From 3bd991674ffa5c88507a4bc5cdd8a7b83e40c946 Mon Sep 17 00:00:00 2001
From: Jojo Ortiz
Date: Sun, 15 Oct 2023 21:59:44 -0700
Subject: [PATCH 1/7] unify flex and retrieval demo into one folder and update
with RAG components
---
example/retrieval_qa/retrieval_qa_demo.py | 72 -------------------
.../retrieval_qa_huggingface_demo.py | 8 +--
.../retrieval_qa_openai_demo.py} | 1 -
3 files changed, 4 insertions(+), 77 deletions(-)
delete mode 100644 example/retrieval_qa/retrieval_qa_demo.py
rename example/{flex/flex_demo.py => retrieval_qa/retrieval_qa_openai_demo.py} (98%)
diff --git a/example/retrieval_qa/retrieval_qa_demo.py b/example/retrieval_qa/retrieval_qa_demo.py
deleted file mode 100644
index 23d1c26..0000000
--- a/example/retrieval_qa/retrieval_qa_demo.py
+++ /dev/null
@@ -1,72 +0,0 @@
-"""Demo for the retrieval_qa application."""
-
-import os
-import argparse
-from pykoi import Application
-from pykoi.chat import QuestionAnswerDatabase
-from pykoi.retrieval import RetrievalFactory
-from pykoi.retrieval import VectorDbFactory
-from pykoi.component import Chatbot, Dashboard, RetrievalQA
-
-
-def main(**kargs):
- # enter openai api key here
- os.environ["OPENAI_API_KEY"] = ""
- os.environ["DOC_PATH"] = os.path.join(os.getcwd(), "temp/docs")
- os.environ["VECTORDB_PATH"] = os.path.join(os.getcwd(), "temp/vectordb")
- MODEL_SOURCE = "openai"
-
- #####################################
- # Creating a retrieval QA component #
- #####################################
- # vector database
- vector_db = VectorDbFactory.create(
- model_source=MODEL_SOURCE, vector_db_name=kargs.get("vectordb"), **kargs
- )
-
- # retrieval model with vector database
- retrieval_model = RetrievalFactory.create(
- model_source=MODEL_SOURCE, vector_db=vector_db
- )
-
- # retrieval, chatbot, and dashboard pykoi components
- retriever = RetrievalQA(retrieval_model=retrieval_model, vector_db=vector_db)
- chatbot = Chatbot(None, feedback="vote", is_retrieval=True)
- dashboard = Dashboard(QuestionAnswerDatabase())
-
- ############################################################
- # Starting the application and retrieval qa as a component #
- ############################################################
- # Create the application
- app = Application(debug=False, share=False)
- app.add_component(retriever)
- app.add_component(chatbot)
- app.add_component(dashboard)
- app.run()
-
-
-if __name__ == "__main__":
- # Parse the command-line arguments
- parser = argparse.ArgumentParser(description="Demo for the Retrieval QA.")
- parser.add_argument(
- "-vectordb",
- type=str,
- default="chroma",
- help="Name of the vector database (default: 'chroma')",
- )
- parser.add_argument(
- "-host",
- type=str,
- default="localhost",
- help="Host address if using Epsilla vector database",
- )
- parser.add_argument(
- "-port",
- type=int,
- default=8888,
- help="Port number if using Epsilla vector database",
- )
- args = parser.parse_args()
-
- # Call the main function with the vector_db_name argument
- main(**vars(args))
diff --git a/example/retrieval_qa/retrieval_qa_huggingface_demo.py b/example/retrieval_qa/retrieval_qa_huggingface_demo.py
index e42dcaf..9de0e8a 100644
--- a/example/retrieval_qa/retrieval_qa_huggingface_demo.py
+++ b/example/retrieval_qa/retrieval_qa_huggingface_demo.py
@@ -3,7 +3,7 @@
import os
import argparse
from pykoi import Application
-from pykoi.chat import QuestionAnswerDatabase
+from pykoi.chat import RAGDatabase
from pykoi.retrieval import RetrievalFactory
from pykoi.retrieval import VectorDbFactory
from pykoi.component import Chatbot, Dashboard, RetrievalQA
@@ -36,9 +36,9 @@ def main(**kwargs):
)
# retrieval, chatbot, and dashboard pykoi components
- retriever = RetrievalQA(retrieval_model=retrieval_model, vector_db=vector_db)
- chatbot = Chatbot(None, feedback="vote", is_retrieval=True)
- dashboard = Dashboard(QuestionAnswerDatabase())
+ retriever = RetrievalQA(retrieval_model=retrieval_model, vector_db=vector_db, feedback="rag")
+ chatbot = Chatbot(None, feedback="rag", is_retrieval=True)
+ dashboard = Dashboard(RAGDatabase(), feedback="rag")
############################################################
# Starting the application and retrieval qa as a component #
diff --git a/example/flex/flex_demo.py b/example/retrieval_qa/retrieval_qa_openai_demo.py
similarity index 98%
rename from example/flex/flex_demo.py
rename to example/retrieval_qa/retrieval_qa_openai_demo.py
index 60bf66c..e02fcf7 100644
--- a/example/flex/flex_demo.py
+++ b/example/retrieval_qa/retrieval_qa_openai_demo.py
@@ -4,7 +4,6 @@
import argparse
from dotenv import load_dotenv
from pykoi import Application
-from pykoi.chat import QuestionAnswerDatabase
from pykoi.retrieval import RetrievalFactory
from pykoi.retrieval import VectorDbFactory
from pykoi.component import Chatbot, Dashboard, RetrievalQA
From 5ec16f8e1b1da14f746dda8f8e86523ac6a12512 Mon Sep 17 00:00:00 2001
From: Jojo Ortiz
Date: Sun, 15 Oct 2023 22:02:11 -0700
Subject: [PATCH 2/7] update Dropdown component and SourceContainer styling
---
pykoi/frontend/dist/assets/index-10f2d9f3.css | 1 -
.../{index-fb0d7a3f.js => index-6fbf13ee.js} | 38 ++++----
pykoi/frontend/dist/assets/index-857a4a8b.css | 1 +
pykoi/frontend/dist/index.html | 4 +-
.../lib/Chatbots/Components/Dropdown.svelte | 96 ++++++++++++++++---
.../Components/SourceContainer.svelte | 2 +-
6 files changed, 105 insertions(+), 37 deletions(-)
delete mode 100644 pykoi/frontend/dist/assets/index-10f2d9f3.css
rename pykoi/frontend/dist/assets/{index-fb0d7a3f.js => index-6fbf13ee.js} (52%)
create mode 100644 pykoi/frontend/dist/assets/index-857a4a8b.css
diff --git a/pykoi/frontend/dist/assets/index-10f2d9f3.css b/pykoi/frontend/dist/assets/index-10f2d9f3.css
deleted file mode 100644
index 8a05e68..0000000
--- a/pykoi/frontend/dist/assets/index-10f2d9f3.css
+++ /dev/null
@@ -1 +0,0 @@
