Conecta un agente de IA a datos de busqueda reales para que deje de alucinar

import os
import requests

API_KEY = os.environ["SCAVIO_API_KEY"]


def web_search(query: str) -> dict:
    r = requests.post(
        "https://api.scavio.dev/api/v1/google",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json={"query": query, "light_request": False},
        timeout=30,
    )
    r.raise_for_status()
    data = r.json()
    return {
        "organic": data.get("organic", [])[:5],
        "knowledge_graph": data.get("knowledge_graph"),
        "people_also_ask": data.get("people_also_ask", []),
    }


def reddit_search(query: str) -> dict:
    r = requests.post(
        "https://api.scavio.dev/api/v1/reddit/search",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json={"query": query},
        timeout=30,
    )
    r.raise_for_status()
    return {"posts": r.json().get("posts", [])[:5]}


SYSTEM = (
    "You are a research agent. Never state a number, date, version, or named "
    "entity unless it came back in a tool result; do not recall from memory. "
    "For any factual claim, call web_search first and narrate only the returned "
    "values, with the source URL next to each figure. If a search returns nothing "
    "useful, say you could not verify it. The search tool sees public indexed data "
    "only; it cannot read private dashboards or login-gated metrics."
)

if __name__ == "__main__":
    # The LLM calls web_search('project X github stars'); you return this JSON;
    # the model then narrates ONLY these values. Example of the grounding call:
    result = web_search("project X github stars")
    print(result["knowledge_graph"])
    print([item["link"] for item in result["organic"]])

const API_KEY = process.env.SCAVIO_API_KEY;

async function webSearch(query) {
  const res = await fetch("https://api.scavio.dev/api/v1/google", {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${API_KEY}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({ query, light_request: false }),
  });
  if (!res.ok) throw new Error(`Scavio ${res.status}`);
  const data = await res.json();
  return {
    organic: (data.organic || []).slice(0, 5),
    knowledge_graph: data.knowledge_graph,
    people_also_ask: data.people_also_ask || [],
  };
}

async function redditSearch(query) {
  const res = await fetch("https://api.scavio.dev/api/v1/reddit/search", {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${API_KEY}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({ query }),
  });
  if (!res.ok) throw new Error(`Scavio ${res.status}`);
  const data = await res.json();
  return { posts: (data.posts || []).slice(0, 5) };
}

const SYSTEM =
  "You are a research agent. Never state a number, date, version, or named " +
  "entity unless it came back in a tool result; do not recall from memory. " +
  "For any factual claim, call web_search first and narrate only the returned " +
  "values, with the source URL next to each figure. If a search returns nothing, " +
  "say you could not verify it. The search tool sees public indexed data only; " +
  "it cannot read private dashboards or login-gated metrics.";

// The LLM calls webSearch('project X github stars'); feed the JSON back so the
// model narrates ONLY these returned values:
webSearch("project X github stars").then((r) => {
  console.log(r.knowledge_graph);
  console.log(r.organic.map((o) => o.link));
});

{
  "organic": [
    {
      "title": "project X - GitHub",
      "link": "https://github.com/org/project-x",
      "snippet": "project X is an open-source ... 12.4k stars"
    }
  ],
  "knowledge_graph": {
    "title": "project X",
    "type": "Software repository"
  },
  "people_also_ask": [
    { "question": "Is project X actively maintained?" }
  ]
}