$T={dont_show:"No mostrar esto de nuevo",cancel:"Cancelar",confirm:"Confirmar",pull_model_name:"Obtener Modelo Ollama",pull_model_desc:"Instalar un modelo desde el registro de Ollama",remove:{name:"Eliminar Modelo Ollama",desc:"Eliminar un modelo instalado de tu sistema",success:"Modelo eliminado con éxito",error:"Error al eliminar el modelo. El modelo podría no estar instalado"}},ek={title:"Ejecutar mediante Servicios de Terceros",description:"¿Estás seguro de que quieres ejecutar mediante servicios de terceros? Tus datos personales serán compartidos con servicios de terceros como OpenAI."},tk={still_running:"Smart Second Brain sigue en ejecución. Por favor, espera.",still_indexing:"Smart Second Brain sigue indexando. Por favor, espera.",ollama_not_running:"Por favor, asegúrate de que Ollama está en ejecución antes de inicializar Smart Second Brain.",ollama_gen_model:"Modelo de Ollama no instalado. Por favor, instala el modelo antes de inicializar Smart Second Brain.",ollama_embed_model:"Modelo de Ollama no instalado. Por favor, instala el modelo antes de inicializar Smart Second Brain.",openai_key:"Por favor, asegúrate de que tu clave API de OpenAI es válida antes de inicializar Smart Second Brain.",failed:"Fallo al inicializar Smart Second Brain (Error: {error}). Por favor, reintenta.",failed_indexing:"Fallo al indexar notas en tu Smart Second Brain. Por favor, reintenta.",done:"Smart Second Brain inicializado.",error:"Ocurrió un error. Por favor, espera a que se resuelva el error.",not_initialized:"Smart Second Brain no inicializado. Por favor, inicializa primero.",plugin_data_cleared:"Datos del complemento eliminados.",api_key_invalid:"¡La clave API no es válida!",error_pulling_model:"Fallo al obtener el modelo de Ollama (Error: {error}). Por favor, reintenta.",copied_to_clipboard:`Copiado al portapapeles:
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> Para que la Clave API funcione, es posible que tengas que actualizar a una cuenta de pago de OpenAI. Esto implica depositar al menos $5 en tu cuenta de OpenAI. Esto podría cambiar en el futuro.`,paste_api_key:"Pega tu clave aquí:",test_api_key:"Prueba tu Clave API",api_key_valid:"¡La Clave API es válida!"}},nk={enable:"Ejecutar en tu máquina",disable:"Ejecutar mediante Servicios de Terceros"},ik={excludeff:"Excluir archivos y carpetas",excludeff_desc:"Estos archivos y carpetas no se incrustarán y no se utilizarán para generar respuestas.",excludeff_add:"Agregar",excludeff_delete:"Eliminar de la lista de excluidos",advanced:"Configuración Avanzada",clear:"Limpiar Datos del Complemento",clear_desc:"Esto eliminará todos los datos del complemento y los datos del almacén de vectores. Todos los archivos de chat dentro de tu bóveda no se verán afectados.",clear_modal:{title:"Limpiar Datos del Complemento",description:"¿Estás seguro de que quieres eliminar los datos del complemento? Ten en cuenta que solo se eliminarán los datos del complemento y los datos del almacén de vectores. Todos los archivos de chat dentro de tu bóveda no se verán afectados."},clear_label:"Limpiar",relaunch:"Reiniciar",autostart:"Inicio Automático",autostart_desc:"Iniciar automáticamente el Smart Second Brain cuando Obsidian se inicia.",num_docs_retrieve:"Documentos a recuperar",num_docs_retrieve_desc:"La cantidad máxima de documentos a recuperar de tu bóveda. Ten en cuenta que los documentos no son equivalentes a las notas.",debugging:"Depuración",langsmith_key:"Clave API de Langsmith",langsmith_key_desc:"Proporciona una Clave API de Langsmith si deseas rastrear y evaluar los componentes internos de tu Smart Second Brain.",verbose:"Registro de la Consola del Desarrollador",verbose_desc:"Habilitar el registro detallado en la Consola del Desarrollador.",openai:{desc:" ",api_key:"Clave API",api_key_desc:"Proporciona tu Clave API de OpenAI para usar los modelos de lenguaje de gran escala de OpenAI.",gen_model:"Modelo de Chat",embed_model:"Modelo de Embedding ",model_descriptions:{"gpt-3.5-turbo":"Modelo GPT-3.5 Turbo más reciente (ventana de contexto de 16385 tokens)","gpt-4":"Modelo GPT-4 más reciente (ventana de contexto de 8192 tokens)","gpt-4-32k":"Modelo GPT-4 (ventana de contexto de 32768 tokens)","gpt-4-turbo":"Modelo GPT-4 Turbo más reciente (ventana de contexto de 128000 tokens)","gpt-4o":"Último modelo GPT-4 Omni (ventana de contexto de 128000 tokens)","text-embedding-3-large":"Modelo de Embedding  más reciente y capaz para tareas tanto en inglés como en otros idiomas","text-embedding-3-small":"Rendimiento mejorado sobre el modelo de Embedding  ada de 2da generación","text-embedding-ada-002":"Modelo de Embedding  de 2da generación más capaz."}},ollama:{desc:" ",gen_model:"Modelo de Chat",base_url:"URL Base de Ollama",base_url_desc:"Establece la URL de tu instancia de Ollama.",embed_model:"Modelo de Embedding",recommended:"Recomendado",other:"Otro",model_descriptions:{llama2:"Llama 2 es una colección de modelos de lenguaje base que varían de 7B a 70B de parámetros.","llama2-uncensored":"Modelo Llama 2 sin censura por George Sung y Jarrad Hope.",mistral:"El modelo de 7B liberado por Mistral AI, actualizado a la versión 0.2.","mistral-openorca":"Mistral OpenOrca es un modelo de 7 mil millones de parámetros, ajustado sobre el modelo Mistral 7B utilizando el conjunto de datos OpenOrca.",gemma:"Gemma es una familia de modelos abiertos ligeros y de última generación construidos por Google DeepMind.",mixtral:"Un modelo de Mezcla de Expertos (MoE) de alta calidad con pesos abiertos por Mistral AI.","dolphin-mixtral":"Dolphin Mixtral (32000 Tokens)",phi:"Phi-2: un modelo de lenguaje de 2.7B de Microsoft Research que demuestra capacidades sobresalientes de razonamiento y comprensión del lenguaje.","nomic-embed-text":"Un modelo de Embedding abierto de alto rendimiento con una amplia ventana de contexto de tokens.","mxbai-embed-large":"Modelo grande de Embedding de última generación de mixedbread.ai."}}},lk={initial
con `,reinitialize:"Reiniciar Smart Second Brain",chat_via:"Chatear mediante {model}",chatview:"Vista de chat cómoda",similarity_threshold:"Similitud",creativity:"Creatividad",settings_changed:`Configuración cambiada.
