How to Add Real-Time News to Your RAG Pipeline
Most RAG pipelines have a freshness problem. Here's how to add verified, confidence-scored intelligence to your vector database so your agent always has current context.
Your RAG pipeline probably has a freshness problem. The documents in your vector database were indexed days or weeks ago. When a user asks “What happened with the Fed rate decision?” your agent retrieves stale context and hallucinates the rest.
The fix: continuously ingest verified intelligence into your vector database, with confidence scores and bias metadata on every document. Here's how.
The Problem with Static RAG
Without real-time intelligence
Agent retrieves a 3-week-old article about the Fed, hallucinates a rate decision that didn't happen.
With Polaris intelligence
Agent retrieves a verified brief from 2 hours ago with confidence score 0.94, source count 7, and counter-arguments.
Ingest Briefs into Your Vector DB
from polaris_news import PolarisClient
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
client = PolarisClient(api_key="pr_live_xxx")
embeddings = OpenAIEmbeddings()
vectorstore = Chroma(embedding_function=embeddings, persist_directory="./polaris_db")
def ingest_latest():
"""Pull latest briefs and add to vector DB with metadata."""
feed = client.feed(per_page=20)
docs = []
for brief in feed.briefs:
docs.append({
"page_content": f"{brief.headline}\n\n{brief.summary}",
"metadata": {
"source": "polaris",
"brief_id": brief.id,
"category": brief.category,
"confidence": brief.confidence,
"bias_score": brief.bias_score,
"published_at": brief.published_at,
"source_count": brief.source_count,
}
})
vectorstore.add_texts(
texts=[d["page_content"] for d in docs],
metadatas=[d["metadata"] for d in docs],
)
print(f"Ingested {len(docs)} briefs")
ingest_latest()Filter by Confidence at Retrieval
Not all sources are equal. Use the confidence score to filter at retrieval time.
# Only retrieve high-confidence documents
results = vectorstore.similarity_search(
"What happened with the Fed rate decision?",
k=5,
filter={"confidence": {"$gte": 0.7}}
)
for doc in results:
conf = doc.metadata.get("confidence", 0)
print(f"[{conf:.0%}] {doc.page_content[:100]}...")Schedule Continuous Ingestion
import schedule, time
schedule.every(10).minutes.do(ingest_latest)
while True:
schedule.run_pending()
time.sleep(1)Or use a GitHub Action, cron job, or the Polaris webhook system to push new briefs to your pipeline as they publish.