Merge pull request #2124 from danielaskdd/label-search

Refactor(WebUI): Change Client-side Search Logic with Server-driven Enity Name Search

env.example

@@ -313,7 +313,7 @@ POSTGRES_IVFFLAT_LISTS=100
 NEO4J_URI=neo4j+s://xxxxxxxx.databases.neo4j.io
 NEO4J_USERNAME=neo4j
 NEO4J_PASSWORD='your_password'
-NEO4J_DATABASE=noe4j
+NEO4J_DATABASE=neo4j
 NEO4J_MAX_CONNECTION_POOL_SIZE=100
 NEO4J_CONNECTION_TIMEOUT=30
 NEO4J_CONNECTION_ACQUISITION_TIMEOUT=30

lightrag/api/__init__.py

@@ -1 +1 @@
-__api_version__ = "0224"
+__api_version__ = "0225"

lightrag/api/routers/graph_routes.py

@@ -45,6 +45,56 @@ def create_graph_routes(rag, api_key: Optional[str] = None):
                 status_code=500, detail=f"Error getting graph labels: {str(e)}"
             )
 
+    @router.get("/graph/label/popular", dependencies=[Depends(combined_auth)])
+    async def get_popular_labels(
+        limit: int = Query(
+            300, description="Maximum number of popular labels to return", ge=1, le=1000
+        ),
+    ):
+        """
+        Get popular labels by node degree (most connected entities)
+
+        Args:
+            limit (int): Maximum number of labels to return (default: 300, max: 1000)
+
+        Returns:
+            List[str]: List of popular labels sorted by degree (highest first)
+        """
+        try:
+            return await rag.chunk_entity_relation_graph.get_popular_labels(limit)
+        except Exception as e:
+            logger.error(f"Error getting popular labels: {str(e)}")
+            logger.error(traceback.format_exc())
+            raise HTTPException(
+                status_code=500, detail=f"Error getting popular labels: {str(e)}"
+            )
+
+    @router.get("/graph/label/search", dependencies=[Depends(combined_auth)])
+    async def search_labels(
+        q: str = Query(..., description="Search query string"),
+        limit: int = Query(
+            50, description="Maximum number of search results to return", ge=1, le=100
+        ),
+    ):
+        """
+        Search labels with fuzzy matching
+
+        Args:
+            q (str): Search query string
+            limit (int): Maximum number of results to return (default: 50, max: 100)
+
+        Returns:
+            List[str]: List of matching labels sorted by relevance
+        """
+        try:
+            return await rag.chunk_entity_relation_graph.search_labels(q, limit)
+        except Exception as e:
+            logger.error(f"Error searching labels with query '{q}': {str(e)}")
+            logger.error(traceback.format_exc())
+            raise HTTPException(
+                status_code=500, detail=f"Error searching labels: {str(e)}"
+            )
+
     @router.get("/graphs", dependencies=[Depends(combined_auth)])
     async def get_knowledge_graph(
         label: str = Query(..., description="Label to get knowledge graph for"),

lightrag/api/webui/assets/_basePickBy-CvPSWTW1.js

@@ -1 +1 @@
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lightrag/api/webui/assets/chunk-353BL4L5-CjoW3Wzn.js

@@ -1 +1 @@
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lightrag/api/webui/assets/chunk-67H74DCK-CnK7iSGJ.js

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lightrag/api/webui/assets/chunk-AACKK3MU-C0N9y2ev.js

@@ -1 +1 @@
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lightrag/api/webui/assets/chunk-BFAMUDN2-BjxoQgRC.js

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+import{_ as a,d as o}from"./mermaid-vendor-Btdx_ET5.js";var d=a((t,e)=>{let n;return e==="sandbox"&&(n=o("#i"+t)),(e==="sandbox"?o(n.nodes()[0].contentDocument.body):o("body")).select(`[id="${t}"]`)},"getDiagramElement");export{d as g};

lightrag/api/webui/assets/chunk-E2GYISFI-3iVC229r.js

@@ -1,4 +1,4 @@
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   /* Font Awesome icon styling - consolidated */
   .label-icon {
     display: inline-block;

lightrag/api/webui/assets/chunk-SKB7J2MH-Cuc7MmmW.js

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lightrag/api/webui/assets/classDiagram-M3E45YP4-CnaJRHbx.js

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lightrag/api/webui/assets/classDiagram-M3E45YP4-CzL1fI5K.js

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lightrag/api/webui/assets/classDiagram-v2-YAWTLIQI-CnaJRHbx.js

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lightrag/api/webui/assets/classDiagram-v2-YAWTLIQI-CzL1fI5K.js

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lightrag/api/webui/assets/clone-CXHLa2LR.js

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lightrag/api/webui/assets/clone-C_6aNc6E.js

@@ -0,0 +1 @@
+import{b as r}from"./_baseUniq-25sRMAXf.js";var e=4;function a(o){return r(o,e)}export{a as c};

lightrag/api/webui/assets/diagram-5UYTHUR4-BpasnX3V.js

@@ -1,4 +1,4 @@
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+import{p as y}from"./chunk-353BL4L5-CjoW3Wzn.js";import{_ as l,s as B,g as S,t as z,q as F,a as P,b as E,F as v,K as W,e as T,z as D,G as _,H as A,l as w}from"./mermaid-vendor-Btdx_ET5.js";import{p as N}from"./treemap-75Q7IDZK-BEnUI63M.js";import"./feature-graph-B2JqR-0F.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-25sRMAXf.js";import"./_basePickBy-CvPSWTW1.js";import"./clone-C_6aNc6E.js";var x={packet:[]},m=structuredClone(x),L=A.packet,Y=l(()=>{const t=v({...L,..._().packet});return t.showBits&&(t.paddingY+=10),t},"getConfig"),G=l(()=>m.packet,"getPacket"),H=l(t=>{t.length>0&&m.packet.push(t)},"pushWord"),I=l(()=>{D(),m=structuredClone(x)},"clear"),u={pushWord:H,getPacket:G,getConfig:Y,clear:I,setAccTitle:E,getAccTitle:P,setDiagramTitle:F,getDiagramTitle:z,getAccDescription:S,setAccDescription:B},K=1e4,M=l(t=>{y(t,u);let e=-1,o=[],n=1;const{bitsPerRow:i}=u.getConfig();for(let{start:a,end:r,bits:c,label:f}of t.blocks){if(a!==void 0&&r!==void 0&&r<a)throw new Error(`Packet block ${a} - ${r} is invalid. End must be greater than start.`);if(a??(a=e+1),a!==e+1)throw new Error(`Packet block ${a} - ${r??a} is not contiguous. It should start from ${e+1}.`);if(c===0)throw new Error(`Packet block ${a} is invalid. Cannot have a zero bit field.`);for(r??(r=a+(c??1)-1),c??(c=r-a+1),e=r,w.debug(`Packet block ${a} - ${e} with label ${f}`);o.length<=i+1&&u.getPacket().length<K;){const[d,p]=O({start:a,end:r,bits:c,label:f},n,i);if(o.push(d),d.end+1===n*i&&(u.pushWord(o),o=[],n++),!p)break;({start:a,end:r,bits:c,label:f}=p)}}u.pushWord(o)},"populate"),O=l((t,e,o)=>{if(t.start===void 0)throw new Error("start should have been set during first phase");if(t.end===void 0)throw new Error("end should have been set during first phase");if(t.start>t.end)throw new Error(`Block start ${t.start} is greater than block end ${t.end}.`);if(t.end+1<=e*o)return[t,void 0];const n=e*o-1,i=e*o;return[{start:t.start,end:n,label:t.label,bits:n-t.start},{start:i,end:t.end,label:t.label,bits:t.end-i}]},"getNextFittingBlock"),q={parse:l(async t=>{const e=await N("packet",t);w.debug(e),M(e)},"parse")},R=l((t,e,o,n)=>{const i=n.db,a=i.getConfig(),{rowHeight:r,paddingY:c,bitWidth:f,bitsPerRow:d}=a,p=i.getPacket(),s=i.getDiagramTitle(),k=r+c,g=k*(p.length+1)-(s?0:r),b=f*d+2,h=W(e);h.attr("viewbox",`0 0 ${b} ${g}`),T(h,g,b,a.useMaxWidth);for(const[C,$]of p.entries())U(h,$,C,a);h.append("text").text(s).attr("x",b/2).attr("y",g-k/2).attr("dominant-baseline","middle").attr("text-anchor","middle").attr("class","packetTitle")},"draw"),U=l((t,e,o,{rowHeight:n,paddingX:i,paddingY:a,bitWidth:r,bitsPerRow:c,showBits:f})=>{const d=t.append("g"),p=o*(n+a)+a;for(const s of e){const k=s.start%c*r+1,g=(s.end-s.start+1)*r-i;if(d.append("rect").attr("x",k).attr("y",p).attr("width",g).attr("height",n).attr("class","packetBlock"),d.append("text").attr("x",k+g/2).attr("y",p+n/2).attr("class","packetLabel").attr("dominant-baseline","middle").attr("text-anchor","middle").text(s.label),!f)continue;const b=s.end===s.start,h=p-2;d.append("text").attr("x",k+(b?g/2:0)).attr("y",h).attr("class","packetByte start").attr("dominant-baseline","auto").attr("text-anchor",b?"middle":"start").text(s.start),b||d.append("text").attr("x",k+g).attr("y",h).attr("class","packetByte end").attr("dominant-baseline","auto").attr("text-anchor","end").text(s.end)}},"drawWord"),X={draw:R},j={byteFontSize:"10px",startByteColor:"black",endByteColor:"black",labelColor:"black",labelFontSize:"12px",titleColor:"black",titleFontSize:"14px",blockStrokeColor:"black",blockStrokeWidth:"1",blockFillColor:"#efefef"},J=l(({packet:t}={})=>{const e=v(j,t);return`
 	.packetByte {
 		font-size: ${e.byteFontSize};
 	}

lightrag/api/webui/assets/flowDiagram-KYDEHFYC-Cfa_r_uV.js

@@ -1,4 +1,4 @@
-import{g as q1}from"./chunk-E2GYISFI-Bj4t4I37.js";import{_ as m,o as O1,l as ee,c as be,d as Se,p as H1,r as X1,u as i1,b as Q1,s as J1,q as Z1,a as $1,g as et,t as tt,k as st,v as it,J as rt,x as nt,y as s1,z as at,A as ut,B as lt,C as ot}from"./mermaid-vendor-BOzHoVUU.js";import{g as ct}from"./chunk-BFAMUDN2-CmfJj-4x.js";import{s as ht}from"./chunk-SKB7J2MH-Bl1qvyQL.js";import"./feature-graph-bahMe5Gt.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var dt="flowchart-",Pe,pt=(Pe=class{constructor(){this.vertexCounter=0,this.config=be(),this.vertices=new Map,this.edges=[],this.classes=new Map,this.subGraphs=[],this.subGraphLookup=new Map,this.tooltips=new Map,this.subCount=0,this.firstGraphFlag=!0,this.secCount=-1,this.posCrossRef=[],this.funs=[],this.setAccTitle=Q1,this.setAccDescription=J1,this.setDiagramTitle=Z1,this.getAccTitle=$1,this.getAccDescription=et,this.getDiagramTitle=tt,this.funs.push(this.setupToolTips.bind(this)),this.addVertex=this.addVertex.bind(this),this.firstGraph=this.firstGraph.bind(this),this.setDirection=this.setDirection.bind(this),this.addSubGraph=this.addSubGraph.bind(this),this.addLink=this.addLink.bind(this),this.setLink=this.setLink.bind(this),this.updateLink=this.updateLink.bind(this),this.addClass=this.addClass.bind(this),this.setClass=this.setClass.bind(this),this.destructLink=this.destructLink.bind(this),this.setClickEvent=this.setClickEvent.bind(this),this.setTooltip=this.setTooltip.bind(this),this.updateLinkInterpolate=this.updateLinkInterpolate.bind(this),this.setClickFun=this.setClickFun.bind(this),this.bindFunctions=this.bindFunctions.bind(this),this.lex={firstGraph:this.firstGraph.bind(this)},this.clear(),this.setGen("gen-2")}sanitizeText(i){return st.sanitizeText(i,this.config)}lookUpDomId(i){for(const n of this.vertices.values())if(n.id===i)return n.domId;return i}addVertex(i,n,a,u,l,f,c={},A){var V,C;if(!i||i.trim().length===0)return;let r;if(A!==void 0){let p;A.includes(`
+import{g as q1}from"./chunk-E2GYISFI-3iVC229r.js";import{_ as m,o as O1,l as ee,c as be,d as Se,p as H1,r as X1,u as i1,b as Q1,s as J1,q as Z1,a as $1,g as et,t as tt,k as st,v as it,J as rt,x as nt,y as s1,z as at,A as ut,B as lt,C as ot}from"./mermaid-vendor-Btdx_ET5.js";import{g as ct}from"./chunk-BFAMUDN2-BjxoQgRC.js";import{s as ht}from"./chunk-SKB7J2MH-Cuc7MmmW.js";import"./feature-graph-B2JqR-0F.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var dt="flowchart-",Pe,pt=(Pe=class{constructor(){this.vertexCounter=0,this.config=be(),this.vertices=new Map,this.edges=[],this.classes=new Map,this.subGraphs=[],this.subGraphLookup=new Map,this.tooltips=new Map,this.subCount=0,this.firstGraphFlag=!0,this.secCount=-1,this.posCrossRef=[],this.funs=[],this.setAccTitle=Q1,this.setAccDescription=J1,this.setDiagramTitle=Z1,this.getAccTitle=$1,this.getAccDescription=et,this.getDiagramTitle=tt,this.funs.push(this.setupToolTips.bind(this)),this.addVertex=this.addVertex.bind(this),this.firstGraph=this.firstGraph.bind(this),this.setDirection=this.setDirection.bind(this),this.addSubGraph=this.addSubGraph.bind(this),this.addLink=this.addLink.bind(this),this.setLink=this.setLink.bind(this),this.updateLink=this.updateLink.bind(this),this.addClass=this.addClass.bind(this),this.setClass=this.setClass.bind(this),this.destructLink=this.destructLink.bind(this),this.setClickEvent=this.setClickEvent.bind(this),this.setTooltip=this.setTooltip.bind(this),this.updateLinkInterpolate=this.updateLinkInterpolate.bind(this),this.setClickFun=this.setClickFun.bind(this),this.bindFunctions=this.bindFunctions.bind(this),this.lex={firstGraph:this.firstGraph.bind(this)},this.clear(),this.setGen("gen-2")}sanitizeText(i){return st.sanitizeText(i,this.config)}lookUpDomId(i){for(const n of this.vertices.values())if(n.id===i)return n.domId;return i}addVertex(i,n,a,u,l,f,c={},A){var V,C;if(!i||i.trim().length===0)return;let r;if(A!==void 0){let p;A.includes(`
 `)?p=A+`
 `:p=`{
 `+A+`