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light){:root{color:#213547;background-color:#fff}a:hover{color:#747bff}button{background-color:#f9f9f9}}:root{--green: #00ebc7;--red: #FF5470;--yellow: #fde24f;--black: #1b2d45;--darkBlue: #00214d;--darkGrey: #222;--grey: #bfbfbf;--lightGrey: #f2f4f6;--white: white;--primary: var(--yellow);--danger: var(--red);--background: var(--lightGrey);--textColor: var(--black);--lineColor: var(--grey);--cardBg: var(--white);--headerBackground: var(--white);--footerBackground: var(--darkBlue);--footerTextColor: var(--lightGrey);--headerTextColor: var(--black);--buttonColor: var(--primary);--buttonTextColor: var(--textColor);--borderBottom: solid 2px var(--primary);--line: solid 1px var(--lineColor);--headingFont: "Lato", monospace;--bodyFont: "Inconsolata", sans-serif;--baseFontSize: 100%;--h1: 3.052em;--h2: 2.441em;--h3: 1.953em;--h4: 1.563em;--h5: 1.25em;--smallText: .8em;--shadow-s: 0 1px 3px 0 rgb(0 0 0 / .1), 0 1px 2px -1px rgb(0 0 0 / .1);--shadow-md: 0 4px 6px -1px rgb(0 0 0 / .1), 0 2px 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diff --git a/pykoi/frontend/dist/assets/index-fb0d7a3f.js b/pykoi/frontend/dist/assets/index-6fbf13ee.js
similarity index 52%
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index 086f063..6563f6e 100644
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*
* Copyright (c) TanStack
@@ -34,10 +34,10 @@
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*
* @license MIT
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l=e.getIsSelected();t.setRowSelection(o=>{if(n=typeof n<"u"?n:!l,l===n)return o;const r={...o};return $o(r,e.id,n,t),r})},getIsSelected:()=>{const{rowSelection:n}=t.getState();return Bo(e,n)},getIsSomeSelected:()=>{const{rowSelection:n}=t.getState();return $s(e,n)==="some"},getIsAllSubRowsSelected:()=>{const{rowSelection:n}=t.getState();return $s(e,n)==="all"},getCanSelect:()=>{var n;return typeof t.options.enableRowSelection=="function"?t.options.enableRowSelection(e):(n=t.options.enableRowSelection)!=null?n:!0},getCanSelectSubRows:()=>{var n;return typeof t.options.enableSubRowSelection=="function"?t.options.enableSubRowSelection(e):(n=t.options.enableSubRowSelection)!=null?n:!0},getCanMultiSelect:()=>{var n;return typeof t.options.enableMultiRowSelection=="function"?t.options.enableMultiRowSelection(e):(n=t.options.enableMultiRowSelection)!=null?n:!0},getToggleSelectedHandler:()=>{const n=e.getCanSelect();return l=>{var o;n&&e.toggleSelected((o=l.target)==null?void 0:o.checked)}}})},$o=(e,t,n,l)=>{var o;const r=l.getRow(t);n?(r.getCanMultiSelect()||Object.keys(e).forEach(s=>delete e[s]),r.getCanSelect()&&(e[t]=!0)):delete e[t],(o=r.subRows)!=null&&o.length&&r.getCanSelectSubRows()&&r.subRows.forEach(s=>$o(e,s.id,n,l))};function Jl(e,t){const n=e.getState().rowSelection,l=[],o={},r=function(s,u){return s.map(a=>{var f;const c=Bo(a,n);if(c&&(l.push(a),o[a.id]=a),(f=a.subRows)!=null&&f.length&&(a={...a,subRows:r(a.subRows)}),c)return a}).filter(Boolean)};return{rows:r(t.rows),flatRows:l,rowsById:o}}function Bo(e,t){var n;return(n=t[e.id])!=null?n:!1}function $s(e,t,n){if(e.subRows&&e.subRows.length){let l=!0,o=!1;return e.subRows.forEach(r=>{o&&!l||(Bo(r,t)?o=!0:l=!1)}),l?"all":o?"some":!1}return!1}const Ao=/([0-9]+)/gm,Km=(e,t,n)=>Mu(Rt(e.getValue(n)).toLowerCase(),Rt(t.getValue(n)).toLowerCase()),Jm=(e,t,n)=>Mu(Rt(e.getValue(n)),Rt(t.getValue(n))),Zm=(e,t,n)=>jo(Rt(e.getValue(n)).toLowerCase(),Rt(t.getValue(n)).toLowerCase()),e_=(e,t,n)=>jo(Rt(e.getValue(n)),Rt(t.getValue(n))),t_=(e,t,n)=>{const l=e.getValue(n),o=t.getValue(n);return l>o?1:ljo(e.getValue(n),t.getValue(n));function jo(e,t){return e===t?0:e>t?1:-1}function Rt(e){return typeof e=="number"?isNaN(e)||e===1/0||e===-1/0?"":String(e):typeof e=="string"?e:""}function Mu(e,t){const n=e.split(Ao).filter(Boolean),l=t.split(Ao).filter(Boolean);for(;n.length&&l.length;){const o=n.shift(),r=l.shift(),s=parseInt(o,10),u=parseInt(r,10),a=[s,u].sort();if(isNaN(a[0])){if(o>r)return 1;if(r>o)return-1;continue}if(isNaN(a[1]))return isNaN(s)?-1:1;if(s>u)return 1;if(u>s)return-1}return n.length-l.length}const hn={alphanumeric:Km,alphanumericCaseSensitive:Jm,text:Zm,textCaseSensitive:e_,datetime:t_,basic:n_},l_={getInitialState:e=>({sorting:[],...e}),getDefaultColumnDef:()=>({sortingFn:"auto",sortUndefined:1}),getDefaultOptions:e=>({onSortingChange:Qe("sorting",e),isMultiSortEvent:t=>t.shiftKey}),createColumn:(e,t)=>({getAutoSortingFn:()=>{const n=t.getFilteredRowModel().flatRows.slice(10);let l=!1;for(const o of n){const r=o==null?void 0:o.getValue(e.id);if(Object.prototype.toString.call(r)==="[object Date]")return hn.datetime;if(typeof r=="string"&&(l=!0,r.split(Ao).length>1))return hn.alphanumeric}return l?hn.text:hn.basic},getAutoSortDir:()=>{const n=t.getFilteredRowModel().flatRows[0];return typeof(n==null?void 0:n.getValue(e.id))=="string"?"asc":"desc"},getSortingFn:()=>{var n,l;if(!e)throw new Error;return Cl(e.columnDef.sortingFn)?e.columnDef.sortingFn:e.columnDef.sortingFn==="auto"?e.getAutoSortingFn():(n=(l=t.options.sortingFns)==null?void 0:l[e.columnDef.sortingFn])!=null?n:hn[e.columnDef.sortingFn]},toggleSorting:(n,l)=>{const o=e.getNextSortingOrder(),r=typeof n<"u"&&n!==null;t.setSorting(s=>{const u=s==null?void 0:s.find(g=>g.id===e.id),a=s==null?void 0:s.findIndex(g=>g.id===e.id);let f=[],c,d=r?n:o==="desc";if(s!=null&&s.length&&e.getCanMultiSort()&&l?u?c="toggle":c="add":s!=null&&s.length&&a!==s.length-1?c="replace":u?c="toggle":c="replace",c==="toggle"&&(r||o||(c="remove")),c==="add"){var p;f=[...s,{id:e.id,desc:d}],f.splice(0,f.length-((p=t.options.maxMultiSortColCount)!=null?p:Number.MAX_SAFE_INTEGER))}else c==="toggle"?f=s.map(g=>g.id===e.id?{...g,desc:d}:g):c==="remove"?f=s.filter(g=>g.id!==e.id):f=[{id:e.id,desc:d}];return f})},getFirstSortDir:()=>{var n,l;return((n=(l=e.columnDef.sortDescFirst)!=null?l:t.options.sortDescFirst)!=null?n:e.getAutoSortDir()==="desc")?"desc":"asc"},getNextSortingOrder:n=>{var l,o;const r=e.getFirstSortDir(),s=e.getIsSorted();return s?s!==r&&((l=t.options.enableSortingRemoval)==null||l)&&(!