Add multi claim runner. Add dbkf fetcher for automated testing. Add visualisation tool plus human score enterer.

This commit is contained in:
William Jeynes
2026-02-13 15:15:01 +00:00
parent fa6e7017b0
commit 6d478fe7ec
9 changed files with 1238 additions and 24 deletions
+3 -2
View File
@@ -14,8 +14,6 @@ Include a concise but specific search query that can be looked up on a search en
Include a url to a source for your trigger event (not a web search, a specific url from a reputuable source). Do not use OAI cite, include url as text in response. Include a url to a source for your trigger event (not a web search, a specific url from a reputuable source). Do not use OAI cite, include url as text in response.
"The COVID-19 Pandemic","Escalation of Russia-Ukraine conflict" are too general, cannot be a trigger event
If you are referencing another disinformation campaign, provide the specific narrative used, not just sentiment, and ensure it is sufficiently different from the claim we are analysing. If you are referencing another disinformation campaign, provide the specific narrative used, not just sentiment, and ensure it is sufficiently different from the claim we are analysing.
Use a JSON format with each entry containing "Event,ReasoningWhyRelevant,SearchQuery,Url,IsItselfDisinformation". Use a JSON format with each entry containing "Event,ReasoningWhyRelevant,SearchQuery,Url,IsItselfDisinformation".
@@ -23,4 +21,7 @@ Use a JSON format with each entry containing "Event,ReasoningWhyRelevant,SearchQ
Multiple tool invocations should be requested at once, if applicable. 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. Use your abilities to look between the lines and produce some insightful analysis, thinking both short and long term.
Events will be reordered as part of processing, each statement must stand alone
Lets go through it step by step Lets go through it step by step
-22
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@@ -1,22 +0,0 @@
import { Client } from "@langchain/langgraph-sdk";
// only set the apiUrl if you changed the default port when calling langgraph dev
const client = new Client({ apiUrl: "http://localhost:2024"});
const thread = await client.threads.create();
const streamResponse = client.runs.stream(
thread["thread_id"],
"agent",
{
input: {
"messages": ["3+5" ]
},
streamMode: "messages-tuple",
}
);
for await (const chunk of streamResponse) {
console.log(`Receiving new event of type: ${chunk.event}...`);
console.log(JSON.stringify(chunk.data));
console.log("\n\n");
}
+148
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@@ -0,0 +1,148 @@
# -------- Ours --------
claims.json
results*.jsonl
# --------- Github -----------
# Logs
logs
*.log
npm-debug.log*
yarn-debug.log*
yarn-error.log*
lerna-debug.log*
# Diagnostic reports (https://nodejs.org/api/report.html)
report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json
# Runtime data
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*.seed
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lib-cov
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coverage
*.lcov
# nyc test coverage
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.grunt
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bower_components
# node-waf configuration
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# Compiled binary addons (https://nodejs.org/api/addons.html)
build/Release
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jspm_packages/
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# LangGraph API
.langgraph_api
+758
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@@ -0,0 +1,758 @@
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+22
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@@ -0,0 +1,22 @@
{
"name": "wrapper",
"version": "1.0.0",
"description": "",
"license": "ISC",
"author": "",
"type": "commonjs",
"main": "run.ts",
"scripts": {
"dev": "npx tsx run.ts"
},
"dependencies": {
"@langchain/langgraph-sdk": "^1.6.2",
"cli-progress": "^3.12.0",
"p-limit": "^7.3.0",
"tsx": "^4.21.0"
},
"devDependencies": {
"@types/cli-progress": "^3.11.6",
"@types/node": "^25.2.3"
}
}
+119
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import fs from "fs";
import path from "path";
import { Client } from "@langchain/langgraph-sdk";
import cliProgress from "cli-progress";
import pLimit from "p-limit";
const INPUT_FILE = "./claims.json";
const OUTPUT_FILE = "./results.jsonl";
const API_URL = "http://localhost:2024";
const AGENT_NAME = "agent";
const MAX_CONCURRENCY = 50;
const client = new Client({ apiUrl: API_URL });
type Claim = {
documentUrl: string;
text: string;
[key: string]: any;
};
type ResultRecord = {
documentUrl: string;
text: string;
status: "success" | "error";
output?: any;
error?: string;
};
function appendResult(record: ResultRecord) {
fs.appendFileSync(OUTPUT_FILE, JSON.stringify(record) + "\n");
}
async function processClaim(claim: Claim): Promise<ResultRecord> {
try {
const thread = await client.threads.create();
const stream = client.runs.stream(
thread.thread_id,
AGENT_NAME,
{
input: {
disinformationTitle: claim.text,
},
streamMode: "messages-tuple",
recursionLimit: 100,
}
);
let lastMessage: any = null;
for await (const chunk of stream) {
// capture latest output
if (chunk?.data) {
lastMessage = chunk.data;
}
}
return {
documentUrl: claim.documentUrl,
text: claim.text,
status: "success",
output: lastMessage,
};
} catch (err: any) {
return {
documentUrl: claim.documentUrl,
text: claim.text,
status: "error",
error: err?.message ?? String(err),
};
}
}
async function main() {
console.log("Reading input file...");
const raw = fs.readFileSync(INPUT_FILE, "utf-8");
const claims: Claim[] = JSON.parse(raw);
console.log(`Loaded ${claims.length} records`);
fs.writeFileSync(OUTPUT_FILE, "", { flag: "a" });
const limit = pLimit(MAX_CONCURRENCY);
const progressBar = new cliProgress.SingleBar(
{
format:
"Progress |{bar}| {percentage}% | {value}/{total} | ETA: {eta}s",
},
cliProgress.Presets.shades_classic
);
progressBar.start(claims.length, 0);
let completed = 0;
const tasks = claims.map((claim) =>
limit(async () => {
const result = await processClaim(claim);
appendResult(result);
completed++;
progressBar.update(completed);
})
);
await Promise.all(tasks);
progressBar.stop();
console.log("Processing complete");
}
main().catch((err) => {
console.error("Fatal error:", err);
});
+61
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@@ -0,0 +1,61 @@
import requests
import json
import random
from urllib.parse import urlencode
BASE_URL = "https://dbkf.ontotext.com/rest-api/search/documents"
# search parameters
# Ukraine: http://weverify.eu/resource/Concept/Q212
# COVID: http://weverify.eu/resource/Concept/Q84263196
DEFAULT_PARAMS = {
"concept": "http://weverify.eu/resource/Concept/Q212",
"documentTypes": "http://schema.org/Claim",
"from": "2000-01-01",
"to": "2023-10-17",
"lang": "en",
"limit": 50, # Max per page
"page": 1,
"orderBy": "date"
}
NUM_RANDOM_CLAIMS = 10
OUTPUT_FILE = "../Wrapper/claims.json"
def fetch_claims(params=None):
if params is None:
params = DEFAULT_PARAMS
query_string = urlencode(params)
url = f"{BASE_URL}?{query_string}"
print(f"Fetching data from: {url}\n")
try:
response = requests.get(url, timeout=15)
response.raise_for_status()
data = response.json()
documents = data.get("documents", [])
print(f"Fetched {len(documents)} documents.")
