Vsetko

documents/ai_intro.txt

@@ -0,0 +1,4 @@
+Python is widely used in Machine Learning.
+Machine Learning uses neural networks and statistical models.
+Neural networks are used in deep learning.
+Deep learning is part of machine learning.

graph_builder.py

@@ -0,0 +1,411 @@
+import json
+import os
+import networkx as nx
+import re
+import hashlib
+
+from ingest import load_documents
+from llm import llm_call
+
+# Cleans response to avoid errors
+def clean_llm_response(text: str) -> str:
+    """
+    Removes markdown, extra text, and extracts JSON safely.
+    """
+
+    if not text:
+        raise ValueError("LLM returned empty response")
+
+    text = text.strip()
+
+    # Remove ```json or ``` wrappers
+    text = re.sub(r"^```json", "", text)
+    text = re.sub(r"^```", "", text)
+    text = re.sub(r"```$", "", text)
+
+    text = text.strip()
+
+    # Try to extract JSON object if model adds extra text
+    match = re.search(r"\{.*\}", text, re.DOTALL)
+    if match:
+        return match.group(0)
+
+    return text
+
+# Gets cached extraction for a chunk, or extracts and caches it
+def get_cached_extraction(chunk, cache_dir="cache"):
+
+    os.makedirs(cache_dir, exist_ok=True)
+
+    # Create unique hash for chunk
+    chunk_hash = hashlib.md5(
+        chunk.encode("utf-8")
+    ).hexdigest()
+
+    cache_path = os.path.join(
+        cache_dir,
+        f"{chunk_hash}.json"
+    )
+
+    
+    # Cache Load (if exists) 
+    if os.path.exists(cache_path):
+
+        print(f"[CACHE HIT] {chunk_hash}")
+
+        with open(cache_path, "r", encoding="utf-8") as f:
+            return json.load(f)
+
+   
+    # Extract json and store in cache
+    print(f"[CACHE MISS] {chunk_hash}")
+
+    extraction = extract_entities_and_relations(chunk)
+
+    
+    # Save cache
+    with open(cache_path, "w", encoding="utf-8") as f:
+        json.dump(
+            extraction,
+            f,
+            indent=2,
+            ensure_ascii=False
+        )
+
+    return extraction
+
+# Calls LLM to get entities and realtions (.json) and saves them to cache
+def extract_entities_and_relations(text):
+
+    text = text[:8000]  # prevent context overflow
+
+    prompt = f"""
+You are an educational knowledge graph extraction system.
+
+Your task:
+
+Extract educational entities and relations.
+
+Rules:
+- Return ONLY EXACT JSON
+- Use ONLY meaningful educational concepts
+- NO markdown
+- NO explanations
+- NO extra text
+- Use these relations ONLY:
+[  "is_a",
+  "part_of",
+  "uses",
+  "teaches",
+  "requires",
+  "depends_on",
+  "implements",
+  "applies_to",
+  "subfield_of",
+  "contains",
+  "defined_by"]
+- Entity names must be normalized:
+    GOOD:
+    - "Machine Learning"
+    - "Artificial Intelligence"
+    BAD:
+    - "machine learning"
+    - "ML"
+    - "AI systems"
+- Prefer fewer HIGH QUALITY relations over many weak relations
+- Every entity should participate in at least one relation
+
+OUTPUT FORMAT EXACTLY:
+{{
+  "entities": [
+    {{"name": "...", "type": "..."}}
+  ],
+  "relations": [
+    {{
+      "source": "...",
+      "target": "...",
+      "relation": "..."
+    }}
+  ]
+}}
+
+TEXT:
+{text}
+"""
+
+    response = llm_call([
+        {"role": "user", "content": prompt}
+    ])
+
+    cleaned = clean_llm_response(response)
+
+    try:
+        data = json.loads(cleaned)
+
+    except json.JSONDecodeError:
+        print("\nINVALID JSON")
+        print(cleaned)
+        raise
+
+    # Checks if llm output is valid
+    entities = data.get("entities", [])
+    relations = data.get("relations", [])
+
+    # Normalize entity names
+    normalized_entities = {}
+
+    for ent in entities:
+
+        name = ent["name"].strip()
+
+        # title case normalization
+        name = " ".join(word.capitalize() for word in name.split())
+
+        normalized_entities[name] = {
+            "name": name,
+            "type": ent.get("type", "concept")
+        }
+
+    entity_names = set(normalized_entities.keys())
+
+    valid_relations = []
+
+    for rel in relations:
+
+        source = rel["source"].strip()
+        target = rel["target"].strip()
+        relation = rel["relation"].strip()
+
+        source = " ".join(word.capitalize() for word in source.split())
+        target = " ".join(word.capitalize() for word in target.split())
+
+        # Skip self-loops
+        if source == target:
+            continue
+
+        # Ensure both entities exist
+        if source not in entity_names:
+            continue
+
+        if target not in entity_names:
+            continue
+
+        valid_relations.append({
+            "source": source,
+            "target": target,
+            "relation": relation
+        })
+
+    # Remove isolated entities
+    connected = set()
+
+    for rel in valid_relations:
+        connected.add(rel["source"])
+        connected.add(rel["target"])
+
+    final_entities = [
+        ent for ent in normalized_entities.values()
+        if ent["name"] in connected
+    ]
+
+    return {
+        "entities": final_entities,
+        "relations": valid_relations
+    }
+
+# Chunks document into chunks (with overlap)
+def chunk_text(text, chunk_size=8000, overlap=300):
+
+    text = text.replace("\r", " ")
+
+    paragraphs = text.split("\n")
+
+    chunks = []
+    current = ""
+
+    for para in paragraphs:
+
+        para = para.strip()
+
+        # Skip useless tiny paragraphs
+        if len(para) < 40:
+            continue
+
+        if len(current) + len(para) < chunk_size:
+
+            current += para + "\n"
+
+        else:
+
+            chunks.append(current)
+
+            # overlap keeps context continuity
+            current = current[-overlap:] + "\n" + para + "\n"
+
+    if current:
+        chunks.append(current)
+
+    return chunks
+
+# Builds graph from documents (must be in documents folder [.pdf, .txt, .docx])
+def build_graph_from_documents(folder="documents"):
+
+    G = nx.DiGraph()
+
+    docs = load_documents(folder)
+
+    for doc in docs:
+
+        print(f"\n[INFO] Processing document: {doc['filename']}")
+
+        # Document is split into chunks
+        chunks = chunk_text(doc["content"])
+
+        print(f"[INFO] Total chunks: {len(chunks)}")
+
+        # Process chunk by chunk
+        for idx, chunk in enumerate(chunks):
+
+            print(f"[INFO] Processing chunk {idx + 1}/{len(chunks)}")
+
+            try:
+                extraction = get_cached_extraction(chunk)
+
+            except Exception as e:
+
+                print(f"[WARN] Extraction failed on chunk {idx + 1}")
+                print(e)
+
+                continue
+
+            # Add entities (nodes) to graph
+            for ent in extraction["entities"]:
+
+                G.add_node(
+                    ent["name"],
+                    type=ent["type"],
+                    source_doc=doc["filename"]
+                )
+
+            # Adds entity realtions (links nodes)
+            for rel in extraction["relations"]:
+
+                G.add_edge(
+                    rel["source"],
+                    rel["target"],
+                    relation=rel["relation"],
+                    source_doc=doc["filename"]
+                )
+
+    return G
+
+# Saves graph to file 
+def save_graph(G, path="graph/kg.graphml"):
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    nx.write_graphml(G, path)
+    print(f"Graph saved to {path}")
+
+# Loads graph from file
+def load_graph(path="graph/kg.graphml"):
+    return nx.read_graphml(path)
+
+# Adds document to graph
+def add_document(filepath, G):
+
+    docs = load_documents(folder=filepath)
+
+    if not docs:
+        print(f"[WARN] No documents found in: {filepath}")
+        return
+
+    doc = docs[0]
+
+    print(f"\n[INFO] Adding document: {doc['filename']}")
+
+    # Document is split into chunks 
+    chunks = chunk_text(doc["content"])
+
+    print(f"[INFO] Total chunks: {len(chunks)}")
+
+    # Process chunk by chunk
+    for idx, chunk in enumerate(chunks):
+
+        print(f"[INFO] Processing chunk {idx + 1}/{len(chunks)}")
+
+        try:
+
+            extraction = get_cached_extraction(chunk)
+
+        except Exception as e:
+
+            print(f"[WARN] Extraction failed on chunk {idx + 1}")
+            print(e)
+
+            continue
+
+        # Add entities (nodes) to graph
+        for ent in extraction["entities"]:
+
+            G.add_node(
+                ent["name"],
+                type=ent["type"],
+                source_doc=doc["filename"]
+            )
+
+        # Adds entity realtions (links nodes)
+        for rel in extraction["relations"]:
+
+            G.add_edge(
+                rel["source"],
+                rel["target"],
+                relation=rel["relation"],
+                source_doc=doc["filename"]
+            )
+
+    print(f"[INFO] Finished adding {doc['filename']}")
+
+# Removes document from graph
+def remove_document(filename, G):
+
+    edges_to_remove = []
+
+    for u, v, data in G.edges(data=True):
+
+        if data.get("source_doc") == filename:
+            edges_to_remove.append((u, v))
+
+    G.remove_edges_from(edges_to_remove)
+
+    # Remove orphan nodes
+    isolated_nodes = list(nx.isolates(G))
+
+    G.remove_nodes_from(isolated_nodes)
+
+    print(f"[INFO] Removed document: {filename}")
+
+'''
+#test
+if __name__ == "__main__":
+
+    docs = load_documents()
+
+    extraction = extract_entities_and_relations(
+        docs[0]["content"]
+    )
+
+    print(extraction)
+'''
+
+#test
+if __name__ == "__main__":
+
+    #G2 = load_graph()
+
+    #print(G2.nodes(data=True))
+    G = build_graph_from_documents()
+
+    save_graph(G)
+    #print(G.nodes(data=True))
+
+    #print(G.edges(data=True))
+

