536 lines
20 KiB
Python
536 lines
20 KiB
Python
import datetime
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import itertools
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import json
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import logging
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import os
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import re
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import string
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from typing import Any
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from schema import Optional, Or, Schema
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from tqdm import tqdm
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from embedchain.models.data_type import DataType
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logger = logging.getLogger(__name__)
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def parse_content(content, type):
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implemented = ["html.parser", "lxml", "lxml-xml", "xml", "html5lib"]
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if type not in implemented:
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raise ValueError(f"Parser type {type} not implemented. Please choose one of {implemented}")
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from bs4 import BeautifulSoup
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soup = BeautifulSoup(content, type)
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original_size = len(str(soup.get_text()))
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tags_to_exclude = [
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"nav",
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"aside",
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"form",
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"header",
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"noscript",
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"svg",
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"canvas",
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"footer",
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"script",
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"style",
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]
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for tag in soup(tags_to_exclude):
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tag.decompose()
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ids_to_exclude = ["sidebar", "main-navigation", "menu-main-menu"]
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for id in ids_to_exclude:
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tags = soup.find_all(id=id)
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for tag in tags:
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tag.decompose()
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classes_to_exclude = [
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"elementor-location-header",
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"navbar-header",
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"nav",
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"header-sidebar-wrapper",
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"blog-sidebar-wrapper",
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"related-posts",
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]
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for class_name in classes_to_exclude:
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tags = soup.find_all(class_=class_name)
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for tag in tags:
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tag.decompose()
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content = soup.get_text()
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content = clean_string(content)
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cleaned_size = len(content)
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if original_size != 0:
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logger.info(
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f"Cleaned page size: {cleaned_size} characters, down from {original_size} (shrunk: {original_size-cleaned_size} chars, {round((1-(cleaned_size/original_size)) * 100, 2)}%)" # noqa:E501
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)
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return content
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def clean_string(text):
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"""
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This function takes in a string and performs a series of text cleaning operations.
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Args:
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text (str): The text to be cleaned. This is expected to be a string.
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Returns:
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cleaned_text (str): The cleaned text after all the cleaning operations
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have been performed.
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"""
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# Stripping and reducing multiple spaces to single:
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cleaned_text = re.sub(r"\s+", " ", text.strip())
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# Removing backslashes:
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cleaned_text = cleaned_text.replace("\\", "")
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# Replacing hash characters:
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cleaned_text = cleaned_text.replace("#", " ")
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# Eliminating consecutive non-alphanumeric characters:
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# This regex identifies consecutive non-alphanumeric characters (i.e., not
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# a word character [a-zA-Z0-9_] and not a whitespace) in the string
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# and replaces each group of such characters with a single occurrence of
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# that character.
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# For example, "!!! hello !!!" would become "! hello !".
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cleaned_text = re.sub(r"([^\w\s])\1*", r"\1", cleaned_text)
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return cleaned_text
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def is_readable(s):
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"""
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Heuristic to determine if a string is "readable" (mostly contains printable characters and forms meaningful words)
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:param s: string
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:return: True if the string is more than 95% printable.
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"""
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len_s = len(s)
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if len_s == 0:
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return False
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printable_chars = set(string.printable)
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printable_ratio = sum(c in printable_chars for c in s) / len_s
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return printable_ratio > 0.95 # 95% of characters are printable
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def use_pysqlite3():
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"""
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Swap std-lib sqlite3 with pysqlite3.
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"""
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import platform
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import sqlite3
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if platform.system() == "Linux" and sqlite3.sqlite_version_info < (3, 35, 0):
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try:
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# According to the Chroma team, this patch only works on Linux
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import datetime
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import subprocess
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import sys
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subprocess.check_call(
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[sys.executable, "-m", "pip", "install", "pysqlite3-binary", "--quiet", "--disable-pip-version-check"]
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)
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__import__("pysqlite3")
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sys.modules["sqlite3"] = sys.modules.pop("pysqlite3")
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# Let the user know what happened.
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current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S,%f")[:-3]
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print(
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f"{current_time} [embedchain] [INFO]",
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"Swapped std-lib sqlite3 with pysqlite3 for ChromaDb compatibility.",
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f"Your original version was {sqlite3.sqlite_version}.",
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)
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except Exception as e:
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# Escape all exceptions
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current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S,%f")[:-3]
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print(
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f"{current_time} [embedchain] [ERROR]",
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"Failed to swap std-lib sqlite3 with pysqlite3 for ChromaDb compatibility.",
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"Error:",
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e,
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)
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def format_source(source: str, limit: int = 20) -> str:
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"""
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Format a string to only take the first x and last x letters.
