Refactor example retreiving, add option for dynamic data. Add hybrid reranking to tooling. Add parsing and loop infrastructure for trigger event processing

agent/agent.ts

@@ -9,9 +9,9 @@ import { verificationSetup } from "./nodes/verificationSetup";
 import { dummyRagasMetrics } from "./nodes/dummyRagasMetrics";
 import { produceRanking } from "./nodes/produceRanking";
 import { createModelNode } from "./nodes/model";
+import { loopEndConditional } from "./conditionals/loop_end";
 
 const triggerEventToolNode = createToolNode(triggerEventToolsByName);
-const verificationToolNode = createToolNode([]);
 
 const dummyVerificationModel = createDummyModelNode("verification of");
 
@@ -20,8 +20,6 @@ const triggerEventModel = createModelNode(triggerEventToolsByName, "trigger.txt"
 
 
 const triggerEventToolConditional = createToolConditional("triggerEventToolNode", verificationSetup.name);
-const verificationToolConditional = createToolConditional("verificationToolNode", produceRanking.name);
-
 
 const agent = new StateGraph(MessagesState)
   
@@ -36,7 +34,6 @@ const agent = new StateGraph(MessagesState)
   .addNode(verificationSetup.name, verificationSetup)
   .addNode("dummyVerificationModel", dummyVerificationModel)
   .addNode(dummyRagasMetrics.name, dummyRagasMetrics)
-  .addNode("verificationToolNode", verificationToolNode)
   .addNode(produceRanking.name, produceRanking)
   
   .addEdge(START, normalizationSetup.name)
@@ -50,11 +47,11 @@ const agent = new StateGraph(MessagesState)
   .addEdge(verificationSetup.name, "dummyVerificationModel")
   .addEdge(verificationSetup.name, dummyRagasMetrics.name)
 
-  // @ts-expect-error
-  .addConditionalEdges("dummyVerificationModel", verificationToolConditional, ["verificationToolNode", produceRanking.name])
-  .addEdge("verificationToolNode", "dummyVerificationModel")
-  
   .addEdge(dummyRagasMetrics.name, produceRanking.name)
+  .addEdge("dummyVerificationModel", produceRanking.name)
+
+  
+  .addConditionalEdges(produceRanking.name, loopEndConditional, [verificationSetup.name, END])
   
   .compile();
 

agent/conditionals/loop_end.ts

@@ -0,0 +1,16 @@
+import { ConditionalEdgeRouter, END } from "@langchain/langgraph";
+import { MessagesState } from "../state";
+
+
+export const loopEndConditional: ConditionalEdgeRouter<typeof MessagesState, String> = (state) => {
+    const triggerEvents = state.proposedTriggerEvent;
+    const triggerEventsIndex = state.proposedTriggerEventIndex;
+
+    if (triggerEventsIndex == triggerEvents.length-1) {
+      return END
+    }
+    else {
+      return "verificationSetup"
+    }
+  };
+

agent/nodes/normalizationSetup.ts

@@ -1,10 +1,10 @@
 import { GraphNode } from "@langchain/langgraph";
 import { MessagesState } from "../state";
 import { AIMessage, BaseMessage, HumanMessage } from "@langchain/core/messages";
-import { calculateSimilarity } from "../tools/clan/retreiveExamples";
+import { rankFromCSV } from "../tools/clan/retreiveExamples";
 
 export const normalizationSetup: GraphNode<typeof MessagesState> = async (state) => {
-  let similarityResults = await calculateSimilarity(state.disinformationTitle)
+  let similarityResults = await rankFromCSV(state.disinformationTitle)
 
   console.log(similarityResults)
 

agent/nodes/verificationSetup.ts

@@ -1,9 +1,22 @@
 import { GraphNode } from "@langchain/langgraph";
-import { MessagesState } from "../state";
+import { MessagesState, ProposedTriggerEventArray } from "../state";
-import { HumanMessage } from "@langchain/core/messages";
+import { logger } from "../utils/logger";
 
 export const verificationSetup: GraphNode<typeof MessagesState> = async (state) => {
-  //TODO: this might not be needed, looks nice on the graph tho
+  //this is kinda doing two things, but having two nodes for it seems overkill
+  console.log(state.proposedTriggerEvent)
+  console.log(state.proposedTriggerEventIndex)
+  if (state.proposedTriggerEvent == undefined) {
+    logger.warn("No trigger events in memory, parsing")
 
