Improve response extraction

.gitignore

@@ -1,2 +1,3 @@
 # TEMP
+literature/
 backup.tar.gz

README.md

@@ -1,55 +1,20 @@
 # AI models for identifying trigger events in disinformation analysis
+Final Dissertation Submission Repository
 
-## Abstract
-Disinformation on the internet has become a significant and growing challenge, driven by the sheer volume and speed of content generation across digital platforms. 
-
-While prior work has thoroughly investigated tasks such as automatic disinformation detection, especially using large language models, comparatively little attention has been paid to applying these techniques to generative analyses of claims.  In particular, the retrieval of structured contextual background information that explains why disinformation spreads remains underexplored.
- 
-This paper introduces a dataset of trigger events, defined as actions or statements that influence the creation, spread or believability of disinformation claims. 
-The dataset is created using large language models as few-shot retrievers and subsequently validated through an ensemble classifier to ensure consistency.
-
-We find that we can successfully retrieve a large dataset of trigger events with high accuracy, and that the dataset proves useful in applications that can be useful in the real-world scenarios of debunking and prebunking. 
-
-
-[Project Writeup](/literature/WillJeynes-Dissertation.pdf)
-
-[Project Presentation](https://jillweynes.github.io/LLMsForDisinformationPrediction-GraphVizBuilt/presentation)
-
-## Repository Information
-
-This repository contains the code for main dataset generation, as a [langchain](https://www.langchain.com/) project. 
-
-![diagram](literature/design.png)
-
-In **green** is claim normalisation based on [this paper](https://arxiv.org/abs/2508.17402)
-
-In **red** is the main AI processing, based on a simple tool invocation setup
-
-![tool invocation](literature/prompting.png)
-
-Finally, in **yellow** is an ensemble model to quantify the usefullness of the AI generated responses, compared against a human-labelled dataset
-
-## Generated Dataset Link
-
-A dataset of this pipeline ran against 2000 input claims from [StopFake](https://www.stopfake.org/en/main/) and [ScienceFeedback](https://science.feedback.org/) is available on [HuggingFace](https://huggingface.co/datasets/WillJeynes/LLMsForDisinformationAnalysis-Dataset)
-
-
-## More information
-A Graph-Based Dataset Visualisation tool is available [online](https://jillweynes.github.io/LLMsForDisinformationPrediction-GraphVizBuilt/), to show the potential usability of the content.
-
-This and other usage experiments can be seen on the [sister repository](https://git.host.jeynes.uk/jill/LLMsForDisinformationPrediction)
-
-
-
-# This repository:
+## Project Description
+-- todo --
 
+## Solution Diagram
+-- todo --
 
 ## Classifier Refinement
-To see more detail about the ensembkle classifier, see [this folder](/supporting/RAGAS_Service/)
+[See RAGAS_Service](/supporting/RAGAS_Service/)
 
 ## Agent Refinement
-To see more detail about experiments to improve the output quality, see [this folder](/agent/)
+[See agent](/agent/)
 
+## Generated Database Link and Usage Experiments
+-- todo --
 
 ## Repository Structure
 ```
@@ -64,6 +29,8 @@ To see more detail about experiments to improve the output quality, see [this fo
 |   ├── input.jsonl                 # Response in cleaned format to give as context to agent
 |   ├── ranked.jsonl                # Cleaned trigger event response from scorer frontend
 |   └── results.jsonl               # Output from wrapper script, read and modified by scorer
+├── literature/
+|   └── report.pdf                  # Final submission report
 ├── agent/                          # Code for main project pipeline
 |   ├── agent.ts                    # Graph definition file
 |   ├── conditionals/               # Conditional translations