return documents
except Exception as e:
print(f"Error fetching data: {e}")
return []
def save_random_claims(documents, output_file, num_claims=20):
if not documents:
print("No documents to save.")
return
sample_size = min(num_claims, len(documents))
selected = random.sample(documents, sample_size)
with open(output_file, "w", encoding="utf-8") as f:
json.dump(selected, f, indent=4, ensure_ascii=False)
print(f"Saved {sample_size} random claims to {output_file}")
if __name__ == "__main__":
docs = fetch_claims()
save_random_claims(docs, OUTPUT_FILE, NUM_RANDOM_CLAIMS)
+126
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import streamlit as st
import json
import random
from pathlib import Path
# Path to your JSONL file
DATA_FILE = "../Wrapper/results.jsonl"
# --------------------------
# Helper functions
# --------------------------
def load_data(file_path):
"""Load JSONL file into a list of dicts with parsed content."""
data = []
if Path(file_path).exists():
with open(file_path, "r", encoding="utf-8") as f:
for line in f:
if line.strip():
entry = json.loads(line)
for o in entry.get("output", []):
if "content" in o:
try:
o["content_parsed"] = json.loads(o["content"])
except json.JSONDecodeError:
o["content_parsed"] = []
data.append(entry)
return data
def save_data(file_path, data):
"""Save the updated data back to JSONL file."""
with open(file_path, "w", encoding="utf-8") as f:
for entry in data:
for o in entry.get("output", []):
if "content_parsed" in o:
o["content"] = json.dumps(o["content_parsed"], ensure_ascii=False)
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
# --------------------------
# Initialize session state
# --------------------------
if "data" not in st.session_state:
st.session_state.data = load_data(DATA_FILE)
if "current_claim" not in st.session_state:
st.session_state.current_claim = None
st.title("Claim Visualizer")
# --------------------------
# Sidebar
# --------------------------
view = st.sidebar.selectbox("Choose View", ["All Claims", "Single Claim Random"])
# --------------------------
# All Claims View
# --------------------------
if view == "All Claims":
st.header("All Claims")
for entry in st.session_state.data:
st.subheader(f"{entry.get('text')}")
for o in entry.get("output", []):
for c in o.get("content_parsed", []):
st.markdown(f"**Event:** {c.get('event')}")
st.markdown(f"**Reasoning:** {c.get('reasoningWhyRelevant')}")
st.markdown(f"**Score:** {c.get('score')}")
st.markdown(f"**Human Score:** {c.get('human_score')}")
st.markdown("---")
# --------------------------
# Single Claim Random View
# --------------------------
elif view == "Single Claim Random":
# Rebuild the list of unscored claims only when needed
if st.session_state.current_claim is None:
single_claims = []
for entry in st.session_state.data:
url = entry.get("documentUrl")
text = entry.get("text")
for o in entry.get("output", []):
for c in o.get("content_parsed", []):
if "human_score" not in c or c.get("human_score") is None:
single_claims.append({
"documentUrl": url,
"text": text,
"event": c.get("event"),
"reasoningWhyRelevant": c.get("reasoningWhyRelevant"),
"raw_obj": c # reference to original object
})
if single_claims:
st.session_state.current_claim = random.choice(single_claims)
else:
st.session_state.current_claim = None
claim = st.session_state.current_claim
if claim is None:
st.info("No claims available without a human score.")
else:
st.subheader(f"{claim['text']}")
st.markdown(f"**Event:** {claim['event']}")
st.markdown(f"**Reasoning:** {claim['reasoningWhyRelevant']}")
# Input for new human score
new_score = st.number_input(
"Provide a score (0 to 1)",
min_value=0.0,
max_value=1.0,
value=0.5,
step=0.01,
format="%.2f"
)
if st.button("Submit Score"):
# Update the original object
claim["raw_obj"]["human_score"] = new_score
# Save immediately
save_data(DATA_FILE, st.session_state.data)
st.success("Score saved!")
# Clear current claim so a new one will be selected next run
st.session_state.current_claim = None
# Rerun app to show a new claim
st.rerun()
+1
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@@ -0,0 +1 @@
streamlit