(n&&(o=t.options.enableMultiRemove)!=null)||o)?!1:s==="desc"?"asc":"desc":r},getCanSort:()=>{var n,l;return((n=e.columnDef.enableSorting)!=null?n:!0)&&((l=t.options.enableSorting)!=null?l:!0)&&!!e.accessorFn},getCanMultiSort:()=>{var n,l;return(n=(l=e.columnDef.enableMultiSort)!=null?l:t.options.enableMultiSort)!=null?n:!!e.accessorFn},getIsSorted:()=>{var n;const l=(n=t.getState().sorting)==null?void 0:n.find(o=>o.id===e.id);return l?l.desc?"desc":"asc":!1},getSortIndex:()=>{var n,l;return(n=(l=t.getState().sorting)==null?void 0:l.findIndex(o=>o.id===e.id))!=null?n:-1},clearSorting:()=>{t.setSorting(n=>n!=null&&n.length?n.filter(l=>l.id!==e.id):[])},getToggleSortingHandler:()=>{const n=e.getCanSort();return l=>{n&&(l.persist==null||l.persist(),e.toggleSorting==null||e.toggleSorting(void 0,e.getCanMultiSort()?t.options.isMultiSortEvent==null?void 0:t.options.isMultiSortEvent(l):!1))}}}),createTable:e=>({setSorting:t=>e.options.onSortingChange==null?void 0:e.options.onSortingChange(t),resetSorting:t=>{var n,l;e.setSorting(t?[]:(n=(l=e.initialState)==null?void 0:l.sorting)!=null?n:[])},getPreSortedRowModel:()=>e.getGroupedRowModel(),getSortedRowModel:()=>(!e._getSortedRowModel&&e.options.getSortedRowModel&&(e._getSortedRowModel=e.options.getSortedRowModel(e)),e.options.manualSorting||!e._getSortedRowModel?e.getPreSortedRowModel():e._getSortedRowModel())})},o_={getInitialState:e=>({columnVisibility:{},...e}),getDefaultOptions:e=>({onColumnVisibilityChange:Qe("columnVisibility",e)}),createColumn:(e,t)=>({toggleVisibility:n=>{e.getCanHide()&&t.setColumnVisibility(l=>({...l,[e.id]:n??!e.getIsVisible()}))},getIsVisible:()=>{var n,l;return(n=(l=t.getState().columnVisibility)==null?void 0:l[e.id])!=null?n:!0},getCanHide:()=>{var n,l;return((n=e.columnDef.enableHiding)!=null?n:!0)&&((l=t.options.enableHiding)!=null?l:!0)},getToggleVisibilityHandler:()=>n=>{e.toggleVisibility==null||e.toggleVisibility(n.target.checked)}}),createRow:(e,t)=>({_getAllVisibleCells:oe(()=>[e.getAllCells(),t.getState().columnVisibility],n=>n.filter(l=>l.column.getIsVisible()),{key:"row._getAllVisibleCells",debug:()=>{var n;return(n=t.options.debugAll)!=null?n:t.options.debugRows}}),getVisibleCells:oe(()=>[e.getLeftVisibleCells(),e.getCenterVisibleCells(),e.getRightVisibleCells()],(n,l,o)=>[...n,...l,...o],{key:!1,debug:()=>{var n;return(n=t.options.debugAll)!=null?n:t.options.debugRows}})}),createTable:e=>{const t=(n,l)=>oe(()=>[l(),l().filter(o=>o.getIsVisible()).map(o=>o.id).join("_")],o=>o.filter(r=>r.getIsVisible==null?void 0:r.getIsVisible()),{key:n,debug:()=>{var o;return(o=e.options.debugAll)!=null?o:e.options.debugColumns}});return{getVisibleFlatColumns:t("getVisibleFlatColumns",()=>e.getAllFlatColumns()),getVisibleLeafColumns:t("getVisibleLeafColumns",()=>e.getAllLeafColumns()),getLeftVisibleLeafColumns:t("getLeftVisibleLeafColumns",()=>e.getLeftLeafColumns()),getRightVisibleLeafColumns:t("getRightVisibleLeafColumns",()=>e.getRightLeafColumns()),getCenterVisibleLeafColumns:t("getCenterVisibleLeafColumns",()=>e.getCenterLeafColumns()),setColumnVisibility:n=>e.options.onColumnVisibilityChange==null?void 0:e.options.onColumnVisibilityChange(n),resetColumnVisibility:n=>{var l;e.setColumnVisibility(n?{}:(l=e.initialState.columnVisibility)!=null?l:{})},toggleAllColumnsVisible:n=>{var l;n=(l=n)!=null?l:!e.getIsAllColumnsVisible(),e.setColumnVisibility(e.getAllLeafColumns().reduce((o,r)=>({...o,[r.id]:n||!(r.getCanHide!=null&&r.getCanHide())}),{}))},getIsAllColumnsVisible:()=>!e.getAllLeafColumns().some(n=>!(n.getIsVisible!=null&&n.getIsVisible())),getIsSomeColumnsVisible:()=>e.getAllLeafColumns().some(n=>n.getIsVisible==null?void 0:n.getIsVisible()),getToggleAllColumnsVisibilityHandler:()=>n=>{var l;e.toggleAllColumnsVisible((l=n.target)==null?void 0:l.checked)}}}},As=[Dm,o_,Wm,Ym,Tm,l_,xm,Pm,Xm,Qm,Im];function i_(e){var t;(e.debugAll||e.debugTable)&&console.info("Creating Table Instance...");let n={_features:As};const l=n._features.reduce((c,d)=>Object.assign(c,d.getDefaultOptions==null?void 0:d.getDefaultOptions(n)),{}),o=c=>n.options.mergeOptions?n.options.mergeOptions(l,c):{...l,...c};let s={...{},...(t=e.initialState)!=null?t:{}};n._features.forEach(c=>{var d;s=(d=c.getInitialState==null?void 0:c.getInitialState(s))!=null?d:s});const u=[];let a=!1;const f={_features:As,options:{...l,...e},initialState:s,_queue:c=>{u.push(c),a||(a=!0,Promise.resolve().then(()=>{for(;u.length;)u.shift()();a=!1}).catch(d=>setTimeout(()=>{throw d})))},reset:()=>{n.setState(n.initialState)},setOptions:c=>{const d=$t(c,n.options);n.options=o(d)},getState:()=>n.options.state,setState:c=>{n.options.onStateChange==null||n.options.onStateChange(c)},_getRowId:(c,d,p)=>{var g;return(g=n.options.getRowId==null?void 0:n.options.getRowId(c,d,p))!=null?g:`${p?[p.id,d].join("."):d}`},getCoreRowModel:()=>(n._getCoreRowModel||(n._getCoreRowModel=n.options.getCoreRowModel(n)),n._getCoreRowModel()),getRowModel:()=>n.getPaginationRowModel(),getRow:c=>{const d=n.getRowModel().rowsById[c];if(!d)throw new Error;return d},_getDefaultColumnDef:oe(()=>[n.options.defaultColumn],c=>{var d;return c=(d=c)!=null?d:{},{header:p=>{const g=p.header.column.columnDef;return g.accessorKey?g.accessorKey:g.accessorFn?g.id:null},cell:p=>{var g,m;return(g=(m=p.renderValue())==null||m.toString==null?void 0:m.toString())!=null?g:null},...n._features.reduce((p,g)=>Object.assign(p,g.getDefaultColumnDef==null?void 0:g.getDefaultColumnDef()),{}),...c}},{debug:()=>{var c;return(c=n.options.debugAll)!=null?c:n.options.debugColumns},key:!1}),_getColumnDefs:()=>n.options.columns,getAllColumns:oe(()=>[n._getColumnDefs()],c=>{const d=function(p,g,m){return m===void 0&&(m=0),p.map(_=>{const v=Fm(n,_,m,g),y=_;return v.columns=y.columns?d(y.columns,v,m+1):[],v})};return d(c)},{key:!1,debug:()=>{var c;return(c=n.options.debugAll)!=null?c:n.options.debugColumns}}),getAllFlatColumns:oe(()=>[n.getAllColumns()],c=>c.flatMap(d=>d.getFlatColumns()),{key:!1,debug:()=>{var c;return(c=n.options.debugAll)!