chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,409 @@
|
||||
"""
|
||||
FetchNode Module
|
||||
"""
|
||||
|
||||
import json
|
||||
from typing import List, Optional
|
||||
import concurrent.futures
|
||||
|
||||
import requests
|
||||
from langchain_core.documents import Document
|
||||
from langchain_openai import AzureChatOpenAI, ChatOpenAI
|
||||
|
||||
from ..docloaders import ChromiumLoader
|
||||
from ..utils.cleanup_html import cleanup_html
|
||||
from ..utils.convert_to_md import convert_to_md
|
||||
from .base_node import BaseNode
|
||||
|
||||
|
||||
class FetchNode(BaseNode):
|
||||
"""
|
||||
A node responsible for fetching the HTML content of a specified URL and updating
|
||||
the graph's state with this content. It uses ChromiumLoader to fetch
|
||||
the content from a web page asynchronously (with proxy protection).
|
||||
|
||||
This node acts as a starting point in many scraping workflows, preparing the state
|
||||
with the necessary HTML content for further processing by subsequent nodes in the graph.
|
||||
|
||||
Attributes:
|
||||
headless (bool): A flag indicating whether the browser should run in headless mode.
|
||||
verbose (bool): A flag indicating whether to print verbose output during execution.
|
||||
|
||||
Args:
|
||||
input (str): Boolean expression defining the input keys needed from the state.
|
||||
output (List[str]): List of output keys to be updated in the state.
|
||||
node_config (Optional[dict]): Additional configuration for the node.
|
||||
node_name (str): The unique identifier name for the node, defaulting to "Fetch".
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
input: str,
|
||||
output: List[str],
|
||||
node_config: Optional[dict] = None,
|
||||
node_name: str = "Fetch",
|
||||
):
|
||||
super().__init__(node_name, "node", input, output, 1, node_config)
|
||||
|
||||
self.headless = (
|
||||
True if node_config is None else node_config.get("headless", True)
|
||||
)
|
||||
self.verbose = (
|
||||
False if node_config is None else node_config.get("verbose", False)
|
||||
)
|
||||
self.use_soup = (
|
||||
False if node_config is None else node_config.get("use_soup", False)
|
||||
)
|
||||
self.loader_kwargs = (
|
||||
{} if node_config is None else node_config.get("loader_kwargs", {})
|
||||
)
|
||||
self.llm_model = {} if node_config is None else node_config.get("llm_model", {})
|
||||
self.force = False if node_config is None else node_config.get("force", False)
|
||||
self.script_creator = (
|
||||
False if node_config is None else node_config.get("script_creator", False)
|
||||
)
|
||||
self.openai_md_enabled = (
|
||||
False
|
||||
if node_config is None
|
||||
else node_config.get("openai_md_enabled", False)
|
||||
)
|
||||
|
||||
# Timeout in seconds for blocking operations (HTTP requests, PDF parsing, etc.).
|
||||
# If set to None, no timeout will be applied.
|
||||
self.timeout = None if node_config is None else node_config.get("timeout", 30)
|
||||
|
||||
self.cut = False if node_config is None else node_config.get("cut", True)
|
||||
|
||||
self.browser_base = (
|
||||
None if node_config is None else node_config.get("browser_base", None)
|
||||
)
|
||||
|
||||
self.scrape_do = (
|
||||
None if node_config is None else node_config.get("scrape_do", None)
|
||||
)
|
||||
|
||||
self.plasmate = (
|
||||
None if node_config is None else node_config.get("plasmate", None)
|
||||
)
|
||||
|
||||
self.storage_state = (
|
||||
None if node_config is None else node_config.get("storage_state", None)
|
||||
)
|
||||
|
||||
def execute(self, state):
|
||||
"""
|
||||
Executes the node's logic to fetch HTML content from a specified URL and
|
||||
update the state with this content.