lightrag/api/webui/assets/infoDiagram-LHK5PUON-o7mbbhko.js

@@ -1,2 +1,2 @@
-import{_ as e,l as o,K as i,e as n,L as p}from"./mermaid-vendor-BOzHoVUU.js";import{p as m}from"./treemap-75Q7IDZK-BCGfM6IV.js";import"./feature-graph-bahMe5Gt.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-DkG1DiI3.js";import"./_basePickBy-BZlRisJu.js";import"./clone-CXHLa2LR.js";var g={parse:e(async r=>{const a=await m("info",r);o.debug(a)},"parse")},v={version:p.version+""},d=e(()=>v.version,"getVersion"),c={getVersion:d},l=e((r,a,s)=>{o.debug(`rendering info diagram
+import{_ as e,l as o,K as i,e as n,L as p}from"./mermaid-vendor-Btdx_ET5.js";import{p as m}from"./treemap-75Q7IDZK-BEnUI63M.js";import"./feature-graph-B2JqR-0F.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-25sRMAXf.js";import"./_basePickBy-CvPSWTW1.js";import"./clone-C_6aNc6E.js";var g={parse:e(async r=>{const a=await m("info",r);o.debug(a)},"parse")},v={version:p.version+""},d=e(()=>v.version,"getVersion"),c={getVersion:d},l=e((r,a,s)=>{o.debug(`rendering info diagram
 `+r);const t=i(a);n(t,100,400,!0),t.append("g").append("text").attr("x",100).attr("y",40).attr("class","version").attr("font-size",32).style("text-anchor","middle").text(`v${s}`)},"draw"),f={draw:l},L={parser:g,db:c,renderer:f};export{L as diagram};

lightrag/api/webui/assets/journeyDiagram-EWQZEKCU-CT-vmtiQ.js

@@ -1,4 +1,4 @@
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lightrag/api/webui/assets/kanban-definition-ZSS6B67P-BAAUdg37.js

@@ -1,4 +1,4 @@
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lightrag/api/webui/assets/pieDiagram-NIOCPIFQ-DelZtU7_.js

@@ -1,4 +1,4 @@
-import{p as N}from"./chunk-353BL4L5-DshwP1ma.js";import{_ as i,g as B,s as U,a as q,b as H,t as K,q as V,l as C,c as Z,F as j,K as J,M as Q,N as z,O as X,e as Y,z as tt,P as et,H as at}from"./mermaid-vendor-BOzHoVUU.js";import{p as rt}from"./treemap-75Q7IDZK-BCGfM6IV.js";import"./feature-graph-bahMe5Gt.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-DkG1DiI3.js";import"./_basePickBy-BZlRisJu.js";import"./clone-CXHLa2LR.js";var it=at.pie,D={sections:new Map,showData:!1},f=D.sections,w=D.showData,st=structuredClone(it),ot=i(()=>structuredClone(st),"getConfig"),nt=i(()=>{f=new Map,w=D.showData,tt()},"clear"),lt=i(({label:t,value:a})=>{f.has(t)||(f.set(t,a),C.debug(`added new section: ${t}, with value: ${a}`))},"addSection"),ct=i(()=>f,"getSections"),pt=i(t=>{w=t},"setShowData"),dt=i(()=>w,"getShowData"),F={getConfig:ot,clear:nt,setDiagramTitle:V,getDiagramTitle:K,setAccTitle:H,getAccTitle:q,setAccDescription:U,getAccDescription:B,addSection:lt,getSections:ct,setShowData:pt,getShowData:dt},gt=i((t,a)=>{N(t,a),a.setShowData(t.showData),t.sections.map(a.addSection)},"populateDb"),ut={parse:i(async t=>{const a=await rt("pie",t);C.debug(a),gt(a,F)},"parse")},mt=i(t=>`
+import{p as N}from"./chunk-353BL4L5-CjoW3Wzn.js";import{_ as i,g as B,s as U,a as q,b as H,t as K,q as V,l as C,c as Z,F as j,K as J,M as Q,N as z,O as X,e as Y,z as tt,P as et,H as at}from"./mermaid-vendor-Btdx_ET5.js";import{p as rt}from"./treemap-75Q7IDZK-BEnUI63M.js";import"./feature-graph-B2JqR-0F.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";import"./_baseUniq-25sRMAXf.js";import"./_basePickBy-CvPSWTW1.js";import"./clone-C_6aNc6E.js";var it=at.pie,D={sections:new Map,showData:!1},f=D.sections,w=D.showData,st=structuredClone(it),ot=i(()=>structuredClone(st),"getConfig"),nt=i(()=>{f=new Map,w=D.showData,tt()},"clear"),lt=i(({label:t,value:a})=>{f.has(t)||(f.set(t,a),C.debug(`added new section: ${t}, with value: ${a}`))},"addSection"),ct=i(()=>f,"getSections"),pt=i(t=>{w=t},"setShowData"),dt=i(()=>w,"getShowData"),F={getConfig:ot,clear:nt,setDiagramTitle:V,getDiagramTitle:K,setAccTitle:H,getAccTitle:q,setAccDescription:U,getAccDescription:B,addSection:lt,getSections:ct,setShowData:pt,getShowData:dt},gt=i((t,a)=>{N(t,a),a.setShowData(t.showData),t.sections.map(a.addSection)},"populateDb"),ut={parse:i(async t=>{const a=await rt("pie",t);C.debug(a),gt(a,F)},"parse")},mt=i(t=>`
   .pieCircle{
     stroke: ${t.pieStrokeColor};
     stroke-width : ${t.pieStrokeWidth};

lightrag/api/webui/assets/stateDiagram-v2-5AN5P6BG-CsZdnMDv.js

@@ -1 +1 @@
-import{s as r,b as e,a,S as i}from"./chunk-OW32GOEJ-Cv4eoNWH.js";import{_ as s}from"./mermaid-vendor-BOzHoVUU.js";import"./chunk-BFAMUDN2-CmfJj-4x.js";import"./chunk-SKB7J2MH-Bl1qvyQL.js";import"./feature-graph-bahMe5Gt.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var f={parser:a,get db(){return new i(2)},renderer:e,styles:r,init:s(t=>{t.state||(t.state={}),t.state.arrowMarkerAbsolute=t.arrowMarkerAbsolute},"init")};export{f as diagram};
+import{s as r,b as e,a,S as i}from"./chunk-OW32GOEJ-ZlCav04U.js";import{_ as s}from"./mermaid-vendor-Btdx_ET5.js";import"./chunk-BFAMUDN2-BjxoQgRC.js";import"./chunk-SKB7J2MH-Cuc7MmmW.js";import"./feature-graph-B2JqR-0F.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var f={parser:a,get db(){return new i(2)},renderer:e,styles:r,init:s(t=>{t.state||(t.state={}),t.state.arrowMarkerAbsolute=t.arrowMarkerAbsolute},"init")};export{f as diagram};

lightrag/api/webui/assets/timeline-definition-MYPXXCX6-C9DDJBMx.js

@@ -1,4 +1,4 @@
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+import{_ as s,c as xt,l as E,d as q,a3 as kt,a4 as _t,a5 as bt,a6 as vt,N as nt,D as wt,a7 as St,z as Et}from"./mermaid-vendor-Btdx_ET5.js";import"./feature-graph-B2JqR-0F.js";import"./react-vendor-DEwriMA6.js";import"./graph-vendor-B-X5JegA.js";import"./ui-vendor-CeCm8EER.js";import"./utils-vendor-BysuhMZA.js";var X=function(){var n=s(function(f,r,a,h){for(a=a||{},h=f.length;h--;a[f[h]]=r);return a},"o"),t=[6,8,10,11,12,14,16,17,20,21],e=[1,9],l=[1,10],i=[1,11],d=[1,12],c=[1,13],g=[1,16],m=[1,17],p={trace:s(function(){},"trace"),yy:{},symbols_:{error:2,start:3,timeline:4,document:5,EOF:6,line:7,SPACE:8,statement:9,NEWLINE:10,title:11,acc_title:12,acc_title_value:13,acc_descr:14,acc_descr_value:15,acc_descr_multiline_value:16,section:17,period_statement:18,event_statement:19,period:20,event:21,$accept:0,$end:1},terminals_:{2:"error",4:"timeline",6:"EOF",8:"SPACE",10:"NEWLINE",11:"title",12:"acc_title",13:"acc_title_value",14:"acc_descr",15:"acc_descr_value",16:"acc_descr_multiline_value",17:"section",20:"period",21:"event"},productions_:[0,[3,3],[5,0],[5,2],[7,2],[7,1],[7,1],[7,1],[9,1],[9,2],[9,2],[9,1],[9,1],[9,1],[9,1],[18,1],[19,1]],performAction:s(function(r,a,h,u,y,o,S){var k=o.length-1;switch(y){case 1:return o[k-1];case 2:this.$=[];break;case 3:o[k-1].push(o[k]),this.$=o[k-1];break;case 4:case 5:this.$=o[k];break;case 6:case 7:this.$=[];break;case 8:u.getCommonDb().setDiagramTitle(o[k].substr(6)),this.$=o[k].substr(6);break;case 9:this.$=o[k].trim(),u.getCommonDb().setAccTitle(this.$);break;case 10:case 11:this.$=o[k].trim(),u.getCommonDb().setAccDescription(this.$);break;case 12:u.addSection(o[k].substr(8)),this.$=o[k].substr(8);break;case 15:u.addTask(o[k],0,""),this.$=o[k];break;case 16:u.addEvent(o[k].substr(2)),this.$=o[k];break}},"anonymous"),table:[{3:1,4:[1,2]},{1:[3]},n(t,[2,2],{5:3}),{6:[1,4],7:5,8:[1,6],9:7,10:[1,8],11:e,12:l,14:i,16:d,17:c,18:14,19:15,20:g,21:m},n(t,[2,7],{1:[2,1]}),n(t,[2,3]),{9:18,11:e,12:l,14:i,16:d,17:c,18:14,19:15,20:g,21:m},n(t,[2,5]),n(t,[2,6]),n(t,[2,8]),{13:[1,19]},{15:[1,20]},n(t,[2,11]),n(t,[2,12]),n(t,[2,13]),n(t,[2,14]),n(t,[2,15]),n(t,[2,16]),n(t,[2,4]),n(t,[2,9]),n(t,[2,10])],defaultActions:{},parseError:s(function(r,a){if(a.recoverable)this.trace(r);else{var h=new Error(r);throw h.hash=a,h}},"parseError"),parse:s(function(r){var a=this,h=[0],u=[],y=[null],o=[],S=this.table,k="",M=0,C=0,B=2,J=1,O=o.slice.call(arguments,1),_=Object.create(this.lexer),N={yy:{}};for(var L in this.yy)Object.prototype.hasOwnProperty.call(this.yy,L)&&(N.yy[L]=this.yy[L]);_.setInput(r,N.yy),N.yy.lexer=_,N.yy.parser=this,typeof _.yylloc>"u"&&(_.yylloc={});var v=_.yylloc;o.push(v);var $=_.options&&_.options.ranges;typeof N.yy.parseError=="function"?this.parseError=N.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function R(T){h.length=h.length-2*T,y.length=y.length-T,o.length=o.length-T}s(R,"popStack");function A(){var T;return T=u.pop()||_.lex()||J,typeof T!="number"&&(T instanceof Array&&(u=T,T=u.pop()),T=a.symbols_[T]||T),T}s(A,"lex");for(var w,H,I,K,F={},j,P,et,G;;){if(H=h[h.length-1],this.defaultActions[H]?I=this.defaultActions[H]:((w===null||typeof w>"u")&&(w=A()),I=S[H]&&S[H][w]),typeof I>"u"||!I.length||!I[0]){var Q="";G=[];for(j in S[H])this.terminals_[j]&&j>B&&G.push("'"+this.terminals_[j]+"'");_.showPosition?Q="Parse error on line "+(M+1)+`:
 `+_.showPosition()+`
 Expecting `+G.join(", ")+", got '"+(this.terminals_[w]||w)+"'":Q="Parse error on line "+(M+1)+": Unexpected "+(w==J?"end of input":"'"+(this.terminals_[w]||w)+"'"),this.parseError(Q,{text:_.match,token:this.terminals_[w]||w,line:_.yylineno,loc:v,expected:G})}if(I[0]instanceof Array&&I.length>1)throw new Error("Parse Error: multiple actions possible at state: "+H+", token: "+w);switch(I[0]){case 1:h.push(w),y.push(_.yytext),o.push(_.yylloc),h.push(I[1]),w=null,C=_.yyleng,k=_.yytext,M=_.yylineno,v=_.yylloc;break;case 2:if(P=this.productions_[I[1]][1],F.$=y[y.length-P],F._$={first_line:o[o.length-(P||1)].first_line,last_line:o[o.length-1].last_line,first_column:o[o.length-(P||1)].first_column,last_column:o[o.length-1].last_column},$&&(F._$.range=[o[o.length-(P||1)].range[0],o[o.length-1].range[1]]),K=this.performAction.apply(F,[k,C,M,N.yy,I[1],y,o].concat(O)),typeof K<"u")return K;P&&(h=h.slice(0,-1*P*2),y=y.slice(0,-1*P),o=o.slice(0,-1*P)),h.push(this.productions_[I[1]][0]),y.push(F.$),o.push(F._$),et=S[h[h.length-2]][h[h.length-1]],h.push(et);break;case 3:return!0}}return!0},"parse")},x=function(){var f={EOF:1,parseError:s(function(a,h){if(this.yy.parser)this.yy.parser.parseError(a,h);else throw new Error(a)},"parseError"),setInput:s(function(r,a){return this.yy=a||this.yy||{},this._input=r,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:s(function(){var r=this._input[0];this.yytext+=r,this.yyleng++,this.offset++,this.match+=r,this.matched+=r;var a=r.match(/(?:\r\n?|\n).*/g);return a?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),r},"input"),unput:s(function(r){var a=r.length,h=r.split(/(?:\r\n?|\n)/g);this._input=r+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-a),this.offset-=a;var u=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),h.length-1&&(this.yylineno-=h.length-1);var y=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:h?(h.length===u.length?this.yylloc.first_column:0)+u[u.length-h.length].length-h[0].length:this.yylloc.first_column-a},this.options.ranges&&(this.yylloc.range=[y[0],y[0]+this.yyleng-a]),this.yyleng=this.yytext.length,this},"unput"),more:s(function(){return this._more=!0,this},"more"),reject:s(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
 `+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:s(function(r){this.unput(this.match.slice(r))},"less"),pastInput:s(function(){var r=this.matched.substr(0,this.matched.length-this.match.length);return(r.length>20?"...":"")+r.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:s(function(){var r=this.match;return r.length<20&&(r+=this._input.substr(0,20-r.length)),(r.substr(0,20)+(r.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:s(function(){var r=this.pastInput(),a=new Array(r.length+1).join("-");return r+this.upcomingInput()+`