Reiniciar Smart Second Brain.`,open:"Abrir configuración rápida",close:"Cerrar configuración rápida",error:{install_model:"Primero instala el modelo {model}",other:`Ocurrió un error.
Por favor, reintenta la inicialización...`}},sk={creativity:"Ajusta el nivel de originalidad en las respuestas del modelo según tu preferencia.",similarity:"Qué tan similares deben ser los documentos recuperados a la consulta del usuario."},ok={general:zT,ribbon:jT,cmd:qT,modal:$T,init_third_party_modal:ek,notice:tk,chat:rk,onboarding:ak,incognito_toggle:nk,settings:ik,quick_settings:lk,tooltip:sk},dk={delete:"Sil",install:"Kur"},uk={chat:"S2B Sohbetini Aç"},pk={chat:"Sohbeti Aç",pull_model:"Ollama Modelini Çek",remove_model:"Ollama Modelini Kaldır"},ck={dont_show:"Bunu bir daha gösterme",cancel:"İptal",confirm:"Onayla",pull_model_name:"Ollama Modelini Çek",pull_model_desc:"Ollama kayıt defterinden bir model kur",remove:{name:"Ollama Modelini Kaldır",desc:"Kurulu bir modeli sisteminizden kaldırın",success:"Model başarıyla kaldırıldı",error:"Model kaldırılamadı. Model kurulu olmayabilir"}},mk={title:"Üçüncü Parti Hizmetler Üzerinden Çalıştır",description:"Üçüncü parti hizmetler üzerinden çalıştırmak istediğinizden emin misiniz? Kişisel verileriniz OpenAI gibi üçüncü parti hizmetlerle paylaşılacaktır."},gk={still_running:"Smart Second Brain hala çalışıyor. Lütfen bekleyin.",still_indexing:"Smart Second Brain hala dizinliyor. Lütfen bekleyin.",ollama_not_running:"Smart Second Brain'i başlatmadan önce Ollama'nın çalıştığından emin olun.",ollama_gen_model:"Ollama modeli kurulu değil. Smart Second Brain'i başlatmadan önce lütfen modeli kurun.",ollama_embed_model:"Ollama modeli kurulu değil. Smart Second Brain'i başlatmadan önce lütfen modeli kurun.",openai_key:"Smart Second Brain'i başlatmadan önce OpenAI API Anahtarınızın geçerli olduğundan emin olun.",failed:"Smart Second Brain başlatılamadı (Hata: {error}). Lütfen tekrar deneyin.",failed_indexing:"Notlarınız smart second brain'e dizinlenemedi. Lütfen tekrar deneyin.",done:"Smart Second Brain başlatıldı.",error:"Bir hata oluştu. Lütfen hatanın düzelmesini bekleyin.",not_initialized:"Smart Second Brain başlatılmadı. Lütfen önce başlatın.",plugin_data_cleared:"Eklenti verileri temizlendi.",api_key_invalid:"API Anahtarı geçersiz!",error_pulling_model:"Ollama'dan model çekilemedi (Hata: {error}). Lütfen tekrar deneyin.",copied_to_clipboard:`Panoya kopyalandı:
{text}`,no_notes_retrieved:"Hiçbir not alınamadı. Belki benzerlik eşiğini düşürün.",run_failed:"Smart Second Brain çalıştırılamadı (Hata: {error}). Lütfen tekrar deneyin.",models_deleted:"{model} Modeli silindi",no_models:"{model} Modeli bulunamadı",model_pull_canceled:"Model çekme iptal edildi"},Ak={edit:"Sorguyu düzenle ve cevabı yeniden oluştur",cancel_edit:"Düzenlemeyi iptal et",copy:"Metni Kopyala",regenerate:"Tüm sonraki mesajları siler ve mevcut sorguya cevabı yeniden oluşturur",change_assistant_prompt:"İlk asistan mesajını değiştir",reset_assistant_prompt:"İlk asistan mesajını sıfırla",retrieving:"Alınıyor",reducing:"{num} Not Azaltılıyor",stopped:"Durduruldu!",user:"Siz",assistant:"Asistan",save:"Sohbeti Not olarak kaydet",delete:"Sohbet Geçmişini Sil",toggle_papa:"Notlarınızla Sohbet Ediliyor",toggle_llm:"Sade LLM ile Sohbet Ediliyor",stop:"Smart Second Brain'inizi Durdurun",send:"Smart Second Brain'inizi Çalıştırın",retry_error:"Bir hata oluştu. Başlatmayı tekrar deneyin",reinitialize:"Ayarlar değişti, Yeniden Başlat",input_placeholder:"Smart Second Brain'inizle sohbet edin..."