Add relation model. Add calculate score initial version

agent/agent.ts

@@ -15,13 +15,13 @@ const triggerEventToolNode = createToolNode(triggerEventToolsByName);
 const normalisationModel = createModelNode([], "normalization.txt");
 const triggerEventModel = createModelNode(triggerEventToolsByName, "trigger.txt");
 const verificationModel = createModelNode([], "verify.txt");
+const relationModel = createModelNode([], "relation.txt");
 
 const triggerEventToolConditional = createToolConditional("triggerEventToolNode", verificationSetup.name);
 
 const agent = new StateGraph(MessagesState)
   
   //NODES
-  
   .addNode(normalizationSetup.name, normalizationSetup)
   .addNode("normalisationModel", normalisationModel)
   
@@ -31,6 +31,8 @@ const agent = new StateGraph(MessagesState)
   .addNode(verificationSetup.name, verificationSetup)
   .addNode("verificationModel", verificationModel)
   .addNode(ragasMetrics.name, ragasMetrics)
+  .addNode("relationModel", relationModel)
+  
   .addNode(produceRanking.name, produceRanking)
   
   .addEdge(START, normalizationSetup.name)
@@ -43,9 +45,11 @@ const agent = new StateGraph(MessagesState)
   
   .addEdge(verificationSetup.name, "verificationModel")
   .addEdge(verificationSetup.name, ragasMetrics.name)
+  .addEdge(verificationSetup.name, "relationModel")
 
   .addEdge(ragasMetrics.name, produceRanking.name)
   .addEdge("verificationModel", produceRanking.name)
+  .addEdge("relationModel", produceRanking.name)
 
   // @ts-expect-error
   .addConditionalEdges(produceRanking.name, loopEndConditional, [verificationSetup.name, END])

agent/nodes/dummyModel.ts

@@ -1,13 +0,0 @@
-import { GraphNode } from "@langchain/langgraph";
-import { MessagesState } from "../state";
-import { AIMessage } from "@langchain/core/messages";
-
-export function createDummyModelNode(addition): GraphNode<typeof MessagesState> {
-    return async (state) => {
-        //TODO: call AI model with collected data
-
-        return {
-            messages: [new AIMessage(addition + " : " + state.messages.at(-1)?.content)]
-        };
-    };
-}

agent/nodes/produceRanking.ts

@@ -1,9 +1,50 @@
 import { GraphNode } from "@langchain/langgraph";
 import { MessagesState } from "../state";
-import { AIMessage, HumanMessage } from "@langchain/core/messages";
+import { BaseMessage } from "@langchain/core/messages";
+
+type Priority = keyof typeof mapping;
+
+const mapping = {
+  VERYHIGH: 1.0,
+  HIGH: 0.75,
+  MEDIUM: 0.5,
+  LOW: 0.25,
+  VERYLOW: 0.0
+} as const;
+
+function mapResponse(value: string): number {
+  const upper = value.toUpperCase() as Priority;
+
+  if (upper in mapping) {
+    return mapping[upper];
+  }
+
+  return 0;
+}
+
+function getLastMessageContaining(
+  messages: BaseMessage[],
+  searchString: string
+): string {
+  for (let i = messages.length - 1; i >= 0; i--) {
+    const content = messages[i].content;
+    if (typeof content === "string" && content.includes(searchString)) {
+      return content;
+    }
+  }
+  return "";
+}
 
 export const produceRanking: GraphNode<typeof MessagesState> = async (state) => {
-  //TODO: produce ranking here
+  //TODO: what should these weights be
+  let conf = getLastMessageContaining(state.messages, "CONFIDENCE")?.split(":")[1] //TODO: we can better error handle here
+  let ragas = getLastMessageContaining(state.messages, "RAGAS")?.split(":")[1] //TODO: we can genericify this too surely
+  let rel = getLastMessageContaining(state.messages, "RELATION")?.split(":")[1]
+
+  let result = mapResponse(conf) * Number.parseFloat(ragas) * mapResponse(rel)
   
-  return { messages: [ new AIMessage(state.messages?.length.toString() ?? "0")] };
+  let current = state.proposedTriggerEvent;
+  current[state.proposedTriggerEventIndex].score = result;
+
+  return  { proposedTriggerEvent: current };
 };

agent/prompts/relation.txt

@@ -0,0 +1,9 @@
+Could the following real-world event:
+###TECLAIM###
+
+Be a trigger for the following disinformation:
+###TITLE###
+
+Respond with "RELATION", followed by : followed by a confidence score (VERYHIGH, HIGH, MEDIUM, LOW, VERYLOW) followed by : followed by the reason. Use no other words, just return the score and reason in format.
+
+Ignore wether the event happened or not, purely consider the likiness of causation

agent/state.ts

@@ -16,7 +16,8 @@ export const ProposedTriggerEvent = z.object({
   SearchQuery: z.string(),
   Url: z.url(),
   IsItselfDisinformation: z.boolean(),
-  context: z.string().optional()
+  context: z.string().optional(),
+  score: z.number().optional
 })
 
 export const ProposedTriggerEventArray = z.array(ProposedTriggerEvent);