lightrag/api/webui/index.html

@@ -8,18 +8,18 @@
     <link rel="icon" type="image/png" href="favicon.png" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Lightrag</title>
-    <script type="module" crossorigin src="/webui/assets/index-BqzR_4SY.js"></script>
+    <script type="module" crossorigin src="/webui/assets/index-Bgbh_BKw.js"></script>
     <link rel="modulepreload" crossorigin href="/webui/assets/react-vendor-DEwriMA6.js">
     <link rel="modulepreload" crossorigin href="/webui/assets/ui-vendor-CeCm8EER.js">
     <link rel="modulepreload" crossorigin href="/webui/assets/graph-vendor-B-X5JegA.js">
     <link rel="modulepreload" crossorigin href="/webui/assets/utils-vendor-BysuhMZA.js">
-    <link rel="modulepreload" crossorigin href="/webui/assets/feature-graph-bahMe5Gt.js">
-    <link rel="modulepreload" crossorigin href="/webui/assets/feature-documents-nDnos1H0.js">
-    <link rel="modulepreload" crossorigin href="/webui/assets/mermaid-vendor-BOzHoVUU.js">
+    <link rel="modulepreload" crossorigin href="/webui/assets/feature-graph-B2JqR-0F.js">
+    <link rel="modulepreload" crossorigin href="/webui/assets/feature-documents-5YuUz-b0.js">
+    <link rel="modulepreload" crossorigin href="/webui/assets/mermaid-vendor-Btdx_ET5.js">
     <link rel="modulepreload" crossorigin href="/webui/assets/markdown-vendor-DmIvJdn7.js">
-    <link rel="modulepreload" crossorigin href="/webui/assets/feature-retrieval-DiuUfFgI.js">
+    <link rel="modulepreload" crossorigin href="/webui/assets/feature-retrieval-D3LH9QjI.js">
     <link rel="stylesheet" crossorigin href="/webui/assets/feature-graph-BipNuM18.css">
-    <link rel="stylesheet" crossorigin href="/webui/assets/index-BvrNHAMA.css">
+    <link rel="stylesheet" crossorigin href="/webui/assets/index-CCBiRoRx.css">
   </head>
   <body>
     <div id="root"></div>

lightrag/base.py

@@ -671,6 +671,29 @@ class BaseGraphStorage(StorageNameSpace, ABC):
             A list of all edges, where each edge is a dictionary of its properties
         """
 
+    @abstractmethod
+    async def get_popular_labels(self, limit: int = 300) -> list[str]:
+        """Get popular labels by node degree (most connected entities)
+
+        Args:
+            limit: Maximum number of labels to return
+
+        Returns:
+            List of labels sorted by degree (highest first)
+        """
+
+    @abstractmethod
+    async def search_labels(self, query: str, limit: int = 50) -> list[str]:
+        """Search labels with fuzzy matching
+
+        Args:
+            query: Search query string
+            limit: Maximum number of results to return
+
+        Returns:
+            List of matching labels sorted by relevance
+        """
+
 
 class DocStatus(str, Enum):
     """Document processing status"""

lightrag/kg/memgraph_impl.py

@@ -1089,3 +1089,100 @@ class MemgraphStorage(BaseGraphStorage):
                 edges.append(edge_properties)
             await result.consume()
             return edges
+
+    async def get_popular_labels(self, limit: int = 300) -> list[str]:
+        """Get popular labels by node degree (most connected entities)
+
+        Args:
+            limit: Maximum number of labels to return
+
+        Returns:
+            List of labels sorted by degree (highest first)
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+
+        try:
+            workspace_label = self._get_workspace_label()
+            async with self._driver.session(
+                database=self._DATABASE, default_access_mode="READ"
+            ) as session:
+                query = f"""
+                MATCH (n:`{workspace_label}`)
+                WHERE n.entity_id IS NOT NULL
+                OPTIONAL MATCH (n)-[r]-()
+                WITH n.entity_id AS label, count(r) AS degree
+                ORDER BY degree DESC, label ASC
+                LIMIT {limit}
+                RETURN label
+                """
+                result = await session.run(query)
+                labels = []
+                async for record in result:
+                    labels.append(record["label"])
+                await result.consume()
+
+                logger.debug(
+                    f"[{self.workspace}] Retrieved {len(labels)} popular labels (limit: {limit})"
+                )
+                return labels
+        except Exception as e:
+            logger.error(f"[{self.workspace}] Error getting popular labels: {str(e)}")
+            return []
+
+    async def search_labels(self, query: str, limit: int = 50) -> list[str]:
+        """Search labels with fuzzy matching
+
+        Args:
+            query: Search query string
+            limit: Maximum number of results to return
+
+        Returns:
+            List of matching labels sorted by relevance
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+
+        query_lower = query.lower().strip()
+
+        if not query_lower:
+            return []
+
+        try:
+            workspace_label = self._get_workspace_label()
+            async with self._driver.session(
+                database=self._DATABASE, default_access_mode="READ"
+            ) as session:
+                cypher_query = f"""
+                MATCH (n:`{workspace_label}`)
+                WHERE n.entity_id IS NOT NULL
+                WITH n.entity_id AS label, toLower(n.entity_id) AS label_lower
+                WHERE label_lower CONTAINS $query_lower
+                WITH label, label_lower,
+                     CASE
+                         WHEN label_lower = $query_lower THEN 1000
+                         WHEN label_lower STARTS WITH $query_lower THEN 500
+                         ELSE 100 - size(label)
+                     END AS score
+                ORDER BY score DESC, label ASC
+                LIMIT {limit}
+                RETURN label
+                """
+
+                result = await session.run(cypher_query, query_lower=query_lower)
+                labels = []
+                async for record in result:
+                    labels.append(record["label"])
+                await result.consume()
+
+                logger.debug(
+                    f"[{self.workspace}] Search query '{query}' returned {len(labels)} results (limit: {limit})"
+                )
+                return labels
+        except Exception as e:
+            logger.error(f"[{self.workspace}] Error searching labels: {str(e)}")
+            return []

lightrag/kg/mongo_impl.py

@@ -1,4 +1,5 @@
 import os
+import re
 import time
 from dataclasses import dataclass, field
 import numpy as np
@@ -112,6 +113,7 @@ class MongoKVStorage(BaseKVStorage):
         # Keep original namespace unchanged for type detection logic
         if effective_workspace:
             self.final_namespace = f"{effective_workspace}_{self.namespace}"
+            self.workspace = effective_workspace
             logger.debug(
                 f"Final namespace with workspace prefix: '{self.final_namespace}'"
             )
@@ -335,6 +337,7 @@ class MongoDocStatusStorage(DocStatusStorage):
         # Keep original namespace unchanged for type detection logic
         if effective_workspace:
             self.final_namespace = f"{effective_workspace}_{self.namespace}"
+            self.workspace = effective_workspace
             logger.debug(
                 f"Final namespace with workspace prefix: '{self.final_namespace}'"
             )
@@ -474,6 +477,7 @@ class MongoDocStatusStorage(DocStatusStorage):
     async def create_and_migrate_indexes_if_not_exists(self):
         """Create indexes to optimize pagination queries and migrate file_path indexes for Chinese collation"""
         try:
+            # Get indexes for the current collection only
             indexes_cursor = await self._data.list_indexes()
             existing_indexes = await indexes_cursor.to_list(length=None)
             existing_index_names = {idx.get("name", "") for idx in existing_indexes}
@@ -481,80 +485,67 @@ class MongoDocStatusStorage(DocStatusStorage):
             # Define collation configuration for Chinese pinyin sorting
             collation_config = {"locale": "zh", "numericOrdering": True}
 
-            # 1. Define all indexes needed (including original pagination indexes and new collation indexes)
+            # Use workspace-specific index names to avoid cross-workspace conflicts
+            workspace_prefix = f"{self.workspace}_" if self.workspace != "_" else ""
+
+            # 1. Define all indexes needed with workspace-specific names
             all_indexes = [
                 # Original pagination indexes
                 {
-                    "name": "status_updated_at",
+                    "name": f"{workspace_prefix}status_updated_at",
                     "keys": [("status", 1), ("updated_at", -1)],
                 },
                 {
-                    "name": "status_created_at",
+                    "name": f"{workspace_prefix}status_created_at",
                     "keys": [("status", 1), ("created_at", -1)],
                 },
-                {"name": "updated_at", "keys": [("updated_at", -1)]},
-                {"name": "created_at", "keys": [("created_at", -1)]},
-                {"name": "id", "keys": [("_id", 1)]},
-                {"name": "track_id", "keys": [("track_id", 1)]},
-                # New file_path indexes with Chinese collation
+                {"name": f"{workspace_prefix}updated_at", "keys": [("updated_at", -1)]},
+                {"name": f"{workspace_prefix}created_at", "keys": [("created_at", -1)]},
+                {"name": f"{workspace_prefix}id", "keys": [("_id", 1)]},
+                {"name": f"{workspace_prefix}track_id", "keys": [("track_id", 1)]},
+                # New file_path indexes with Chinese collation and workspace-specific names
                 {
-                    "name": "file_path_zh_collation",
+                    "name": f"{workspace_prefix}file_path_zh_collation",
                     "keys": [("file_path", 1)],
                     "collation": collation_config,
                 },
                 {
-                    "name": "status_file_path_zh_collation",
+                    "name": f"{workspace_prefix}status_file_path_zh_collation",
                     "keys": [("status", 1), ("file_path", 1)],
                     "collation": collation_config,
                 },
             ]
 
-            # 2. Handle index migration: drop conflicting indexes with different names but same key patterns
-            for index_info in all_indexes:
-                target_keys = index_info["keys"]
-                target_name = index_info["name"]
-                target_collation = index_info.get("collation")
+            # 2. Handle legacy index cleanup: only drop old indexes that exist in THIS collection
+            legacy_index_names = [
+                "file_path_zh_collation",
+                "status_file_path_zh_collation",
+                "status_updated_at",
+                "status_created_at",
+                "updated_at",
+                "created_at",
+                "id",
+                "track_id",
+            ]
 