agent/.env.example

@@ -3,4 +3,5 @@ LANGSMITH_TRACING=true
 LANGSMITH_API_KEY=123456
 LANGSMITH_ENDPOINT=https://eu.api.smith.langchain.com
 SCRAPER_INSTANCE=https://example.com
-SCRAPER_PARAM_ANYTHING=else
+SCRAPER_PARAM_ANYTHING=else
+RANKING_URL=http://localhost:8000/evaluate

agent/README.md

@@ -1,32 +1,3 @@
 ## Refining the agent output
 
-Experiments modifying pipeline
-
-| Model            | % Correct | % Change |
-|------------------|----------:|---------:|
-| BASELINE         | 33        | 0        |
-| Improv Prompt    | 39.96     | 0.21     |
-| Add Examples     | 44.67     | 0.35     |
-| Date             | 45.51     | 0.38     |
-| Chain of Thought | 43.38     | 0.31     |
-| Self-Critique    | 44.36     | 0.34     |
-
-Experiments with different model types:
-| Model                         | % Correct | % Change |
-|-------------------------------|----------:|---------:|
-| gpt-5-mini                    | 45.51     |          |
-| gpt-5.4-mini                  | 32.4      |          |
-| gpt-5.4-nano                  | 23.28     |          |
-| gpt-4.1-mini                  | 27.85     |          |
-| gpt-4o-mini                   | 32.47     |          |
-| llama3.1:8b-instruct-q4_K_M   | ?         |          |
-| qwen3.5:9b                    | 0         |          |
-
-%age valid URLS
-| Model                         | Number    | % Age    |
-|-------------------------------|----------:|---------:|
-| gpt-5-mini                    | 22/405    | 5.43     |
-| gpt-5.4-mini                  | 29/278    | 10.43    |
-| gpt-5.4-nano                  | 6/210     | 2.85     |
-| gpt-4.1-mini                  | 15/269    | 5.57     |
-| gpt-4o-mini                   | 27/287    | 9.407    |
+TODO: Table and document experiments

agent/nodes/model.ts

@@ -1,25 +1,28 @@
-import { HumanMessage, SystemMessage } from "@langchain/core/messages";
+import { SystemMessage } from "@langchain/core/messages";
 import { GraphNode } from "@langchain/langgraph";
 import { MessagesState } from "../state";
-import { ChatOpenAI } from "@langchain/openai"
+import { ChatOllama } from "@langchain/ollama";
 import { hydratePrompt } from "../prompts/hydratePrompt";
+import { logger } from "../utils/logger";
 
 export function createModelNode(tools: any, promptPath: string): GraphNode<typeof MessagesState> {
     return async (state) => {
         const sysPrompt = await hydratePrompt(promptPath, state);
 
-        const model = new ChatOpenAI({
-            model: "gpt-5-mini"
+        const model = new ChatOllama({
+            model: "llama3.1:8b-instruct-q4_K_M",
+            temperature: 0.3
         });
+
         const modelWithTools = model.bindTools(Object.values(tools));
 
         const response = await modelWithTools.invoke([
-            new SystemMessage(
-                sysPrompt
-            ),
+            new SystemMessage(sysPrompt),
             ...state.messages,
         ]);
 
+        logger.error(response);
+
         return {
             messages: [response]
         };

agent/nodes/triggerEventSetup.ts

@@ -3,9 +3,17 @@ import { MessagesState } from "../state";
 import { AIMessage, BaseMessage } from "@langchain/core/messages";
 import { rankExampleTriggerEvents } from "../tools/retreiveExamples";
 
+function extractTE(text: string) {
+  const match = text.match(/<norm>([\s\S]*?)<\/norm>/);
+  if (!match) throw new Error("Nothing found between <norm> tags");
+  return match[1].trim();
+}
+
+
 export const triggerEventSetup: GraphNode<typeof MessagesState> = async (state) => {
-  let nc = state?.messages?.at(-1)?.content ?? "" //keep a copy of normalized trigger event. Again two things, womp womp
-  
+  let raw = state?.messages?.at(-1)?.content ?? "" //keep a copy of normalized trigger event. Again two things, womp womp
+  let nc = extractTE(raw.toString())
+
   //Now give in-context examples. hopwfully we can self-teach?