-  return { messages: [ new HumanMessage(state.messages.at(-1)?.content ?? "undefined")] };
+    let genResponse = state.messages.at(-1)?.content.toString() ?? "";
+    const parsed = ProposedTriggerEventArray.parse(JSON.parse(genResponse));
+
+    return { proposedTriggerEvent: parsed, proposedTriggerEventIndex: 0 };
+  }
+  else {
+    logger.info("Trigger event index %s", state.proposedTriggerEventIndex+1)
+    
+    return { proposedTriggerEvent: state.proposedTriggerEvent, proposedTriggerEventIndex: state.proposedTriggerEventIndex+1 };
+  }
 };

agent/package-lock.json

@@ -21,6 +21,8 @@
         "fs": "^0.0.1-security",
         "langchain": "^1.2.14",
         "selenium-webdriver": "^4.40.0",
+        "wink-bm25-text-search": "^3.1.2",
+        "wink-nlp-utils": "^2.1.0",
         "winston": "^3.19.0"
       },
       "devDependencies": {
@@ -1640,6 +1642,12 @@
         "node": ">= 0.4"
       }
     },
+    "node_modules/emoji-regex": {
+      "version": "9.2.2",
+      "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-9.2.2.tgz",
+      "integrity": "sha512-L18DaJsXSUk2+42pv8mLs5jJT2hqFkFE4j21wOmgbUqsZ2hL72NsUU785g9RXgo3s0ZNgVl42TiHp3ZtOv/Vyg==",
+      "license": "MIT"
+    },
     "node_modules/enabled": {
       "version": "2.0.0",
       "resolved": "https://registry.npmjs.org/enabled/-/enabled-2.0.0.tgz",
@@ -2787,6 +2795,84 @@
       "resolved": "https://registry.npmjs.org/validate.io-function/-/validate.io-function-1.0.2.tgz",
       "integrity": "sha512-LlFybRJEriSuBnUhQyG5bwglhh50EpTL2ul23MPIuR1odjO7XaMLFV8vHGwp7AZciFxtYOeiSCT5st+XSPONiQ=="
     },
+    "node_modules/wink-bm25-text-search": {
+      "version": "3.1.2",
+      "resolved": "https://registry.npmjs.org/wink-bm25-text-search/-/wink-bm25-text-search-3.1.2.tgz",
+      "integrity": "sha512-s+xY0v/yurUhiUop/XZnf9IvO9XVuwI14X+QTW0JqlmQCg+9ZgVXTMudXKqZuQVsnm5J+RjLnqrOflnD5BLApA==",
+      "license": "MIT",
+      "dependencies": {
+        "wink-eng-lite-web-model": "^1.4.3",
+        "wink-helpers": "^2.0.0",
+        "wink-nlp": "^1.12.2",
+        "wink-nlp-utils": "^2.0.4"
+      }
+    },
+    "node_modules/wink-distance": {
+      "version": "2.0.2",
+      "resolved": "https://registry.npmjs.org/wink-distance/-/wink-distance-2.0.2.tgz",
+      "integrity": "sha512-pyEhUB/OKFYcgOC4J6E+c+gwVA/8qg2s5n49mIcUsJZM5iDSa17uOxRQXR4rvfp+gbj55K/I08FwjFBwb6fq3g==",
+      "license": "MIT",
+      "dependencies": {
+        "wink-helpers": "^2.0.0",
+        "wink-jaro-distance": "^2.0.0"
+      }
+    },
+    "node_modules/wink-eng-lite-web-model": {
+      "version": "1.8.1",
+      "resolved": "https://registry.npmjs.org/wink-eng-lite-web-model/-/wink-eng-lite-web-model-1.8.1.tgz",