-                # Find existing indexes with the same key pattern but different names or collation
-                conflicting_indexes = []
-                for idx in existing_indexes:
-                    idx_name = idx.get("name", "")
-                    idx_keys = list(idx.get("key", {}).items())
-                    idx_collation = idx.get("collation")
-
-                    # Skip the _id_ index (MongoDB default)
-                    if idx_name == "_id_":
-                        continue
-
-                    # Check if keys match but name or collation differs
-                    if idx_keys == target_keys:
-                        if (
-                            idx_name != target_name
-                            or (target_collation and not idx_collation)
-                            or (not target_collation and idx_collation)
-                            or (
-                                target_collation
-                                and idx_collation
-                                and target_collation != idx_collation
-                            )
-                        ):
-                            conflicting_indexes.append(idx_name)
-
-                # Drop conflicting indexes
-                for conflicting_name in conflicting_indexes:
+            for legacy_name in legacy_index_names:
+                if (
+                    legacy_name in existing_index_names
+                    and legacy_name
+                    != f"{workspace_prefix}{legacy_name.replace(workspace_prefix, '')}"
+                ):
                     try:
-                        await self._data.drop_index(conflicting_name)
-                        logger.info(
-                            f"[{self.workspace}] Migrated: dropped conflicting index '{conflicting_name}' for collection {self._collection_name}"
+                        await self._data.drop_index(legacy_name)
+                        logger.debug(
+                            f"[{self.workspace}] Migrated: dropped legacy index '{legacy_name}' from collection {self._collection_name}"
                         )
-                        # Remove from existing_index_names to allow recreation
-                        existing_index_names.discard(conflicting_name)
+                        existing_index_names.discard(legacy_name)
                     except PyMongoError as drop_error:
                         logger.warning(
-                            f"[{self.workspace}] Failed to drop conflicting index '{conflicting_name}': {drop_error}"
+                            f"[{self.workspace}] Failed to drop legacy index '{legacy_name}' from collection {self._collection_name}: {drop_error}"
                         )
 
-            # 3. Create all needed indexes
+            # 3. Create all needed indexes with workspace-specific names
             for index_info in all_indexes:
                 index_name = index_info["name"]
                 if index_name not in existing_index_names:
@@ -566,7 +557,7 @@ class MongoDocStatusStorage(DocStatusStorage):
                         await self._data.create_index(
                             index_info["keys"], **create_kwargs
                         )
-                        logger.info(
+                        logger.debug(
                             f"[{self.workspace}] Created index '{index_name}' for collection {self._collection_name}"
                         )
                     except PyMongoError as create_error:
@@ -734,6 +725,7 @@ class MongoGraphStorage(BaseGraphStorage):
         # Keep original namespace unchanged for type detection logic
         if effective_workspace:
             self.final_namespace = f"{effective_workspace}_{self.namespace}"
+            self.workspace = effective_workspace
             logger.debug(
                 f"Final namespace with workspace prefix: '{self.final_namespace}'"
             )
@@ -757,6 +749,10 @@ class MongoGraphStorage(BaseGraphStorage):
             self.edge_collection = await get_or_create_collection(
                 self.db, self._edge_collection_name
             )
+
+            # Create Atlas Search index for better search performance if possible
+            await self.create_search_index_if_not_exists()
+
             logger.debug(
                 f"[{self.workspace}] Use MongoDB as KG {self._collection_name}"
             )
@@ -1612,6 +1608,403 @@ class MongoGraphStorage(BaseGraphStorage):
             edges.append(edge_dict)
         return edges
 
+    async def get_popular_labels(self, limit: int = 300) -> list[str]:
+        """Get popular labels by node degree (most connected entities)
+
+        Args:
+            limit: Maximum number of labels to return
+
+        Returns:
+            List of labels sorted by degree (highest first)
+        """
+        try:
+            # Use aggregation pipeline to count edges per node and sort by degree
+            pipeline = [
+                # Count outbound edges
+                {"$group": {"_id": "$source_node_id", "out_degree": {"$sum": 1}}},
+                # Union with inbound edges count
+                {
+                    "$unionWith": {
+                        "coll": self._edge_collection_name,
+                        "pipeline": [
+                            {
+                                "$group": {
+                                    "_id": "$target_node_id",
+                                    "in_degree": {"$sum": 1},
+                                }
+                            }
+                        ],
+                    }
+                },
+                # Group by node_id and sum degrees
+                {
+                    "$group": {
+                        "_id": "$_id",
+                        "total_degree": {
+                            "$sum": {
+                                "$add": [
+                                    {"$ifNull": ["$out_degree", 0]},
+                                    {"$ifNull": ["$in_degree", 0]},
+                                ]
+                            }
+                        },
+                    }
+                },
+                # Sort by degree descending, then by label ascending
+                {"$sort": {"total_degree": -1, "_id": 1}},
+                # Limit results
+                {"$limit": limit},
+                # Project only the label
+                {"$project": {"_id": 1}},
+            ]
+
+            cursor = await self.edge_collection.aggregate(pipeline, allowDiskUse=True)
+            labels = []
+            async for doc in cursor:
+                if doc.get("_id"):
+                    labels.append(doc["_id"])
+
+            logger.debug(
+                f"[{self.workspace}] Retrieved {len(labels)} popular labels (limit: {limit})"
+            )
+            return labels
+        except Exception as e:
+            logger.error(f"[{self.workspace}] Error getting popular labels: {str(e)}")
+            return []
+
+    async def _try_atlas_text_search(self, query_strip: str, limit: int) -> list[str]:
+        """Try Atlas Search using simple text search."""
+        try:
+            pipeline = [
+                {
+                    "$search": {
+                        "index": "entity_id_search_idx",
+                        "text": {"query": query_strip, "path": "_id"},
+                    }
+                },
+                {"$project": {"_id": 1, "score": {"$meta": "searchScore"}}},
+                {"$limit": limit},
+            ]
+            cursor = await self.collection.aggregate(pipeline)
+            labels = [doc["_id"] async for doc in cursor if doc.get("_id")]
+            if labels:
+                logger.debug(
+                    f"[{self.workspace}] Atlas text search returned {len(labels)} results"
+                )
+                return labels
+            return []
+        except PyMongoError as e:
+            logger.debug(f"[{self.workspace}] Atlas text search failed: {e}")
+            return []
+
+    async def _try_atlas_autocomplete_search(
+        self, query_strip: str, limit: int
+    ) -> list[str]:
+        """Try Atlas Search using autocomplete for prefix matching."""
+        try:
+            pipeline = [
+                {
+                    "$search": {
+                        "index": "entity_id_search_idx",
+                        "autocomplete": {
+                            "query": query_strip,
+                            "path": "_id",
+                            "fuzzy": {"maxEdits": 1, "prefixLength": 1},
+                        },
+                    }
+                },
+                {"$project": {"_id": 1, "score": {"$meta": "searchScore"}}},
+                {"$limit": limit},
+            ]
+            cursor = await self.collection.aggregate(pipeline)
+            labels = [doc["_id"] async for doc in cursor if doc.get("_id")]
+            if labels:
+                logger.debug(
+                    f"[{self.workspace}] Atlas autocomplete search returned {len(labels)} results"
+                )
+                return labels
+            return []
+        except PyMongoError as e:
+            logger.debug(f"[{self.workspace}] Atlas autocomplete search failed: {e}")
+            return []
+
+    async def _try_atlas_compound_search(
+        self, query_strip: str, limit: int
+    ) -> list[str]:
+        """Try Atlas Search using compound query for comprehensive matching."""
+        try:
+            pipeline = [
+                {
+                    "$search": {
+                        "index": "entity_id_search_idx",
+                        "compound": {
+                            "should": [
+                                {
+                                    "text": {
+                                        "query": query_strip,
+                                        "path": "_id",
+                                        "score": {"boost": {"value": 10}},
+                                    }
+                                },
+                                {
+                                    "autocomplete": {
+                                        "query": query_strip,
+                                        "path": "_id",
+                                        "score": {"boost": {"value": 5}},
+                                        "fuzzy": {"maxEdits": 1, "prefixLength": 1},
+                                    }
+                                },
+                                {
+                                    "wildcard": {
+                                        "query": f"*{query_strip}*",
+                                        "path": "_id",
+                                        "score": {"boost": {"value": 2}},
+                                    }
+                                },
+                            ],
+                            "minimumShouldMatch": 1,
+                        },
+                    }
+                },
+                {"$project": {"_id": 1, "score": {"$meta": "searchScore"}}},
+                {"$sort": {"score": {"$meta": "searchScore"}}},
+                {"$limit": limit},
+            ]
+            cursor = await self.collection.aggregate(pipeline)
+            labels = [doc["_id"] async for doc in cursor if doc.get("_id")]
+            if labels:
+                logger.debug(
+                    f"[{self.workspace}] Atlas compound search returned {len(labels)} results"
+                )
+                return labels
+            return []
+        except PyMongoError as e:
+            logger.debug(f"[{self.workspace}] Atlas compound search failed: {e}")
+            return []
+
+    async def _fallback_regex_search(self, query_strip: str, limit: int) -> list[str]:
+        """Fallback to regex-based search when Atlas Search fails."""
+        try:
+            logger.debug(
+                f"[{self.workspace}] Using regex fallback search for: '{query_strip}'"
+            )
+
+            escaped_query = re.escape(query_strip)
+            regex_condition = {"_id": {"$regex": escaped_query, "$options": "i"}}
+            cursor = self.collection.find(regex_condition, {"_id": 1}).limit(limit * 2)
+            docs = await cursor.to_list(length=limit * 2)
+
+            # Extract labels
+            labels = []
+            for doc in docs:
+                doc_id = doc.get("_id")
+                if doc_id:
+                    labels.append(doc_id)
+
+            # Sort results to prioritize exact matches and starts-with matches
+            def sort_key(label):
+                label_lower = label.lower()
+                query_lower_strip = query_strip.lower()
+
+                if label_lower == query_lower_strip:
+                    return (0, label_lower)  # Exact match - highest priority
+                elif label_lower.startswith(query_lower_strip):
+                    return (1, label_lower)  # Starts with - medium priority
+                else:
+                    return (2, label_lower)  # Contains - lowest priority
+
+            labels.sort(key=sort_key)
+            labels = labels[:limit]  # Apply final limit after sorting
+
+            logger.debug(
+                f"[{self.workspace}] Regex fallback search returned {len(labels)} results (limit: {limit})"
+            )
+            return labels
+
+        except Exception as e:
+            logger.error(f"[{self.workspace}] Regex fallback search failed: {e}")
+            import traceback
+
+            logger.error(f"[{self.workspace}] Traceback: {traceback.format_exc()}")
+            return []
+
+    async def search_labels(self, query: str, limit: int = 50) -> list[str]:
+        """
+        Search labels with progressive fallback strategy:
+        1. Atlas text search (simple and fast)
+        2. Atlas autocomplete search (prefix matching with fuzzy)
+        3. Atlas compound search (comprehensive matching)
+        4. Regex fallback (when Atlas Search is unavailable)
+        """
+        query_strip = query.strip()
+        if not query_strip:
+            return []
+
+        # First check if we have any nodes at all
+        try:
+            node_count = await self.collection.count_documents({})
+            if node_count == 0:
+                logger.debug(
+                    f"[{self.workspace}] No nodes found in collection {self._collection_name}"
+                )
+                return []
+        except PyMongoError as e:
+            logger.error(f"[{self.workspace}] Error counting nodes: {e}")
+            return []
+
+        # Progressive search strategy
+        search_methods = [
+            ("text", self._try_atlas_text_search),
+            ("autocomplete", self._try_atlas_autocomplete_search),
+            ("compound", self._try_atlas_compound_search),
+        ]
+
+        # Try Atlas Search methods in order
+        for method_name, search_method in search_methods:
+            try:
+                labels = await search_method(query_strip, limit)
+                if labels:
+                    logger.debug(
+                        f"[{self.workspace}] Search successful using {method_name} method: {len(labels)} results"
+                    )
+                    return labels
+                else:
+                    logger.debug(
+                        f"[{self.workspace}] {method_name} search returned no results, trying next method"
+                    )
+            except Exception as e:
+                logger.debug(
+                    f"[{self.workspace}] {method_name} search failed: {e}, trying next method"
+                )
+                continue
+
+        # If all Atlas Search methods fail, use regex fallback
+        logger.info(
+            f"[{self.workspace}] All Atlas Search methods failed, using regex fallback search for: '{query_strip}'"
+        )
+        return await self._fallback_regex_search(query_strip, limit)
+
+    async def _check_if_index_needs_rebuild(
+        self, indexes: list, index_name: str
+    ) -> bool:
+        """Check if the existing index needs to be rebuilt due to configuration issues."""
+        for index in indexes:
+            if index["name"] == index_name:
+                # Check if the index has the old problematic configuration
+                definition = index.get("latestDefinition", {})
+                mappings = definition.get("mappings", {})
+                fields = mappings.get("fields", {})
+                id_field = fields.get("_id", {})
+
+                # If it's the old single-type autocomplete configuration, rebuild
+                if (
+                    isinstance(id_field, dict)
+                    and id_field.get("type") == "autocomplete"
+                ):
+                    logger.info(
+                        f"[{self.workspace}] Found old index configuration for '{index_name}', will rebuild"
+                    )
+                    return True
+
+                # If it's not a list (multi-type configuration), rebuild
+                if not isinstance(id_field, list):
+                    logger.info(
+                        f"[{self.workspace}] Index '{index_name}' needs upgrade to multi-type configuration"
+                    )
+                    return True
+
+                logger.info(
+                    f"[{self.workspace}] Index '{index_name}' has correct configuration"
+                )
+                return False
+        return True  # Index doesn't exist, needs creation
+
+    async def _safely_drop_old_index(self, index_name: str):
+        """Safely drop the old search index."""
+        try:
+            await self.collection.drop_search_index(index_name)
+            logger.info(
+                f"[{self.workspace}] Successfully dropped old search index '{index_name}'"
+            )
+        except PyMongoError as e:
+            logger.warning(
+                f"[{self.workspace}] Could not drop old index '{index_name}': {e}"
+            )
+
+    async def _create_improved_search_index(self, index_name: str):
+        """Create an improved search index with multiple field types."""
+        search_index_model = SearchIndexModel(
+            definition={
+                "mappings": {
+                    "dynamic": False,
+                    "fields": {
+                        "_id": [
+                            {
+                                "type": "string",
+                            },
+                            {
+                                "type": "token",
+                            },
+                            {
+                                "type": "autocomplete",
+                                "maxGrams": 15,
+                                "minGrams": 2,
+                            },
+                        ]
+                    },
+                },
+                "analyzer": "lucene.standard",  # Index-level analyzer for text processing
+            },
+            name=index_name,
+            type="search",
+        )
+
+        await self.collection.create_search_index(search_index_model)
+        logger.info(
+            f"[{self.workspace}] Created improved Atlas Search index '{index_name}' for collection {self._collection_name}. "
+        )
+        logger.info(
+            f"[{self.workspace}] Index will be built asynchronously, using regex fallback until ready."
+        )
+
+    async def create_search_index_if_not_exists(self):
+        """Creates an improved Atlas Search index for entity search, rebuilding if necessary."""
+        index_name = "entity_id_search_idx"
+
+        try:
+            # Check if we're using MongoDB Atlas (has search index capabilities)
+            indexes_cursor = await self.collection.list_search_indexes()
+            indexes = await indexes_cursor.to_list(length=None)
+
+            # Check if we need to rebuild the index
+            needs_rebuild = await self._check_if_index_needs_rebuild(
+                indexes, index_name
+            )
+
+            if needs_rebuild:
+                # Check if index exists and drop it
+                index_exists = any(idx["name"] == index_name for idx in indexes)
+                if index_exists:
+                    await self._safely_drop_old_index(index_name)
+
+                # Create the improved search index (async, no waiting)
+                await self._create_improved_search_index(index_name)
+            else:
+                logger.info(
+                    f"[{self.workspace}] Atlas Search index '{index_name}' already exists with correct configuration"
+                )
+
+        except PyMongoError as e:
+            # This is expected if not using MongoDB Atlas or if search indexes are not supported
+            logger.info(
+                f"[{self.workspace}] Could not create Atlas Search index for {self._collection_name}: {e}. "
+                "This is normal if not using MongoDB Atlas - search will use regex fallback."
+            )
+        except Exception as e:
+            logger.warning(
+                f"[{self.workspace}] Unexpected error creating Atlas Search index for {self._collection_name}: {e}"
+            )
+
     async def drop(self) -> dict[str, str]:
         """Drop the storage by removing all documents in the collection.
 