|
||||
"""
|
||||
self.logger.info(f"--- Executing {self.node_name} Node ---")
|
||||
|
||||
input_keys = self.get_input_keys(state)
|
||||
input_data = [state[key] for key in input_keys]
|
||||
|
||||
source = input_data[0]
|
||||
input_type = input_keys[0]
|
||||
|
||||
handlers = {
|
||||
"json_dir": self.handle_directory,
|
||||
"xml_dir": self.handle_directory,
|
||||
"csv_dir": self.handle_directory,
|
||||
"pdf_dir": self.handle_directory,
|
||||
"md_dir": self.handle_directory,
|
||||
"pdf": self.handle_file,
|
||||
"csv": self.handle_file,
|
||||
"json": self.handle_file,
|
||||
"xml": self.handle_file,
|
||||
"md": self.handle_file,
|
||||
}
|
||||
|
||||
if input_type in handlers:
|
||||
return handlers[input_type](state, input_type, source)
|
||||
elif input_type == "local_dir":
|
||||
return self.handle_local_source(state, source)
|
||||
elif input_type == "url":
|
||||
return self.handle_web_source(state, source)
|
||||
else:
|
||||
raise ValueError(f"Invalid input type: {input_type}")
|
||||
|
||||
def handle_directory(self, state, input_type, source):
|
||||
"""
|
||||
Handles the directory by compressing the source document and updating the state.
|
||||
|
||||
Parameters:
|
||||
state (dict): The current state of the graph.
|
||||
input_type (str): The type of input being processed.
|
||||
source (str): The source document to be compressed.
|
||||
|
||||
Returns:
|
||||
dict: The updated state with the compressed document.
|
||||
"""
|
||||
|
||||
compressed_document = [source]
|
||||
state.update({self.output[0]: compressed_document})
|
||||
return state
|
||||
|
||||
def handle_file(self, state, input_type, source):
|
||||
"""
|
||||
Loads the content of a file based on its input type.
|
||||
|
||||
Parameters:
|
||||
state (dict): The current state of the graph.
|
||||
input_type (str): The type of the input file (e.g., "pdf", "csv", "json", "xml", "md").
|
||||
source (str): The path to the source file.
|
||||
|
||||
Returns:
|
||||
dict: The updated state with the compressed document.
|
||||
|
||||
The function supports the following input types:
|
||||
- "pdf": Uses PyPDFLoader to load the content of a PDF file.
|
||||
- "csv": Reads the content of a CSV file using pandas and converts it to a string.
|
||||
- "json": Loads the content of a JSON file.
|
||||
- "xml": Reads the content of an XML file as a string.
|
||||
- "md": Reads the content of a Markdown file as a string.
|
||||
"""
|
||||
|
||||
compressed_document = self.load_file_content(source, input_type)
|
||||
|
||||
# return self.update_state(state, compressed_document)
|
||||
state.update({self.output[0]: compressed_document})
|
||||
return state
|
||||
|
||||
def load_file_content(self, source, input_type):
|
||||
"""
|
||||
Loads the content of a file based on its input type.
|
||||
|
||||
Parameters:
|
||||
source (str): The path to the source file.
|
||||
input_type (str): The type of the input file (e.g., "pdf", "csv", "json", "xml", "md").
|
||||
|
||||
Returns:
|
||||
list: A list containing a Document object with the loaded content and metadata.
|
||||
"""
|
||||
|
||||
if input_type == "pdf":
|
||||
from langchain_community.document_loaders import PyPDFLoader
|
||||
loader = PyPDFLoader(source)
|
||||
# PyPDFLoader.load() can be blocking for large PDFs. Run it in a thread and
|
||||
# enforce the configured timeout if provided.
|
||||
if self.timeout is None:
|
||||
return loader.load()
|
||||
else:
|
||||
with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
|
||||
future = executor.submit(loader.load)
|
||||
try:
|
||||
return future.result(timeout=self.timeout)
|
||||
except concurrent.futures.TimeoutError:
|
||||
raise TimeoutError(
|
||||
f"PDF parsing exceeded timeout of {self.timeout} seconds"
|
||||
)
|
||||
elif input_type == "csv":
|
||||
try:
|
||||
import pandas as pd
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"pandas is not installed. Please install it using `pip install pandas`."