+      "integrity": "sha512-M2tSOU/rVNkDj8AS8IoKJaM7apJJjS0cN+hE8CPazfnB4A/ojyc9+7RMPk18UOiIdSyWk7MR6w8z9lWix2l5tA==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=16.0.0"
+      }
+    },
+    "node_modules/wink-helpers": {
+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/wink-helpers/-/wink-helpers-2.0.0.tgz",
+      "integrity": "sha512-I/ZzXrHcNRXuoeFJmp2vMVqDI6UCK02Tds1WP4kSGAmx520gjL1BObVzF7d2ps24tyHIly9ngdB2jwhlFUjPvg==",
+      "license": "MIT"
+    },
+    "node_modules/wink-jaro-distance": {
+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/wink-jaro-distance/-/wink-jaro-distance-2.0.0.tgz",
+      "integrity": "sha512-9bcUaXCi9N8iYpGWbFkf83OsBkg17r4hEyxusEzl+nnReLRPqxhB9YNeRn3g54SYnVRNXP029lY3HDsbdxTAuA==",
+      "license": "MIT"
+    },
+    "node_modules/wink-nlp": {
+      "version": "1.14.3",
+      "resolved": "https://registry.npmjs.org/wink-nlp/-/wink-nlp-1.14.3.tgz",
+      "integrity": "sha512-lvY5iCs3T8I34F8WKS70+2P0U9dWLn3vdPf/Z+m2VK14N7OmqnPzmHfh3moHdusajoQ37Em39z0IZB9K4x/96A==",
+      "license": "MIT"
+    },
+    "node_modules/wink-nlp-utils": {
+      "version": "2.1.0",
+      "resolved": "https://registry.npmjs.org/wink-nlp-utils/-/wink-nlp-utils-2.1.0.tgz",
+      "integrity": "sha512-b7PcRhEBNxQmsmht70jLOkwYUyie3da4/cgEXL+CumYO5b/nwV+W7fuMXToh5BtGq1RABznmc2TGTp1Qf/JUXg==",
+      "license": "MIT",
+      "dependencies": {
+        "wink-distance": "^2.0.1",
+        "wink-eng-lite-web-model": "^1.4.3",
+        "wink-helpers": "^2.0.0",
+        "wink-nlp": "^1.12.0",
+        "wink-porter2-stemmer": "^2.0.1",
+        "wink-tokenizer": "^5.2.3"
+      }
+    },
+    "node_modules/wink-porter2-stemmer": {
+      "version": "2.0.1",
+      "resolved": "https://registry.npmjs.org/wink-porter2-stemmer/-/wink-porter2-stemmer-2.0.1.tgz",
+      "integrity": "sha512-0g+RkkqhRXFmSpJQStVXW5N/WsshWpJXsoDRW7DwVkGI2uDT6IBCoq3xdH5p6IHLaC6ygk7RWUsUx4alKxoagQ==",
+      "license": "MIT"
+    },
+    "node_modules/wink-tokenizer": {
+      "version": "5.3.0",
+      "resolved": "https://registry.npmjs.org/wink-tokenizer/-/wink-tokenizer-5.3.0.tgz",
+      "integrity": "sha512-O/yAw0g3FmSgeeQuYAJJfP7fVPB4A6ay0018qASh79aWmIOyPYy4j4r9EQT8xBjicja6lCLvgRVAybmEBaATQA==",
+      "license": "MIT",
+      "dependencies": {
+        "emoji-regex": "^9.0.0"
+      }
+    },
     "node_modules/winston": {
       "version": "3.19.0",
       "resolved": "https://registry.npmjs.org/winston/-/winston-3.19.0.tgz",

agent/package.json

@@ -22,6 +22,8 @@
     "fs": "^0.0.1-security",
     "langchain": "^1.2.14",
     "selenium-webdriver": "^4.40.0",
+    "wink-bm25-text-search": "^3.1.2",
+    "wink-nlp-utils": "^2.1.0",
     "winston": "^3.19.0"
   },
   "devDependencies": {

agent/state.ts

@@ -10,8 +10,20 @@ import {
 } from "@langchain/langgraph";
 import { z } from "zod/v4";
 