@@ -1685,6 +2078,7 @@ class MongoVectorDBStorage(BaseVectorStorage):
         # Keep original namespace unchanged for type detection logic
         if effective_workspace:
             self.final_namespace = f"{effective_workspace}_{self.namespace}"
+            self.workspace = effective_workspace
             logger.debug(
                 f"Final namespace with workspace prefix: '{self.final_namespace}'"
             )

lightrag/kg/neo4j_impl.py

@@ -70,6 +70,11 @@ class Neo4JStorage(BaseGraphStorage):
         """Return workspace label (guaranteed non-empty during initialization)"""
         return self.workspace
 
+    def _is_chinese_text(self, text: str) -> bool:
+        """Check if text contains Chinese characters."""
+        chinese_pattern = re.compile(r"[\u4e00-\u9fff]+")
+        return bool(chinese_pattern.search(text))
+
     async def initialize(self):
         async with get_data_init_lock():
             URI = os.environ.get("NEO4J_URI", config.get("neo4j", "uri", fallback=None))
@@ -201,44 +206,132 @@ class Neo4JStorage(BaseGraphStorage):
                                 raise e
 
                 if connected:
-                    # Create index for workspace nodes on entity_id if it doesn't exist
                     workspace_label = self._get_workspace_label()
+                    # Create B-Tree index for entity_id for faster lookups
                     try:
                         async with self._driver.session(database=database) as session:
-                            # Check if index exists first
-                            check_query = f"""
-                            CALL db.indexes() YIELD name, labelsOrTypes, properties
-                            WHERE labelsOrTypes = ['{workspace_label}'] AND properties = ['entity_id']
-                            RETURN count(*) > 0 AS exists
-                            """
-                            try:
-                                check_result = await session.run(check_query)
-                                record = await check_result.single()
-                                await check_result.consume()
-
-                                index_exists = record and record.get("exists", False)
-
-                                if not index_exists:
-                                    # Create index only if it doesn't exist
-                                    result = await session.run(
-                                        f"CREATE INDEX FOR (n:`{workspace_label}`) ON (n.entity_id)"
-                                    )
-                                    await result.consume()
-                                    logger.info(
-                                        f"[{self.workspace}] Created index for {workspace_label} nodes on entity_id in {database}"
-                                    )
-                            except Exception:
-                                # Fallback if db.indexes() is not supported in this Neo4j version
-                                result = await session.run(
-                                    f"CREATE INDEX IF NOT EXISTS FOR (n:`{workspace_label}`) ON (n.entity_id)"
-                                )
-                                await result.consume()
+                            await session.run(
+                                f"CREATE INDEX IF NOT EXISTS FOR (n:`{workspace_label}`) ON (n.entity_id)"
+                            )
+                            logger.info(
+                                f"[{self.workspace}] Ensured B-Tree index on entity_id for {workspace_label} in {database}"
+                            )
                     except Exception as e:
                         logger.warning(
-                            f"[{self.workspace}] Failed to create index: {str(e)}"
+                            f"[{self.workspace}] Failed to create B-Tree index: {str(e)}"
                         )
+
+                    # Create full-text index for entity_id for faster text searches
+                    await self._create_fulltext_index(
+                        self._driver, self._DATABASE, workspace_label
+                    )
                     break
 
+    async def _create_fulltext_index(
+        self, driver: AsyncDriver, database: str, workspace_label: str
+    ):
+        """Create a full-text index on the entity_id property with Chinese tokenizer support."""
+        index_name = "entity_id_fulltext_idx"
+        try:
+            async with driver.session(database=database) as session:
+                # Check if the full-text index exists and get its configuration
+                check_index_query = "SHOW FULLTEXT INDEXES"
+                result = await session.run(check_index_query)
+                indexes = await result.data()
+                await result.consume()
+
+                existing_index = None
+                for idx in indexes:
+                    if idx["name"] == index_name:
+                        existing_index = idx
+                        break
+
+                # Check if we need to recreate the index
+                needs_recreation = False
+                if existing_index:
+                    # Check if the existing index has CJK analyzer
+                    index_config = existing_index.get("options", {})
+                    current_analyzer = index_config.get("indexConfig", {}).get(
+                        "fulltext.analyzer", "standard"
+                    )
+
+                    if current_analyzer != "cjk":
+                        logger.info(
+                            f"[{self.workspace}] Existing index '{index_name}' uses '{current_analyzer}' analyzer. "
+                            "Recreating with CJK analyzer for Chinese support."
+                        )
+                        needs_recreation = True
+                    else:
+                        logger.debug(
+                            f"[{self.workspace}] Full-text index '{index_name}' already exists with CJK analyzer."
+                        )
+                        return
+
+                if not existing_index or needs_recreation:
+                    # Drop existing index if it needs recreation
+                    if needs_recreation:
+                        try:
+                            drop_query = f"DROP INDEX {index_name}"
+                            result = await session.run(drop_query)
+                            await result.consume()
+                            logger.info(
+                                f"[{self.workspace}] Dropped existing index '{index_name}'"
+                            )
+                        except Exception as drop_error:
+                            logger.warning(
+                                f"[{self.workspace}] Failed to drop existing index: {str(drop_error)}"
+                            )
+
+                    # Create new index with CJK analyzer
+                    logger.info(
+                        f"[{self.workspace}] Creating full-text index '{index_name}' with Chinese tokenizer support."
+                    )
+
+                    try:
+                        create_index_query = f"""
+                        CREATE FULLTEXT INDEX {index_name}
+                        FOR (n:`{workspace_label}`) ON EACH [n.entity_id]
+                        OPTIONS {{
+                            indexConfig: {{
+                                `fulltext.analyzer`: 'cjk',
+                                `fulltext.eventually_consistent`: true
+                            }}
+                        }}
+                        """
+                        result = await session.run(create_index_query)
+                        await result.consume()
+                        logger.info(
+                            f"[{self.workspace}] Successfully created full-text index '{index_name}' with CJK analyzer."
+                        )
+                    except Exception as cjk_error:
+                        # Fallback to standard analyzer if CJK is not supported
+                        logger.warning(
+                            f"[{self.workspace}] CJK analyzer not supported: {str(cjk_error)}. "
+                            "Falling back to standard analyzer."
+                        )
+                        create_index_query = f"""
+                        CREATE FULLTEXT INDEX {index_name}
+                        FOR (n:`{workspace_label}`) ON EACH [n.entity_id]
+                        """
+                        result = await session.run(create_index_query)
+                        await result.consume()
+                        logger.info(
+                            f"[{self.workspace}] Successfully created full-text index '{index_name}' with standard analyzer."
+                        )
+
+        except Exception as e:
+            # Handle cases where the command might not be supported
+            if "Unknown command" in str(e) or "invalid syntax" in str(e).lower():
+                logger.warning(
+                    f"[{self.workspace}] Could not create or verify full-text index '{index_name}'. "
+                    "This might be because you are using a Neo4j version that does not support it. "
+                    "Search functionality will fall back to slower, non-indexed queries."
+                )
+            else:
+                logger.error(
+                    f"[{self.workspace}] Failed to create or verify full-text index '{index_name}': {str(e)}"
+                )
+
     async def finalize(self):
         """Close the Neo4j driver and release all resources"""
         async with get_graph_db_lock():
@@ -251,7 +344,7 @@ class Neo4JStorage(BaseGraphStorage):
         await self.finalize()
 
     async def index_done_callback(self) -> None:
-        # Noe4J handles persistence automatically
+        # Neo4J handles persistence automatically
         pass
 
     async def has_node(self, node_id: str) -> bool:
@@ -1523,6 +1616,180 @@ class Neo4JStorage(BaseGraphStorage):
             await result.consume()
             return edges
 