|
||||
)
|
||||
return [
|
||||
Document(
|
||||
page_content=str(pd.read_csv(source)), metadata={"source": "csv"}
|
||||
)
|
||||
]
|
||||
elif input_type == "json":
|
||||
with open(source, encoding="utf-8") as f:
|
||||
return [
|
||||
Document(
|
||||
page_content=str(json.load(f)), metadata={"source": "json"}
|
||||
)
|
||||
]
|
||||
elif input_type == "xml" or input_type == "md":
|
||||
with open(source, "r", encoding="utf-8") as f:
|
||||
data = f.read()
|
||||
return [Document(page_content=data, metadata={"source": input_type})]
|
||||
|
||||
def handle_local_source(self, state, source):
|
||||
"""
|
||||
Handles the local source by fetching HTML content, optionally converting it to Markdown,
|
||||
and updating the state.
|
||||
|
||||
Parameters:
|
||||
state (dict): The current state of the graph.
|
||||
source (str): The HTML content from the local source.
|
||||
|
||||
Returns:
|
||||
dict: The updated state with the processed content.
|
||||
|
||||
Raises:
|
||||
ValueError: If the source is empty or contains only whitespace.
|
||||
"""
|
||||
|
||||
self.logger.info(f"--- (Fetching HTML from: {source}) ---")
|
||||
if not source.strip():
|
||||
raise ValueError("No HTML body content found in the local source.")
|
||||
|
||||
parsed_content = source
|
||||
|
||||
if (
|
||||
(
|
||||
isinstance(self.llm_model, ChatOpenAI)
|
||||
or isinstance(self.llm_model, AzureChatOpenAI)
|
||||
)
|
||||
and not self.script_creator
|
||||
or self.force
|
||||
and not self.script_creator
|
||||
):
|
||||
parsed_content = convert_to_md(source)
|
||||
else:
|
||||
parsed_content = source
|
||||
|
||||
compressed_document = [
|
||||
Document(page_content=parsed_content, metadata={"source": "local_dir"})
|
||||
]
|
||||
|
||||
# return self.update_state(state, compressed_document)
|
||||
state.update({self.output[0]: compressed_document})
|
||||
return state
|
||||
|
||||
def handle_web_source(self, state, source):
|
||||
"""
|
||||
Handles the web source by fetching HTML content from a URL,
|
||||
optionally converting it to Markdown, and updating the state.
|
||||
|
||||
Parameters:
|
||||
state (dict): The current state of the graph.
|
||||
source (str): The URL of the web source to fetch HTML content from.
|
||||
|
||||
Returns:
|
||||
dict: The updated state with the processed content.
|
||||
|
||||
Raises:
|
||||
ValueError: If the fetched HTML content is empty or contains only whitespace.
|
||||
"""
|
||||
|
||||
self.logger.info(f"--- (Fetching HTML from: {source}) ---")
|
||||
if self.use_soup:
|
||||
# Apply configured timeout to blocking HTTP requests. If timeout is None,
|
||||
# don't pass the timeout argument (requests will block until completion).
|
||||
if self.timeout is None:
|
||||
response = requests.get(source)
|
||||
else:
|
||||
response = requests.get(source, timeout=self.timeout)
|
||||
if response.status_code == 200:
|
||||
if not response.text.strip():
|
||||
raise ValueError("No HTML body content found in the response.")
|
||||
|
||||
if not self.cut:
|
||||
parsed_content = cleanup_html(response, source)
|
||||
|
||||
if (
|
||||
isinstance(self.llm_model, (ChatOpenAI, AzureChatOpenAI))
|
||||
and not self.script_creator
|
||||
or (self.force and not self.script_creator)
|
||||
):
|
||||
parsed_content = convert_to_md(source, parsed_content)
|
||||
|
||||
compressed_document = [Document(page_content=parsed_content)]
|
||||
else:
|
||||
self.logger.warning(
|
||||
f"Failed to retrieve contents from the webpage at url: {source}"
|
||||
)
|
||||
else:
|
||||
loader_kwargs = {}
|
||||
|
||||
if self.node_config:
|
||||
loader_kwargs = self.node_config.get("loader_kwargs", {})