   let similarityResults = await rankExampleTriggerEvents(state.disinformationTitle)
 

agent/nodes/verificationSetup.ts

@@ -1,20 +1,31 @@
 import { GraphNode } from "@langchain/langgraph";
 import { MessagesState, ProposedTriggerEventArray } from "../state";
 import { logger } from "../utils/logger";
-import { jsonrepair } from 'jsonrepair'
+import { jsonrepair } from 'jsonrepair';
+
+function extractJSON(text: string) {
+  const match = text.match(/<json>([\s\S]*?)<\/json>/);
+  if (!match) throw new Error("No JSON found between <json> tags");
+  return match[1].trim();
+}
 
 export const verificationSetup: GraphNode<typeof MessagesState> = async (state) => {
-  //this is kinda doing two things, but having two nodes for it seems overkill
-
   if (state.proposedTriggerEvent == undefined) {
-    logger.warn("No trigger events in memory, parsing")
+    logger.warn("No trigger events in memory, parsing");
 
-    let genResponse = state.messages.at(-1)?.content.toString() ?? "";
+    const genResponse = state.messages.at(-1)?.content.toString() ?? "";
 
-    const repaired = jsonrepair(genResponse);
+    let repaired: string;
+    try {
+      let extracted = extractJSON(genResponse)
+      repaired = jsonrepair(extracted);
+    } catch (repairErr: any) {
+      logger.error("Failed to repair JSON from LLM response.");
+      logger.error("Original LLM response:\n%s", genResponse);
+      throw new Error(`JSON repair failed: ${repairErr.message}`);
+    }
 
     let parsed;
-
     try {
       const json = JSON.parse(repaired);
 
@@ -27,19 +38,23 @@ export const verificationSetup: GraphNode<typeof MessagesState> = async (state)
         if (Array.isArray(firstValue)) {
           parsed = ProposedTriggerEventArray.parse(firstValue);
         } else {
-          throw new Error("No array found in JSON");
+          logger.error("No array found in JSON after parsing.");
+          logger.error("Repaired JSON:\n%s", repaired);
+          logger.error("Original LLM response:\n%s", genResponse);
+          throw new Error("No array found in JSON structure");
         }
       }
-    } catch (err: any) {
-      logger.error(`Failed to parse LLM response: ${err.message}`);
-      throw new Error(`Failed to parse LLM response: ${err}`);
+    } catch (parseErr: any) {
+      logger.error("Failed to parse LLM response to JSON or validate array.");
+      logger.error("Repaired JSON:\n%s", repaired);
+      logger.error("Original LLM response:\n%s", genResponse);
+      throw new Error(`Parsing failed: ${parseErr.message}`);
     }
-    
+
     return { proposedTriggerEvent: parsed, proposedTriggerEventIndex: 0 };
-  }
-  else {
-    logger.info("Trigger event index %s", state.proposedTriggerEventIndex+1)
-    
-    return { proposedTriggerEvent: state.proposedTriggerEvent, proposedTriggerEventIndex: state.proposedTriggerEventIndex+1 };
+  } else {
+    logger.info("Trigger event index %s", state.proposedTriggerEventIndex + 1);
+
+    return { proposedTriggerEvent: state.proposedTriggerEvent, proposedTriggerEventIndex: state.proposedTriggerEventIndex + 1 };
   }
 };

agent/package.json

@@ -17,6 +17,7 @@
     "@langchain/core": "^1.1.17",
     "@langchain/langgraph": "^1.1.2",
     "@langchain/langgraph-sdk": "^1.5.5",
+    "@langchain/ollama": "^1.2.6",
     "@langchain/openai": "^1.2.3",
     "axios": "^1.13.5",
     "compute-cosine-similarity": "^1.1.0",

agent/prompts/normalization.txt

@@ -16,4 +16,7 @@ Relevent examples are included in preceeding messages, use these as exact inspir
 The claim to normalize is:
 ###TITLE###
 
-Produce no other text other than the condensed claim.
+Produce no other text other than the condensed claim, surrounded <norm></norm>
+
+For example: BREAKING: the sky is green!