},hk={welcome_msg:"Smart Second Brain'inize hoş geldiniz! Notlarınızdaki bilgileri bulmaya ve işlemeye yardımcı olan kişisel AI asistanınız.",setup:"Kurulum",test:"Test",privacy_mode_note:"Asistanınız gizlilik modunda çalışacak. Bu, internete bağlı olmadığı ve Ollama'yı kullanarak tamamen yerel olarak çalıştığı anlamına gelir.",openai_mode_note:"Asistanınız çalışmak için üçüncü parti hizmetleri kullanacak. Bu, tüm kişisel bilgilerinizi bu hizmetlerle paylaşmanız gerektiği ve Smart Second Brain'inizin ChatGPT gibi OpenAI'ın büyük dil modellerini kullanabilmek için internete bağlı olması gerektiği anlamına gelir.",init:"Smart Second Brain'inizi başlatın",init_label:"Başlatmak için tıklayın",ollama:{deamon:{install:"Ollama'yı terminal üzerinden kurun:",set_baseurl:"Ollama Temel URL'sini Ayarlayın",start:"Bu komutu terminal üzerinden çalıştırarak Ollama'yı başlatın"},app:{download:"Uygulamayı İndirin",download_link:"buradan",extract:".zip'i çıkarın ve Ollama'yı başlatın",run:"setup.exe'yi çalıştırın",test_label:"Ollama'nın çalıştığını test edin",set_origins:"Streaming yanıtları etkinleştirmek için terminal üzerinden Ollama kökenlerini ayarlayın:",restart:"Ollama servisini yeniden başlatın",restart_label:"Ollama menü çubuğu simgesine tıklayın ve sonra Çık'a basın",quit:"Ollama servisinden çıkın",quit_label:"Ollama menü çubuğu simgesine tıklayın ve sonra Çık'a basın",start_origins:"Bu komutu powershell üzerinden çalıştırarak Ollama'yı başlatın"},test_origins:"Kökenlerin doğru şekilde ayarlandığını test edin",install_model:"Bir Ollama Embedding Modeli kurun.",recommended:"Önerilen: ",set_model:"Embedding Modelinizi ayarlayın:"},openai:{create_account:"Bir OpenAI ",create_account_link:"Hesabı Oluşturun",create_api_key:"Bir ",create_api_key_link:"API Anahtarı Oluşturun",api_key_warning:`> [!Warning] API Anahtarını Etkinleştir
> API Anahtarının çalışması için OpenAI ücretli hesabına yükseltmeniz gerekebilir. Bu, en az 5$ OpenAI hesabınıza yatırmanız gerektiği anlamına gelir. Bu gelecekte değişebilir.`,paste_api_key:"Anahtarınızı buraya yapıştırın:",test_api_key:"API Anahtarınızı test edin",api_key_valid:"API Anahtarı geçerli!"}},Bk={enable:"Kendi makinenizde Çalıştır",disable:"Üçüncü Parti Hizmetler Üzerinden Çalıştır"},Ck={excludeff:"Dosya ve klasörleri hariç tut",excludeff_desc:"Bu dosya ve klasörler gömülmeyecek ve cevaplar üretilirken kullanılmayacaktır.",excludeff_add:"Ekle",excludeff_delete:"Hariç tutma listesinden kaldır",advanced:"Gelişmiş Ayarlar",clear:"Eklenti Verilerini Temizle",clear_desc:"Bu, tüm eklenti verilerini ve vektör depolama verilerini silecektir. Arşiviniz içindeki tüm sohbet dosyaları etkilenmeyecektir.",clear_modal:{title:"Eklenti Verilerini Temizle",description:"Eklenti verilerini silmek istediğinizden emin misiniz? Sadece eklenti verileri ve vektör depolama verileri kaldırılacaktır. Arşiviniz içindeki tüm sohbet dosyaları etkilenmeyecektir."},clear_label:"Temizle",relaunch:"Yeniden Başlat",autostart:"Otomatik Başlat",autostart_desc:"Obsidian başladığında Smart Second Brain'i otomatik olarak başlatın.",num_docs_retrieve:"Alınacak Doküman Sayısı",num_docs_retrieve_desc:"Arşivinizden alınacak maksimum doküman sayısı. Dokümanlar notlara eşdeğer değildir.",debugging:"Hata Ayıklama",langsmith_key:"Langsmith API Anahtarı",langsmith_key_desc:"Smart Second Brain'inizin iç işleyişini izlemek ve değerlendirmek istiyorsanız bir Langsmith API Anahtarı sağlayın.",verbose:"Geliştirici Konsolu günlüğü",verbose_desc:"Geliştirici Konsolunda ayrıntılı günlüğü etkinleştirin.",openai:{desc:" ",api_key:"API Anahtarı",api_key_desc:"OpenAI'ın büyük dil modellerini kullanmak için OpenAI API Anahtarınızı sağlayın.",gen_model:"Sohbet Modeli",embed_model:"Embedding Modeli",model_descriptions:{"gpt-3.5-turbo":"En son GPT-3.5 Turbo modeli (16385 Token bağlam penceresi)","gpt-4":"En son GPT-4 modeli (8192 Token bağlam penceresi)","gpt-4-32k":"GPT-4 modeli (32768 Token bağlam penceresi)","gpt-4-turbo":"En son GPT-4 Turbo modeli (128000 Token bağlam penceresi)","gpt-4o":"En son GPT-4 Omni modeli (128000 Token bağlam penceresi)","text-embedding-3-large":"Hem İngilizce hem de İngilizce olmayan görevler için en son ve en yetenekli embedding modeli","text-embedding-3-small":"2. nesil 'ada' embedding modeline göre artırılmış performans","text-embedding-ada-002":"En yetenekli 2. nesil embedding modeli."