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This makes it easier to display a URL, keeping familiarity while ensuring a consistent length.
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If the string is too short, it is not sliced.
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"""
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if len(source) > 2 * limit:
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return source[:limit] + "..." + source[-limit:]
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return source
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def detect_datatype(source: Any) -> DataType:
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"""
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Automatically detect the datatype of the given source.
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:param source: the source to base the detection on
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:return: data_type string
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"""
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from urllib.parse import urlparse
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import requests
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import yaml
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def is_openapi_yaml(yaml_content):
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# currently the following two fields are required in openapi spec yaml config
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return "openapi" in yaml_content and "info" in yaml_content
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def is_google_drive_folder(url):
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# checks if url is a Google Drive folder url against a regex
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regex = r"^drive\.google\.com\/drive\/(?:u\/\d+\/)folders\/([a-zA-Z0-9_-]+)$"
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return re.match(regex, url)
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try:
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if not isinstance(source, str):
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raise ValueError("Source is not a string and thus cannot be a URL.")
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url = urlparse(source)
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# Check if both scheme and netloc are present. Local file system URIs are acceptable too.
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if not all([url.scheme, url.netloc]) and url.scheme != "file":
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raise ValueError("Not a valid URL.")
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except ValueError:
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url = False
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formatted_source = format_source(str(source), 30)
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if url:
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YOUTUBE_ALLOWED_NETLOCKS = {
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"www.youtube.com",
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"m.youtube.com",
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"youtu.be",
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"youtube.com",
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"vid.plus",
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"www.youtube-nocookie.com",
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}
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if url.netloc in YOUTUBE_ALLOWED_NETLOCKS:
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logger.debug(f"Source of `{formatted_source}` detected as `youtube_video`.")
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return DataType.YOUTUBE_VIDEO
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if url.netloc in {"notion.so", "notion.site"}:
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logger.debug(f"Source of `{formatted_source}` detected as `notion`.")
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return DataType.NOTION
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if url.path.endswith(".pdf"):
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logger.debug(f"Source of `{formatted_source}` detected as `pdf_file`.")
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return DataType.PDF_FILE
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if url.path.endswith(".xml"):
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logger.debug(f"Source of `{formatted_source}` detected as `sitemap`.")
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return DataType.SITEMAP
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if url.path.endswith(".csv"):
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logger.debug(f"Source of `{formatted_source}` detected as `csv`.")
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return DataType.CSV
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if url.path.endswith(".mdx") or url.path.endswith(".md"):
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logger.debug(f"Source of `{formatted_source}` detected as `mdx`.")
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return DataType.MDX
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if url.path.endswith(".docx"):
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logger.debug(f"Source of `{formatted_source}` detected as `docx`.")
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return DataType.DOCX
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if url.path.endswith(
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(".mp3", ".mp4", ".mp2", ".aac", ".wav", ".flac", ".pcm", ".m4a", ".ogg", ".opus", ".webm")
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):
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logger.debug(f"Source of `{formatted_source}` detected as `audio`.")
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return DataType.AUDIO
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if url.path.endswith(".yaml"):
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try:
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response = requests.get(source)
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response.raise_for_status()
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try:
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yaml_content = yaml.safe_load(response.text)
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except yaml.YAMLError as exc:
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logger.error(f"Error parsing YAML: {exc}")
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raise TypeError(f"Not a valid data type. Error loading YAML: {exc}")
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if is_openapi_yaml(yaml_content):
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logger.debug(f"Source of `{formatted_source}` detected as `openapi`.")
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return DataType.OPENAPI
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else:
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logger.error(
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f"Source of `{formatted_source}` does not contain all the required \
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fields of OpenAPI yaml. Check 'https://spec.openapis.org/oas/v3.1.0'"
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)
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raise TypeError(
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"Not a valid data type. Check 'https://spec.openapis.org/oas/v3.1.0', \
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make sure you have all the required fields in YAML config data"
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)
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except requests.exceptions.RequestException as e:
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logger.error(f"Error fetching URL {formatted_source}: {e}")
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if url.path.endswith(".json"):
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logger.debug(f"Source of `{formatted_source}` detected as `json_file`.")