=null?c:n.options.debugColumns}}),_getAllFlatColumnsById:oe(()=>[n.getAllFlatColumns()],c=>c.reduce((d,p)=>(d[p.id]=p,d),{}),{key:!1,debug:()=>{var c;return(c=n.options.debugAll)!=null?c:n.options.debugColumns}}),getAllLeafColumns:oe(()=>[n.getAllColumns(),n._getOrderColumnsFn()],(c,d)=>{let p=c.flatMap(g=>g.getLeafColumns());return d(p)},{key:!1,debug:()=>{var c;return(c=n.options.debugAll)!=null?c:n.options.debugColumns}}),getColumn:c=>n._getAllFlatColumnsById()[c]};return Object.assign(n,f),n._features.forEach(c=>Object.assign(n,c.createTable==null?void 0:c.createTable(n))),n}function r_(e,t,n,l){const o=()=>{var s;return(s=r.getValue())!=null?s:e.options.renderFallbackValue},r={id:`${t.id}_${n.id}`,row:t,column:n,getValue:()=>t.getValue(l),renderValue:o,getContext:oe(()=>[e,n,t,r],(s,u,a,f)=>({table:s,column:u,row:a,cell:f,getValue:f.getValue,renderValue:f.renderValue}),{key:!1,debug:()=>e.options.debugAll})};return e._features.forEach(s=>{Object.assign(r,s.createCell==null?void 0:s.createCell(r,n,t,e))},{}),r}const xo=(e,t,n,l,o,r,s)=>{let u={id:t,index:l,original:n,depth:o,parentId:s,_valuesCache:{},_uniqueValuesCache:{},getValue:a=>{if(u._valuesCache.hasOwnProperty(a))return u._valuesCache[a];const f=e.getColumn(a);if(f!=null&&f.accessorFn)return u._valuesCache[a]=f.accessorFn(u.original,l),u._valuesCache[a]},getUniqueValues:a=>{if(u._uniqueValuesCache.hasOwnProperty(a))return u._uniqueValuesCache[a];const f=e.getColumn(a);if(f!=null&&f.accessorFn)return f.columnDef.getUniqueValues?(u._uniqueValuesCache[a]=f.columnDef.getUniqueValues(u.original,l),u._uniqueValuesCache[a]):(u._uniqueValuesCache[a]=[u.getValue(a)],u._uniqueValuesCache[a])},renderValue:a=>{var f;return(f=u.getValue(a))!=null?f:e.options.renderFallbackValue},subRows:r??[],getLeafRows:()=>Em(u.subRows,a=>a.subRows),getParentRow:()=>u.parentId?e.getRow(u.parentId):void 0,getParentRows:()=>{let a=[],f=u;for(;;){const c=f.getParentRow();if(!c)break;a.push(c),f=c}return a.reverse()},getAllCells:oe(()=>[e.getAllLeafColumns()],a=>a.map(f=>r_(e,u,f,f.id)),{key:!1,debug:()=>{var a;return(a=e.options.debugAll)!=null?a:e.options.debugRows}}),_getAllCellsByColumnId:oe(()=>[u.getAllCells()],a=>a.reduce((f,c)=>(f[c.column.id]=c,f),{}),{key:"row.getAllCellsByColumnId",debug:()=>{var a;return(a=e.options.debugAll)!=null?a:e.options.debugRows}})};for(let a=0;atypeof e=="function"?{...t,accessorFn:e}:{...t,accessorKey:e},display:e=>e,group:e=>e}}function Rs(){return e=>oe(()=>[e.options.data],t=>{const n={rows:[],flatRows:[],rowsById:{}},l=function(o,r,s){r===void 0&&(r=0);const u=[];for(let f=0;f{var t;return(t=e.options.debugAll)!=null?t:e.options.debugTable},onChange:()=>{e._autoResetPageIndex()}})}function Eu(e,t,n){return n.options.filterFromLeafRows?a_(e,t,n):u_(e,t,n)}function a_(e,t,n){var l;const o=[],r={},s=(l=n.options.maxLeafRowFilterDepth)!=null?l:100,u=function(a,f){f===void 0&&(f=0);const c=[];for(let p=0;poe(()=>[e.getPreFilteredRowModel(),e.getState().columnFilters,e.getState().globalFilter],(t,n,l)=>{if(!t.rows.length||!(n!=null&&n.length)&&!l){for(let p=0;p{var g;const m=e.getColumn(p.id);if(!m)return;const _=m.getFilterFn();_&&o.push({id:p.id,filterFn:_,resolvedValue:(g=_.resolveFilterValue==null?void 0:_.resolveFilterValue(p.value))!=null?g:p.value})});const s=n.map(p=>p.id),u=e.getGlobalFilterFn(),a=e.getAllLeafColumns().filter(p=>p.getCanGlobalFilter());l&&u&&a.length&&(s.push("__global__"),a.forEach(p=>{var g;r.push({id:p.id,filterFn:u,resolvedValue:(g=u.resolveFilterValue==null?void 0:u.resolveFilterValue(l))!=null?g:l})}));let f,c;for(let p=0;p{g.columnFiltersMeta[_]=v})}if(r.length){for(let m=0;m{g.columnFiltersMeta[_]=v})){g.columnFilters.__global__=!0;break}}g.columnFilters.__global__!==!0&&(g.columnFilters.__global__=!1)}}const d=p=>{for(let g=0;g{var t;return(t=e.options.debugAll)!=null?t:e.options.debugTable},onChange:()=>{e._autoResetPageIndex()}})}function Es(){return(e,t)=>oe(()=>[e.getPreFilteredRowModel(),e.getState().columnFilters,e.getState().globalFilter,e.getFilteredRowModel()],(n,l,o)=>{if(!n.rows.length||!(l!=null&&l.length)&&!o)return n;const r=[...l.map(u=>u.id).filter(u=>u!==t),o?"__global__":void 0].filter(Boolean),s=u=>{for(let a=0;a{var n;return(n=e.options.debugAll)!=null?n:e.options.debugTable},onChange:()=>{}})}function Fs(){return e=>oe(()=>[e.getState().sorting,e.getPreSortedRowModel()],(t,n)=>{if(!n.rows.length||!(t!=null&&t.length))return n;const l=e.getState().sorting,o=[],r=l.filter(a=>{var f;return(f=e.getColumn(a.id))==null?void 0:f.getCanSort()}),s={};r.forEach(a=>{const f=e.getColumn(a.id);f&&(s[a.id]={sortUndefined:f.columnDef.sortUndefined,invertSorting:f.columnDef.invertSorting,sortingFn:f.getSortingFn()})});const u=a=>{const f=[...a];return f.sort((c,d)=>{for(let g=0;g{var d;o.push(c),(d=c.subRows)!=null&&d.length&&(c.subRows=u(c.subRows))}),f};return{rows:u(n.rows),flatRows:o,rowsById:n.rowsById}},{key:!1,debug:()=>{var t;return(t=e.options.debugAll)!=null?t:e.options.debugTable},onChange:()=>{e._autoResetPageIndex()}})}function f_(e){const t=[],n=l=>{var o;t.push(l),(o=l.subRows)!=null&&o.length&&l.getIsExpanded()&&l.subRows.forEach(n)};return e.rows.forEach(n),{rows:t,flatRows:e.flatRows,rowsById:e.rowsById}}function Ds(e){return t=>oe(()=>[t.getState().pagination,t.getPrePaginationRowModel(),t.options.paginateExpandedRows?void 0:t.getState().expanded],(n,l)=>{if(!l.rows.length)return l;const{pageSize:o,pageIndex:r}=n;let{rows:s,flatRows:u,rowsById:a}=l;const f=o*r,c=f+o;s=s.slice(f,c);let d;t.options.paginateExpandedRows?d={rows:s,flatRows:u,rowsById:a}:d=f_({rows:s,flatRows:u,rowsById:a}),d.flatRows=[];const p=g=>{d.flatRows.push(g),g.subRows.length&&g.subRows.forEach(p)};return d.rows.forEach(p),d},{key:!1,debug:()=>{var n;return(n=t.options.debugAll)!=null?n:t.options.debugTable}})}/**
* svelte-table
*
* Copyright (c) TanStack
@@ -46,7 +46,7 @@
* LICENSE.md file in the root directory of this source tree.