ingest.py

@@ -0,0 +1,43 @@
+from pathlib import Path
+from pypdf import PdfReader
+from docx import Document
+
+def load_documents(folder="documents"):
+    docs = []
+
+    for file in Path(folder).glob("*"):
+        if file.suffix == ".txt":
+            text = file.read_text(encoding="utf-8")
+
+            docs.append({
+                "filename": file.name,
+                "content": text
+            })
+            
+        elif file.suffix == ".pdf":
+            reader = PdfReader(file)
+            text = ""
+            for page in reader.pages:
+                text += page.extract_text()
+
+            docs.append({
+                "filename": file.name,
+                "content": text
+            })
+
+        elif file.suffix == ".docx":
+            doc = Document(file)
+            text = "\n".join([paragraph.text for paragraph in doc.paragraphs])
+
+            docs.append({
+                "filename": file.name,
+                "content": text
+            })
+
+    return docs
+
+#test
+if __name__ == "__main__":
+    docs = load_documents()
+
+    print(docs)

lib/bindings/utils.js

@@ -0,0 +1,189 @@
+function neighbourhoodHighlight(params) {
+  // console.log("in nieghbourhoodhighlight");
+  allNodes = nodes.get({ returnType: "Object" });
+  // originalNodes = JSON.parse(JSON.stringify(allNodes));
+  // if something is selected:
+  if (params.nodes.length > 0) {
+    highlightActive = true;
+    var i, j;
+    var selectedNode = params.nodes[0];
+    var degrees = 2;
+
+    // mark all nodes as hard to read.
+    for (let nodeId in allNodes) {
+      // nodeColors[nodeId] = allNodes[nodeId].color;
+      allNodes[nodeId].color = "rgba(200,200,200,0.5)";
+      if (allNodes[nodeId].hiddenLabel === undefined) {
+        allNodes[nodeId].hiddenLabel = allNodes[nodeId].label;
+        allNodes[nodeId].label = undefined;
+      }
+    }
+    var connectedNodes = network.getConnectedNodes(selectedNode);
+    var allConnectedNodes = [];
+
+    // get the second degree nodes
+    for (i = 1; i < degrees; i++) {
+      for (j = 0; j < connectedNodes.length; j++) {
+        allConnectedNodes = allConnectedNodes.concat(
+          network.getConnectedNodes(connectedNodes[j])
+        );
+      }
+    }
+
+    // all second degree nodes get a different color and their label back
+    for (i = 0; i < allConnectedNodes.length; i++) {
+      // allNodes[allConnectedNodes[i]].color = "pink";
+      allNodes[allConnectedNodes[i]].color = "rgba(150,150,150,0.75)";
+      if (allNodes[allConnectedNodes[i]].hiddenLabel !== undefined) {
+        allNodes[allConnectedNodes[i]].label =
+          allNodes[allConnectedNodes[i]].hiddenLabel;
+        allNodes[allConnectedNodes[i]].hiddenLabel = undefined;
+      }
+    }
+
+    // all first degree nodes get their own color and their label back
+    for (i = 0; i < connectedNodes.length; i++) {
+      // allNodes[connectedNodes[i]].color = undefined;
+      allNodes[connectedNodes[i]].color = nodeColors[connectedNodes[i]];
+      if (allNodes[connectedNodes[i]].hiddenLabel !== undefined) {
+        allNodes[connectedNodes[i]].label =
+          allNodes[connectedNodes[i]].hiddenLabel;
+        allNodes[connectedNodes[i]].hiddenLabel = undefined;
+      }
+    }
+
+    // the main node gets its own color and its label back.
+    // allNodes[selectedNode].color = undefined;
+    allNodes[selectedNode].color = nodeColors[selectedNode];
+    if (allNodes[selectedNode].hiddenLabel !== undefined) {
+      allNodes[selectedNode].label = allNodes[selectedNode].hiddenLabel;
+      allNodes[selectedNode].hiddenLabel = undefined;
+    }
+  } else if (highlightActive === true) {
+    // console.log("highlightActive was true");
+    // reset all nodes
+    for (let nodeId in allNodes) {
+      // allNodes[nodeId].color = "purple";
+      allNodes[nodeId].color = nodeColors[nodeId];
+      // delete allNodes[nodeId].color;
+      if (allNodes[nodeId].hiddenLabel !== undefined) {
+        allNodes[nodeId].label = allNodes[nodeId].hiddenLabel;
+        allNodes[nodeId].hiddenLabel = undefined;
+      }
+    }
+    highlightActive = false;
+  }
+
+  // transform the object into an array
+  var updateArray = [];
+  if (params.nodes.length > 0) {
+    for (let nodeId in allNodes) {
+      if (allNodes.hasOwnProperty(nodeId)) {
+        // console.log(allNodes[nodeId]);
+        updateArray.push(allNodes[nodeId]);
+      }
+    }
+    nodes.update(updateArray);
+  } else {
+    // console.log("Nothing was selected");
+    for (let nodeId in allNodes) {
+      if (allNodes.hasOwnProperty(nodeId)) {
+        // console.log(allNodes[nodeId]);
+        // allNodes[nodeId].color = {};
+        updateArray.push(allNodes[nodeId]);
+      }
+    }
+    nodes.update(updateArray);
+  }
+}
+
+function filterHighlight(params) {
+  allNodes = nodes.get({ returnType: "Object" });
+  // if something is selected:
+  if (params.nodes.length > 0) {
+    filterActive = true;
+    let selectedNodes = params.nodes;
+
+    // hiding all nodes and saving the label
+    for (let nodeId in allNodes) {
+      allNodes[nodeId].hidden = true;
+      if (allNodes[nodeId].savedLabel === undefined) {
+        allNodes[nodeId].savedLabel = allNodes[nodeId].label;
+        allNodes[nodeId].label = undefined;
+      }
+    }
+
+    for (let i=0; i < selectedNodes.length; i++) {
+      allNodes[selectedNodes[i]].hidden = false;
+      if (allNodes[selectedNodes[i]].savedLabel !== undefined) {
+        allNodes[selectedNodes[i]].label = allNodes[selectedNodes[i]].savedLabel;
+        allNodes[selectedNodes[i]].savedLabel = undefined;
+      }
+    }
+
+  } else if (filterActive === true) {
+    // reset all nodes
+    for (let nodeId in allNodes) {
+      allNodes[nodeId].hidden = false;
+      if (allNodes[nodeId].savedLabel !== undefined) {
+        allNodes[nodeId].label = allNodes[nodeId].savedLabel;
+        allNodes[nodeId].savedLabel = undefined;
+      }
+    }
+    filterActive = false;
+  }
+
+  // transform the object into an array
+  var updateArray = [];
+  if (params.nodes.length > 0) {
+    for (let nodeId in allNodes) {
+      if (allNodes.hasOwnProperty(nodeId)) {
+        updateArray.push(allNodes[nodeId]);
+      }
+    }
+    nodes.update(updateArray);
+  } else {
+    for (let nodeId in allNodes) {
+      if (allNodes.hasOwnProperty(nodeId)) {
+        updateArray.push(allNodes[nodeId]);
+      }
+    }
+    nodes.update(updateArray);
+  }
+}
+
+function selectNode(nodes) {
+  network.selectNodes(nodes);
+  neighbourhoodHighlight({ nodes: nodes });
+  return nodes;
+}
+
+function selectNodes(nodes) {
+  network.selectNodes(nodes);
+  filterHighlight({nodes: nodes});
+  return nodes;
+}
+
+function highlightFilter(filter) {
+  let selectedNodes = []
+  let selectedProp = filter['property']
+  if (filter['item'] === 'node') {
+    let allNodes = nodes.get({ returnType: "Object" });
+    for (let nodeId in allNodes) {
+      if (allNodes[nodeId][selectedProp] && filter['value'].includes((allNodes[nodeId][selectedProp]).toString())) {
+        selectedNodes.push(nodeId)
+      }
+    }
+  }
+  else if (filter['item'] === 'edge'){
+    let allEdges = edges.get({returnType: 'object'});
+    // check if the selected property exists for selected edge and select the nodes connected to the edge
+    for (let edge in allEdges) {
+      if (allEdges[edge][selectedProp] && filter['value'].includes((allEdges[edge][selectedProp]).toString())) {
+        selectedNodes.push(allEdges[edge]['from'])
+        selectedNodes.push(allEdges[edge]['to'])
+      }
+    }
+  }
+  selectNodes(selectedNodes)
+}