+export const ProposedTriggerEvent = z.object({
+  Event: z.string(),
+  ReasoningWhyRelevant: z.string(),
+  SearchQuery: z.string(),
+  Url: z.url(),
+  IsItselfDisinformation: z.boolean()
+})
+
+export const ProposedTriggerEventArray = z.array(ProposedTriggerEvent);
+
 export const MessagesState = new StateSchema({
+  disinformationTitle: z.string(),
   messages: MessagesValue,
-  // normalizationContext: z.map(z.string(), z.string()),
+  proposedTriggerEvent: ProposedTriggerEventArray,
-  disinformationTitle: z.string()
+  proposedTriggerEventIndex: z.int(),
 });
+

agent/tools/clan/retreiveExamples.ts

@@ -1,16 +1,16 @@
 import { parse } from "csv-parse";
 import fs from "fs";
 import { pipeline, cos_sim } from "@huggingface/transformers";
+import bm25Factory from "wink-bm25-text-search";
+import nlp from "wink-nlp-utils";
 import { logger } from "../../utils/logger";
 
-//TODO, am getting duplicates, is it from the multi files?
 const CSV_PATHS = [
   "./tools/clan/dev-eng.csv",
-  // "./tools/clan/test-eng.csv",
   "./tools/clan/train-eng.csv",
 ];
 
-const CACHE_PATH = "./tools/clan/dev.embeddings.json";
+const CACHE_PATH = "./tools/clan/csv.cache.json";
 
 type EmbeddingCache = {
   rawtexts: string[];
@@ -18,104 +18,262 @@ type EmbeddingCache = {
   embeddings: number[][];
 };
 
-export type NormalisedMatch = {
+export type RetrievalItem = {
-  index: number;
+  id: string | number;
-  score: number;
   rawtext: string;
-  cleantext: string;
+  cleantext?: string;
 };
 
-let rawtexts: string[] = [];
+export type RankedResult = RetrievalItem & {
-let cleantexts: string[] = [];
+  denseScore: number;
-let embeddings: number[][] = [];
+  sparseScore: number;
+  fusedScore: number;
+};
 
+let csvRawtexts: string[] = [];
+let csvCleantexts: string[] = [];
+let csvEmbeddings: number[][] = [];
+let csvBM25: any = null;
+let csvLoaded = false;
+
+logger.info("Loading embedding model...");
 const featureExtractor = await pipeline(
   "feature-extraction",
   "Xenova/all-MiniLM-L6-v2"
 );
+logger.info("Embedding model loaded");
 
+//Cached entrypoint
+export async function rankFromCSV(
+  query: string,
+  topK = 5
+): Promise<RankedResult[]> {
+  await ensureCSVLoaded();
+
+  logger.info("Ranking from CSV cache...");
+
+  const queryEmbedding = await embedText(query);
+
+  const denseScores = csvEmbeddings.map((docEmbedding) =>
+    cos_sim(docEmbedding, queryEmbedding)
+  );
+
+  const sparseScores = computeSparseScores(query, csvBM25, csvRawtexts);
+
+  const fusedScores = reciprocalRankFusion([denseScores, sparseScores]);
+
+  const ranked = csvRawtexts
+    .map((text, i) => ({
+      id: i,
+      rawtext: text,
+      cleantext: csvCleantexts[i],
+      denseScore: denseScores[i],
+      sparseScore: sparseScores[i],
+      fusedScore: fusedScores[i],
+    }))
+    .sort((a, b) => b.fusedScore - a.fusedScore);
+
+  logger.info("Ranking complete (CSV mode)");
+
+  return ranked.slice(0, topK);
+}
+
+//Dynamic Entrypoint
+export async function rankDynamically(
+  query: string,
+  items: RetrievalItem[],
+  topK = 5
+): Promise<RankedResult[]> {
+  logger.info("Ranking dynamically (no cache)...");
+
+  if (!items.length) return [];
+
+  const texts = items.map((i) => i.rawtext);
+
+  const queryEmbedding = await embedText(query);
+
+  const docEmbeddings = await Promise.all(
+    texts.map((text) => embedText(text))
+  );
+
+  const denseScores = docEmbeddings.map((embedding) =>
+    cos_sim(embedding, queryEmbedding)
+  );
+
+  const localBM25 = buildBM25(texts);
+
+  const sparseScores = computeSparseScores(query, localBM25, texts);
+
+  const fusedScores = reciprocalRankFusion([denseScores, sparseScores]);
+
+  const ranked = items
+    .map((item, i) => ({
+      ...item,
+      denseScore: denseScores[i],
+      sparseScore: sparseScores[i],
+      fusedScore: fusedScores[i],
+    }))
+    .sort((a, b) => b.fusedScore - a.fusedScore);
+
+  logger.info("Ranking complete (dynamic mode)");
+
+  return ranked.slice(0, topK);
+}
+
+//CSV stuff
+async function ensureCSVLoaded(): Promise<void> {
+  if (csvLoaded) return;
+
+  logger.info("Initializing CSV ranking mode...");
 