}},ollama:{desc:" ",gen_model:"Sohbet Modeli",base_url:"Ollama Temel URL'si",base_url_desc:"Ollama örneğinizin URL'sini ayarlayın.",embed_model:"Embedding Modeli",recommended:"Önerilen",other:"Diğer",model_descriptions:{llama2:"Llama 2, 7B ila 70B parametreli çeşitli temel dil modellerinden oluşur.","llama2-uncensored":"George Sung ve Jarrad Hope tarafından sansürsüz Llama 2 modeli.",mistral:"Mistral AI tarafından yayınlanan ve 0.2 sürümüne güncellenen 7B model.","mistral-openorca":"OpenOrca veri kümesi kullanılarak Mistral 7B modelinin üzerine ince ayar yapılmış 7 milyar parametreli Mistral OpenOrca modeli.",gemma:"Google DeepMind tarafından oluşturulan, son derece gelişmiş açık kaynaklı model ailesi.",mixtral:"Mistral AI tarafından oluşturulan, yüksek kaliteli, açık ağırlıklı, 'Mixture of Experts' (MoE) modeli.","dolphin-mixtral":"Dolphin Mixtral (32000 Token)",phi:"Microsoft Araştırma tarafından geliştirilen, üstün akıl yürütme ve dil anlama yetenekleri sergileyen 2,7B dil modeli.","nomic-embed-text":"Geniş bir token bağlam penceresi ile yüksek performanslı açık embedding modeli.","mxbai-embed-large":"Mixedbread.ai'den son teknoloji büyük embedding modeli."}}},wk={initialize:"Smart Second Brain'inizi başlatın",indexing_vault:"Arşiv dizinleniyor",time_left_minutes:"Yaklaşık {minutes}dk {seconds}sn kaldı...",time_left_seconds:"Yaklaşık {seconds}sn kaldı.
Smart Second Brain'i Yeniden Başlat`,reinitialize:"Smart Second Brain'i Yeniden Başlat",chat_via:"{model} üzerinden sohbet et",chatview:"Rahat Sohbet Görünümü",similarity_threshold:"Benzerlik",creativity:"Yaratıcılık",settings_changed:`Ayarlar değişti.
Smart Second Brain'i Yeniden Başlat.`,open:"Hızlı ayarları aç",close:"Hızlı ayarları kapat",error:{install_model:"Önce {model} modelini kurun",other:`Bir hata oluştu.
Lütfen başlatmayı tekrar deneyin...`}},bk={creativity:"Modelin çıktılarındaki özgünlük seviyesini tercihlerinize göre ayarlar.",similarity:"Alınan dokümanların kullanıcı sorgusuyla ne kadar benzer olması gerektiğini belirler."},Kk={general:dk,ribbon:uk,cmd:pk,modal:ck,init_third_party_modal:mk,notice:gk,chat:Ak,onboarding:hk,incognito_toggle:Bk,settings:Ck,quick_settings:wk,tooltip:bk},fk={delete:"Supprimer",install:"Installer"},Qk={chat:"Ouvrir le chat S2B"},Wk={chat:"Ouvrir le chat",pull_model:"Récupérer le modèle Ollama",remove_model:"Supprimer le modèle Ollama"},vk={dont_show:"Ne plus afficher ceci",cancel:"Annuler",confirm:"Confirmer",pull_model_name:"Récupérer le modèle Ollama",pull_model_desc:"Installer un modèle depuis le registre Ollama",remove:{name:"Supprimer le modèle Ollama",desc:"Supprimer un modèle installé de votre système",success:"Modèle supprimé avec succès",error:"Échec de la suppression du modèle. Le modèle n'est peut-être pas installé"}},Fk={title:"Exécuter via des services tiers",description:"Êtes-vous sûr de vouloir exécuter via des tiers ? Vos données personnelles seront partagées avec des services tiers comme OpenAI."},yk={still_running:"Smart Second Brain est toujours en cours d'exécution. Veuillez patienter.",still_indexing:"Smart Second Brain est toujours en cours d'indexation. Veuillez patienter.",ollama_not_running:"Veuillez vous assurer qu'Ollama est en cours d'exécution avant d'initialiser Smart Second Brain.",ollama_gen_model:"Modèle Ollama non installé. Veuillez installer le modèle avant d'initialiser Smart Second Brain.",ollama_embed_model:"Modèle Ollama non installé. Veuillez installer le modèle avant d'initialiser Smart Second Brain.",openai_key:"Veuillez vous assurer que votre clé API OpenAI est valide avant d'initialiser Smart Second Brain.",failed:"Échec de l'initialisation de Smart Second Brain (Erreur : {error}). Veuillez réessayer.",failed_indexing:"Échec de l'indexation des notes dans votre Smart Second Brain. Veuillez réessayer.",done:"Smart Second Brain initialisé.",error:"Une erreur est survenue. Veuillez attendre que l'erreur soit résolue.",not_initialized:"Smart Second Brain non initialisé. Veuillez initialiser d'abord.",plugin_data_cleared:"Données du plugin effacées.",api_key_invalid:"La clé API n'est pas valide !",error_pulling_model:"Échec de la récupération du modèle depuis Ollama (Erreur : {error}). Veuillez réessayer.",copied_to_clipboard:`Copié dans le presse-papiers :