lib/tom-select/tom-select.complete.min.js

@@ -0,0 +1,356 @@
+/**
+* Tom Select v2.0.0-rc.4
+* Licensed under the Apache License, Version 2.0 (the "License");
+*/
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).TomSelect=t()}(this,(function(){"use strict"
+function e(e,t){e.split(/\s+/).forEach((e=>{t(e)}))}class t{constructor(){this._events={}}on(t,i){e(t,(e=>{this._events[e]=this._events[e]||[],this._events[e].push(i)}))}off(t,i){var s=arguments.length
+0!==s?e(t,(e=>{if(1===s)return delete this._events[e]
+e in this._events!=!1&&this._events[e].splice(this._events[e].indexOf(i),1)})):this._events={}}trigger(t,...i){var s=this
+e(t,(e=>{if(e in s._events!=!1)for(let t of s._events[e])t.apply(s,i)}))}}var i
+const s="[̀-ͯ·ʾ]",n=new RegExp(s,"g")
+var o
+const r={"æ":"ae","ⱥ":"a","ø":"o"},l=new RegExp(Object.keys(r).join("|"),"g"),a=[[67,67],[160,160],[192,438],[452,652],[961,961],[1019,1019],[1083,1083],[1281,1289],[1984,1984],[5095,5095],[7429,7441],[7545,7549],[7680,7935],[8580,8580],[9398,9449],[11360,11391],[42792,42793],[42802,42851],[42873,42897],[42912,42922],[64256,64260],[65313,65338],[65345,65370]],c=e=>e.normalize("NFKD").replace(n,"").toLowerCase().replace(l,(function(e){return r[e]})),d=(e,t="|")=>{if(1==e.length)return e[0]
+var i=1
+return e.forEach((e=>{i=Math.max(i,e.length)})),1==i?"["+e.join("")+"]":"(?:"+e.join(t)+")"},p=e=>{if(1===e.length)return[[e]]
+var t=[]
+return p(e.substring(1)).forEach((function(i){var s=i.slice(0)
+s[0]=e.charAt(0)+s[0],t.push(s),(s=i.slice(0)).unshift(e.charAt(0)),t.push(s)})),t},u=e=>{void 0===o&&(o=(()=>{var e={}
+a.forEach((t=>{for(let s=t[0];s<=t[1];s++){let t=String.fromCharCode(s),n=c(t)
+if(n!=t.toLowerCase()){n in e||(e[n]=[n])
+var i=new RegExp(d(e[n]),"iu")
+t.match(i)||e[n].push(t)}}}))
+var t=Object.keys(e)
+t=t.sort(((e,t)=>t.length-e.length)),i=new RegExp("("+d(t)+"[̀-ͯ·ʾ]*)","g")
+var s={}
+return t.sort(((e,t)=>e.length-t.length)).forEach((t=>{var i=p(t).map((t=>(t=t.map((t=>e.hasOwnProperty(t)?d(e[t]):t)),d(t,""))))
+s[t]=d(i)})),s})())
+return e.normalize("NFKD").toLowerCase().split(i).map((e=>{if(""==e)return""
+const t=c(e)
+if(o.hasOwnProperty(t))return o[t]
+const i=e.normalize("NFC")
+return i!=e?d([e,i]):e})).join("")},h=(e,t)=>{if(e)return e[t]},g=(e,t)=>{if(e){for(var i,s=t.split(".");(i=s.shift())&&(e=e[i]););return e}},f=(e,t,i)=>{var s,n
+return e?-1===(n=(e+="").search(t.regex))?0:(s=t.string.length/e.length,0===n&&(s+=.5),s*i):0},v=e=>(e+"").replace(/([\$\(-\+\.\?\[-\^\{-\}])/g,"\\$1"),m=(e,t)=>{var i=e[t]
+if("function"==typeof i)return i
+i&&!Array.isArray(i)&&(e[t]=[i])},y=(e,t)=>{if(Array.isArray(e))e.forEach(t)
+else for(var i in e)e.hasOwnProperty(i)&&t(e[i],i)},O=(e,t)=>"number"==typeof e&&"number"==typeof t?e>t?1:e<t?-1:0:(e=c(e+"").toLowerCase())>(t=c(t+"").toLowerCase())?1:t>e?-1:0
+class b{constructor(e,t){this.items=e,this.settings=t||{diacritics:!0}}tokenize(e,t,i){if(!e||!e.length)return[]
+const s=[],n=e.split(/\s+/)
+var o
+return i&&(o=new RegExp("^("+Object.keys(i).map(v).join("|")+"):(.*)$")),n.forEach((e=>{let i,n=null,r=null
+o&&(i=e.match(o))&&(n=i[1],e=i[2]),e.length>0&&(r=v(e),this.settings.diacritics&&(r=u(r)),t&&(r="\\b"+r)),s.push({string:e,regex:r?new RegExp(r,"iu"):null,field:n})})),s}getScoreFunction(e,t){var i=this.prepareSearch(e,t)
+return this._getScoreFunction(i)}_getScoreFunction(e){const t=e.tokens,i=t.length
+if(!i)return function(){return 0}
+const s=e.options.fields,n=e.weights,o=s.length,r=e.getAttrFn
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+if("click"===(i=t&&t.type.toLowerCase())&&K("shiftKey",t)&&a.activeItems.length){for(l=a.getLastActive(),(n=Array.prototype.indexOf.call(a.control.children,l))>(o=Array.prototype.indexOf.call(a.control.children,e))&&(r=n,n=o,o=r),s=n;s<=o;s++)e=a.control.children[s],-1===a.activeItems.indexOf(e)&&a.setActiveItemClass(e)
+H(t)}else"click"===i&&K(V,t)||"keydown"===i&&K("shiftKey",t)?e.classList.contains("active")?a.removeActiveItem(e):a.setActiveItemClass(e):(a.clearActiveItems(),a.setActiveItemClass(e))
+a.hideInput(),a.isFocused||a.focus()}}setActiveItemClass(e){const t=this,i=t.control.querySelector(".last-active")
+i&&S(i,"last-active"),C(e,"active last-active"),t.trigger("item_select",e),-1==t.activeItems.indexOf(e)&&t.activeItems.push(e)}removeActiveItem(e){var t=this.activeItems.indexOf(e)
+this.activeItems.splice(t,1),S(e,"active")}clearActiveItems(){S(this.activeItems,"active"),this.activeItems=[]}setActiveOption(e){e!==this.activeOption&&(this.clearActiveOption(),e&&(this.activeOption=e,P(this.focus_node,{"aria-activedescendant":e.getAttribute("id")}),P(e,{"aria-selected":"true"}),C(e,"active"),this.scrollToOption(e)))}scrollToOption(e,t){if(!e)return
+const i=this.dropdown_content,s=i.clientHeight,n=i.scrollTop||0,o=e.offsetHeight,r=e.getBoundingClientRect().top-i.getBoundingClientRect().top+n
+r+o>s+n?this.scroll(r-s+o,t):r<n&&this.scroll(r,t)}scroll(e,t){const i=this.dropdown_content
+t&&(i.style.scrollBehavior=t),i.scrollTop=e,i.style.scrollBehavior=""}clearActiveOption(){this.activeOption&&(S(this.activeOption,"active"),P(this.activeOption,{"aria-selected":null})),this.activeOption=null,P(this.focus_node,{"aria-activedescendant":null})}selectAll(){if("single"===this.settings.mode)return
+const e=this.controlChildren()
+e.length&&(this.hideInput(),this.close(),this.activeItems=e,C(e,"active"))}inputState(){var e=this
+e.control.contains(e.control_input)&&(P(e.control_input,{placeholder:e.settings.placeholder}),e.activeItems.length>0||!e.isFocused&&e.settings.hidePlaceholder&&e.items.length>0?(e.setTextboxValue(),e.isInputHidden=!0):(e.settings.hidePlaceholder&&e.items.length>0&&P(e.control_input,{placeholder:""}),e.isInputHidden=!1),e.wrapper.classList.toggle("input-hidden",e.isInputHidden))}hideInput(){this.inputState()}showInput(){this.inputState()}inputValue(){return this.control_input.value.trim()}focus(){var e=this
+e.isDisabled||(e.ignoreFocus=!0,e.control_input.offsetWidth?e.control_input.focus():e.focus_node.focus(),setTimeout((()=>{e.ignoreFocus=!1,e.onFocus()}),0))}blur(){this.focus_node.blur(),this.onBlur()}getScoreFunction(e){return this.sifter.getScoreFunction(e,this.getSearchOptions())}getSearchOptions(){var e=this.settings,t=e.sortField
+return"string"==typeof e.sortField&&(t=[{field:e.sortField}]),{fields:e.searchField,conjunction:e.searchConjunction,sort:t,nesting:e.nesting}}search(e){var t,i,s,n=this,o=this.getSearchOptions()
+if(n.settings.score&&"function"!=typeof(s=n.settings.score.call(n,e)))throw new Error('Tom Select "score" setting must be a function that returns a function')
+if(e!==n.lastQuery?(n.lastQuery=e,i=n.sifter.search(e,Object.assign(o,{score:s})),n.currentResults=i):i=Object.assign({},n.currentResults),n.settings.hideSelected)for(t=i.items.length-1;t>=0;t--){let e=q(i.items[t].id)