*
* @license MIT
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* match-sorter-utils
*
* Copyright (c) TanStack
@@ -55,10 +55,10 @@
* LICENSE.md file in the root directory of this source tree.
*
* @license MIT
- */const Mu={À:"A",Á:"A",Â:"A",Ã:"A",Ä:"A",Å:"A",Ấ:"A",Ắ:"A",Ẳ:"A",Ẵ:"A",Ặ:"A",Æ:"AE",Ầ:"A",Ằ:"A",Ȃ:"A",Ç:"C",Ḉ:"C",È:"E",É:"E",Ê:"E",Ë:"E",Ế:"E",Ḗ:"E",Ề:"E",Ḕ:"E",Ḝ:"E",Ȇ:"E",Ì:"I",Í:"I",Î:"I",Ï:"I",Ḯ:"I",Ȋ:"I",Ð:"D",Ñ:"N",Ò:"O",Ó:"O",Ô:"O",Õ:"O",Ö:"O",Ø:"O",Ố:"O",Ṍ:"O",Ṓ:"O",Ȏ:"O",Ù:"U",Ú:"U",Û:"U",Ü:"U",Ý:"Y",à:"a",á:"a",â:"a",ã:"a",ä:"a",å:"a",ấ:"a",ắ:"a",ẳ:"a",ẵ:"a",ặ:"a",æ:"ae",ầ:"a",ằ:"a",ȃ:"a",ç:"c",ḉ:"c",è:"e",é:"e",ê:"e",ë:"e",ế:"e",ḗ:"e",ề:"e",ḕ:"e",ḝ:"e",ȇ:"e",ì:"i",í:"i",î:"i",ï:"i",ḯ:"i",ȋ:"i",ð:"d",ñ:"n",ò:"o",ó:"o",ô:"o",õ:"o",ö:"o",ø:"o",ố:"o",ṍ:"o",ṓ:"o",ȏ:"o",ù:"u",ú:"u",û:"u",ü:"u",ý:"y",ÿ:"y",Ā:"A",ā:"a",Ă:"A",ă:"a",Ą:"A",ą:"a",Ć:"C",ć:"c",Ĉ:"C",ĉ:"c",Ċ:"C",ċ:"c",Č:"C",č:"c",C̆:"C",c̆:"c",Ď:"D",ď:"d",Đ:"D",đ:"d",Ē:"E",ē:"e",Ĕ:"E",ĕ:"e",Ė:"E",ė:"e",Ę:"E",ę:"e",Ě:"E",ě:"e",Ĝ:"G",Ǵ:"G",ĝ:"g",ǵ:"g",Ğ:"G",ğ:"g",Ġ:"G",ġ:"g",Ģ:"G",ģ:"g",Ĥ:"H",ĥ:"h",Ħ:"H",ħ:"h",Ḫ:"H",ḫ:"h",Ĩ:"I",ĩ:"i",Ī:"I",ī:"i",Ĭ:"I",ĭ:"i",Į:"I",į:"i",İ:"I",ı:"i",IJ:"IJ",ij:"ij",Ĵ:"J",ĵ:"j",Ķ:"K",ķ:"k",Ḱ:"K",ḱ:"k",K̆:"K",k̆:"k",Ĺ:"L",ĺ:"l",Ļ:"L",ļ:"l",Ľ:"L",ľ:"l",Ŀ:"L",ŀ:"l",Ł:"l",ł:"l",Ḿ:"M",ḿ:"m",M̆:"M",m̆:"m",Ń:"N",ń:"n",Ņ:"N",ņ:"n",Ň:"N",ň:"n",ʼn:"n",N̆:"N",n̆:"n",Ō:"O",ō:"o",Ŏ:"O",ŏ:"o",Ő:"O",ő:"o",Œ:"OE",œ:"oe",P̆:"P",p̆:"p",Ŕ:"R",ŕ:"r",Ŗ:"R",ŗ:"r",Ř:"R",ř:"r",R̆:"R",r̆:"r",Ȓ:"R",ȓ:"r",Ś:"S",ś:"s",Ŝ:"S",ŝ:"s",Ş:"S",Ș:"S",ș:"s",ş:"s",Š:"S",š:"s",Ţ:"T",ţ:"t",ț:"t",Ț:"T",Ť:"T",ť:"t",Ŧ:"T",ŧ:"t",T̆:"T",t̆:"t",Ũ:"U",ũ:"u",Ū:"U",ū:"u",Ŭ:"U",ŭ:"u",Ů:"U",ů:"u",Ű:"U",ű:"u",Ų:"U",ų:"u",Ȗ:"U",ȗ:"u",V̆:"V",v̆:"v",Ŵ:"W",ŵ:"w",Ẃ:"W",ẃ:"w",X̆:"X",x̆:"x",Ŷ:"Y",ŷ:"y",Ÿ:"Y",Y̆:"Y",y̆:"y",Ź:"Z",ź:"z",Ż:"Z",ż:"z",Ž:"Z",ž:"z",ſ:"s",ƒ:"f",Ơ:"O",ơ:"o",Ư:"U",ư:"u",Ǎ:"A",ǎ:"a",Ǐ:"I",ǐ:"i",Ǒ:"O",ǒ:"o",Ǔ:"U",ǔ:"u",Ǖ:"U",ǖ:"u",Ǘ:"U",ǘ:"u",Ǚ:"U",ǚ:"u",Ǜ:"U",ǜ:"u",Ứ:"U",ứ:"u",Ṹ:"U",ṹ:"u",Ǻ:"A",ǻ:"a",Ǽ:"AE",ǽ:"ae",Ǿ:"O",ǿ:"o",Þ:"TH",þ:"th",Ṕ:"P",ṕ:"p",Ṥ:"S",ṥ:"s",X́:"X",x́:"x",Ѓ:"Г",ѓ:"г",Ќ:"К",ќ:"к",A̋:"A",a̋:"a",E̋:"E",e̋:"e",I̋:"I",i̋:"i",Ǹ:"N",ǹ:"n",Ồ:"O",ồ:"o",Ṑ:"O",ṑ:"o",Ừ:"U",ừ:"u",Ẁ:"W",ẁ:"w",Ỳ:"Y",ỳ:"y",Ȁ:"A",ȁ:"a",Ȅ:"E",ȅ:"e",Ȉ:"I",ȉ:"i",Ȍ:"O",ȍ:"o",Ȑ:"R",ȑ:"r",Ȕ:"U",ȕ:"u",B̌:"B",b̌:"b",Č̣:"C",č̣:"c",Ê̌:"E",ê̌:"e",F̌:"F",f̌:"f",Ǧ:"G",ǧ:"g",Ȟ:"H",ȟ:"h",J̌:"J",ǰ:"j",Ǩ:"K",ǩ:"k",M̌:"M",m̌:"m",P̌:"P",p̌:"p",Q̌:"Q",q̌:"q",Ř̩:"R",ř̩:"r",Ṧ:"S",ṧ:"s",V̌:"V",v̌:"v",W̌:"W",w̌:"w",X̌:"X",x̌:"x",Y̌:"Y",y̌:"y",A̧:"A",a̧:"a",B̧:"B",b̧:"b",Ḑ:"D",ḑ:"d",Ȩ:"E",ȩ:"e",Ɛ̧:"E",ɛ̧:"e",Ḩ:"H",ḩ:"h",I̧:"I",i̧:"i",Ɨ̧:"I",ɨ̧:"i",M̧:"M",m̧:"m",O̧:"O",o̧:"o",Q̧:"Q",q̧:"q",U̧:"U",u̧:"u",X̧:"X",x̧:"x",Z̧:"Z",z̧:"z"},v_=Object.keys(Mu).join("|"),b_=new RegExp(v_,"g");function w_(e){return e.replace(b_,t=>Mu[t])}/**