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return DataType.JSON
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if "docs" in url.netloc or ("docs" in url.path and url.scheme != "file"):
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# `docs_site` detection via path is not accepted for local filesystem URIs,
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# because that would mean all paths that contain `docs` are now doc sites, which is too aggressive.
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logger.debug(f"Source of `{formatted_source}` detected as `docs_site`.")
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return DataType.DOCS_SITE
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if "github.com" in url.netloc:
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logger.debug(f"Source of `{formatted_source}` detected as `github`.")
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return DataType.GITHUB
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if is_google_drive_folder(url.netloc + url.path):
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logger.debug(f"Source of `{formatted_source}` detected as `google drive folder`.")
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return DataType.GOOGLE_DRIVE_FOLDER
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# If none of the above conditions are met, it's a general web page
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logger.debug(f"Source of `{formatted_source}` detected as `web_page`.")
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return DataType.WEB_PAGE
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elif not isinstance(source, str):
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# For datatypes where source is not a string.
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if isinstance(source, tuple) and len(source) == 2 and isinstance(source[0], str) and isinstance(source[1], str):
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logger.debug(f"Source of `{formatted_source}` detected as `qna_pair`.")
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return DataType.QNA_PAIR
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# Raise an error if it isn't a string and also not a valid non-string type (one of the previous).
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# We could stringify it, but it is better to raise an error and let the user decide how they want to do that.
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raise TypeError(
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"Source is not a string and a valid non-string type could not be detected. If you want to embed it, please stringify it, for instance by using `str(source)` or `(', ').join(source)`." # noqa: E501
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)
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elif os.path.isfile(source):
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# For datatypes that support conventional file references.
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# Note: checking for string is not necessary anymore.
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if source.endswith(".docx"):
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logger.debug(f"Source of `{formatted_source}` detected as `docx`.")
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return DataType.DOCX
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if source.endswith(".csv"):
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logger.debug(f"Source of `{formatted_source}` detected as `csv`.")
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return DataType.CSV
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if source.endswith(".xml"):
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logger.debug(f"Source of `{formatted_source}` detected as `xml`.")
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return DataType.XML
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if source.endswith(".mdx") or source.endswith(".md"):
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logger.debug(f"Source of `{formatted_source}` detected as `mdx`.")
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return DataType.MDX
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if source.endswith(".txt"):
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logger.debug(f"Source of `{formatted_source}` detected as `text`.")
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return DataType.TEXT_FILE
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if source.endswith(".pdf"):
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logger.debug(f"Source of `{formatted_source}` detected as `pdf_file`.")
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return DataType.PDF_FILE
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if source.endswith(".yaml"):
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with open(source, "r") as file:
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yaml_content = yaml.safe_load(file)
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if is_openapi_yaml(yaml_content):
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logger.debug(f"Source of `{formatted_source}` detected as `openapi`.")
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return DataType.OPENAPI
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else:
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logger.error(
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f"Source of `{formatted_source}` does not contain all the required \
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fields of OpenAPI yaml. Check 'https://spec.openapis.org/oas/v3.1.0'"
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)
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raise ValueError(
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"Invalid YAML data. Check 'https://spec.openapis.org/oas/v3.1.0', \
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make sure to add all the required params"
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)
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if source.endswith(".json"):
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logger.debug(f"Source of `{formatted_source}` detected as `json`.")
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return DataType.JSON
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if os.path.exists(source) and is_readable(open(source).read()):
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logger.debug(f"Source of `{formatted_source}` detected as `text_file`.")
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return DataType.TEXT_FILE
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# If the source is a valid file, that's not detectable as a type, an error is raised.
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# It does not fall back to text.
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raise ValueError(
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"Source points to a valid file, but based on the filename, no `data_type` can be detected. Please be aware, that not all data_types allow conventional file references, some require the use of the `file URI scheme`. Please refer to the embedchain documentation (https://docs.embedchain.ai/advanced/data_types#remote-data-types)." # noqa: E501
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)
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else:
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# Source is not a URL.
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# TODO: check if source is gmail query
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# check if the source is valid json string
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if is_valid_json_string(source):
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logger.debug(f"Source of `{formatted_source}` detected as `json`.")
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return DataType.JSON
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# Use text as final fallback.
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logger.debug(f"Source of `{formatted_source}` detected as `text`.")