+e&&-1!==n.items.indexOf(e)&&i.items.splice(t,1)}return i}refreshOptions(e=!0){var t,i,s,n,o,r,l,a,c,d,p
+const u={},h=[]
+var g,f=this,v=f.inputValue(),m=f.search(v),O=f.activeOption,b=f.settings.shouldOpen||!1,w=f.dropdown_content
+for(O&&(c=O.dataset.value,d=O.closest("[data-group]")),n=m.items.length,"number"==typeof f.settings.maxOptions&&(n=Math.min(n,f.settings.maxOptions)),n>0&&(b=!0),t=0;t<n;t++){let e=m.items[t].id,n=f.options[e],l=f.getOption(e,!0)
+for(f.settings.hideSelected||l.classList.toggle("selected",f.items.includes(e)),o=n[f.settings.optgroupField]||"",i=0,s=(r=Array.isArray(o)?o:[o])&&r.length;i<s;i++)o=r[i],f.optgroups.hasOwnProperty(o)||(o=""),u.hasOwnProperty(o)||(u[o]=document.createDocumentFragment(),h.push(o)),i>0&&(l=l.cloneNode(!0),P(l,{id:n.$id+"-clone-"+i,"aria-selected":null}),l.classList.add("ts-cloned"),S(l,"active")),c==e&&d&&d.dataset.group===o&&(O=l),u[o].appendChild(l)}this.settings.lockOptgroupOrder&&h.sort(((e,t)=>(f.optgroups[e]&&f.optgroups[e].$order||0)-(f.optgroups[t]&&f.optgroups[t].$order||0))),l=document.createDocumentFragment(),y(h,(e=>{if(f.optgroups.hasOwnProperty(e)&&u[e].children.length){let t=document.createDocumentFragment(),i=f.render("optgroup_header",f.optgroups[e])
+G(t,i),G(t,u[e])
+let s=f.render("optgroup",{group:f.optgroups[e],options:t})
+G(l,s)}else G(l,u[e])})),w.innerHTML="",G(w,l),f.settings.highlight&&(g=w.querySelectorAll("span.highlight"),Array.prototype.forEach.call(g,(function(e){var t=e.parentNode
+t.replaceChild(e.firstChild,e),t.normalize()})),m.query.length&&m.tokens.length&&y(m.tokens,(e=>{T(w,e.regex)})))
+var _=e=>{let t=f.render(e,{input:v})
+return t&&(b=!0,w.insertBefore(t,w.firstChild)),t}
+if(f.loading?_("loading"):f.settings.shouldLoad.call(f,v)?0===m.items.length&&_("no_results"):_("not_loading"),(a=f.canCreate(v))&&(p=_("option_create")),f.hasOptions=m.items.length>0||a,b){if(m.items.length>0){if(!w.contains(O)&&"single"===f.settings.mode&&f.items.length&&(O=f.getOption(f.items[0])),!w.contains(O)){let e=0
+p&&!f.settings.addPrecedence&&(e=1),O=f.selectable()[e]}}else p&&(O=p)
+e&&!f.isOpen&&(f.open(),f.scrollToOption(O,"auto")),f.setActiveOption(O)}else f.clearActiveOption(),e&&f.isOpen&&f.close(!1)}selectable(){return this.dropdown_content.querySelectorAll("[data-selectable]")}addOption(e,t=!1){const i=this
+if(Array.isArray(e))return i.addOptions(e,t),!1
+const s=q(e[i.settings.valueField])
+return null!==s&&!i.options.hasOwnProperty(s)&&(e.$order=e.$order||++i.order,e.$id=i.inputId+"-opt-"+e.$order,i.options[s]=e,i.lastQuery=null,t&&(i.userOptions[s]=t,i.trigger("option_add",s,e)),s)}addOptions(e,t=!1){y(e,(e=>{this.addOption(e,t)}))}registerOption(e){return this.addOption(e)}registerOptionGroup(e){var t=q(e[this.settings.optgroupValueField])
+return null!==t&&(e.$order=e.$order||++this.order,this.optgroups[t]=e,t)}addOptionGroup(e,t){var i
+t[this.settings.optgroupValueField]=e,(i=this.registerOptionGroup(t))&&this.trigger("optgroup_add",i,t)}removeOptionGroup(e){this.optgroups.hasOwnProperty(e)&&(delete this.optgroups[e],this.clearCache(),this.trigger("optgroup_remove",e))}clearOptionGroups(){this.optgroups={},this.clearCache(),this.trigger("optgroup_clear")}updateOption(e,t){const i=this
+var s,n
+const o=q(e),r=q(t[i.settings.valueField])
+if(null===o)return
+if(!i.options.hasOwnProperty(o))return
+if("string"!=typeof r)throw new Error("Value must be set in option data")
+const l=i.getOption(o),a=i.getItem(o)
+if(t.$order=t.$order||i.options[o].$order,delete i.options[o],i.uncacheValue(r),i.options[r]=t,l){if(i.dropdown_content.contains(l)){const e=i._render("option",t)
+E(l,e),i.activeOption===l&&i.setActiveOption(e)}l.remove()}a&&(-1!==(n=i.items.indexOf(o))&&i.items.splice(n,1,r),s=i._render("item",t),a.classList.contains("active")&&C(s,"active"),E(a,s)),i.lastQuery=null}removeOption(e,t){const i=this
+e=D(e),i.uncacheValue(e),delete i.userOptions[e],delete i.options[e],i.lastQuery=null,i.trigger("option_remove",e),i.removeItem(e,t)}clearOptions(){this.loadedSearches={},this.userOptions={},this.clearCache()
+var e={}
+y(this.options,((t,i)=>{this.items.indexOf(i)>=0&&(e[i]=this.options[i])})),this.options=this.sifter.items=e,this.lastQuery=null,this.trigger("option_clear")}getOption(e,t=!1){const i=q(e)
+if(null!==i&&this.options.hasOwnProperty(i)){const e=this.options[i]
+if(e.$div)return e.$div
+if(t)return this._render("option",e)}return null}getAdjacent(e,t,i="option"){var s
+if(!e)return null
+s="item"==i?this.controlChildren():this.dropdown_content.querySelectorAll("[data-selectable]")
+for(let i=0;i<s.length;i++)if(s[i]==e)return t>0?s[i+1]:s[i-1]
+return null}getItem(e){if("object"==typeof e)return e
+var t=q(e)
+return null!==t?this.control.querySelector(`[data-value="${Q(t)}"]`):null}addItems(e,t){var i=this,s=Array.isArray(e)?e:[e]
+for(let e=0,n=(s=s.filter((e=>-1===i.items.indexOf(e)))).length;e<n;e++)i.isPending=e<n-1,i.addItem(s[e],t)}addItem(e,t){R(this,t?[]:["change"],(()=>{var i,s
+const n=this,o=n.settings.mode,r=q(e)
+if((!r||-1===n.items.indexOf(r)||("single"===o&&n.close(),"single"!==o&&n.settings.duplicates))&&null!==r&&n.options.hasOwnProperty(r)&&("single"===o&&n.clear(t),"multi"!==o||!n.isFull())){if(i=n._render("item",n.options[r]),n.control.contains(i)&&(i=i.cloneNode(!0)),s=n.isFull(),n.items.splice(n.caretPos,0,r),n.insertAtCaret(i),n.isSetup){if(!n.isPending&&n.settings.hideSelected){let e=n.getOption(r),t=n.getAdjacent(e,1)
+t&&n.setActiveOption(t)}n.isPending||n.refreshOptions(n.isFocused&&"single"!==o),0!=n.settings.closeAfterSelect&&n.isFull()?n.close():n.isPending||n.positionDropdown(),n.trigger("item_add",r,i),n.isPending||n.updateOriginalInput({silent:t})}(!n.isPending||!s&&n.isFull())&&(n.inputState(),n.refreshState())}}))}removeItem(e=null,t){const i=this
+if(!(e=i.getItem(e)))return
+var s,n
+const o=e.dataset.value
+s=L(e),e.remove(),e.classList.contains("active")&&(n=i.activeItems.indexOf(e),i.activeItems.splice(n,1),S(e,"active")),i.items.splice(s,1),i.lastQuery=null,!i.settings.persist&&i.userOptions.hasOwnProperty(o)&&i.removeOption(o,t),s<i.caretPos&&i.setCaret(i.caretPos-1),i.updateOriginalInput({silent:t}),i.refreshState(),i.positionDropdown(),i.trigger("item_remove",o,e)}createItem(e=null,t=!0,i=(()=>{})){var s,n=this,o=n.caretPos
+if(e=e||n.inputValue(),!n.canCreate(e))return i(),!1
+n.lock()
+var r=!1,l=e=>{if(n.unlock(),!e||"object"!=typeof e)return i()
+var s=q(e[n.settings.valueField])
+if("string"!=typeof s)return i()
+n.setTextboxValue(),n.addOption(e,!0),n.setCaret(o),n.addItem(s),n.refreshOptions(t&&"single"!==n.settings.mode),i(e),r=!0}
+return s="function"==typeof n.settings.create?n.settings.create.call(this,e,l):{[n.settings.labelField]:e,[n.settings.valueField]:e},r||l(s),!0}refreshItems(){var e=this
