Integrazioni
LlamaIndex
Inserisci Thunderbit in una pipeline LlamaIndex come Reader o Tool
LlamaIndex li chiama "Reader" invece di loader, ma il pattern è identico a LangChain — Thunderbit produce Markdown pulito, LlamaIndex lo segmenta e lo indicizza.
Installazione
pip install llama-index-core httpxCome Reader
from llama_index.core import Document
import httpx
API = "https://openapi.thunderbit.com/openapi/v1"
H = {"Authorization": "Bearer YOUR_API_KEY"}
class ThunderbitReader:
def load_data(self, urls: list[str]) -> list[Document]:
job = httpx.post(f"{API}/batch/distill",
headers=H,
json={"urls": urls,
"include": ["metadata"]}).json()
# poll until COMPLETED — see Batch Job Lifecycle guide
return [
Document(text=r["markdown"],
metadata={"source": r["url"], **r.get("metadata", {})})
for r in job["data"]["results"] if r["status"] == "SUCCEEDED"
]
docs = ThunderbitReader().load_data(["https://docs.example.com"])Manda i documenti in VectorStoreIndex.from_documents(docs) come al solito.
Come Tool dell'agent (FunctionTool)
from llama_index.core.tools import FunctionTool
def read_url(url: str) -> str:
"""Fetch a URL and return clean Markdown."""
resp = httpx.post(f"{API}/distill",
headers=H,
json={"url": url, "renderMode": "basic"},
timeout=60.0)
resp.raise_for_status()
return resp.json()["data"]["markdown"]
read_tool = FunctionTool.from_defaults(fn=read_url)Correlati
Questa integrazione sarà ampliata con un pacchetto llama-index-readers-thunderbit — torna a controllare presto.