+ */const Fu={À:"A",Á:"A",Â:"A",Ã:"A",Ä:"A",Å:"A",Ấ:"A",Ắ:"A",Ẳ:"A",Ẵ:"A",Ặ:"A",Æ:"AE",Ầ:"A",Ằ:"A",Ȃ:"A",Ç:"C",Ḉ:"C",È:"E",É:"E",Ê:"E",Ë:"E",Ế:"E",Ḗ:"E",Ề:"E",Ḕ:"E",Ḝ:"E",Ȇ:"E",Ì:"I",Í:"I",Î:"I",Ï:"I",Ḯ:"I",Ȋ:"I",Ð:"D",Ñ:"N",Ò:"O",Ó:"O",Ô:"O",Õ:"O",Ö:"O",Ø:"O",Ố:"O",Ṍ:"O",Ṓ:"O",Ȏ:"O",Ù:"U",Ú:"U",Û:"U",Ü:"U",Ý:"Y",à:"a",á:"a",â:"a",ã:"a",ä:"a",å:"a",ấ:"a",ắ:"a",ẳ:"a",ẵ:"a",ặ:"a",æ:"ae",ầ:"a",ằ:"a",ȃ:"a",ç:"c",ḉ:"c",è:"e",é:"e",ê:"e",ë:"e",ế:"e",ḗ:"e",ề:"e",ḕ:"e",ḝ:"e",ȇ:"e",ì:"i",í:"i",î:"i",ï:"i",ḯ:"i",ȋ:"i",ð:"d",ñ:"n",ò:"o",ó:"o",ô:"o",õ:"o",ö:"o",ø:"o",ố:"o",ṍ:"o",ṓ:"o",ȏ:"o",ù:"u",ú:"u",û:"u",ü:"u",ý:"y",ÿ:"y",Ā:"A",ā:"a",Ă:"A",ă:"a",Ą:"A",ą:"a",Ć:"C",ć:"c",Ĉ:"C",ĉ:"c",Ċ:"C",ċ:"c",Č:"C",č:"c",C̆:"C",c̆:"c",Ď:"D",ď:"d",Đ:"D",đ:"d",Ē:"E",ē:"e",Ĕ:"E",ĕ:"e",Ė:"E",ė:"e",Ę:"E",ę:"e",Ě:"E",ě:"e",Ĝ:"G",Ǵ:"G",ĝ:"g",ǵ:"g",Ğ:"G",ğ:"g",Ġ:"G",ġ:"g",Ģ:"G",ģ:"g",Ĥ:"H",ĥ:"h",Ħ:"H",ħ:"h",Ḫ:"H",ḫ:"h",Ĩ:"I",ĩ:"i",Ī:"I",ī:"i",Ĭ:"I",ĭ:"i",Į:"I",į:"i",İ:"I",ı:"i",IJ:"IJ",ij:"ij",Ĵ:"J",ĵ:"j",Ķ:"K",ķ:"k",Ḱ:"K",ḱ:"k",K̆:"K",k̆:"k",Ĺ:"L",ĺ:"l",Ļ:"L",ļ:"l",Ľ:"L",ľ:"l",Ŀ:"L",ŀ:"l",Ł:"l",ł:"l",Ḿ:"M",ḿ:"m",M̆:"M",m̆:"m",Ń:"N",ń:"n",Ņ:"N",ņ:"n",Ň:"N",ň:"n",ʼn:"n",N̆:"N",n̆:"n",Ō:"O",ō:"o",Ŏ:"O",ŏ:"o",Ő:"O",ő:"o",Œ:"OE",œ:"oe",P̆:"P",p̆:"p",Ŕ:"R",ŕ:"r",Ŗ:"R",ŗ:"r",Ř:"R",ř:"r",R̆:"R",r̆:"r",Ȓ:"R",ȓ:"r",Ś:"S",ś:"s",Ŝ:"S",ŝ:"s",Ş:"S",Ș:"S",ș:"s",ş:"s",Š:"S",š:"s",Ţ:"T",ţ:"t",ț:"t",Ț:"T",Ť:"T",ť:"t",Ŧ:"T",ŧ:"t",T̆:"T",t̆:"t",Ũ:"U",ũ:"u",Ū:"U",ū:"u",Ŭ:"U",ŭ:"u",Ů:"U",ů:"u",Ű:"U",ű:"u",Ų:"U",ų:"u",Ȗ:"U",ȗ:"u",V̆:"V",v̆:"v",Ŵ:"W",ŵ:"w",Ẃ:"W",ẃ:"w",X̆:"X",x̆:"x",Ŷ:"Y",ŷ:"y",Ÿ:"Y",Y̆:"Y",y̆:"y",Ź:"Z",ź:"z",Ż:"Z",ż:"z",Ž:"Z",ž:"z",ſ:"s",ƒ:"f",Ơ:"O",ơ:"o",Ư:"U",ư:"u",Ǎ:"A",ǎ:"a",Ǐ:"I",ǐ:"i",Ǒ:"O",ǒ:"o",Ǔ:"U",ǔ:"u",Ǖ:"U",ǖ:"u",Ǘ:"U",ǘ:"u",Ǚ:"U",ǚ:"u",Ǜ:"U",ǜ:"u",Ứ:"U",ứ:"u",Ṹ:"U",ṹ:"u",Ǻ:"A",ǻ:"a",Ǽ:"AE",ǽ:"ae",Ǿ:"O",ǿ:"o",Þ:"TH",þ:"th",Ṕ:"P",ṕ:"p",Ṥ:"S",ṥ:"s",X́:"X",x́:"x",Ѓ:"Г",ѓ:"г",Ќ:"К",ќ:"к",A̋:"A",a̋:"a",E̋:"E",e̋:"e",I̋:"I",i̋:"i",Ǹ:"N",ǹ:"n",Ồ:"O",ồ:"o",Ṑ:"O",ṑ:"o",Ừ:"U",ừ:"u",Ẁ:"W",ẁ:"w",Ỳ:"Y",ỳ:"y",Ȁ:"A",ȁ:"a",Ȅ:"E",ȅ:"e",Ȉ:"I",ȉ:"i",Ȍ:"O",ȍ:"o",Ȑ:"R",ȑ:"r",Ȕ:"U",ȕ:"u",B̌:"B",b̌:"b",Č̣:"C",č̣:"c",Ê̌:"E",ê̌:"e",F̌:"F",f̌:"f",Ǧ:"G",ǧ:"g",Ȟ:"H",ȟ:"h",J̌:"J",ǰ:"j",Ǩ:"K",ǩ:"k",M̌:"M",m̌:"m",P̌:"P",p̌:"p",Q̌:"Q",q̌:"q",Ř̩:"R",ř̩:"r",Ṧ:"S",ṧ:"s",V̌:"V",v̌:"v",W̌:"W",w̌:"w",X̌:"X",x̌:"x",Y̌:"Y",y̌:"y",A̧:"A",a̧:"a",B̧:"B",b̧:"b",Ḑ:"D",ḑ:"d",Ȩ:"E",ȩ:"e",Ɛ̧:"E",ɛ̧:"e",Ḩ:"H",ḩ:"h",I̧:"I",i̧:"i",Ɨ̧:"I",ɨ̧:"i",M̧:"M",m̧:"m",O̧:"O",o̧:"o",Q̧:"Q",q̧:"q",U̧:"U",u̧:"u",X̧:"X",x̧:"x",Z̧:"Z",z̧:"z"},y_=Object.keys(Fu).join("|"),k_=new RegExp(y_,"g");function S_(e){return e.replace(k_,t=>Fu[t])}/**
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* @license MIT license.