+e.lastQuery=null,e.isSetup&&e.addItems(e.items),e.updateOriginalInput(),e.refreshState()}refreshState(){const e=this
+e.refreshValidityState()
+const t=e.isFull(),i=e.isLocked
+e.wrapper.classList.toggle("rtl",e.rtl)
+const s=e.wrapper.classList
+var n
+s.toggle("focus",e.isFocused),s.toggle("disabled",e.isDisabled),s.toggle("required",e.isRequired),s.toggle("invalid",!e.isValid),s.toggle("locked",i),s.toggle("full",t),s.toggle("input-active",e.isFocused&&!e.isInputHidden),s.toggle("dropdown-active",e.isOpen),s.toggle("has-options",(n=e.options,0===Object.keys(n).length)),s.toggle("has-items",e.items.length>0)}refreshValidityState(){var e=this
+e.input.checkValidity&&(e.isValid=e.input.checkValidity(),e.isInvalid=!e.isValid)}isFull(){return null!==this.settings.maxItems&&this.items.length>=this.settings.maxItems}updateOriginalInput(e={}){const t=this
+var i,s
+const n=t.input.querySelector('option[value=""]')
+if(t.is_select_tag){const e=[]
+function o(i,s,o){return i||(i=w('<option value="'+N(s)+'">'+N(o)+"</option>")),i!=n&&t.input.append(i),e.push(i),i.selected=!0,i}t.input.querySelectorAll("option:checked").forEach((e=>{e.selected=!1})),0==t.items.length&&"single"==t.settings.mode?o(n,"",""):t.items.forEach((n=>{if(i=t.options[n],s=i[t.settings.labelField]||"",e.includes(i.$option)){o(t.input.querySelector(`option[value="${Q(n)}"]:not(:checked)`),n,s)}else i.$option=o(i.$option,n,s)}))}else t.input.value=t.getValue()
+t.isSetup&&(e.silent||t.trigger("change",t.getValue()))}open(){var e=this
+e.isLocked||e.isOpen||"multi"===e.settings.mode&&e.isFull()||(e.isOpen=!0,P(e.focus_node,{"aria-expanded":"true"}),e.refreshState(),I(e.dropdown,{visibility:"hidden",display:"block"}),e.positionDropdown(),I(e.dropdown,{visibility:"visible",display:"block"}),e.focus(),e.trigger("dropdown_open",e.dropdown))}close(e=!0){var t=this,i=t.isOpen
+e&&(t.setTextboxValue(),"single"===t.settings.mode&&t.items.length&&t.hideInput()),t.isOpen=!1,P(t.focus_node,{"aria-expanded":"false"}),I(t.dropdown,{display:"none"}),t.settings.hideSelected&&t.clearActiveOption(),t.refreshState(),i&&t.trigger("dropdown_close",t.dropdown)}positionDropdown(){if("body"===this.settings.dropdownParent){var e=this.control,t=e.getBoundingClientRect(),i=e.offsetHeight+t.top+window.scrollY,s=t.left+window.scrollX
+I(this.dropdown,{width:t.width+"px",top:i+"px",left:s+"px"})}}clear(e){var t=this
+if(t.items.length){var i=t.controlChildren()
+y(i,(e=>{t.removeItem(e,!0)})),t.showInput(),e||t.updateOriginalInput(),t.trigger("clear")}}insertAtCaret(e){const t=this,i=t.caretPos,s=t.control
+s.insertBefore(e,s.children[i]),t.setCaret(i+1)}deleteSelection(e){var t,i,s,n,o,r=this
+t=e&&8===e.keyCode?-1:1,i={start:(o=r.control_input).selectionStart||0,length:(o.selectionEnd||0)-(o.selectionStart||0)}
+const l=[]
+if(r.activeItems.length)n=F(r.activeItems,t),s=L(n),t>0&&s++,y(r.activeItems,(e=>l.push(e)))
+else if((r.isFocused||"single"===r.settings.mode)&&r.items.length){const e=r.controlChildren()
+t<0&&0===i.start&&0===i.length?l.push(e[r.caretPos-1]):t>0&&i.start===r.inputValue().length&&l.push(e[r.caretPos])}const a=l.map((e=>e.dataset.value))
+if(!a.length||"function"==typeof r.settings.onDelete&&!1===r.settings.onDelete.call(r,a,e))return!1
+for(H(e,!0),void 0!==s&&r.setCaret(s);l.length;)r.removeItem(l.pop())
+return r.showInput(),r.positionDropdown(),r.refreshOptions(!1),!0}advanceSelection(e,t){var i,s,n=this
+n.rtl&&(e*=-1),n.inputValue().length||(K(V,t)||K("shiftKey",t)?(s=(i=n.getLastActive(e))?i.classList.contains("active")?n.getAdjacent(i,e,"item"):i:e>0?n.control_input.nextElementSibling:n.control_input.previousElementSibling)&&(s.classList.contains("active")&&n.removeActiveItem(i),n.setActiveItemClass(s)):n.moveCaret(e))}moveCaret(e){}getLastActive(e){let t=this.control.querySelector(".last-active")
+if(t)return t
+var i=this.control.querySelectorAll(".active")
+return i?F(i,e):void 0}setCaret(e){this.caretPos=this.items.length}controlChildren(){return Array.from(this.control.querySelectorAll("[data-ts-item]"))}lock(){this.close(),this.isLocked=!0,this.refreshState()}unlock(){this.isLocked=!1,this.refreshState()}disable(){var e=this
+e.input.disabled=!0,e.control_input.disabled=!0,e.focus_node.tabIndex=-1,e.isDisabled=!0,e.lock()}enable(){var e=this
+e.input.disabled=!1,e.control_input.disabled=!1,e.focus_node.tabIndex=e.tabIndex,e.isDisabled=!1,e.unlock()}destroy(){var e=this,t=e.revertSettings
+e.trigger("destroy"),e.off(),e.wrapper.remove(),e.dropdown.remove(),e.input.innerHTML=t.innerHTML,e.input.tabIndex=t.tabIndex,S(e.input,"tomselected","ts-hidden-accessible"),e._destroy(),delete e.input.tomselect}render(e,t){return"function"!=typeof this.settings.render[e]?null:this._render(e,t)}_render(e,t){var i,s,n=""
+const o=this
+return"option"!==e&&"item"!=e||(n=D(t[o.settings.valueField])),null==(s=o.settings.render[e].call(this,t,N))||(s=w(s),"option"===e||"option_create"===e?t[o.settings.disabledField]?P(s,{"aria-disabled":"true"}):P(s,{"data-selectable":""}):"optgroup"===e&&(i=t.group[o.settings.optgroupValueField],P(s,{"data-group":i}),t.group[o.settings.disabledField]&&P(s,{"data-disabled":""})),"option"!==e&&"item"!==e||(P(s,{"data-value":n}),"item"===e?(C(s,o.settings.itemClass),P(s,{"data-ts-item":""})):(C(s,o.settings.optionClass),P(s,{role:"option",id:t.$id}),o.options[n].$div=s))),s}clearCache(){y(this.options,((e,t)=>{e.$div&&(e.$div.remove(),delete e.$div)}))}uncacheValue(e){const t=this.getOption(e)
+t&&t.remove()}canCreate(e){return this.settings.create&&e.length>0&&this.settings.createFilter.call(this,e)}hook(e,t,i){var s=this,n=s[t]
+s[t]=function(){var t,o
+return"after"===e&&(t=n.apply(s,arguments)),o=i.apply(s,arguments),"instead"===e?o:("before"===e&&(t=n.apply(s,arguments)),t)}}}return J.define("change_listener",(function(){B(this.input,"change",(()=>{this.sync()}))})),J.define("checkbox_options",(function(){var e=this,t=e.onOptionSelect
+e.settings.hideSelected=!1
+var i=function(e){setTimeout((()=>{var t=e.querySelector("input")
+e.classList.contains("selected")?t.checked=!0:t.checked=!1}),1)}
+e.hook("after","setupTemplates",(()=>{var t=e.settings.render.option
+e.settings.render.option=(i,s)=>{var n=w(t.call(e,i,s)),o=document.createElement("input")
+o.addEventListener("click",(function(e){H(e)})),o.type="checkbox"
+const r=q(i[e.settings.valueField])
+return r&&e.items.indexOf(r)>-1&&(o.checked=!0),n.prepend(o),n}})),e.on("item_remove",(t=>{var s=e.getOption(t)
+s&&(s.classList.remove("selected"),i(s))})),e.hook("instead","onOptionSelect",((s,n)=>{if(n.classList.contains("selected"))return n.classList.remove("selected"),e.removeItem(n.dataset.value),e.refreshOptions(),void H(s,!0)
