Implement ensemble into final model structure
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@@ -0,0 +1,17 @@
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import { GraphNode } from "@langchain/langgraph";
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import { MessagesState } from "../state";
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import { AIMessage } from "@langchain/core/messages";
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import { evaluateWithEnsemble } from "../tools/ensembleCall";
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export function createEnsembleNode(title: string, method: string): GraphNode<typeof MessagesState> {
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return async (state) => {
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const answer = state.proposedTriggerEvent[state.proposedTriggerEventIndex].Event
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const result = await evaluateWithEnsemble({ answer, method })
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const score = result.validProb - result.invalidProb;
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return {
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messages: [new AIMessage(title + ":" + score)]
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};
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};
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};
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@@ -2,31 +2,25 @@ import { GraphNode } from "@langchain/langgraph";
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import { MessagesState } from "../state";
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import { BaseMessage } from "@langchain/core/messages";
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//TODO: Each of these might need different weights
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const keys = ["CONFIDENCE", "RELATION", "RAGAS", "ROBERTA"];
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const mapping = {
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VERYHIGH: 1.0,
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HIGH: 0.75,
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MEDIUM: 0.5,
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LOW: 0.25,
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VERYLOW: 0.0,
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const models = {
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REGRESSION: 0.3,
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ROBERTA: 0.5,
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FLAN: 0.3,
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} as const;
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type Priority = keyof typeof mapping;
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type ModelKey = keyof typeof models;
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function mapResponse(value: string | undefined | null): number {
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if (!value) return 1;
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if (!value) return 0;
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const trimmed = value.trim();
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const num = parseFloat(trimmed);
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// If number, return it
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if (!isNaN(num)) return num;
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// Otherwise, map to value
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const upper = trimmed.toUpperCase() as Priority;
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return mapping[upper] ?? 0;
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if (!isNaN(num)) {
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return num;
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} else {
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return 0;
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}
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}
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function getLastMessageContaining(
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@@ -43,18 +37,18 @@ function getLastMessageContaining(
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}
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export const produceRanking: GraphNode<typeof MessagesState> = async (state) => {
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// Extract and map values
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const values = keys.map((key) => {
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const values = (Object.keys(models) as ModelKey[]).map((key) => {
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const msg = getLastMessageContaining(state.messages, key);
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const part = msg?.split(":").at(1);
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return mapResponse(part);
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const baseValue = mapResponse(part);
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return baseValue * models[key];
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});
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// Multiply!
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const result = values.reduce((acc, val) => acc * val, 1);
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const result = values.reduce((acc, val) => acc + val, 0);
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const current = state.proposedTriggerEvent;
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current[state.proposedTriggerEventIndex].score = result;
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return { proposedTriggerEvent: current };
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};
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};
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@@ -1,16 +0,0 @@
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import { GraphNode } from "@langchain/langgraph";
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import { MessagesState } from "../state";
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import { AIMessage, HumanMessage } from "@langchain/core/messages";
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import { evaluateWithRagas } from "../tools/ragasCall";
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export const ragasMetrics: GraphNode<typeof MessagesState> = async (state) => {
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const question = "A possible trigger event for: " + state.disinformationTitle //Should it be raw, or normalized?
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const answer = state.proposedTriggerEvent[state.proposedTriggerEventIndex].Event
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const contexts = state.proposedTriggerEvent[state.proposedTriggerEventIndex].context?.split("^^^") ?? []
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const results = await evaluateWithRagas({question, answer, contexts})
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return {
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messages: [ new AIMessage("RAGAS:" + results.faithfulness)]
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};
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};
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@@ -1,39 +0,0 @@
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import { GraphNode } from "@langchain/langgraph";
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import { MessagesState } from "../state";
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import { AIMessage } from "@langchain/core/messages";
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import { evaluateWithRoberta } from "../tools/robertaCall";
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export const robertaMetrics: GraphNode<typeof MessagesState> = async (state) => {
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const answer = state.proposedTriggerEvent[state.proposedTriggerEventIndex].Event
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const lrresult = await evaluateWithRoberta({answer, method:"logreg"})
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const lrscore = lrresult.validProb - lrresult.invalidProb;
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const roresult = await evaluateWithRoberta({answer, method:"roberta"})
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const roscore = roresult.validProb - roresult.invalidProb;
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const flresult = await evaluateWithRoberta({answer, method:"flan"})
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const flscore = flresult.validProb - flresult.invalidProb;
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//Option 1: combining scores
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const score = lrscore * 0.3 + roscore * 0.5 + flscore * 0.3
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//Option 2: majority voting
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// const rovote = roscore > 0.6
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// const flvote = flscore > 0.94
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// const lrvote = lrscore > 0.75
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// let counter = 0
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// if (rovote) counter++
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// if (flvote) counter++
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// if (lrvote) counter++
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// let score = 0
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// if (counter >= 2) {
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// score = 0.7 + lrscore + flscore + lrscore
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// }
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return {
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messages: [ new AIMessage("ROBERTA:" + score)]
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};
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};
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