{text}`,no_notes_retrieved:"Aucune note récupérée. Peut-être baisser le seuil de similarité.",run_failed:"Échec de l'exécution de Smart Second Brain (Erreur : {error}). Veuillez réessayer.",models_deleted:"Le modèle {model} a été supprimé",no_models:"Le modèle {model} n'a pas été trouvé",model_pull_canceled:"Annulation de la récupération du modèle"},Rk={edit:"Modifier la requête et régénérer la réponse",cancel_edit:"Annuler la modification",copy:"Copier le texte",regenerate:"Supprimer tous les messages suivants et régénérer la réponse à la requête actuelle",change_assistant_prompt:"Modifier le message initial de l'assistant",reset_assistant_prompt:"Réinitialiser le message initial de l'assistant",retrieving:"Récupération",reducing:"Réduction de {num} notes",stopped:"Arrêté !",user:"Vous",assistant:"Assistant",save:"Sauvegarder le chat dans une note",delete:"Supprimer l'historique du chat",toggle_papa:"Discuter avec vos notes",toggle_llm:"Discuter avec un LLM simple",stop:"Arrêter votre Smart Second Brain",send:"Exécuter votre Smart Second Brain",retry_error:"Une erreur est survenue. Réessayez l'initialisation",reinitialize:"Réinitialiser, les paramètres ont changé",input_placeholder:"Discutez avec votre Smart Second Brain..."},Ek={welcome_msg:"Bienvenue dans votre Smart Second Brain ! Votre assistant personnel IA qui vous aide à trouver et traiter les informations dans vos notes.",setup:"Configuration",test:"Test",privacy_mode_note:"Votre assistant fonctionnera en mode privé. Cela signifie qu'il n'est pas connecté à Internet et fonctionne entièrement localement en utilisant Ollama.",openai_mode_note:"Votre assistant utilisera des services tiers pour fonctionner. Cela signifie que vous devrez partager toutes vos informations personnelles avec ces services et que votre Smart Second Brain doit être connecté à Internet pour utiliser les grands modèles de langage d'OpenAI comme ChatGPT.",init:"Démarrer votre Smart Second Brain",init_label:"Cliquez pour démarrer",ollama:{deamon:{install:"Installer Ollama dans le terminal :",set_baseurl:"Définir l'URL de base d'Ollama",start:"Démarrer Ollama en exécutant cette commande dans le terminal"},app:{download:"Télécharger l'application",download_link:"ici",extract:"Extraire le fichier .zip et démarrer Ollama",run:"Exécuter le setup.exe",test_label:"Tester si Ollama fonctionne",set_origins:"Dans le terminal, définir les origines d'Ollama pour activer les réponses en streaming :",restart:"Redémarrer le service Ollama",restart_label:"Cliquez sur l'icône de la barre de menus d'Ollama puis quittez",quit:"Quitter le service Ollama",quit_label:"Cliquez sur l'icône de la barre de menus d'Ollama puis quittez",start_origins:"Démarrer Ollama en exécutant cette commande dans le powershell"},test_origins:"Tester si les origines sont correctement définies",install_model:"Installer un modèle d'encodage Ollama.",recommended:"Recommandé : ",set_model:"Définir votre modèle d'encodage :"},openai:{create_account:"Créer un ",create_account_link:"compte OpenAI",create_api_key:"Créer une ",create_api_key_link:"clé API",api_key_warning:`> [!Avertissement] Activer la clé API 