+    async def get_popular_labels(self, limit: int = 300) -> list[str]:
+        """Get popular labels by node degree (most connected entities)
+
+        Args:
+            limit: Maximum number of labels to return
+
+        Returns:
+            List of labels sorted by degree (highest first)
+        """
+        workspace_label = self._get_workspace_label()
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            try:
+                query = f"""
+                MATCH (n:`{workspace_label}`)
+                WHERE n.entity_id IS NOT NULL
+                OPTIONAL MATCH (n)-[r]-()
+                WITH n.entity_id AS label, count(r) AS degree
+                ORDER BY degree DESC, label ASC
+                LIMIT $limit
+                RETURN label
+                """
+                result = await session.run(query, limit=limit)
+                labels = []
+                async for record in result:
+                    labels.append(record["label"])
+                await result.consume()
+
+                logger.debug(
+                    f"[{self.workspace}] Retrieved {len(labels)} popular labels (limit: {limit})"
+                )
+                return labels
+            except Exception as e:
+                logger.error(
+                    f"[{self.workspace}] Error getting popular labels: {str(e)}"
+                )
+                await result.consume()
+                raise
+
+    async def search_labels(self, query: str, limit: int = 50) -> list[str]:
+        """
+        Search labels with fuzzy matching, using a full-text index for performance if available.
+        Enhanced with Chinese text support using CJK analyzer.
+        Falls back to a slower CONTAINS search if the index is not available or fails.
+        """
+        workspace_label = self._get_workspace_label()
+        query_strip = query.strip()
+        if not query_strip:
+            return []
+
+        query_lower = query_strip.lower()
+        is_chinese = self._is_chinese_text(query_strip)
+        index_name = "entity_id_fulltext_idx"
+
+        # Attempt to use the full-text index first
+        try:
+            async with self._driver.session(
+                database=self._DATABASE, default_access_mode="READ"
+            ) as session:
+                if is_chinese:
+                    # For Chinese text, use different search strategies
+                    cypher_query = f"""
+                    CALL db.index.fulltext.queryNodes($index_name, $search_query) YIELD node, score
+                    WITH node, score
+                    WHERE node:`{workspace_label}`
+                    WITH node.entity_id AS label, score
+                    WITH label, score,
+                         CASE
+                             WHEN label = $query_strip THEN score + 1000
+                             WHEN label CONTAINS $query_strip THEN score + 500
+                             ELSE score
+                         END AS final_score
+                    RETURN label
+                    ORDER BY final_score DESC, label ASC
+                    LIMIT $limit
+                    """
+                    # For Chinese, don't add wildcard as it may not work properly with CJK analyzer
+                    search_query = query_strip
+                else:
+                    # For non-Chinese text, use the original logic with wildcard
+                    cypher_query = f"""
+                    CALL db.index.fulltext.queryNodes($index_name, $search_query) YIELD node, score
+                    WITH node, score
+                    WHERE node:`{workspace_label}`
+                    WITH node.entity_id AS label, toLower(node.entity_id) AS label_lower, score
+                    WITH label, label_lower, score,
+                         CASE
+                             WHEN label_lower = $query_lower THEN score + 1000
+                             WHEN label_lower STARTS WITH $query_lower THEN score + 500
+                             WHEN label_lower CONTAINS ' ' + $query_lower OR label_lower CONTAINS '_' + $query_lower THEN score + 50
+                             ELSE score
+                         END AS final_score
+                    RETURN label
+                    ORDER BY final_score DESC, label ASC
+                    LIMIT $limit
+                    """
+                    search_query = f"{query_strip}*"
+
+                result = await session.run(
+                    cypher_query,
+                    index_name=index_name,
+                    search_query=search_query,
+                    query_lower=query_lower,
+                    query_strip=query_strip,
+                    limit=limit,
+                )
+                labels = [record["label"] async for record in result]
+                await result.consume()
+
+                logger.debug(
+                    f"[{self.workspace}] Full-text search ({'Chinese' if is_chinese else 'Latin'}) for '{query}' returned {len(labels)} results (limit: {limit})"
+                )
+                return labels
+
+        except Exception as e:
+            # If the full-text search fails, fall back to CONTAINS search
+            logger.warning(
+                f"[{self.workspace}] Full-text search failed with error: {str(e)}. "
+                "Falling back to slower, non-indexed search."
+            )
+
+            # Enhanced fallback implementation
+            async with self._driver.session(
+                database=self._DATABASE, default_access_mode="READ"
+            ) as session:
+                if is_chinese:
+                    # For Chinese text, use direct CONTAINS without case conversion
+                    cypher_query = f"""
+                    MATCH (n:`{workspace_label}`)
+                    WHERE n.entity_id IS NOT NULL
+                    WITH n.entity_id AS label
+                    WHERE label CONTAINS $query_strip
+                    WITH label,
+                         CASE
+                             WHEN label = $query_strip THEN 1000
+                             WHEN label STARTS WITH $query_strip THEN 500
+                             ELSE 100 - size(label)
+                         END AS score
+                    ORDER BY score DESC, label ASC
+                    LIMIT $limit
+                    RETURN label
+                    """
+                    result = await session.run(
+                        cypher_query, query_strip=query_strip, limit=limit
+                    )
+                else:
+                    # For non-Chinese text, use the original fallback logic
+                    cypher_query = f"""
+                    MATCH (n:`{workspace_label}`)
+                    WHERE n.entity_id IS NOT NULL
+                    WITH n.entity_id AS label, toLower(n.entity_id) AS label_lower
+                    WHERE label_lower CONTAINS $query_lower
+                    WITH label, label_lower,
+                         CASE
+                             WHEN label_lower = $query_lower THEN 1000
+                             WHEN label_lower STARTS WITH $query_lower THEN 500
+                             ELSE 100 - size(label)
+                         END AS score
+                    ORDER BY score DESC, label ASC
+                    LIMIT $limit
+                    RETURN label
+                    """
+                    result = await session.run(
+                        cypher_query, query_lower=query_lower, limit=limit
+                    )
+
+                labels = [record["label"] async for record in result]
+                await result.consume()
+                logger.debug(
+                    f"[{self.workspace}] Fallback search ({'Chinese' if is_chinese else 'Latin'}) for '{query}' returned {len(labels)} results (limit: {limit})"
+                )
+                return labels
+
     async def drop(self) -> dict[str, str]:
         """Drop all data from current workspace storage and clean up resources
 

lightrag/kg/networkx_impl.py

@@ -212,6 +212,87 @@ class NetworkXStorage(BaseGraphStorage):
         # Return sorted list
         return sorted(list(labels))
 
+    async def get_popular_labels(self, limit: int = 300) -> list[str]:
+        """
+        Get popular labels by node degree (most connected entities)
+
+        Args:
+            limit: Maximum number of labels to return
+
+        Returns:
+            List of labels sorted by degree (highest first)
+        """
+        graph = await self._get_graph()
+
+        # Get degrees of all nodes and sort by degree descending
+        degrees = dict(graph.degree())
+        sorted_nodes = sorted(degrees.items(), key=lambda x: x[1], reverse=True)
+
+        # Return top labels limited by the specified limit
+        popular_labels = [str(node) for node, _ in sorted_nodes[:limit]]
+
+        logger.debug(
+            f"[{self.workspace}] Retrieved {len(popular_labels)} popular labels (limit: {limit})"
+        )
+
+        return popular_labels
+
+    async def search_labels(self, query: str, limit: int = 50) -> list[str]:
+        """
+        Search labels with fuzzy matching
+
+        Args:
+            query: Search query string
+            limit: Maximum number of results to return
+
+        Returns:
+            List of matching labels sorted by relevance
+        """
+        graph = await self._get_graph()
+        query_lower = query.lower().strip()
+
+        if not query_lower:
+            return []
+
+        # Collect matching nodes with relevance scores
+        matches = []
+        for node in graph.nodes():
+            node_str = str(node)
+            node_lower = node_str.lower()
+
+            # Skip if no match
+            if query_lower not in node_lower:
+                continue
+
+            # Calculate relevance score
+            # Exact match gets highest score
+            if node_lower == query_lower:
+                score = 1000
+            # Prefix match gets high score
+            elif node_lower.startswith(query_lower):
+                score = 500
+            # Contains match gets base score, with bonus for shorter strings
+            else:
+                # Shorter strings with matches are more relevant
+                score = 100 - len(node_str)
+                # Bonus for word boundary matches
+                if f" {query_lower}" in node_lower or f"_{query_lower}" in node_lower:
+                    score += 50
+
+            matches.append((node_str, score))
+
+        # Sort by relevance score (desc) then alphabetically
+        matches.sort(key=lambda x: (-x[1], x[0]))
+
+        # Return top matches limited by the specified limit
+        search_results = [match[0] for match in matches[:limit]]
+
+        logger.debug(
+            f"[{self.workspace}] Search query '{query}' returned {len(search_results)} results (limit: {limit})"
+        )
+
+        return search_results
+
     async def get_knowledge_graph(
         self,
         node_label: str,
@@ -272,6 +353,9 @@ class NetworkXStorage(BaseGraphStorage):
             # Store (node, depth, degree) in the queue
             queue = [(node_label, 0, graph.degree(node_label))]
 
+            # Flag to track if there are unexplored neighbors due to depth limit
+            has_unexplored_neighbors = False
+
             # Modified breadth-first search with degree-based prioritization
             while queue and len(bfs_nodes) < max_nodes:
                 # Get the current depth from the first node in queue
@@ -303,18 +387,30 @@ class NetworkXStorage(BaseGraphStorage):
                             for neighbor in unvisited_neighbors:
                                 neighbor_degree = graph.degree(neighbor)
                                 queue.append((neighbor, depth + 1, neighbor_degree))
+                        else:
+                            # Check if there are unexplored neighbors (skipped due to depth limit)
+                            neighbors = list(graph.neighbors(current_node))
+                            unvisited_neighbors = [
+                                n for n in neighbors if n not in visited
+                            ]
+                            if unvisited_neighbors:
+                                has_unexplored_neighbors = True
 
                     # Check if we've reached max_nodes
                     if len(bfs_nodes) >= max_nodes:
                         break
 
-            # Check if graph is truncated - if we still have nodes in the queue
-            # and we've reached max_nodes, then the graph is truncated
-            if queue and len(bfs_nodes) >= max_nodes:
+            # Check if graph is truncated - either due to max_nodes limit or depth limit
+            if (queue and len(bfs_nodes) >= max_nodes) or has_unexplored_neighbors:
                 result.is_truncated = True
-                logger.info(
-                    f"[{self.workspace}] Graph truncated: breadth-first search limited to {max_nodes} nodes"
-                )
+                if len(bfs_nodes) >= max_nodes:
+                    logger.info(
+                        f"[{self.workspace}] Graph truncated: max_nodes limit {max_nodes} reached"
+                    )
+                else:
+                    logger.info(
+                        f"[{self.workspace}] Graph truncated: only {len(bfs_nodes)} nodes found within max_depth {max_depth}"
+                    )
 
             # Create subgraph with BFS discovered nodes
             subgraph = graph.subgraph(bfs_nodes)

lightrag/kg/postgres_impl.py

@@ -4259,6 +4259,113 @@ class PGGraphStorage(BaseGraphStorage):
             edges.append(edge_properties)
         return edges
 
+    async def get_popular_labels(self, limit: int = 300) -> list[str]:
+        """Get popular labels by node degree (most connected entities) using native SQL for performance."""
+        try:
+            # Native SQL query to calculate node degrees directly from AGE's underlying tables
+            # This is significantly faster than using the cypher() function wrapper
+            query = f"""
+            WITH node_degrees AS (
+                SELECT
+                    node_id,
+                    COUNT(*) AS degree
+                FROM (
+                    SELECT start_id AS node_id FROM {self.graph_name}._ag_label_edge
+                    UNION ALL
+                    SELECT end_id AS node_id FROM {self.graph_name}._ag_label_edge
+                ) AS all_edges
+                GROUP BY node_id
+            )
+            SELECT
+                (ag_catalog.agtype_access_operator(VARIADIC ARRAY[v.properties, '"entity_id"'::agtype]))::text AS label
+            FROM
+                node_degrees d
+            JOIN
+                {self.graph_name}._ag_label_vertex v ON d.node_id = v.id
+            WHERE
+                ag_catalog.agtype_access_operator(VARIADIC ARRAY[v.properties, '"entity_id"'::agtype]) IS NOT NULL
+            ORDER BY
+                d.degree DESC,
+                label ASC
+            LIMIT $1;
+            """
+            results = await self._query(query, params={"limit": limit})
+            labels = [
+                result["label"] for result in results if result and "label" in result
+            ]
+
+            logger.debug(
+                f"[{self.workspace}] Retrieved {len(labels)} popular labels (limit: {limit})"
+            )
+            return labels
+        except Exception as e:
+            logger.error(f"[{self.workspace}] Error getting popular labels: {str(e)}")
+            return []
+
+    async def search_labels(self, query: str, limit: int = 50) -> list[str]:
+        """Search labels with fuzzy matching using native, parameterized SQL for performance and security."""
+        query_lower = query.lower().strip()
+        if not query_lower:
+            return []
+
+        try:
+            # Re-implementing with the correct agtype access operator and full scoring logic.
+            sql_query = f"""
+            WITH ranked_labels AS (
+                SELECT
+                    (ag_catalog.agtype_access_operator(VARIADIC ARRAY[properties, '"entity_id"'::agtype]))::text AS label,
+                    LOWER((ag_catalog.agtype_access_operator(VARIADIC ARRAY[properties, '"entity_id"'::agtype]))::text) AS label_lower
+                FROM
+                    {self.graph_name}._ag_label_vertex
+                WHERE
+                    ag_catalog.agtype_access_operator(VARIADIC ARRAY[properties, '"entity_id"'::agtype]) IS NOT NULL
+                    AND LOWER((ag_catalog.agtype_access_operator(VARIADIC ARRAY[properties, '"entity_id"'::agtype]))::text) ILIKE $1
+            )
+            SELECT
+                label
+            FROM (
+                SELECT
+                    label,
+                    CASE
+                        WHEN label_lower = $2 THEN 1000
+                        WHEN label_lower LIKE $3 THEN 500
+                        ELSE (100 - LENGTH(label))
+                    END +
+                    CASE
+                        WHEN label_lower LIKE $4 OR label_lower LIKE $5 THEN 50
+                        ELSE 0
+                    END AS score
+                FROM
+                    ranked_labels
+            ) AS scored_labels
+            ORDER BY
+                score DESC,
+                label ASC
+            LIMIT $6;
+            """
+            params = (
+                f"%{query_lower}%",  # For the main ILIKE clause ($1)
+                query_lower,  # For exact match ($2)
+                f"{query_lower}%",  # For prefix match ($3)
+                f"% {query_lower}%",  # For word boundary (space) ($4)
+                f"%_{query_lower}%",  # For word boundary (underscore) ($5)
+                limit,  # For LIMIT ($6)
+            )
+            results = await self._query(sql_query, params=dict(enumerate(params, 1)))
+            labels = [
+                result["label"] for result in results if result and "label" in result
+            ]
+
+            logger.debug(
+                f"[{self.workspace}] Search query '{query}' returned {len(labels)} results (limit: {limit})"
+            )
+            return labels
+        except Exception as e:
+            logger.error(
+                f"[{self.workspace}] Error searching labels with query '{query}': {str(e)}"
+            )
+            return []
+
     async def drop(self) -> dict[str, str]:
         """Drop the storage"""
         async with get_graph_db_lock():

lightrag_webui/src/api/lightrag.ts

@@ -1,5 +1,5 @@
 import axios, { AxiosError } from 'axios'
-import { backendBaseUrl } from '@/lib/constants'
+import { backendBaseUrl, popularLabelsDefaultLimit, searchLabelsDefaultLimit } from '@/lib/constants'
 import { errorMessage } from '@/lib/utils'
 import { useSettingsStore } from '@/stores/settings'
 import { navigationService } from '@/services/navigation'
@@ -319,6 +319,16 @@ export const getGraphLabels = async (): Promise<string[]> => {
   return response.data
 }
 