+Becomes: <norm>The sky is green</norm>

agent/prompts/trigger.txt

@@ -17,6 +17,15 @@ Include a url to a source for your trigger event (not a web search, a specific u
 Include the date that the event happened ("March 2022" for exmaple)
 
 Use a JSON format with each entry containing "Event,ReasoningWhyRelevant,SearchQuery,Url,Date".
+Return ONLY JSON, no extra text. Wrap it like this:
+<json>
+[
+    {
+        "Event": "Example"
+        ...
+    }
+]
+</json>
 
 Multiple tool invocations should be requested at once, if applicable.
 Use your abilities to look between the lines and produce some insightful analysis, thinking both short and long term.
@@ -26,4 +35,8 @@ Events will be reordered as part of processing, each statement must stand alone
 The preceeding messages act as examples of previous responses to potentially ficitonal events and scores given.
 Analysis should only be completed for proposed events that would graner >0.7 points
 
+Since URLs change frequently, use tools to retreive up to date informaiton everytime, provided examples or existing knowledge will be wrong or out of date.
+
+Remember to return just json enclosed by <json></json>
+
 Lets go through it step by step

agent/tools/ensembleCall.ts

@@ -7,7 +7,7 @@ export async function evaluateWithEnsemble({
   answer: string;
   method: string
 }): Promise<{ validProb: number; invalidProb: number; }> {
-  const res = await axios.post("http://localhost:8000/evaluate", {
+    const res = await axios.post(process.env.RANKING_URL ?? "http://localhost:8000/evaluate", {
     answer,
     method
   }, {timeout: 0});
@@ -18,11 +18,15 @@ export async function evaluateWithEnsemble({
   return {validProb, invalidProb};
 }
 
-// let res = await evaluateWithRoberta({answer: "High-profile political downplaying of COVID-19 (examples: President Trump saying 'it will go away' in March–August 2020)"});
+// import dotenv from "dotenv";
+
+// dotenv.config();
+
+// let res = await evaluateWithEnsemble({method:"flan" ,answer: "High-profile political downplaying of COVID-19 (examples: President Trump saying 'it will go away' in March–August 2020)"});
 // console.log(res)
 
-// res = await evaluateWithRoberta({answer: "Multiple mirrored reuploads (2020–2023) put the clip on other channels with titles implying it was a genuine 1970s public information film."});
+// res = await evaluateWithEnsemble({method:"roberta" ,answer: "Multiple mirrored reuploads (2020–2023) put the clip on other channels with titles implying it was a genuine 1970s public information film."});
 // console.log(res)
 
-// res = await evaluateWithRoberta({answer: "The COVID-19 Pandemic"});
+// res = await evaluateWithEnsemble({method:"logreg" ,answer: "The COVID-19 Pandemic"});
 // console.log(res)

agent/tools/ragasCall.ts

@@ -1,22 +0,0 @@
-import axios from "axios";
-
-export async function evaluateWithRagas({
-  question,
-  answer,
-  contexts,
-}: {
-  question: string;
-  answer: string;
-  contexts: string[];
-}) {
-  const res = await axios.post("http://localhost:8001/evaluate", {
-    question,
-    answer,
-    contexts,
-  });
-
-  return res.data;
-}
-
-// let res = await evaluateWithRagas({question: "Who was Bill Nye", answer: "Bill Nye was a Scientist", contexts: ["Bill nye was a Scientist"]});
-// console.log(res)

agent/tools/webpageFetch.ts

@@ -26,6 +26,9 @@ async function extractWebpageContentWorker(url: string): Promise<string[]> {
   try {
     const options = new firefox.Options();
     options.addArguments("--headless");
+    options.addArguments("--disable-gpu");
+    options.addArguments("--no-sandbox"); // Linux sandbox issues
+    options.addArguments("--disable-dev-shm-usage"); // /dev/shm issues
     driver = await new Builder()
       .forBrowser(Browser.FIREFOX)
       .setFirefoxOptions(options)

run.sh

@@ -5,7 +5,7 @@ set -e
 run_agent () {
     echo "Starting LangGraph agent..."
     cd agent
-    npx @langchain/langgraph-cli@1.1.17 dev
+    npx @langchain/langgraph-cli dev
 }
 
 run_ensemble_service () {

supporting/RAGAS_Service/README.md

@@ -2,32 +2,14 @@
 
 Made using a dataset of 1000 labeled claims from MVP pipeline.