> Pour que la clé API fonctionne, vous devrez peut-être passer à un compte payant OpenAI. Cela signifie déposer au moins 5 $ sur votre compte OpenAI. Cela pourrait changer à l'avenir.`,paste_api_key:"Collez votre clé ici :",test_api_key:"Tester votre clé API",api_key_valid:"La clé API est valide !"}},Gk={enable:"Exécuter sur votre machine",disable:"Exécuter via des services tiers"},Sk={excludeff:"Exclure fichiers et dossiers",excludeff_desc:"Ces fichiers et dossiers ne seront pas intégrés et ne seront pas utilisés pour générer des réponses.",excludeff_add:"Ajouter",excludeff_delete:"Supprimer de la liste d'exclusion",advanced:"Paramètres avancés",clear:"Effacer les données du plugin",clear_desc:"Cela supprimera toutes les données du plugin et les données du magasin vectoriel. Tous les fichiers de discussion dans votre coffre ne seront pas affectés.",clear_modal:{title:"Effacer les données du plugin",description:"Êtes-vous sûr de vouloir supprimer les données du plugin ? Notez que seules les données du plugin et les données du magasin vectoriel seront supprimées. Tous les fichiers de discussion dans votre coffre ne seront pas affectés."},clear_label:"Effacer",relaunch:"Relancer",autostart:"Démarrage automatique",autostart_desc:"Démarrer automatiquement le Smart Second Brain lorsque Obsidian démarre.",num_docs_retrieve:"Documents à récupérer",num_docs_retrieve_desc:"Le nombre maximal de documents à récupérer de votre coffre. Notez que les documents ne sont pas équivalents aux notes.",debugging:"Débogage",langsmith_key:"Clé API Langsmith",langsmith_key_desc:"Fournissez une clé API Langsmith si vous souhaitez tracer et évaluer les mécanismes internes de votre Smart Second Brain.",verbose:"Journalisation détaillée de la console développeur",verbose_desc:"Activer la journalisation détaillée dans la console développeur.",openai:{desc:" ",api_key:"Clé API",api_key_desc:"Fournissez votre clé API OpenAI pour utiliser les grands modèles de langage d'OpenAI.",gen_model:"Modèle de discussion",embed_model:"Modèle d'encodage",model_descriptions:{"gpt-3.5-turbo":"Dernier modèle GPT-3.5 Turbo (fenêtre de contexte de 16385 tokens)","gpt-4":"Dernier modèle GPT-4 (fenêtre de contexte de 8192 tokens)","gpt-4-32k":"Modèle GPT-4 (fenêtre de contexte de 32768 tokens)","gpt-4-turbo":"Dernier modèle GPT-4 Turbo (fenêtre de contexte de 128000 tokens)","gpt-4o":"Dernier modèle GPT-4 Omni (fenêtre de contexte de 128000 jetons)","text-embedding-3-large":"Dernier modèle d'encodage le plus capable pour les tâches en anglais et non anglais","text-embedding-3-small":"Performances accrues par rapport au modèle d'encodage ada de 2ème génération","text-embedding-ada-002":"Modèle d'encodage le plus capable de 2ème génération."}},ollama:{desc:" ",gen_model:"Modèle de discussion",base_url:"URL de base d'Ollama",base_url_desc:"Définir l'URL de votre instance Ollama.",embed_model:"Modèle d'encodage",recommended:"Recommandé",other:"Autre",model_descriptions:{llama2:"Llama 2 est une collection de modèles de langage de base allant de 7B à 70B de paramètres.","llama2-uncensored":"Modèle Llama 2 non censuré par George Sung et Jarrad Hope.",mistral:"Le modèle 7B publié par Mistral AI, mis à jour en version 0.2.","mistral-openorca":"Mistral OpenOrca est un modèle de 7 milliards de paramètres, affiné sur le modèle Mistral 7B en utilisant le dataset OpenOrca.",gemma:"Gemma est une famille de modèles ouverts, légers et de pointe développés par Google DeepMind.",mixtral:"Un modèle de haute qualité Mixture of Experts (MoE) avec poids ouverts par Mistral AI.","dolphin-mixtral":"Dolphin Mixtral (32000 Tokens)",phi:"Phi-2 : un modèle de langage de 2,7 milliards de paramètres par Microsoft Research qui démontre des capacités exceptionnelles de raisonnement et de compréhension du langage.","nomic-embed-text":"Un modèle d'encodage ouvert performant avec une large fenêtre de contexte de tokens.","mxbai-embed-large":"Modèle d'encodage large de pointe de mixedbread.ai."}}},Zk={
avec `,reinitialize:"Réinitialiser Smart Second Brain",chat_via:"Discuter via {model}",chatview:"Vue de chat confortable",similarity_threshold:"Similarité",creativity:"Créativité",settings_changed:`Paramètres modifiés.