+export const getPopularLabels = async (limit: number = popularLabelsDefaultLimit): Promise<string[]> => {
+  const response = await axiosInstance.get(`/graph/label/popular?limit=${limit}`)
+  return response.data
+}
+
+export const searchLabels = async (query: string, limit: number = searchLabelsDefaultLimit): Promise<string[]> => {
+  const response = await axiosInstance.get(`/graph/label/search?q=${encodeURIComponent(query)}&limit=${limit}`)
+  return response.data
+}
+
 export const checkHealth = async (): Promise<
   LightragStatus | { status: 'error'; message: string }
 > => {

lightrag_webui/src/components/graph/GraphLabels.tsx

@@ -2,124 +2,101 @@ import { useCallback, useEffect } from 'react'
 import { AsyncSelect } from '@/components/ui/AsyncSelect'
 import { useSettingsStore } from '@/stores/settings'
 import { useGraphStore } from '@/stores/graph'
-import { labelListLimit, controlButtonVariant } from '@/lib/constants'
-import MiniSearch from 'minisearch'
+import {
+  dropdownDisplayLimit,
+  controlButtonVariant,
+  popularLabelsDefaultLimit,
+  searchLabelsDefaultLimit
+} from '@/lib/constants'
 import { useTranslation } from 'react-i18next'
 import { RefreshCw } from 'lucide-react'
 import Button from '@/components/ui/Button'
+import { SearchHistoryManager } from '@/utils/SearchHistoryManager'
+import { getPopularLabels, searchLabels } from '@/api/lightrag'
 
 const GraphLabels = () => {
   const { t } = useTranslation()
   const label = useSettingsStore.use.queryLabel()
-  const allDatabaseLabels = useGraphStore.use.allDatabaseLabels()
-  const labelsFetchAttempted = useGraphStore.use.labelsFetchAttempted()
 
-  // Remove initial label fetch effect as it's now handled by fetchGraph based on lastSuccessfulQueryLabel
+  // Initialize search history on component mount
+  useEffect(() => {
+    const initializeHistory = async () => {
+      const history = SearchHistoryManager.getHistory()
 
-  const getSearchEngine = useCallback(() => {
-    // Create search engine
-    const searchEngine = new MiniSearch({
-      idField: 'id',
-      fields: ['value'],
-      searchOptions: {
-        prefix: true,
-        fuzzy: 0.2,
-        boost: {
-          label: 2
+      if (history.length === 0) {
+        // If no history exists, fetch popular labels and initialize
+        try {
+          const popularLabels = await getPopularLabels(popularLabelsDefaultLimit)
+          await SearchHistoryManager.initializeWithDefaults(popularLabels)
+        } catch (error) {
+          console.error('Failed to initialize search history:', error)
+          // No fallback needed, API is the source of truth
         }
       }
-    })
-
-    // Add documents
-    const documents = allDatabaseLabels.map((str, index) => ({ id: index, value: str }))
-    searchEngine.addAll(documents)
-
-    return {
-      labels: allDatabaseLabels,
-      searchEngine
     }
-  }, [allDatabaseLabels])
+
+    initializeHistory()
+  }, [])
 
   const fetchData = useCallback(
     async (query?: string): Promise<string[]> => {
-      const { labels, searchEngine } = getSearchEngine()
+      let results: string[] = [];
+      if (!query || query.trim() === '' || query.trim() === '*') {
+        // Empty query: return search history
+        results = SearchHistoryManager.getHistoryLabels(dropdownDisplayLimit)
+      } else {
+        // Non-empty query: call backend search API
+        try {
+          const apiResults = await searchLabels(query.trim(), searchLabelsDefaultLimit)
+          results = apiResults.length <= dropdownDisplayLimit
+            ? apiResults
+            : [...apiResults.slice(0, dropdownDisplayLimit), '...']
+        } catch (error) {
+          console.error('Search API failed, falling back to local history search:', error)
 
-      let result: string[] = labels
-      if (query) {
-        // Search labels using MiniSearch
-        result = searchEngine.search(query).map((r: { id: number }) => labels[r.id])
-
-        // Add middle-content matching if results are few
-        // This enables matching content in the middle of text, not just from the beginning
-        if (result.length < 15) {
-          // Get already matched labels to avoid duplicates
-          const matchedLabels = new Set(result)
-
-          // Perform middle-content matching on all labels
-          const middleMatchResults = labels.filter(label => {
-            // Skip already matched labels
-            if (matchedLabels.has(label)) return false
-
-            // Match if label contains query string but doesn't start with it
-            return label &&
-                   typeof label === 'string' &&
-                   !label.toLowerCase().startsWith(query.toLowerCase()) &&
-                   label.toLowerCase().includes(query.toLowerCase())
-          })
-
-          // Merge results
-          result = [...result, ...middleMatchResults]
+          // Fallback to local history search
+          const history = SearchHistoryManager.getHistory()
+          const queryLower = query.toLowerCase().trim()
+          results = history
+            .filter(item => item.label.toLowerCase().includes(queryLower))
+            .map(item => item.label)
+            .slice(0, dropdownDisplayLimit)
         }
       }
-
-      return result.length <= labelListLimit
-        ? result
-        : [...result.slice(0, labelListLimit), '...']
+      // Always show '*' at the top, and remove duplicates
+      const finalResults = ['*', ...results.filter(label => label !== '*')];
+      return finalResults;
     },
-    [getSearchEngine]
+    []
   )
 
-  // Validate label
-  useEffect(() => {
+  const handleRefresh = useCallback(async () => {
+    // Clear search history
+    SearchHistoryManager.clearHistory()
 
-    if (labelsFetchAttempted) {
-      if (allDatabaseLabels.length > 1) {
-        if (label && label !== '*' && !allDatabaseLabels.includes(label)) {
-          console.log(`Label "${label}" not in available labels, setting to "*"`);
-          useSettingsStore.getState().setQueryLabel('*');
-        } else {
-          console.log(`Label "${label}" is valid`);
-        }
-      } else if (label && allDatabaseLabels.length <= 1 && label && label !== '*') {
-        console.log('Available labels list is empty, setting label to empty');
-        useSettingsStore.getState().setQueryLabel('');
-      }
-      useGraphStore.getState().setLabelsFetchAttempted(false)
+    // Reinitialize with popular labels
+    try {
+      const popularLabels = await getPopularLabels(popularLabelsDefaultLimit)
+      await SearchHistoryManager.initializeWithDefaults(popularLabels)
+    } catch (error) {
+      console.error('Failed to reload popular labels:', error)
+      // No fallback needed
     }
 
-  }, [allDatabaseLabels, label, labelsFetchAttempted]);
-
-  const handleRefresh = useCallback(() => {
-    // Reset fetch status flags
+    // Reset fetch status flags to trigger UI refresh
     useGraphStore.getState().setLabelsFetchAttempted(false)
     useGraphStore.getState().setGraphDataFetchAttempted(false)
 
-    // Clear last successful query label to ensure labels are fetched
+    // Clear last successful query label to ensure labels are fetched,
+    // which is the key to forcing a data refresh.
     useGraphStore.getState().setLastSuccessfulQueryLabel('')
 
-    // Get current label
-    const currentLabel = useSettingsStore.getState().queryLabel
+    // Reset to default label to ensure consistency
+    useSettingsStore.getState().setQueryLabel('*')
 
-    // If current label is empty, use default label '*'
-    if (!currentLabel) {
-      useSettingsStore.getState().setQueryLabel('*')
-    } else {
-      // Trigger data reload
-      useSettingsStore.getState().setQueryLabel('')
-      setTimeout(() => {
-        useSettingsStore.getState().setQueryLabel(currentLabel)
-      }, 0)
-    }
+    // Force a data refresh by incrementing the version counter in the graph store.
+    // This is the reliable way to trigger a re-fetch of the graph data.
+    useGraphStore.getState().incrementGraphDataVersion()
   }, []);
 
   return (
@@ -160,6 +137,11 @@ const GraphLabels = () => {
             newLabel = '*';
           }
 
+          // Add selected label to search history (except for special cases)
+          if (newLabel && newLabel !== '*' && newLabel !== '...' && newLabel.trim() !== '') {
+            SearchHistoryManager.addToHistory(newLabel);
+          }
+
           // Reset graphDataFetchAttempted flag to ensure data fetch is triggered
           useGraphStore.getState().setGraphDataFetchAttempted(false);
 
@@ -167,6 +149,7 @@ const GraphLabels = () => {
           useSettingsStore.getState().setQueryLabel(newLabel);
         }}
         clearable={false}  // Prevent clearing value on reselect
+        debounceTime={500}
       />
     </div>
   )

lightrag_webui/src/components/retrieval/QuerySettings.tsx

@@ -419,7 +419,7 @@ export default function QuerySettings() {
                   className="mr-1 cursor-pointer"
                   id="only_need_context"
                   checked={querySettings.only_need_context}
-                  onCheckedChange={(checked) => { 
+                  onCheckedChange={(checked) => {
                     handleChange('only_need_context', checked)
                     if (checked) {
                       handleChange('only_need_prompt', false)

lightrag_webui/src/components/ui/AsyncSelect.tsx

@@ -63,6 +63,8 @@ export interface AsyncSelectProps<T> {
   triggerTooltip?: string
   /** Allow clearing the selection */
   clearable?: boolean
+  /** Debounce time in milliseconds */
+  debounceTime?: number
 }
 
 export function AsyncSelect<T>({
@@ -84,7 +86,8 @@ export function AsyncSelect<T>({
   searchInputClassName,
   noResultsMessage,
   triggerTooltip,
-  clearable = true
+  clearable = true,
+  debounceTime = 150
 }: AsyncSelectProps<T>) {
   const [mounted, setMounted] = useState(false)
   const [open, setOpen] = useState(false)
@@ -94,7 +97,7 @@ export function AsyncSelect<T>({
   const [selectedValue, setSelectedValue] = useState(value)
   const [selectedOption, setSelectedOption] = useState<T | null>(null)
   const [searchTerm, setSearchTerm] = useState('')
-  const debouncedSearchTerm = useDebounce(searchTerm, preload ? 0 : 150)
+  const debouncedSearchTerm = useDebounce(searchTerm, preload ? 0 : debounceTime)
   const [originalOptions, setOriginalOptions] = useState<T[]>([])
   const [initialValueDisplay, setInitialValueDisplay] = useState<React.ReactNode | null>(null)
 
@@ -130,8 +133,8 @@ export function AsyncSelect<T>({
       try {
         setLoading(true)
         setError(null)
-        // If we have a value, use it for the initial search
-        const data = await fetcher(value)
+        // Always use empty query for initial load to show search history
+        const data = await fetcher('')
         setOriginalOptions(data)
         setOptions(data)
       } catch (err) {
@@ -144,7 +147,7 @@ export function AsyncSelect<T>({
     if (!mounted) {
       initializeOptions()
     }
-  }, [mounted, fetcher, value])
+  }, [mounted, fetcher])
 
   useEffect(() => {
     const fetchOptions = async () => {
@@ -245,21 +248,18 @@ export function AsyncSelect<T>({
                 </CommandEmpty>
               ))}
             <CommandGroup>
-              {options.map((option, index) => {
+              {options.map((option) => {
                 const optionValue = getOptionValue(option);
-                // Use index as a safe value that won't be trimmed by cmdk
-                const safeValue = `option-${index}-${optionValue.length}`;
+                // Fix cmdk filtering issue: use empty string when search is empty
+                // This ensures all items are shown when searchTerm is empty
+                const itemValue = searchTerm.trim() === '' ? '' : optionValue;
 
                 return (
                   <CommandItem
                     key={optionValue}
-                    value={safeValue}
-                    onSelect={(selectedSafeValue) => {
-                      // Extract the original value from the safe value
-                      const selectedIndex = parseInt(selectedSafeValue.split('-')[1]);
-                      const originalValue = getOptionValue(options[selectedIndex]);
-                      console.log(`CommandItem onSelect: safeValue='${selectedSafeValue}', originalValue='${originalValue}' (length: ${originalValue.length})`);
-                      handleSelect(originalValue);
+                    value={itemValue}
+                    onSelect={() => {
+                      handleSelect(optionValue);
                     }}
                     className="truncate"
                   >

lightrag_webui/src/hooks/useLightragGraph.tsx

@@ -225,18 +225,6 @@ export type EdgeType = {
 const fetchGraph = async (label: string, maxDepth: number, maxNodes: number) => {
   let rawData: any = null;
 
-  // Check if we need to fetch all database labels first
-  const lastSuccessfulQueryLabel = useGraphStore.getState().lastSuccessfulQueryLabel;
-  if (!lastSuccessfulQueryLabel) {
-    console.log('Last successful queryLabel is empty');
-    try {
-      await useGraphStore.getState().fetchAllDatabaseLabels();
-    } catch (e) {
-      console.error('Failed to fetch all database labels:', e);
-      // Continue with graph fetch even if labels fetch fails
-    }
-  }
-
   // Trigger GraphLabels component to check if the label is valid
   // console.log('Setting labelsFetchAttempted to true');
   useGraphStore.getState().setLabelsFetchAttempted(true)
@@ -411,6 +399,7 @@ const useLightrangeGraph = () => {
   const isFetching = useGraphStore.use.isFetching()
   const nodeToExpand = useGraphStore.use.nodeToExpand()
   const nodeToPrune = useGraphStore.use.nodeToPrune()
+  const graphDataVersion = useGraphStore.use.graphDataVersion()
 