* @copyright (c) 2099 Kent C. Dodds
* @author Kent C. Dodds (https://kentcdodds.com)
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new file mode 100644
index 0000000..32009ad
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+++ b/pykoi/frontend/dist/assets/index-857a4a8b.css
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12.5%}.left-text.svelte-14znspc{text-align:center;border-bottom:var(--line);border-right:var(--line);display:grid;grid-template-rows:100%;grid-template-columns:50% 50%}.left-confidence.svelte-14znspc{text-align:center;border-bottom:var(--line);border-right:var(--line);display:grid;grid-template-rows:40% 55%}.left-question.svelte-14znspc,.left-filter.svelte-14znspc{border-bottom:var(--line);border-right:var(--line)}.feedback-right.svelte-14znspc{border-bottom:var(--line);border-right:var(--line);display:grid;grid-template-columns:100%;grid-template-rows:30% 70%;grid-template-rows:0% 100%}.right-chart.svelte-14znspc{border-bottom:var(--line);border-right:var(--line);display:grid;grid-template-columns:50% 50%;grid-template-rows:100%}.right-chart-1.svelte-14znspc,.right-chart-2.svelte-14znspc{border-right:var(--line)}.right-table.svelte-14znspc{border-bottom:var(--line);border-right:var(--line)}.card-1.svelte-14znspc{border-right:var(--line)}
diff --git a/pykoi/frontend/dist/index.html b/pykoi/frontend/dist/index.html
index ee742e2..bf2e096 100644
--- a/pykoi/frontend/dist/index.html
+++ b/pykoi/frontend/dist/index.html
@@ -5,8 +5,8 @@
Vite + Svelte
-
-
+
+
diff --git a/pykoi/frontend/src/lib/Chatbots/Components/Dropdown.svelte b/pykoi/frontend/src/lib/Chatbots/Components/Dropdown.svelte
index f435a81..a9dee91 100644
--- a/pykoi/frontend/src/lib/Chatbots/Components/Dropdown.svelte
+++ b/pykoi/frontend/src/lib/Chatbots/Components/Dropdown.svelte
@@ -1,15 +1,16 @@
@@ -39,17 +61,30 @@
class="dropdown-content"
style="display: {expanded ? 'block' : 'none'};"
>
- {#each documents as doc, index}
-
-
- handleCheckboxChange(doc.name, event)}
- />{doc.name}
-
- {/each}
+ {#if documents.length === 0}
+
+ No documents found. Please upload via the RetrievalQA component.
+
+ {:else}
+
+ Select All
+ Deselect All
+
+
+ {#each documents as doc, index}
+
+
+ handleCheckboxChange(doc.name, event)}
+ />
+ {centerTruncate(doc.name)}
+
+ {/each}
+
+ {/if}
@@ -85,5 +120,38 @@
border: 1px #dadada solid;
background-color: white;
z-index: 1;
+ padding: 0.5em;
+ }
+ .checkbox-container {
+ display: flex;
+ flex-direction: column;
+ align-items: flex-start;
+ }
+
+ .checkbox-container label {
+ display: inline-block;
+ padding-right: 10px;
+ white-space: nowrap;
+ }
+ .checkbox-container input {
+ cursor: pointer;
+ vertical-align: middle;
+ }
+ .checkbox-container label span {
+ cursor: pointer;
+ vertical-align: middle;
+ }
+
+ .select-button-container {
+ width: 100%;
+ display: flex;
+ justify-content: center;
+ padding-top: 1em;
+ }
+ .select-button-container button {
+ color: var(--darkGrey);
+ font-size: small;
+ margin: 0 2px;
+ padding: 1em;
}
diff --git a/pykoi/frontend/src/lib/Chatbots/Components/SourceContainer.svelte b/pykoi/frontend/src/lib/Chatbots/Components/SourceContainer.svelte
index 900288f..4cfd325 100644
--- a/pykoi/frontend/src/lib/Chatbots/Components/SourceContainer.svelte
+++ b/pykoi/frontend/src/lib/Chatbots/Components/SourceContainer.svelte
@@ -8,7 +8,7 @@
{#if sources !== undefined}
{#if sources[0] === "N/A"}
-
⚠️ No Retrieval Sources selected
+
⚠️ No Retrieval Sources selected or received
{:else if sources[0] === "Loading..."}
📖 Loading...
{:else if sources[0] === "Not loaded"}
From cae1fb1cacfe6bc6b18d25794cb50b91ceb5a6ef Mon Sep 17 00:00:00 2001
From: Jojo Ortiz
Date: Sun, 15 Oct 2023 22:04:05 -0700
Subject: [PATCH 3/7] WIP updating hugginface model to work with new RAG
features
---
pykoi/application.py | 6 +++-
pykoi/retrieval/llm/huggingface.py | 49 ++++++++++++++++++++++++------
2 files changed, 44 insertions(+), 11 deletions(-)
diff --git a/pykoi/application.py b/pykoi/application.py
index 44dd8f0..ad6f53c 100644
--- a/pykoi/application.py
+++ b/pykoi/application.py
@@ -606,7 +606,11 @@ async def inference(
component["component"].retrieval_model.re_init(request_body.file_names)
output = component["component"].retrieval_model.run_with_return_source_documents({"query": request_body.prompt})
print('output', output, output["result"])
- if output["source_documents"] == []:
+ if "source_documents" not in output:
+ print('no source documents', output)
+ source = ["N/A"]
+ source_content = ["N/A"]
+ elif output["source_documents"] == []:
source = ["N/A"]
source_content = ["N/A"]
else:
diff --git a/pykoi/retrieval/llm/huggingface.py b/pykoi/retrieval/llm/huggingface.py
index fe59b46..bb2de3a 100644
--- a/pykoi/retrieval/llm/huggingface.py
+++ b/pykoi/retrieval/llm/huggingface.py
@@ -13,15 +13,15 @@
class HuggingFaceModel(AbsLlm):
"""
- A class representing a language model that uses OpenAI's GPT-3 to generate text.
+ A class representing a language model that uses Huggingface's model to generate text.
"""
def __init__(self, vector_db: AbsVectorDb, **kwargs):
"""
- Initializes the OpenAIModel class.
+ Initializes the HuggingFaceModel class.
"""
try:
- llm = HuggingFacePipeline.from_model_id(
+ self._llm = HuggingFacePipeline.from_model_id(
model_id=kwargs.get("model_name"),
task="text-generation",
device=torch.cuda.device_count() - 1,
@@ -29,20 +29,49 @@ def __init__(self, vector_db: AbsVectorDb, **kwargs):
model_kwargs={
"temperature": 0,
"max_length": kwargs.get("max_length", 500),
- "trust_remote_code": kwargs.get("trust_remote_code", True),
+ # "trust_remote_code": kwargs.get("trust_remote_code", True),
# "load_in_8bit": True,
},
)
- vector_db = vector_db.vector_db
+ self._vector_db = vector_db.vector_db
- retrieve_qa = RetrievalQA.from_chain_type(
- llm=llm,
+ self._retrieve_qa = RetrievalQA.from_chain_type(
+ llm=self._llm,
chain_type="stuff",
- retriever=vector_db.as_retriever(search_kwargs={"k": MIN_DOCS}),
+ retriever=self._vector_db.as_retriever(search_kwargs={"k": MIN_DOCS, "filter": {}}),
verbose=True,
+ return_source_documents=True,
)
- print("HuggingFaceModel initialized successfully")
- super().__init__(retrieve_qa)
+ print("HuggingFaceModel initialized successfully!")
+ super().__init__(self._retrieve_qa)
except Exception as ex:
print("Inference initialization failed: {}".format(ex))
+
+ def re_init(self, file_names: list[str]):
+ """
+ Re-initializes the HuggingFaceModel class.