Réinitialiser Smart Second Brain.`,open:"Ouvrir les paramètres rapides",close:"Fermer les paramètres rapides",error:{install_model:"Installer d'abord le modèle {model}",other:`Une erreur est survenue.
Veuillez réessayer l'initialisation...`}},Xk={creativity:"Ajuste le niveau d'originalité dans les sorties du modèle selon votre préférence.",similarity:"À quel point les documents récupérés doivent être similaires à la requête de l'utilisateur."},Hk={general:fk,ribbon:Qk,cmd:Wk,modal:vk,init_third_party_modal:Fk,notice:yk,chat:Rk,onboarding:Ek,incognito_toggle:Gk,settings:Sk,quick_settings:Zk,tooltip:Xk},Vk={delete:"删除",install:"安装"},Yk={chat:"打开 S2B 聊天"},Nk={chat:"打开聊天窗口",pull_model:"拉取 Ollama 模型",remove_model:"移除 Ollama 模型"},Lk={dont_show:"不再显示此信息",cancel:"取消",confirm:"确认",pull_model_name:"拉取 Ollama 模型",pull_model_desc:"安装在 Ollama 注册的模型",remove:{name:"移除 Ollama 模型",desc:"从系统中移除已安装的模型",success:"模型已成功移除",error:"移除模型失败，模型可能未安装"}},Tk={title:"通过第三方服务运行",description:"您确定要使用第三方服务吗？您的个人数据将与 OpenAI 等第三方服务共享。"},kk={still_running:"Smart Second Brain 仍在运行。请稍候。",still_indexing:"Smart Second Brain 仍在执行索引。请稍候。",ollama_not_running:"在初始化 Smart Second Brain 之前，请确保 Ollama 正在运行。",ollama_gen_model:"Ollama 对话生成模型未安装。请在初始化 Smart Second Brain 之前安装模型。",ollama_embed_model:"Ollama 嵌入模型未安装。请在初始化 Smart Second Brain 之前安装模型。",openai_key:"在初始化 Smart Second Brain 之前，请确保您的 OpenAI API 密钥有效。",failed:"初始化 Smart Second Brain 失败 (Error: {error}) 。请重试。",failed_indexing:"将笔记索引到 Smart Second Brain 失败。请重试。",done:"Smart Second Brain 初始化成功。",error:"发生错误。请等待错误解决。",not_initialized:"Smart Second Brain 未初始化。请先初始化。",plugin_data_cleared:"插件数据已清除。",api_key_invalid:"API Key 无效！",error_pulling_model:"从 Ollama 拉取模型失败  (Error: {error}) 。请重试。",copied_to_clipboard:`已复制到剪贴板：
{text}`,no_notes_retrieved:"未检索到笔记。建议降低相似度阈值。",run_failed:"运行 Smart Second Brain 失败 (Error: {error}) 。请重试。",models_deleted:"模型 {model} 已被删除",no_models:"未找到模型 {model}",model_pull_canceled:"模型拉取已取消"},Uk={edit:"编辑问题并重生成回答",cancel_edit:"取消编辑",copy:"复制文本",regenerate:"删除所有后续聊天记录并重生成当前问题的回答",change_assistant_prompt:"更改初始助手消息 (initial assistant message)",reset_assistant_prompt:"重置初始助手消息 (initial assistant message)",retrieving:"正在检索",reducing:"正在化简 {num} 条笔记",stopped:"已停止！",user:"您",assistant:"助手",save:"将聊天记录保存到笔记",delete:"删除聊天记录",toggle_papa:"与您的笔记聊天",toggle_llm:"仅与 LLM 聊天",stop:"停止 Smart Second Brain",send:"运行 Smart Second Brain",retry_error:"发生了一个错误。重试初始化",reinitialize:"重新初始化，设置已更改",input_placeholder:"与 Smart Second Brain 聊天..."},_k={welcome_msg:"欢迎使用 Smart Second Brain！您的个人 AI 助手，帮助您在笔记中查找和处理信息。",setup:"设置",test:"测试",privacy_mode_note:"您的助手将在隐私模式下运行。它没有联网，以 Ollama 在本地运行。",openai_mode_note:"您的助手将使用第三方服务来运行。您将与服务商共享个人信息，您的 Smart Second Brain 需要联网使用 OpenAI 的 LLMs，如 ChatGPT。",init:"开启 Smart Second Brain",init_label:"点击以开启",ollama:{deamon:{install:"在终端中安装 Ollama：",set_baseurl:"设置 Ollama Base URL",start:"在终端运行此命令以启动 Ollama"},app:{download:"下载应用程序",download_link:"从这里",extract:"解压 .zip 并启动 Ollama",run:"运行 setup.exe",test_label:"测试 Ollama 是否在运行",set_origins:"在终端中设置 Ollama origins 以启用流式响应：",restart:"重启 Ollama 服务",restart_label:"点击 Ollama 菜单栏图标，然后退出",quit:"退出 Ollama 服务",quit_label:"点击 Ollama 菜单栏图标，然后退出",start_origins:"在 PowerShell 中运行此命令以启动 Ollama"},test_origins:"测试 origins 是否设置正确",install_model:"安装 Ollama 嵌入模型。",recommended:"推荐：",set_model:"设置您的嵌入模型："},openai:{create_account:"创建一个 OpenAI ",create_account_link:"账户",create_api_key:"创建一个 ",create_api_key_link:"API Key",api_key_warning:`> [!Warning] 激活 API-Key 
