集成
LlamaIndex
把 Thunderbit 接进 LlamaIndex 流水线，当 Reader 或 Tool 用
LlamaIndex 把 loader 叫 "Reader"，但写法和 LangChain 一模一样 —— Thunderbit 输出干净的 Markdown，LlamaIndex 负责切片和建索引。
安装
pip install llama-index-core httpx当 Reader 用
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"])照常喂给 VectorStoreIndex.from_documents(docs) 即可。
当 Agent Tool 用（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)相关链接
这个集成正在打包成 llama-index-readers-thunderbit，敬请期待。