 
-# Roberta model 
+Roberta model trained on an augmented dataset with LLM generated adversarial examples for low frequency labels.
 
-Trained on an augmented dataset with LLM generated adversarial examples for low frequency labels.
+Flan model trained using raw labelled claims, inherrent natural language ability allows for pattern recognition without the need for fake data.
 
-![ROBERTA Diagram](/literature/classifierROBERTA.png)
+Regression model trained using the roberta dataset.
 
-# Flan model 
-
-Trained using raw labelled claims, inherrent natural language ability allows for pattern recognition without the need for fake data.
-
-![FLAN Diagram](/literature/classifierFLAN.png)
-
-# NN Model
-
-Regression model trained like roberta.
-
-![NN Diagram](/literature/classifierNN.png)
-
-
-# Ensemble 
 Used ensemble model in the final version, with the component models available on Hugging Face. 
 
-
-![Ensemble Diagram](/literature/classifierOverall.png)
-
-
 | Model                                                      | % Correct | % Valid taken forward|Used in ensemble|Link
 |------------------------------------------------------------|-----------|----------------------|----------------|-
 | Original                                                   | 53.22     | 61.72                |

supporting/RAGAS_Service/ensemble_service.py

@@ -92,7 +92,7 @@ LABEL_TO_INT = {v: k for k, v in INT_TO_LABEL.items()}
 flan_tokenizer = AutoTokenizer.from_pretrained(FLAN_PATH)
 flan_model = AutoModelForSeq2SeqLM.from_pretrained(FLAN_PATH)
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+device = torch.device("cpu")
 flan_model.to(device)
 flan_model.eval()
 

supporting/Wrapper/run.ts

@@ -17,10 +17,7 @@ const AGENT_NAME = process.env.AGENT ?? "agent";
  */
 const MODE = process.env.MODE ?? "claim";
 
-const MAX_CONCURRENCY = 5;
-
-const OFFSET = parseInt(process.env.OFFSET ?? "0", 10);
-const LIMIT = process.env.LIMIT ? parseInt(process.env.LIMIT, 10) : null;
+const MAX_CONCURRENCY = 1;
 
 const client = new Client({ apiUrl: API_URL });
 
@@ -167,19 +164,10 @@ async function processRecord(record: any): Promise<ResultRecord> {
 async function main() {
   console.log("Reading input file...");
 
-  const allRecords = await loadInputs();
+  const records = await loadInputs();
 
-  console.log(`Loaded ${allRecords.length} records`);
+  console.log(`Loaded ${records.length} records`);
 
-  const records = allRecords.slice(
-    OFFSET,
-    LIMIT !== null ? OFFSET + LIMIT : undefined
-  );
-
-  console.log(
-    `Processing ${records.length} records (offset=${OFFSET}, limit=${LIMIT ?? "∞"})`
-  );
-  
   fs.writeFileSync(OUTPUT_FILE, "", { flag: "a" });
 
   const limit = pLimit(MAX_CONCURRENCY);

supporting/checker/checker.py

@@ -1,119 +0,0 @@
-import json
-import argparse
-from urllib.parse import urlparse
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.common.exceptions import WebDriverException, TimeoutException, StaleElementReferenceException
-from tqdm import tqdm
-
-def init_driver():
-    options = Options()
-    options.headless = True
-    options.add_argument("--disable-gpu")
-    options.add_argument("--no-sandbox")
-    options.add_argument("--headless")
-    options.add_argument("--disable-blink-features=AutomationControlled")
-    options.add_argument("--window-size=1920,1080")
-    prefs = {
-        "profile.managed_default_content_settings.images": 2,  # block images
-        "profile.default_content_setting_values.stylesheets": 2,  # block CSS
-        "profile.managed_default_content_settings.cookies": 2,  # optional
-    }
-    options.add_experimental_option("prefs", prefs)
-
-    driver = webdriver.Chrome(options=options)
-    driver.set_page_load_timeout(30)
-    return driver
-
-def is_root_url(url):
-    parsed = urlparse(url)
-    return parsed.path in ("", "/")
-
-def is_404_page(driver):
-    """Safely check for 404, handling stale elements."""