> 为使 API-Key 有效，您可能需要升级到 OpenAI 付费账户。这需要在您的 OpenAI 账户上存入至少 $5。该提示未来可能改变。`,paste_api_key:"在此粘贴 API Key：",test_api_key:"测试 API Key",api_key_valid:" API Key 有效！"}},Ik={enable:"本地运行",disable:"通过第三方服务运行"},Dk={excludeff:"排除文件和文件夹",excludeff_desc:"这些文件和文件夹将不做向量嵌入和索引，也不会用于生成聊天记录。",excludeff_add:"添加",excludeff_delete:"从排除列表中删除",advanced:"高级设置",clear:"清除插件数据",clear_desc:"将删除所有插件数据和向量存储数据。仓库中的所有聊天记录文件不受影响。",clear_modal:{title:"清除插件数据",description:"您确定要删除插件数据吗？请注意，只有插件数据和向量存储数据将被移除。仓库中的所有聊天记录文件将不受影响。"},clear_label:"清除",relaunch:"重启",autostart:"自启动",autostart_desc:"Obsidian 开启后，自动运行 Smart Second Brain。",num_docs_retrieve:"检索 (retrieve) 文档数",num_docs_retrieve_desc:"仓库中每次允许检索的最大文档数。注意，文档 (本插件索引的笔记片段) 不等同于笔记。",debugging:"调试",langsmith_key:"LangSmith API Key",langsmith_key_desc:"如需跟踪和评估您的 Smart Second Brain 的内部情况，请提供 LangSmith API Key。",verbose:"开发者控制台日志",verbose_desc:"在 Developer Console 中启用详细日志记录。",openai:{desc:" ",api_key:"API Key",api_key_desc:"提供您的 OpenAI API Key 以使用 OpenAI 的大型语言模型。",gen_model:"对话生成模型",embed_model:"嵌入模型",model_descriptions:{"gpt-3.5-turbo":"最新的 GPT-3.5 Turbo 模型（16385 Token 上下文）","gpt-4":"最新的 GPT-4 模型（8192 Token 上下文）","gpt-4-32k":"GPT-4 模型（32768 Token 上下文）","gpt-4-turbo":"最新的 GPT-4 Turbo 模型（128000 Token 上下文）","gpt-4o":"最新的 GPT-4 Omni 模型 (128000 Token 上下文)","text-embedding-3-large":"最新、最强的嵌入模型，适用于多语言任务","text-embedding-3-small":"性能超过二代 ada 嵌入模型","text-embedding-ada-002":"最好的二代嵌入模型。"}},ollama:{desc:" ",gen_model:"对话生成模型",base_url:"Ollama 基础 URL",base_url_desc:"设置您的 Ollama 实例的 URL。",embed_model:"嵌入模型",recommended:"推荐",other:"其他",model_descriptions:{llama2:"Llama 2 是一系列基础语言模型，参数范围从 7B 到 70B。","llama2-uncensored":"由 George Sung 和 Jarrad Hope 提供的未审查的 Llama 2 模型。",mistral:"由 Mistral AI 发布的 7B 模型，已更新至版本 0.2。","mistral-openorca":"Mistral OpenOrca 是 70 亿参数的模型，基于 Mistral 7B 模型并使用 OpenOrca 数据集进行了微调。",gemma:"Gemma 是由 Google DeepMind 构建的一系列轻量级、最先进的开放模型。",mixtral:"由 Mistral AI 提供的高质量 MoE 模型，具有开放权重。","dolphin-mixtral":"Dolphin Mixtral（32000 个令牌）",phi:"Phi-2：微软研究院的 2.7B 语言模型，展示了出色的推理和语言理解能力。","nomic-embed-text":"具有较大上下文的高性能开放嵌入模型。","mxbai-embed-large":"来自 mixedbread.ai 的最先进大型嵌入模型。"}}},Mk={initialize:"启动 Smart Second Brain",indexing_vault:"索引仓库",time_left_minutes:"约剩余 {minutes} 分 {seconds} 秒...",time_left_seconds:"约剩余 {seconds} 秒...",assistant_language:"助手语言",pause_indexing:"暂停索引",resume_indexing:"恢复索引",cancel_indexing:"取消索引",retry_initialization:"重试初始化",mode_changed:`重新初始化 Smart Second Brain 
使用以下模型 `,reinitialize:"重新初始化 Smart Second Brain",chat_via:"通过 {model} 聊天",chatview:"Comfy 视图",similarity_threshold:"相似度",creativity:"创意",settings_changed:`设置已更改。
重新初始化 Smart Second Brain。`,open:"打开快速设置",close:"关闭快速设置",error:{install_model:"请先安装 {model} 模型",other:`发生错误。
请重试初始化...`}},xk={creativity:"调整模型输出的创意以适应您的偏好。",similarity:"检索文档与用户提问的相似度。"},Jk={general:Vk,ribbon:Yk,cmd:Nk,modal:Lk,init_third_party_modal:Tk,notice:kk,chat:Uk,onboarding:_k,incognito_toggle:Ik,settings:Dk,quick_settings:Mk,tooltip:xk},Ok={delete:"삭제",install:"설치"},Pk={chat:"S2B 채팅 열기"},zk={chat:"채팅 열기",pull_model:"Ollama 모델 가져오기",remove_model:"Ollama 모델 제거하기"},jk={dont_show:"다시 보지 않기",cancel:"취소",confirm:"확인",pull_model_name:"Ollama 모델 가져오기",pull_model_desc:"Ollama 레지스트리에서 모델 설치하기",remove:{name:"Ollama 모델 제거하기",desc:"시스템에서 설치된 모델 제거하기",success:"모델이 성공적으로 제거됨",error:"모델 제거 실패. 모델이 설치되지 않았을 수 있음"}},qk={title:"타사 서비스를 통해 실행",description:"타사를 통해 실행하시겠습니까? 귀하의 개인 데이터가 OpenAI와 같은 타사 서비스와 공유됩니다."},$k={still_running:"Smart Second Brain이 아직 실행 중입니다. 기다려 주세요.",still_indexing:"Smart Second Brain이 아직 색인 중입니다. 기다려 주세요.",ollama_not_running:"Smart Second Brain을 초기화하기 전에 Ollama가 실행 중인지 확인해 주세요.",ollama_gen_model:"Ollama 모델이 설치되지 않았습니다. Smart Second Brain을 초기화하기 전에 모델을 설치해 주세요.",ollama_embed_model:"Ollama 모델이 설치되지 않았습니다. Smart Second Brain을 초기화하기 전에 모델을 설치해 주세요.",openai_key:"Smart Second Brain을 초기화하기 전에 OpenAI API 키가 유효한지 확인해 주세요.",failed:"Smart Second Brain 초기화에 실패했습니다 (오류: {error}). 다시 시도해 주세요.",failed_indexing:"Smart Second Brain에 노트를 색인하는 데 실패했습니다. 다시 시도해 주세요.",done:"Smart Second Brain이 초기화되었습니다.",error:"오류가 발생했습니다. 오류가 해결될 때까지 기다려 주세요.",not_initialized:"Smart Second Brain이 초기화되지 않았습니다. 먼저 초기화해 주세요.",plugin_data_cleared:"플러그인 데이터가 지워졌습니다.",api_key_invalid:"API 키가 유효하지 않습니다!",error_pulling_model:"Ollama에서 모델을 가져오는 데 실패했습니다 (오류: {error}). 다시 시도해 주세요.",copied_to_clipboard:`클립보드에 복사됨:
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