+t.call(e,s,n),i(n)}))})),J.define("clear_button",(function(e){const t=this,i=Object.assign({className:"clear-button",title:"Clear All",html:e=>`<div class="${e.className}" title="${e.title}">&times;</div>`},e)
+t.on("initialize",(()=>{var e=w(i.html(i))
+e.addEventListener("click",(e=>{t.clear(),"single"===t.settings.mode&&t.settings.allowEmptyOption&&t.addItem(""),e.preventDefault(),e.stopPropagation()})),t.control.appendChild(e)}))})),J.define("drag_drop",(function(){var e=this
+if(!$.fn.sortable)throw new Error('The "drag_drop" plugin requires jQuery UI "sortable".')
+if("multi"===e.settings.mode){var t=e.lock,i=e.unlock
+e.hook("instead","lock",(()=>{var i=$(e.control).data("sortable")
+return i&&i.disable(),t.call(e)})),e.hook("instead","unlock",(()=>{var t=$(e.control).data("sortable")
+return t&&t.enable(),i.call(e)})),e.on("initialize",(()=>{var t=$(e.control).sortable({items:"[data-value]",forcePlaceholderSize:!0,disabled:e.isLocked,start:(e,i)=>{i.placeholder.css("width",i.helper.css("width")),t.css({overflow:"visible"})},stop:()=>{t.css({overflow:"hidden"})
+var i=[]
+t.children("[data-value]").each((function(){this.dataset.value&&i.push(this.dataset.value)})),e.setValue(i)}})}))}})),J.define("dropdown_header",(function(e){const t=this,i=Object.assign({title:"Untitled",headerClass:"dropdown-header",titleRowClass:"dropdown-header-title",labelClass:"dropdown-header-label",closeClass:"dropdown-header-close",html:e=>'<div class="'+e.headerClass+'"><div class="'+e.titleRowClass+'"><span class="'+e.labelClass+'">'+e.title+'</span><a class="'+e.closeClass+'">&times;</a></div></div>'},e)
+t.on("initialize",(()=>{var e=w(i.html(i)),s=e.querySelector("."+i.closeClass)
+s&&s.addEventListener("click",(e=>{H(e,!0),t.close()})),t.dropdown.insertBefore(e,t.dropdown.firstChild)}))})),J.define("caret_position",(function(){var e=this
+e.hook("instead","setCaret",(t=>{"single"!==e.settings.mode&&e.control.contains(e.control_input)?(t=Math.max(0,Math.min(e.items.length,t)))==e.caretPos||e.isPending||e.controlChildren().forEach(((i,s)=>{s<t?e.control_input.insertAdjacentElement("beforebegin",i):e.control.appendChild(i)})):t=e.items.length,e.caretPos=t})),e.hook("instead","moveCaret",(t=>{if(!e.isFocused)return
+const i=e.getLastActive(t)
+if(i){const s=L(i)
+e.setCaret(t>0?s+1:s),e.setActiveItem()}else e.setCaret(e.caretPos+t)}))})),J.define("dropdown_input",(function(){var e=this
+e.settings.shouldOpen=!0,e.hook("before","setup",(()=>{e.focus_node=e.control,C(e.control_input,"dropdown-input")
+const t=w('<div class="dropdown-input-wrap">')
+t.append(e.control_input),e.dropdown.insertBefore(t,e.dropdown.firstChild)})),e.on("initialize",(()=>{e.control_input.addEventListener("keydown",(t=>{switch(t.keyCode){case 27:return e.isOpen&&(H(t,!0),e.close()),void e.clearActiveItems()
+case 9:e.focus_node.tabIndex=-1}return e.onKeyDown.call(e,t)})),e.on("blur",(()=>{e.focus_node.tabIndex=e.isDisabled?-1:e.tabIndex})),e.on("dropdown_open",(()=>{e.control_input.focus()}))
+const t=e.onBlur
+e.hook("instead","onBlur",(i=>{if(!i||i.relatedTarget!=e.control_input)return t.call(e)})),B(e.control_input,"blur",(()=>e.onBlur())),e.hook("before","close",(()=>{e.isOpen&&e.focus_node.focus()}))}))})),J.define("input_autogrow",(function(){var e=this
+e.on("initialize",(()=>{var t=document.createElement("span"),i=e.control_input
+t.style.cssText="position:absolute; top:-99999px; left:-99999px; width:auto; padding:0; white-space:pre; ",e.wrapper.appendChild(t)
+for(const e of["letterSpacing","fontSize","fontFamily","fontWeight","textTransform"])t.style[e]=i.style[e]
+var s=()=>{e.items.length>0?(t.textContent=i.value,i.style.width=t.clientWidth+"px"):i.style.width=""}
+s(),e.on("update item_add item_remove",s),B(i,"input",s),B(i,"keyup",s),B(i,"blur",s),B(i,"update",s)}))})),J.define("no_backspace_delete",(function(){var e=this,t=e.deleteSelection
+this.hook("instead","deleteSelection",(i=>!!e.activeItems.length&&t.call(e,i)))})),J.define("no_active_items",(function(){this.hook("instead","setActiveItem",(()=>{})),this.hook("instead","selectAll",(()=>{}))})),J.define("optgroup_columns",(function(){var e=this,t=e.onKeyDown
+e.hook("instead","onKeyDown",(i=>{var s,n,o,r
+if(!e.isOpen||37!==i.keyCode&&39!==i.keyCode)return t.call(e,i)
+r=k(e.activeOption,"[data-group]"),s=L(e.activeOption,"[data-selectable]"),r&&(r=37===i.keyCode?r.previousSibling:r.nextSibling)&&(n=(o=r.querySelectorAll("[data-selectable]"))[Math.min(o.length-1,s)])&&e.setActiveOption(n)}))})),J.define("remove_button",(function(e){const t=Object.assign({label:"&times;",title:"Remove",className:"remove",append:!0},e)
+var i=this
+if(t.append){var s='<a href="javascript:void(0)" class="'+t.className+'" tabindex="-1" title="'+N(t.title)+'">'+t.label+"</a>"
+i.hook("after","setupTemplates",(()=>{var e=i.settings.render.item
+i.settings.render.item=(t,n)=>{var o=w(e.call(i,t,n)),r=w(s)
+return o.appendChild(r),B(r,"mousedown",(e=>{H(e,!0)})),B(r,"click",(e=>{if(H(e,!0),!i.isLocked){var t=o.dataset.value
+i.removeItem(t),i.refreshOptions(!1)}})),o}}))}})),J.define("restore_on_backspace",(function(e){const t=this,i=Object.assign({text:e=>e[t.settings.labelField]},e)
+t.on("item_remove",(function(e){if(""===t.control_input.value.trim()){var s=t.options[e]
+s&&t.setTextboxValue(i.text.call(t,s))}}))})),J.define("virtual_scroll",(function(){const e=this,t=e.canLoad,i=e.clearActiveOption,s=e.loadCallback
+var n,o={},r=!1
+if(!e.settings.firstUrl)throw"virtual_scroll plugin requires a firstUrl() method"
+function l(t){return!("number"==typeof e.settings.maxOptions&&n.children.length>=e.settings.maxOptions)&&!(!(t in o)||!o[t])}e.settings.sortField=[{field:"$order"},{field:"$score"}],e.setNextUrl=function(e,t){o[e]=t},e.getUrl=function(t){if(t in o){const e=o[t]
+return o[t]=!1,e}return o={},e.settings.firstUrl(t)},e.hook("instead","clearActiveOption",(()=>{if(!r)return i.call(e)})),e.hook("instead","canLoad",(i=>i in o?l(i):t.call(e,i))),e.hook("instead","loadCallback",((t,i)=>{r||e.clearOptions(),s.call(e,t,i),r=!1})),e.hook("after","refreshOptions",(()=>{const t=e.lastValue
+var i
+l(t)?(i=e.render("loading_more",{query:t}))&&i.setAttribute("data-selectable",""):t in o&&!n.querySelector(".no-results")&&(i=e.render("no_more_results",{query:t})),i&&(C(i,e.settings.optionClass),n.append(i))})),e.on("initialize",(()=>{n=e.dropdown_content,e.settings.render=Object.assign({},{loading_more:function(){return'<div class="loading-more-results">Loading more results ... </div>'},no_more_results:function(){return'<div class="no-more-results">No more results</div>'}},e.settings.render),n.addEventListener("scroll",(function(){n.clientHeight/(n.scrollHeight-n.scrollTop)<.95||l(e.lastValue)&&(r||(r=!0,e.load.call(e,e.lastValue)))}))}))})),J}))
+var tomSelect=function(e,t){return new TomSelect(e,t)}
+//# sourceMappingURL=tom-select.complete.min.js.map