 
   // Use ref to track if data has been loaded and initial load
@@ -597,7 +586,7 @@ const useLightrangeGraph = () => {
         state.setLastSuccessfulQueryLabel('') // Clear last successful query label on error
       })
     }
-  }, [queryLabel, maxQueryDepth, maxNodes, isFetching, t])
+  }, [queryLabel, maxQueryDepth, maxNodes, isFetching, t, graphDataVersion])
 
   // Handle node expansion
   useEffect(() => {

lightrag_webui/src/lib/constants.ts

@@ -20,6 +20,17 @@ export const edgeColorHighlighted = '#B2EBF2'
 export const searchResultLimit = 50
 export const labelListLimit = 100
 
+// Search History Configuration
+export const searchHistoryMaxItems = 500
+export const searchHistoryVersion = '1.0'
+
+// API Request Limits
+export const popularLabelsDefaultLimit = 300
+export const searchLabelsDefaultLimit = 50
+
+// UI Display Limits
+export const dropdownDisplayLimit = 300
+
 export const minNodeSize = 4
 export const maxNodeSize = 20
 

lightrag_webui/src/locales/ar.json

@@ -336,7 +336,7 @@
       "label": "التسمية",
       "placeholder": "ابحث في التسميات...",
       "andOthers": "و {{count}} آخرون",
-      "refreshTooltip": "إعادة تحميل البيانات (بعد إضافة الملف)"
+      "refreshTooltip": "إعادة تعيين بيانات الرسم البياني وسجل البحث (مطلوب بعد تغيير المستندات)"
     },
     "emptyGraph": "فارغ (حاول إعادة التحميل)"
   },

lightrag_webui/src/locales/en.json

@@ -336,7 +336,7 @@
       "label": "Label",
       "placeholder": "Search labels...",
       "andOthers": "And {count} others",
-      "refreshTooltip": "Reload data(After file added)"
+      "refreshTooltip": "Reset graph data and search history (required after document changes)"
     },
     "emptyGraph": "Empty(Try Reload Again)"
   },

lightrag_webui/src/locales/fr.json

@@ -336,7 +336,7 @@
       "label": "Étiquette",
       "placeholder": "Rechercher des étiquettes...",
       "andOthers": "Et {{count}} autres",
-      "refreshTooltip": "Recharger les données (Après l'ajout de fichier)"
+      "refreshTooltip": "Réinitialiser les données du graphe et l'historique de recherche (requis après modification des documents)"
     },
     "emptyGraph": "Vide (Essayez de recharger)"
   },

lightrag_webui/src/locales/zh.json

@@ -336,7 +336,7 @@
       "label": "标签",
       "placeholder": "搜索标签...",
       "andOthers": "还有 {count} 个",
-      "refreshTooltip": "重载图形数据(添加文件后需重载)"
+      "refreshTooltip": "重置图形数据和搜索历史(文档变化后需重置)"
     },
     "emptyGraph": "无数据(请重载图形数据)"
   },

lightrag_webui/src/locales/zh_TW.json

@@ -336,7 +336,7 @@
       "label": "標籤",
       "placeholder": "搜尋標籤...",
       "andOthers": "還有 {count} 個",
-      "refreshTooltip": "重載圖形數據(新增檔案後需重載)"
+      "refreshTooltip": "重設圖形資料和搜尋歷史(文件變化後需重設)"
     },
     "emptyGraph": "無數據(請重載圖形數據)"
   },

lightrag_webui/src/stores/graph.ts

@@ -1,7 +1,6 @@
 import { create } from 'zustand'
 import { createSelectors } from '@/lib/utils'
 import { DirectedGraph } from 'graphology'
-import { getGraphLabels } from '@/api/lightrag'
 import MiniSearch from 'minisearch'
 
 export type RawNodeType = {
@@ -84,7 +83,6 @@ interface GraphState {
   rawGraph: RawGraph | null
   sigmaGraph: DirectedGraph | null
   sigmaInstance: any | null
-  allDatabaseLabels: string[]
 
   searchEngine: MiniSearch | null
 
@@ -113,8 +111,6 @@ interface GraphState {
 
   setRawGraph: (rawGraph: RawGraph | null) => void
   setSigmaGraph: (sigmaGraph: DirectedGraph | null) => void
-  setAllDatabaseLabels: (labels: string[]) => void
-  fetchAllDatabaseLabels: () => Promise<void>
   setIsFetching: (isFetching: boolean) => void
 
   // 搜索引擎方法
@@ -160,7 +156,6 @@ const useGraphStoreBase = create<GraphState>()((set, get) => ({
   rawGraph: null,
   sigmaGraph: null,
   sigmaInstance: null,
-  allDatabaseLabels: ['*'],
 
   typeColorMap: new Map<string, string>(),
 
@@ -207,21 +202,6 @@ const useGraphStoreBase = create<GraphState>()((set, get) => ({
     set({ sigmaGraph });
   },
 
-  setAllDatabaseLabels: (labels: string[]) => set({ allDatabaseLabels: labels }),
-
-  fetchAllDatabaseLabels: async () => {
-    try {
-      console.log('Fetching all database labels...');
-      const labels = await getGraphLabels();
-      set({ allDatabaseLabels: ['*', ...labels] });
-      return;
-    } catch (error) {
-      console.error('Failed to fetch all database labels:', error);
-      set({ allDatabaseLabels: ['*'] });
-      throw error;
-    }
-  },
-
   setMoveToSelectedNode: (moveToSelectedNode?: boolean) => set({ moveToSelectedNode }),
 
   setSigmaInstance: (instance: any) => set({ sigmaInstance: instance }),

lightrag_webui/src/utils/SearchHistoryManager.ts

@@ -0,0 +1,259 @@
+import { searchHistoryMaxItems, searchHistoryVersion } from '@/lib/constants'
+
+/**
+ * SearchHistoryManager - Manages search history persistence in localStorage
+ *
+ * This utility class handles:
+ * - Storing and retrieving search history from localStorage
+ * - Managing history size limits
+ * - Sorting by access time and frequency
+ * - Version compatibility
+ */
+
+export interface SearchHistoryItem {
+  label: string           // Label name
+  lastAccessed: number   // Last access timestamp
+  accessCount: number    // Access count for sorting optimization
+}
+
+export interface SearchHistoryData {
+  items: SearchHistoryItem[]
+  version: string        // Data version for compatibility
+  workspace?: string     // Workspace isolation (if needed)
+}
+
+export class SearchHistoryManager {
+  private static readonly STORAGE_KEY = 'lightrag_search_history'
+  private static readonly MAX_HISTORY = searchHistoryMaxItems
+  private static readonly VERSION = searchHistoryVersion
+
+  /**
+   * Get search history from localStorage
+   * @returns Array of search history items sorted by last accessed time (descending)
+   */
+  static getHistory(): SearchHistoryItem[] {
+    try {
+      const data = localStorage.getItem(this.STORAGE_KEY)
+      if (!data) return []
+
+      const parsed: SearchHistoryData = JSON.parse(data)
+
+      // Version compatibility check
+      if (parsed.version !== this.VERSION) {
+        console.warn(`Search history version mismatch. Expected ${this.VERSION}, got ${parsed.version}. Clearing history.`)
+        this.clearHistory()
+        return []
+      }
+
+      // Ensure items is an array
+      if (!Array.isArray(parsed.items)) {
+        console.warn('Invalid search history format. Clearing history.')
+        this.clearHistory()
+        return []
+      }
+
+      // Sort by last accessed time (descending) then by access count (descending)
+      return parsed.items.sort((a, b) => {
+        if (b.lastAccessed !== a.lastAccessed) {
+          return b.lastAccessed - a.lastAccessed
+        }
+        return (b.accessCount || 0) - (a.accessCount || 0)
+      })
+    } catch (error) {
+      console.error('Error reading search history:', error)
+      this.clearHistory()
+      return []
+    }
+  }
+
+  /**
+   * Add a label to search history (or update if exists)
+   * @param label Label to add to history
+   */
+  static addToHistory(label: string): void {
+    if (!label || typeof label !== 'string' || label.trim() === '') {
+      return
+    }
+
+    try {
+      const history = this.getHistory()
+      const now = Date.now()
+      const trimmedLabel = label.trim()
+
+      // Find existing item
+      const existingIndex = history.findIndex(item => item.label === trimmedLabel)
+
+      if (existingIndex >= 0) {
+        // Update existing item
+        const existingItem = history[existingIndex]
+        existingItem.lastAccessed = now
+        existingItem.accessCount = (existingItem.accessCount || 0) + 1
+
+        // Move to front (will be sorted properly when saved)
+        history.splice(existingIndex, 1)
+        history.unshift(existingItem)
+      } else {
+        // Add new item to the beginning
+        history.unshift({
+          label: trimmedLabel,
+          lastAccessed: now,
+          accessCount: 1
+        })
+      }
+
+      // Limit history size
+      if (history.length > this.MAX_HISTORY) {
+        history.splice(this.MAX_HISTORY)
+      }
+
+      // Save to localStorage
+      const data: SearchHistoryData = {
+        items: history,
+        version: this.VERSION
+      }
+
+      localStorage.setItem(this.STORAGE_KEY, JSON.stringify(data))
+    } catch (error) {
+      console.error('Error saving search history:', error)
+    }
+  }
+
+  /**
+   * Clear all search history
+   */
+  static clearHistory(): void {
+    try {
+      localStorage.removeItem(this.STORAGE_KEY)
+    } catch (error) {
+      console.error('Error clearing search history:', error)
+    }
+  }
+
+  /**
+   * Initialize history with default popular labels if empty
+   * @param popularLabels Array of popular labels to use as defaults
+   */
+  static async initializeWithDefaults(popularLabels: string[]): Promise<void> {
+    const history = this.getHistory()
+
+    if (history.length === 0 && popularLabels.length > 0) {
+      try {
+        const now = Date.now()
+        const defaultItems: SearchHistoryItem[] = popularLabels.map((label, index) => ({
+          label: label.trim(),
+          lastAccessed: now - index, // Ensure proper ordering
+          accessCount: 0 // Mark as default/popular items
+        }))
+
+        const data: SearchHistoryData = {
+          items: defaultItems,
+          version: this.VERSION
+        }
+
+        localStorage.setItem(this.STORAGE_KEY, JSON.stringify(data))
+      } catch (error) {
+        console.error('Error initializing search history with defaults:', error)
+      }
+    }
+  }
+
+  /**
+   * Get recent searches (items with accessCount > 0)
+   * @param limit Maximum number of recent searches to return
+   * @returns Array of recent search items
+   */
+  static getRecentSearches(limit: number = 10): SearchHistoryItem[] {
+    const history = this.getHistory()
+    return history
+      .filter(item => item.accessCount > 0)
+      .slice(0, limit)
+  }
+
+  /**
+   * Get popular recommendations (items with accessCount = 0, i.e., defaults)
+   * @param limit Maximum number of recommendations to return
+   * @returns Array of popular recommendation items
+   */
+  static getPopularRecommendations(limit?: number): SearchHistoryItem[] {
+    const history = this.getHistory()
+    const recommendations = history.filter(item => item.accessCount === 0)
+    return limit ? recommendations.slice(0, limit) : recommendations
+  }
+
+  /**
+   * Get all history items as simple string array
+   * @param limit Maximum number of items to return
+   * @returns Array of label strings
+   */
+  static getHistoryLabels(limit?: number): string[] {
+    const history = this.getHistory()
+    const labels = history.map(item => item.label)
+    return limit ? labels.slice(0, limit) : labels
+  }
+
+  /**
+   * Check if a label exists in history
+   * @param label Label to check
+   * @returns True if label exists in history
+   */
+  static hasLabel(label: string): boolean {
+    if (!label || typeof label !== 'string') return false
+    const history = this.getHistory()
+    return history.some(item => item.label === label.trim())
+  }
+
+  /**
+   * Remove a specific label from history
+   * @param label Label to remove
+   */
+  static removeLabel(label: string): void {
+    if (!label || typeof label !== 'string') return
+
+    try {
+      const history = this.getHistory()
+      const trimmedLabel = label.trim()
+      const filteredHistory = history.filter(item => item.label !== trimmedLabel)
+
+      if (filteredHistory.length !== history.length) {
+        const data: SearchHistoryData = {
+          items: filteredHistory,
+          version: this.VERSION
+        }
+
+        localStorage.setItem(this.STORAGE_KEY, JSON.stringify(data))
+      }
+    } catch (error) {
+      console.error('Error removing label from search history:', error)
+    }
+  }
+
+  /**
+   * Get storage statistics
+   * @returns Object with history statistics
+   */
+  static getStats(): {
+    totalItems: number
+    recentSearches: number
+    popularRecommendations: number
+    storageSize: number
+  } {
+    const history = this.getHistory()
+    const recentCount = history.filter(item => item.accessCount > 0).length
+    const popularCount = history.filter(item => item.accessCount === 0).length
+
+    let storageSize = 0
+    try {
+      const data = localStorage.getItem(this.STORAGE_KEY)
+      storageSize = data ? data.length : 0
+    } catch {
+      // Ignore error
+    }
+
+    return {
+      totalItems: history.length,
+      recentSearches: recentCount,
+      popularRecommendations: popularCount,
+      storageSize
+    }
+  }
+}