Drop Polaris into any AI stack

from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate
import requests

BASE = "https://api.thepolarisreport.com/api/v1"

@tool
def search_news(query: str) -> str:
    """Search verified news intelligence from The Polaris Report.
    Returns briefs with confidence scores, bias detection, and counter-arguments."""
    res = requests.get(f"{BASE}/search", params={"q": query, "limit": 5, "min_confidence": 0.7})
    briefs = res.json().get("briefs", [])
    return "\n\n".join([
        f"[{b.get('confidence', 0):.0%} confidence] {b['headline']}\n"
        f"{b['summary']}\n"
        f"Bias: {b.get('bias_score', 'N/A')} | Counter: {b.get('counter_argument', 'N/A')}"
        for b in briefs
    ])

@tool
def news_feed(category: str) -> str:
    """Get latest briefs from a category. Options: markets, crypto, ai_ml, tech,
    politics, defense, cybersecurity, climate, energy, biotech, space, sports."""
    res = requests.get(f"{BASE}/agent-feed", params={"category": category, "limit": 5})
    briefs = res.json().get("briefs", [])
    return "\n\n".join([
        f"[{b.get('confidence', 0):.0%}] {b['headline']}: {b['summary']}"
        for b in briefs
    ])

@tool
def similar_briefs(brief_id: str) -> str:
    """Find briefs with similar content to a given brief ID."""
    res = requests.get(f"{BASE}/similar/{brief_id}", params={"limit": 5})
    briefs = res.json().get("briefs", [])
    return "\n\n".join([
        f"[{b.get('similarity', 0):.0%} similar] {b['headline']}"
        for b in briefs
    ])

@tool
def story_clusters(period: str = "24h") -> str:
    """Get story clusters — briefs grouped by topic. Period: 12h, 24h, 48h, 7d."""
    res = requests.get(f"{BASE}/clusters", params={"period": period, "limit": 10})
    clusters = res.json().get("clusters", [])
    return "\n\n".join([
        f"[{c['brief_count']} briefs] {c['topic']} — {', '.join(c.get('categories', []))}"
        for c in clusters
    ])

@tool
def search_data(entity: str, data_type: str = "") -> str:
    """Search structured data points (money, percentages, metrics) by entity."""
    params = {"entity": entity, "limit": 10}
    if data_type:
        params["type"] = data_type
    res = requests.get(f"{BASE}/data", params=params)
    points = res.json().get("data", [])
    return "\n\n".join([
        f"{d['data_point']['value']} ({d['data_point']['type']}) — {d['data_point']['context']}"
        for d in points
    ])

# Create agent
tools = [search_news, news_feed, similar_briefs, story_clusters, search_data]
llm = ChatOpenAI(model="gpt-4o", temperature=0)
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a news analyst with access to The Polaris Report intelligence feed."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])
agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

result = executor.invoke({"input": "What are the latest developments in AI regulation?"})
print(result["output"])

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