lib/tom-select/tom-select.css

@@ -0,0 +1,334 @@
+/**
+ * tom-select.css (v2.0.0-rc.4)
+ * Copyright (c) contributors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not use this
+ * file except in compliance with the License. You may obtain a copy of the License at:
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software distributed under
+ * the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+ * ANY KIND, either express or implied. See the License for the specific language
+ * governing permissions and limitations under the License.
+ *
+ */
+.ts-wrapper.plugin-drag_drop.multi > .ts-control > div.ui-sortable-placeholder {
+  visibility: visible !important;
+  background: #f2f2f2 !important;
+  background: rgba(0, 0, 0, 0.06) !important;
+  border: 0 none !important;
+  box-shadow: inset 0 0 12px 4px #fff; }
+
+.ts-wrapper.plugin-drag_drop .ui-sortable-placeholder::after {
+  content: '!';
+  visibility: hidden; }
+
+.ts-wrapper.plugin-drag_drop .ui-sortable-helper {
+  box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2); }
+
+.plugin-checkbox_options .option input {
+  margin-right: 0.5rem; }
+
+.plugin-clear_button .ts-control {
+  padding-right: calc( 1em + (3 * 6px)) !important; }
+
+.plugin-clear_button .clear-button {
+  opacity: 0;
+  position: absolute;
+  top: 8px;
+  right: calc(8px - 6px);
+  margin-right: 0 !important;
+  background: transparent !important;
+  transition: opacity 0.5s;
+  cursor: pointer; }
+
+.plugin-clear_button.single .clear-button {
+  right: calc(8px - 6px +  2rem); }
+
+.plugin-clear_button.focus.has-items .clear-button,
+.plugin-clear_button:hover.has-items .clear-button {
+  opacity: 1; }
+
+.ts-wrapper .dropdown-header {
+  position: relative;
+  padding: 10px 8px;
+  border-bottom: 1px solid #d0d0d0;
+  background: #f8f8f8;
+  border-radius: 3px 3px 0 0; }
+
+.ts-wrapper .dropdown-header-close {
+  position: absolute;
+  right: 8px;
+  top: 50%;
+  color: #303030;
+  opacity: 0.4;
+  margin-top: -12px;
+  line-height: 20px;
+  font-size: 20px !important; }
+
+.ts-wrapper .dropdown-header-close:hover {
+  color: black; }
+
+.plugin-dropdown_input.focus.dropdown-active .ts-control {
+  box-shadow: none;
+  border: 1px solid #d0d0d0; }
+
+.plugin-dropdown_input .dropdown-input {
+  border: 1px solid #d0d0d0;
+  border-width: 0 0 1px 0;
+  display: block;
+  padding: 8px 8px;
+  box-shadow: none;
+  width: 100%;
+  background: transparent; }
+
+.ts-wrapper.plugin-input_autogrow.has-items .ts-control > input {
+  min-width: 0; }
+
+.ts-wrapper.plugin-input_autogrow.has-items.focus .ts-control > input {
+  flex: none;
+  min-width: 4px; }
+  .ts-wrapper.plugin-input_autogrow.has-items.focus .ts-control > input::-webkit-input-placeholder {
+    color: transparent; }
+  .ts-wrapper.plugin-input_autogrow.has-items.focus .ts-control > input::-ms-input-placeholder {
+    color: transparent; }
+  .ts-wrapper.plugin-input_autogrow.has-items.focus .ts-control > input::placeholder {
+    color: transparent; }
+
+.ts-dropdown.plugin-optgroup_columns .ts-dropdown-content {
+  display: flex; }
+
+.ts-dropdown.plugin-optgroup_columns .optgroup {
+  border-right: 1px solid #f2f2f2;
+  border-top: 0 none;
+  flex-grow: 1;
+  flex-basis: 0;
+  min-width: 0; }
+
+.ts-dropdown.plugin-optgroup_columns .optgroup:last-child {
+  border-right: 0 none; }
+
+.ts-dropdown.plugin-optgroup_columns .optgroup:before {
+  display: none; }
+
+.ts-dropdown.plugin-optgroup_columns .optgroup-header {
+  border-top: 0 none; }
+
+.ts-wrapper.plugin-remove_button .item {
+  display: inline-flex;
+  align-items: center;
+  padding-right: 0 !important; }
+
+.ts-wrapper.plugin-remove_button .item .remove {
+  color: inherit;
+  text-decoration: none;
+  vertical-align: middle;
+  display: inline-block;
+  padding: 2px 6px;
+  border-left: 1px solid #d0d0d0;
+  border-radius: 0 2px 2px 0;
+  box-sizing: border-box;
+  margin-left: 6px; }
+
+.ts-wrapper.plugin-remove_button .item .remove:hover {
+  background: rgba(0, 0, 0, 0.05); }
+
+.ts-wrapper.plugin-remove_button .item.active .remove {
+  border-left-color: #cacaca; }
+
+.ts-wrapper.plugin-remove_button.disabled .item .remove:hover {
+  background: none; }
+
+.ts-wrapper.plugin-remove_button.disabled .item .remove {
+  border-left-color: white; }
+
+.ts-wrapper.plugin-remove_button .remove-single {
+  position: absolute;
+  right: 0;
+  top: 0;
+  font-size: 23px; }
+
+.ts-wrapper {
+  position: relative; }
+
+.ts-dropdown,
+.ts-control,
+.ts-control input {
+  color: #303030;
+  font-family: inherit;
+  font-size: 13px;
+  line-height: 18px;
+  font-smoothing: inherit; }
+
+.ts-control,
+.ts-wrapper.single.input-active .ts-control {
+  background: #fff;
+  cursor: text; }
+
+.ts-control {
+  border: 1px solid #d0d0d0;
+  padding: 8px 8px;
+  width: 100%;
+  overflow: hidden;
+  position: relative;
+  z-index: 1;
+  box-sizing: border-box;
+  box-shadow: none;
+  border-radius: 3px;
+  display: flex;
+  flex-wrap: wrap; }
+  .ts-wrapper.multi.has-items .ts-control {
+    padding: calc( 8px - 2px - 0) 8px calc( 8px - 2px - 3px - 0); }
+  .full .ts-control {
+    background-color: #fff; }
+  .disabled .ts-control,
+  .disabled .ts-control * {
+    cursor: default !important; }
+  .focus .ts-control {
+    box-shadow: none; }
+  .ts-control > * {
+    vertical-align: baseline;
+    display: inline-block; }
+  .ts-wrapper.multi .ts-control > div {
+    cursor: pointer;
+    margin: 0 3px 3px 0;
+    padding: 2px 6px;
+    background: #f2f2f2;
+    color: #303030;
+    border: 0 solid #d0d0d0; }
+    .ts-wrapper.multi .ts-control > div.active {
+      background: #e8e8e8;
+      color: #303030;
+      border: 0 solid #cacaca; }
+  .ts-wrapper.multi.disabled .ts-control > div, .ts-wrapper.multi.disabled .ts-control > div.active {
+    color: #7d7c7c;
+    background: white;
+    border: 0 solid white; }
+  .ts-control > input {
+    flex: 1 1 auto;
+    min-width: 7rem;
+    display: inline-block !important;
+    padding: 0 !important;
+    min-height: 0 !important;
+    max-height: none !important;
+    max-width: 100% !important;
+    margin: 0 !important;
+    text-indent: 0 !important;
+    border: 0 none !important;
+    background: none !important;
+    line-height: inherit !important;
+    -webkit-user-select: auto !important;
+       -moz-user-select: auto !important;
+        -ms-user-select: auto !important;
+            user-select: auto !important;
+    box-shadow: none !important; }
+    .ts-control > input::-ms-clear {
+      display: none; }
+    .ts-control > input:focus {
+      outline: none !important; }
+  .has-items .ts-control > input {
+    margin: 0 4px !important; }
+  .ts-control.rtl {
+    text-align: right; }
+    .ts-control.rtl.single .ts-control:after {
+      left: 15px;
+      right: auto; }
+    .ts-control.rtl .ts-control > input {
+      margin: 0 4px 0 -2px !important; }
+  .disabled .ts-control {
+    opacity: 0.5;
+    background-color: #fafafa; }
+  .input-hidden .ts-control > input {
+    opacity: 0;
+    position: absolute;
+    left: -10000px; }
+
+.ts-dropdown {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  width: 100%;
+  z-index: 10;
+  border: 1px solid #d0d0d0;
+  background: #fff;
+  margin: 0.25rem 0 0 0;
+  border-top: 0 none;
+  box-sizing: border-box;
+  box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
+  border-radius: 0 0 3px 3px; }
+  .ts-dropdown [data-selectable] {
+    cursor: pointer;
+    overflow: hidden; }
+    .ts-dropdown [data-selectable] .highlight {
+      background: rgba(125, 168, 208, 0.2);
+      border-radius: 1px; }
+  .ts-dropdown .option,
+  .ts-dropdown .optgroup-header,
+  .ts-dropdown .no-results,
+  .ts-dropdown .create {
+    padding: 5px 8px; }
+  .ts-dropdown .option, .ts-dropdown [data-disabled], .ts-dropdown [data-disabled] [data-selectable].option {
+    cursor: inherit;
+    opacity: 0.5; }
+  .ts-dropdown [data-selectable].option {
+    opacity: 1;
+    cursor: pointer; }
+  .ts-dropdown .optgroup:first-child .optgroup-header {
+    border-top: 0 none; }
+  .ts-dropdown .optgroup-header {
+    color: #303030;
+    background: #fff;
+    cursor: default; }
+  .ts-dropdown .create:hover,
+  .ts-dropdown .option:hover,
+  .ts-dropdown .active {
+    background-color: #f5fafd;
+    color: #495c68; }
+    .ts-dropdown .create:hover.create,
+    .ts-dropdown .option:hover.create,
+    .ts-dropdown .active.create {
+      color: #495c68; }
+  .ts-dropdown .create {
+    color: rgba(48, 48, 48, 0.5); }
+  .ts-dropdown .spinner {
+    display: inline-block;
+    width: 30px;
+    height: 30px;
+    margin: 5px 8px; }
+    .ts-dropdown .spinner:after {
+      content: " ";
+      display: block;
+      width: 24px;
+      height: 24px;
+      margin: 3px;
+      border-radius: 50%;
+      border: 5px solid #d0d0d0;
+      border-color: #d0d0d0 transparent #d0d0d0 transparent;
+      animation: lds-dual-ring 1.2s linear infinite; }
+
+@keyframes lds-dual-ring {
+  0% {
+    transform: rotate(0deg); }
+  100% {
+    transform: rotate(360deg); } }
+
+.ts-dropdown-content {
+  overflow-y: auto;
+  overflow-x: hidden;
+  max-height: 200px;
+  overflow-scrolling: touch;
+  scroll-behavior: smooth; }
+
+.ts-hidden-accessible {
+  border: 0 !important;
+  clip: rect(0 0 0 0) !important;
+  -webkit-clip-path: inset(50%) !important;
+          clip-path: inset(50%) !important;
+  height: 1px !important;
+  overflow: hidden !important;
+  padding: 0 !important;
+  position: absolute !important;
+  width: 1px !important;
+  white-space: nowrap !important; }
+
+/*# sourceMappingURL=tom-select.css.map */