-async function loadOrBuildCache(): Promise<void> {
   if (fs.existsSync(CACHE_PATH)) {
-    logger.info("Loading embeddings from cache");
+    logger.info("Loading CSV cache from disk...");
 
     const raw = fs.readFileSync(CACHE_PATH, "utf-8");
     const cache: EmbeddingCache = JSON.parse(raw);
 
-    rawtexts = cache.rawtexts;
+    csvRawtexts = cache.rawtexts;
-    cleantexts = cache.cleantexts;
+    csvCleantexts = cache.cleantexts;
-    embeddings = cache.embeddings.map(e => Array.from(e));
+    csvEmbeddings = cache.embeddings;
 
-    logger.info("Loaded %s embeddings", embeddings.length);
+    logger.info("Loaded %s cached embeddings", csvEmbeddings.length);
-    return;
+  } else {
-  }
+    logger.warn("CSV cache not found. Building embeddings...");
 
-  logger.warn("Cache not found. Generating embeddings");
+    const seen = new Set<string>();
 
-  for (const csvPath of CSV_PATHS) {
+    for (const path of CSV_PATHS) {
-    await buildCacheFromCSV(csvPath);
+      await processCSV(path, seen);
     }
 
     const cache: EmbeddingCache = {
-    rawtexts,
+      rawtexts: csvRawtexts,
-    cleantexts,
+      cleantexts: csvCleantexts,
-    embeddings,
+      embeddings: csvEmbeddings,
     };
 
     fs.writeFileSync(CACHE_PATH, JSON.stringify(cache));
-
+    logger.info("Cache written (%s embeddings)", csvEmbeddings.length);
-  logger.info("Cached %s embeddings", embeddings.length);
   }
 
-async function buildCacheFromCSV(csvPath: string): Promise<void> {
+  csvBM25 = buildBM25(csvRawtexts);
-  let count = 0;
 
-  logger.info("Processing CSV: %s", csvPath);
+  csvLoaded = true;
+  logger.info("CSV mode ready");
+}
 
-  const stream = fs.createReadStream(csvPath).pipe(parse());
+async function processCSV(
+  path: string,
+  seen: Set<string>
+): Promise<void> {
+  logger.info("Processing CSV: %s", path);
+
+  const stream = fs.createReadStream(path).pipe(parse());
 
   for await (const row of stream) {
     const text = row[0];
-    if (!text) continue;
+    if (!text || seen.has(text)) continue;
 
+    seen.add(text);
+
+    const embedding = await embedText(text);
+
+    csvRawtexts.push(text);
+    csvCleantexts.push(row[1]);
+    csvEmbeddings.push(embedding);
+
+    if (csvRawtexts.length % 100 === 0) {
+      logger.info("Embedded %s documents...", csvRawtexts.length);
+    }
+  }
+
+  logger.info("Finished CSV: %s", path);
+}
+
+
+async function embedText(text: string): Promise<number[]> {
   const output = await featureExtractor(text, {
     pooling: "mean",
     normalize: true,
   });
 
-    rawtexts.push(text);
+  return Array.from(output.data as Float32Array);
-    cleantexts.push(row[1]);
-    const vector = Array.from(output.data as Float32Array);
-    embeddings.push(vector);
-
-
-    count++;
-    if (count % 100 === 0) {
-      logger.info("[%s] Processed %s rows", csvPath, count);
-    }
 }
 
-  logger.info("[%s] Finished (%s rows)", csvPath, count);
+function buildBM25(texts: string[]) {
-}
+  logger.info("Building BM25 index (%s docs)...", texts.length);
 