|
||||
|
||||
# If a global timeout is configured on the node and no loader-specific timeout
|
||||
# was provided, propagate it to ChromiumLoader so it can apply the same limit.
|
||||
if "timeout" not in loader_kwargs and self.timeout is not None:
|
||||
loader_kwargs["timeout"] = self.timeout
|
||||
|
||||
if self.browser_base:
|
||||
try:
|
||||
from ..docloaders.browser_base import browser_base_fetch
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"""The browserbase module is not installed.
|
||||
Please install it using `pip install browserbase`."""
|
||||
)
|
||||
|
||||
data = browser_base_fetch(
|
||||
self.browser_base.get("api_key"),
|
||||
self.browser_base.get("project_id"),
|
||||
[source],
|
||||
)
|
||||
|
||||
document = [
|
||||
Document(page_content=content, metadata={"source": source})
|
||||
for content in data
|
||||
]
|
||||
elif self.scrape_do:
|
||||
from ..docloaders.scrape_do import scrape_do_fetch
|
||||
|
||||
if (
|
||||
(self.scrape_do.get("use_proxy") is None)
|
||||
or self.scrape_do.get("geoCode") is None
|
||||
or self.scrape_do.get("super_proxy") is None
|
||||
):
|
||||
data = scrape_do_fetch(self.scrape_do.get("api_key"), source)
|
||||
else:
|
||||
data = scrape_do_fetch(
|
||||
self.scrape_do.get("api_key"),
|
||||
source,
|
||||
self.scrape_do.get("use_proxy"),
|
||||
self.scrape_do.get("geoCode"),
|
||||
self.scrape_do.get("super_proxy"),
|
||||
)
|
||||
|
||||
document = [Document(page_content=data, metadata={"source": source})]
|
||||
elif self.plasmate is not None:
|
||||
from ..docloaders.plasmate import PlasmateLoader
|
||||
|
||||
plasmate_cfg = self.plasmate if isinstance(self.plasmate, dict) else {}
|
||||
loader = PlasmateLoader(
|
||||
[source],
|
||||
output_format=plasmate_cfg.get("output_format", "text"),
|
||||
timeout=plasmate_cfg.get("timeout", self.timeout or 30),
|
||||
selector=plasmate_cfg.get("selector"),
|
||||
extra_headers=plasmate_cfg.get("extra_headers", {}),
|
||||
fallback_to_chrome=plasmate_cfg.get("fallback_to_chrome", False),
|
||||
)
|
||||
document = loader.load()
|
||||
else:
|
||||
loader = ChromiumLoader(
|
||||
[source],
|
||||
headless=self.headless,
|
||||
storage_state=self.storage_state,
|
||||
**loader_kwargs,
|
||||
)
|
||||
document = loader.load()
|
||||
|
||||
if not document or not document[0].page_content.strip():
|
||||
raise ValueError(
|
||||
"""No HTML body content found in
|
||||
the document fetched by ChromiumLoader."""
|
||||
)
|
||||
|
||||
parsed_content = document[0].page_content
|
||||
|
||||
if (
|
||||
(
|
||||
isinstance(self.llm_model, ChatOpenAI)
|
||||
or isinstance(self.llm_model, AzureChatOpenAI)
|
||||
)
|
||||
and not self.script_creator
|
||||
or self.force
|
||||
and not self.script_creator
|
||||
and not self.openai_md_enabled
|
||||
):
|
||||
parsed_content = convert_to_md(document[0].page_content, parsed_content)
|
||||
|
||||
compressed_document = [
|
||||
Document(page_content=parsed_content, metadata={"source": "html file"})
|
||||
]
|
||||
state["doc"] = document
|
||||
state.update(
|
||||
{
|
||||
self.output[0]: compressed_document,
|
||||
}
|
||||
)
|
||||
return state
|
||||
Reference in New Issue
Block a user