mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-08-20 14:42:28 +02:00
135 lines
4.2 KiB
Python
135 lines
4.2 KiB
Python
from contextlib import asynccontextmanager, contextmanager
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from typing import AsyncGenerator, Callable, FrozenSet, Generator, Optional, TypedDict
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import httpx
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from langchain_core.documents import Document
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from langchain_core.runnables import RunnableLambda, RunnablePassthrough
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from langchain_core.utils.html import extract_sub_links
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from langgraph.channels.context import Context
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from langgraph.channels.topic import Topic
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from langgraph.pregel import Channel, Pregel
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# Load url with sync httpx client
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@contextmanager
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def httpx_client() -> Generator[httpx.Client, None, None]:
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with httpx.HTTPTransport(retries=3) as transport, httpx.Client(
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transport=transport
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) as client:
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yield client
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class LoadUrlInput(TypedDict):
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url: str
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visited: FrozenSet[str]
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client: httpx.Client
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def load_url(input: LoadUrlInput) -> str:
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response = input["client"].get(input["url"])
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return response.text
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# Same as above but with async httpx client
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@asynccontextmanager
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async def httpx_aclient() -> AsyncGenerator[httpx.AsyncClient, None]:
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async with httpx.AsyncHTTPTransport(retries=3) as transport, httpx.AsyncClient(
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transport=transport
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) as client:
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yield client
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class LoadUrlInputAsync(TypedDict):
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url: str
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visited: FrozenSet[str]
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client: httpx.AsyncClient
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async def load_url_async(input: LoadUrlInputAsync) -> str:
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response = await input["client"].get(input["url"])
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return response.text
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# default metadata extractor copied from langchain.document_loaders
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def _metadata_extractor(raw_html: str, url: str) -> dict:
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"""Extract metadata from raw html using BeautifulSoup."""
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metadata = {"source": url}
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try:
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from bs4 import BeautifulSoup
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except ImportError:
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return metadata
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soup = BeautifulSoup(raw_html, "html.parser")
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if title := soup.find("title"):
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metadata["title"] = title.get_text()
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if description := soup.find("meta", attrs={"name": "description"}):
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metadata["description"] = description.get("content", None)
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if html := soup.find("html"):
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metadata["language"] = html.get("lang", None)
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return metadata
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def recursive_web_loader(
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*,
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max_depth: int = 2,
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extractor: Optional[Callable[[str], str]] = None,
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metadata_extractor: Optional[Callable[[str, str], dict]] = None,
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) -> Pregel:
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# assign default extractors
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extractor = extractor or (lambda x: x)
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metadata_extractor = metadata_extractor or _metadata_extractor
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# define the channels
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channels = {
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"next_urls": Topic(str, unique=True),
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"documents": Topic(Document, accumulate=True),
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"client": Context(httpx_client, httpx_aclient),
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}
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# the main chain that gets executed recursively
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# while there are urls in next_urls
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visitor = (
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# run the chain below for each url in next_urls
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# adding the current values of base_url and httpx client
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Channel.subscribe_to_each("next_urls", key="url").join(["client", "base_url"])
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# load the url (with sync and async implementations)
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| RunnablePassthrough.assign(body=RunnableLambda(load_url, load_url_async))
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| Channel.write_to(
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# send a new document to the documents stream
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documents=lambda x: Document(
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page_content=extractor(x["body"]),
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metadata=metadata_extractor(x["body"], x["url"]),
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),
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# send the next urls to the next_urls topic
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next_urls=lambda x: extract_sub_links(
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x["body"], x["url"], base_url=x["base_url"]
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),
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)
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)
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return Pregel(
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channels=channels,
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chains={
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# use the base_url as the first url to visit
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"input": Channel.subscribe_to("base_url") | Channel.write_to("next_urls"),
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# add the main chain
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"visitor": visitor,
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},
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# this will accept a string as input
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input="base_url",
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# and return a dict with documents and visited set
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output=["documents", "visited"],
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# debug logging
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debug=True,
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).with_config({"recursion_limit": max_depth + 1})
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loader = recursive_web_loader(max_depth=3)
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documents = loader.invoke("https://docs.python.org/3.9/")
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print(len(documents["documents"]))
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