-export async function calculateSimilarity(
+  const bm25 = bm25Factory();
-  query: string,
-  topK = 5
-): Promise<NormalisedMatch[]> {
-  await loadOrBuildCache()
 
-  const queryEmbedding = await featureExtractor(query, {
+  bm25.defineConfig({
-    pooling: "mean",
+    fldWeights: { text: 1 },
-    normalize: true,
+    bm25Params: { k1: 1.2, b: 0.75 },
   });
 
-  return embeddings
+  bm25.definePrepTasks([
-    .map((embedding, index) => ({
+    nlp.string.lowerCase,
-      index,
+    nlp.string.tokenize0,
-      score: cos_sim(embedding, queryEmbedding.data as number[]),
+    nlp.tokens.removeWords,
-      rawtext: rawtexts[index],
+  ]);
-      cleantext: cleantexts[index]
+
-    }))
+  texts.forEach((text, i) => {
-    .sort((a, b) => b.score - a.score)
+    bm25.addDoc({ text }, i);
-    .slice(0, topK);
+  });
+
+  bm25.consolidate();
+
+  logger.info("BM25 ready");
+
+  return bm25;
 }
+
+function computeSparseScores(
+  query: string,
+  bm25: any,
+  texts: string[]
+): number[] {
+  const results = bm25.search(query);
+
+  const scores = new Array(texts.length).fill(0);
+
+  results.forEach((r: any) => {
+    scores[r[0]] = r[1];
+  });
+
+  return scores;
+}
+
+function reciprocalRankFusion(
+  scoreLists: number[][],
+  k = 60
+): number[] {
+  const length = scoreLists[0].length;
+  const fused = new Array(length).fill(0);
+
+  for (const scores of scoreLists) {
+    const ranked = scores
+      .map((score, i) => ({ score, i }))
+      .sort((a, b) => b.score - a.score)
+      .map((x) => x.i);
+
+    ranked.forEach((docIndex, rank) => {
+      fused[docIndex] += 1 / (k + rank);
+    });
+  }
+
+  return fused;
+}
+
+// console.log(await rankFromCSV("barrack obama"))
+// console.log(
+//   await rankDynamically(
+//     "i fell over",
+//     [
+//       { id: 1, rawtext: "I slipped and fell on the floor." },
+//       { id: 2, rawtext: "Barack Obama was the 44th president." },
+//       { id: 3, rawtext: "He tripped and hurt his knee badly." },
+//       { id: 4, rawtext: "The weather is sunny today." },
+//       { id: 5, rawtext: "She lost her balance and fell down the stairs." },
+//     ]
+//   )
+// );

agent/tools/triggerEventTools.ts

@@ -2,18 +2,20 @@ import { tool } from "@langchain/core/tools";
 import * as z from "zod";
 import { queryScraper } from "./webSearch";
 import { extractWebpageContent } from "./webpageFetch";
+import { rankDynamically } from "./clan/retreiveExamples";
 
 
-function rankAndDisplayData(data: string[]):string {
+async function rankAndDisplayData(data: string[], context: string):Promise<string> {
-  //TODO: hybrid re-ranking of the provided data
+  let index = 0;
-  return data.join("\n")
+  let ranked = await rankDynamically(context, data.map(irm => ({ id: index++, rawtext: irm })))
+  return ranked.map(itm => itm.rawtext).join("\n")
 }
 
 // Define tools
 const webSearch = tool(
   async ({ a }) => {
     const data = await queryScraper(a);
-    return rankAndDisplayData(data);
+    return await rankAndDisplayData(data, a);
   },
   {
     name: "WebSearch",
@@ -25,15 +27,16 @@ const webSearch = tool(
 );
 
 const openWebpage = tool(
-  async ({ a }) => {
+  async ({ a, b }) => {
     const data = await extractWebpageContent(a);
-    return rankAndDisplayData(data);
+    return rankAndDisplayData(data, b);
   },
   {
     name: "OpenWebpage",
     description: "Opens webpage and returns most relevent snippets",
     schema: z.object({
       a: z.string().describe("URL"),
+      b: z.string().describe("What to match against in webpage content"),
     }),
   }
 );