-    try:
-        title = driver.title.lower()
-        body_text = driver.find_element("tag name", "body").text.lower()
-        return "404" in title or "404" in body_text
-    except StaleElementReferenceException:
-        return False
-    except Exception:
-        return False
-
-def check_url_selenium(url):
-    driver = None
-    try:
-        driver = init_driver()
-        driver.get(url)
-        # 404 check
-        if is_404_page(driver):
-            return False, "404 page detected"
-        # Root URL after redirects
-        final_url = driver.current_url
-        if is_root_url(final_url):
-            return False, f"Redirected to root URL ({final_url})"
-        return True, None
-    except (WebDriverException, TimeoutException) as e:
-        return False, str(e)
-    finally:
-        if driver:
-            driver.quit()
-
-def process_event(event):
-    """Process an event only if score > 0.4."""
-    score = event.get("score", 0)
-    if score <= 0.4:
-        return None, False, "Score too low"
-    url = event.get("Url")
-    if not url:
-        return None, False, "No URL"
-    is_valid, error_msg = check_url_selenium(url)
-    event["url_valid"] = is_valid
-    return url, is_valid, error_msg
-
-def process_jsonl_file(file_path, max_workers=4):
-    invalid_urls = []
-    valid_urls = 0
-
-    # Gather events with score > 0.4
-    urls_to_check = []
-    with open(file_path, "r", encoding="utf-8") as f:
-        for line in f:
-            line_data = json.loads(line)
-            if line_data.get("status") != "success":
-                continue
-            for event in line_data.get("events", []):
-                if event.get("score", 0) > 0.4:
-                    urls_to_check.append(event)
-
-    total_urls = len(urls_to_check)
-
-    # ThreadPoolExecutor with tqdm progress bar
-    with ThreadPoolExecutor(max_workers=max_workers) as executor:
-        future_to_event = {executor.submit(process_event, e): e for e in urls_to_check}
-        for future in tqdm(as_completed(future_to_event), total=total_urls, desc="Checking URLs"):
-            url, is_valid, error_msg = future.result()
-            if not is_valid and url:
-                invalid_urls.append((url, error_msg))
-            else:
-                valid_urls += 1
-
-    # Summary
-    if invalid_urls:
-        print("\nList of invalid URLs and reasons:")
-        for url, err in invalid_urls:
-            print(f"{url} --> {err}")
-    print("\n=== URL Validation Summary ===")
-    print(f"Total URLs processed: {total_urls}")
-    print(f"Valid URLs (loaded successfully): {valid_urls}")
-    print(f"Invalid URLs: {len(invalid_urls)}")
-    
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Validate URLs in JSONL file events using Selenium")
-    parser.add_argument("file_path", type=str, help="Path to the JSONL file")
-    parser.add_argument("--workers", type=int, default=4, help="Number of parallel Selenium workers")
-    args = parser.parse_args()
-
-    process_jsonl_file(args.file_path, max_workers=args.workers)

supporting/dbkf/fetch.py

@@ -27,7 +27,7 @@ DEFAULT_PARAMS = [
     ("organization", "http://weverify.eu/resource/Organization/3727f7b2aa90ec0716693e5464b28d18"), # StopFake
 ]
 
-NUM_RANDOM_CLAIMS = 2000
+NUM_RANDOM_CLAIMS = 200
 
 INPUT_FILE = "../../data/input.jsonl"
 OUTPUT_FILE = "../../data/claims.json"