llm.py

@@ -0,0 +1,35 @@
+from __future__ import annotations
+from dataclasses import dataclass
+from typing import Dict, List
+import requests
+
+# TUKE LLM Connection
+UNIVERSITY_BASE_URL = "https://ui.tukekemt.xyz/api/v1/chat/completions"
+UNIVERSITY_API_KEY  = "sk-06098ff9afb946c2b9d197cb400cd752"
+UNIVERSITY_MODEL    = "model2"
+
+# LLM call
+def llm_call(messages: List[Dict[str, str]]) -> str:
+    """Send a list of {role, content} dicts to the university LLM and return the reply text."""
+    resp = requests.post(
+        UNIVERSITY_BASE_URL,
+        json={"model": UNIVERSITY_MODEL, "messages": messages, "stream": False},
+        headers={
+            "Authorization": f"Bearer {UNIVERSITY_API_KEY}",
+            "Content-Type": "application/json",
+        },
+        timeout=600,
+    )
+    resp.raise_for_status()
+    return resp.json()["choices"][0]["message"]["content"]
+
+#test
+if __name__ == "__main__":
+    response = llm_call([
+        {
+            "role": "user",
+            "content": "Say hello"
+        }
+    ])
+
+    print(response)

main.py

@@ -0,0 +1,88 @@
+from graph_builder import (
+    build_graph_from_documents,
+    save_graph,
+    load_graph
+)
+from react_agent import react_agent  
+from visualization import visualize_graph
+
+GRAPH_PATH = "graph/kg.graphml"
+
+# Create graph or load existing one
+def get_or_build_graph(force_rebuild=False):
+
+    if not force_rebuild:
+        try:
+            print("\n[INFO] Loading existing graph...")
+            G = load_graph(GRAPH_PATH)
+
+            print(f"[INFO] Loaded graph: {len(G.nodes())} nodes, {len(G.edges())} edges")
+            return G
+
+        except Exception as e:
+            print(f"[WARN] Could not load graph: {e}")
+            print("[INFO] Rebuilding graph...")
+
+    print("\n[INFO] Building graph from documents...")
+    G = build_graph_from_documents()
+
+    print(f"[INFO] Built graph: {len(G.nodes())} nodes, {len(G.edges())} edges")
+
+    print("\n[INFO] Saving graph...")
+    save_graph(G, GRAPH_PATH)
+    return G
+
+# Testing questions (query)
+def ask_question(G, question):
+
+    print("\n" + "=" * 60)
+    print(f"QUESTION: {question}")
+    print("=" * 60)
+
+    history = []
+
+    result = react_agent(
+        user_message=question,
+        history=history,
+        G=G
+    )
+
+    print("\n--- FINAL ANSWER ---")
+    print(result.answer)
+
+    print("\n--- EVIDENCE (GRAPH) ---")
+    print(result.evidence)
+
+    return result
+
+
+# Visualisation
+def show_graph(G):
+
+    print("\n[INFO] Generating visualization...")
+
+    visualize_graph(G)
+
+    print("[INFO] Graph saved as kg.html")
+
+# Run main
+if __name__ == "__main__":
+
+    # Create graph or load existing one
+    G = get_or_build_graph(force_rebuild=False)
+
+    # Run visualisation.py (optional and for testing)
+    #show_graph(G)
+
+    # Testing questions (query)
+    test_questions = [
+        #"What is taught in Informatics?",
+        #"What is used in machine learning?",
+        #"What Literary Texts are taught in Slovak Language And Slovak Literature?",
+        "Čo sa učí na predmete Slovenský jazyk a literatúra?"
+    ]
+
+    for q in test_questions:
+        ask_question(G, q)
+
+    print("\n[INFO] PIPELINE COMPLETED")

models.py

@@ -0,0 +1,30 @@
+from dataclasses import dataclass
+from typing import Optional, List
+
+@dataclass
+class GraphRAGResponse:
+
+    question: str
+
+    answer: str
+
+    evidence: Optional[str] = None
+
+    entities_used: Optional[List[str]] = None
+
+    relations_used: Optional[List[str]] = None
+
+    source_documents: Optional[List[str]] = None
+
+    def __str__(self):
+
+        return (
+            "\nGraphRAGResponse(\n"
+            f"  question          = {self.question!r}\n"
+            f"  answer            = {self.answer!r}\n"
+            f"  evidence          = {self.evidence!r}\n"
+            f"  entities_used     = {self.entities_used!r}\n"
+            f"  relations_used    = {self.relations_used!r}\n"
+            f"  source_documents  = {self.source_documents!r}\n"
+            ")"
+        )

react_agent.py

@@ -0,0 +1,82 @@
+from tools import TOOL_MAP
+from llm import llm_call
+import re
+from typing import List, Dict
+from models import GraphRAGResponse
+
+SYSTEM_PROMPT = """
+You are an educational GraphRAG assistant for schools.
+
+You MUST use tools when answering knowledge questions.
+
+Available tool:
+- query_knowledge_graph
+
+Format:
+Action: query_knowledge_graph
+Action Input: <question>
+
+OR:
+
+Final Answer: <answer>
+"""
+
+def parse_action(text: str):
+    action = re.search(r"Action:\s*(\w+)", text)
+    input_ = re.search(r"Action Input:\s*(.+)", text)
+
+    if action and input_:
+        return action.group(1), input_.group(1)
+
+    return None
+
+def parse_final(text: str):
+    m = re.search(r"Final Answer:\s*(.+)", text, re.DOTALL)
+    return m.group(1).strip() if m else None
+
+def react_agent(user_message: str, history: List[Dict], G=None, max_steps: int = 5):
+
+    messages = (
+        [{"role": "system", "content": SYSTEM_PROMPT}]
+        + history
+        + [{"role": "user", "content": user_message}]
+    )
+
+    last = ""
+
+    for _ in range(max_steps):
+
+        reply = llm_call(messages)
+        last = reply
+
+        final = parse_final(reply)
+        if final:
+            return GraphRAGResponse(
+                question=user_message,
+                answer=final,
+                evidence=""
+            )
+
+        action = parse_action(reply)
+
+        if action:
+
+            tool_name, tool_input = action
+
+            if tool_name == "query_knowledge_graph":
+                result = TOOL_MAP[tool_name](tool_input, G)
+
+                messages.append({"role": "assistant", "content": reply})
+                messages.append({"role": "user", "content": f"Observation: {result}"})
+
+                return GraphRAGResponse(
+                    question=user_message,
+                    answer=result["answer"],
+                    evidence=result["evidence"]
+                )
+
+    return GraphRAGResponse(
+        question=user_message,
+        answer=last,
+        evidence=""
+)

retrieval.py

@@ -0,0 +1,37 @@
+def retrieve_subgraph(G, query):
+    query = query.lower()
+
+    context = []
+
+    for node in G.nodes(data=True):
+
+        node_name = node[0].lower()
+
+        if (
+            query in node_name
+            or node_name in query
+        ):
+
+            for neighbor in G.neighbors(node[0]):
+
+                edge = G[node[0]][neighbor]
+
+                context.append(
+                    f"{node[0]} --{edge.get('relation','related_to')}--> {neighbor}"
+                )
+
+    return "\n".join(context)
+
+#test
+from graph_builder import load_graph
+
+if __name__ == "__main__":
+
+    G = load_graph()
+
+    result = retrieve_subgraph(
+        G,
+        "Python"
+    )
+
+    print(result)

tools.py

@@ -0,0 +1,50 @@
+from retrieval import retrieve_subgraph
+from llm import llm_call
+
+def query_knowledge_graph(question: str, G):
+
+    graph_context = retrieve_subgraph(G, question)
+
+    prompt = f"""
+You are an educational assistant.
+
+Use ONLY the graph knowledge below to answer.
+
+Graph Knowledge:
+{graph_context}
+
+Question:
+{question}
+
+Rules:
+- If graph is empty, say "No information in knowledge graph"
+- Be concise and educational
+"""
+
+    response = llm_call([
+        {"role": "user", "content": prompt}
+    ])
+
+    return {
+        "answer": response,
+        "evidence": graph_context
+    }
+
+
+TOOL_MAP = {
+    "query_knowledge_graph": query_knowledge_graph,
+}
+
+#test
+from graph_builder import load_graph
+
+if __name__ == "__main__":
+
+    G = load_graph()
+
+    result = query_knowledge_graph(
+        "What is used in machine learning?",
+        G
+    )
+
+    print(result)

visualization.py

@@ -0,0 +1,31 @@
+from pyvis.network import Network
+from graph_builder import load_graph
+
+def visualize_graph(G):
+    net = Network(
+        directed=True,
+        notebook=False   
+    )
+
+    for node in G.nodes():
+        net.add_node(node)
+
+    for u, v, data in G.edges(data=True):
+        net.add_edge(
+            u,
+            v,
+            label=data.get("relation", "")
+        )
+
+    output_path = "kg.html"
+
+    net.write_html(output_path)
+
+    print(f"Graph saved to {output_path}")
+    
+#test
+if __name__ == "__main__":
+
+    G = load_graph()
+
+    visualize_graph(G)