+ """
+ try:
+ if file_names == []:
+ metadata_filename_filter = {"file_name": ""}
+ elif len(file_names) == 1:
+ metadata_filename_filter = {"file_name": file_names[0]}
+ else:
+ metadata_filename_filter = {
+ "$or": [{"file_name": name} for name in file_names]
+ }
+
+ self._retrieve_qa = RetrievalQA.from_chain_type(
+ llm=self._llm,
+ chain_type="stuff",
+ retriever=self._vector_db.as_retriever(search_kwargs={"k": MIN_DOCS, "filter": metadata_filename_filter}),
+ verbose=True,
+ return_source_documents=True,
+ )
+
+ print("Re-initialized HuggingFaceModel successfully with filter: ", metadata_filename_filter)
+
+ super().__init__(self._retrieve_qa)
+ except Exception as ex:
+ print("Inference re-init failed: {}".format(ex))
From 810aec56966f5089d3a4a8dac63bfb561b89b671 Mon Sep 17 00:00:00 2001
From: Jojo Ortiz
Date: Sun, 15 Oct 2023 22:04:46 -0700
Subject: [PATCH 4/7] update pyproject for huggingface RAG
---
pyproject.toml | 17 +++++++++--------
1 file changed, 9 insertions(+), 8 deletions(-)
diff --git a/pyproject.toml b/pyproject.toml
index 7592087..1972cd1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -48,16 +48,17 @@ huggingface = [
"einops",
"accelerate",
"bitsandbytes",
- "sentence-transformers"
+ "sentence-transformers",
+ "xformers"
]
rag = [
- "langchain",
- "scikit-learn",
- "chromadb",
- "pyepsilla",
- "pdfminer-six",
- "docx2txt",
- "python-multipart",
+ "langchain",
+ "scikit-learn",
+ "chromadb",
+ "pyepsilla",
+ "pdfminer-six",
+ "docx2txt",
+ "python-multipart",
"tiktoken"
]
rlhf = [
From 082a532124f7571288d80e72276f1c2a4c4283fb Mon Sep 17 00:00:00 2001
From: Jojo Ortiz
Date: Sun, 15 Oct 2023 22:05:15 -0700
Subject: [PATCH 5/7] add RAG section with gifs and copy to README
---
README.md | 33 ++++++++++++------
example/image/pykoi_demo_rag_chatbot_chat.gif | Bin 0 -> 2989050 bytes
.../image/pykoi_demo_rag_chatbot_modify.gif | Bin 0 -> 488208 bytes
.../image/pykoi_demo_rag_chatbot_upload.gif | Bin 0 -> 669024 bytes
4 files changed, 23 insertions(+), 10 deletions(-)
create mode 100644 example/image/pykoi_demo_rag_chatbot_chat.gif
create mode 100644 example/image/pykoi_demo_rag_chatbot_modify.gif
create mode 100644 example/image/pykoi_demo_rag_chatbot_upload.gif
diff --git a/README.md b/README.md
index 291c1ff..a85bafd 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@
-[pykoi](https://www.cambioml.com/pykoi/) is an open-source python library for improving LLMs with RLHF. We provide a unified interface including RLHF/RLAIF data and feedback collection, finetuning with reinforcement learning and reward modeling, and LLM comparisons.
+[pykoi](https://www.cambioml.com/pykoi/) is an open-source python library for improving LLMs with RLHF. We provide a unified interface including RLHF/RLAIF data and feedback collection, finetuning with reinforcement learning and reward modeling, and LLM comparisons.
## Features
@@ -26,12 +26,12 @@ Do you want to store your chat history with LLMs from OpenAI, Amazon Bedrock(:wo
### Model comparison
-Comparing models is a difficult task. `pykoi` makes it easy by allowing one to directly compare the performance of multiple models to each other, with just a few lines of code. If you have multiple language models that you’d like to compare to each other on a set of prompts or via an interactive session, you can use `pk.Compare`. Check out any of the demo below:
+Comparing models is a difficult task. `pykoi` makes it easy by allowing one to directly compare the performance of multiple models to each other, with just a few lines of code. If you have multiple language models that you’d like to compare to each other on a set of prompts or via an interactive session, you can use `pk.Compare`. Check out any of the demo below:
- If you're using a CPU instance, check out [demo_launch_app_cpu.ipynb](https://nbviewer.org/github/CambioML/pykoi/blob/main/example/chatbot/demo_model_comparator_openai.ipynb)
- If you're using a GPU instance, check out [demo_launch_app_gpu.ipynb](https://nbviewer.org/github/CambioML/pykoi/blob/main/example/chatbot/demo_model_comparator_gpu_huggingface.ipynb)
- Alternatively, read our [blog](https://www.cambioml.com/docs/model_comparison.html) for more information!
-
+
![Watch the video](example/image/pykoi_demo_model_comparison.gif)
### RLHF
@@ -40,6 +40,19 @@ Reinforcement Learning with Human Feedback (RLHF) is a unique training paradigm
`pykoi` allows you to easily fine-tune your model on the datasets you've collected via your `pykoi` chat or rank databases. Check our [blog](https://www.cambioml.com/docs/rlhf.html) for detailed instructions on how to use it.
+### RAG
+Implement a Retrieval-Augmented Generation (RAG) Chatbot quickly with `pykoi`. Upload your own documents to create context-aware responses on top of a pretrained LLM, such as from OpenAI or Huggingface.
+
+![Watch the video](example/image/pykoi_demo_rag_chatbot_upload.gif)
+
+Then chat with the RAG Chatbot, where you can select specific sources for the text generation, as well as see which sources the model used to generate the response.
+
+![Watch the video](example/image/pykoi_demo_rag_chatbot_chat.gif)
+
+Finally, you can save a modified response to help improve the model. This is a great way to collect data for RLHF.
+
+![Watch the video](example/image/pykoi_demo_rag_chatbot_modify.gif)
+
## Installation
To get started with pykoi, you can choose from any of the installation options. The choice should be based on the features you need (e.g., RAG, RLHF or all) and the compute resources you have, such as a CPU (e.g., your laptop) or GPU (e.g., AWS EC2 or SageMaker).
@@ -56,10 +69,10 @@ conda activate pykoi # some OS requires `source activate pykoi`
Then install `pykoi` and the compatible [pytorch based on your os](https://pytorch.org/get-started/locally/)
```
pip3 install "pykoi[rag]"
-pip3 install torch
+pip3 install torch
```
-### Option 2: RAG (GPU)
+### Option 2: RAG (GPU)
This option allows you to run RAG on a GPU using an open-source LLM from HuggingFace. Here's a quick [tutorial](#ec2-dev-setup) on setting up an EC2 GPU instance for the installation below.
On your GPU instance terminal, create a conda environment using:
@@ -74,7 +87,7 @@ pip3 install "pykoi[rag, huggingface]"
pip3 install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu121 # cu121 means cuda 12.1
```
-### Option 3: RLHF (GPU)
+### Option 3: RLHF (GPU)
This option allows you to train LLM via RLHF on a GPU. Here's a quick [tutorial](#ec2-dev-setup) on setting up an EC2 GPU instance for the installation below.
On your GPU instance terminal, create a conda environment using:
@@ -89,7 +102,7 @@ pip3 install "pykoi[rlhf]"
pip3 install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu121 # cu121 means cuda 12.1
```
-Congrats you have finished the installation!
+Congrats you have finished the installation!
## Dev Setup
If you are interested to contribute to us, here are the preliminary development setup.
@@ -104,15 +117,15 @@ pip3 install poetry
Then, based the feature you need to develop, run one or more installation options below. We recommend install all the options below although it may take ~3 minutes longer.
-- Option 1: RAG (CPU)
+- Option 1: RAG (CPU)
```
poetry install --no-root --extras rag
```
-- Option 2: RAG (GPU)
+- Option 2: RAG (GPU)
```
poetry install --no-root --extras "rag huggingface"
```
-- Option 3: RLHF (GPU)
+- Option 3: RLHF (GPU)
```
poetry install --no-root --extras rlhf
```
diff --git a/example/image/pykoi_demo_rag_chatbot_chat.gif b/example/image/pykoi_demo_rag_chatbot_chat.gif
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