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return DataType.TEXT
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# check if the source is valid json string
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def is_valid_json_string(source: str):
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try:
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_ = json.loads(source)
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return True
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except json.JSONDecodeError:
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return False
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def validate_config(config_data):
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schema = Schema(
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{
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Optional("app"): {
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Optional("config"): {
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Optional("id"): str,
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Optional("name"): str,
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Optional("log_level"): Or("DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"),
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Optional("collect_metrics"): bool,
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Optional("collection_name"): str,
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}
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},
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Optional("llm"): {
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Optional("provider"): Or(
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"openai",
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"azure_openai",
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"anthropic",
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"huggingface",
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"cohere",
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"together",
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"gpt4all",
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"ollama",
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"jina",
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"llama2",
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"vertexai",
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"google",
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"aws_bedrock",
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"mistralai",
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"clarifai",
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"vllm",
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"groq",
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"nvidia",
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),
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Optional("config"): {
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Optional("model"): str,
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Optional("model_name"): str,
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Optional("number_documents"): int,
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Optional("temperature"): float,
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Optional("max_tokens"): int,
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Optional("top_p"): Or(float, int),
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Optional("stream"): bool,
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Optional("online"): bool,
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Optional("template"): str,
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Optional("prompt"): str,
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Optional("system_prompt"): str,
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Optional("deployment_name"): str,
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Optional("where"): dict,
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Optional("query_type"): str,
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Optional("api_key"): str,
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Optional("base_url"): str,
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Optional("endpoint"): str,
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Optional("model_kwargs"): dict,
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Optional("local"): bool,
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Optional("base_url"): str,
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Optional("default_headers"): dict,
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Optional("api_version"): Or(str, datetime.date),
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},
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},
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Optional("vectordb"): {
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Optional("provider"): Or(
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"chroma", "elasticsearch", "opensearch", "pinecone", "qdrant", "weaviate", "zilliz"
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),
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Optional("config"): object, # TODO: add particular config schema for each provider
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},
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Optional("embedder"): {
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Optional("provider"): Or(
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"openai",
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"gpt4all",
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"huggingface",
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"vertexai",
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"azure_openai",
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"google",
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"mistralai",
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"clarifai",
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"nvidia",
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"ollama",
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"cohere",
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),
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Optional("config"): {
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Optional("model"): Optional(str),
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Optional("deployment_name"): Optional(str),
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Optional("api_key"): str,
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Optional("api_base"): str,
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Optional("title"): str,
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Optional("task_type"): str,
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Optional("vector_dimension"): int,
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Optional("base_url"): str,
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Optional("endpoint"): str,
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},
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},
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Optional("embedding_model"): {
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Optional("provider"): Or(
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"openai",
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"gpt4all",
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"huggingface",
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"vertexai",
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"azure_openai",
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"google",
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"mistralai",
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"clarifai",
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"nvidia",
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"ollama",
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),
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Optional("config"): {
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Optional("model"): str,
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Optional("deployment_name"): str,
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Optional("api_key"): str,
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Optional("title"): str,
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Optional("task_type"): str,
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Optional("vector_dimension"): int,
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Optional("base_url"): str,
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},
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},
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Optional("chunker"): {
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Optional("chunk_size"): int,
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Optional("chunk_overlap"): int,
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|
Optional("length_function"): str,
|
|
Optional("min_chunk_size"): int,
|
|
},
|
|
Optional("cache"): {
|
|
Optional("similarity_evaluation"): {
|
|
Optional("strategy"): Or("distance", "exact"),
|
|
Optional("max_distance"): float,
|
|
Optional("positive"): bool,
|
|
},
|
|
Optional("config"): {
|
|
Optional("similarity_threshold"): float,
|
|
Optional("auto_flush"): int,
|
|
},
|
|
},
|
|
}
|
|
)
|
|
|
|
return schema.validate(config_data)
|
|
|
|
|
|
def chunks(iterable, batch_size=100, desc="Processing chunks"):
|
|
"""A helper function to break an iterable into chunks of size batch_size."""
|
|
it = iter(iterable)
|
|
total_size = len(iterable)
|
|
|
|
with tqdm(total=total_size, desc=desc, unit="batch") as pbar:
|
|
chunk = tuple(itertools.islice(it, batch_size))
|
|
while chunk:
|
|
yield chunk
|
|
pbar.update(len(chunk))
|
|
chunk = tuple(itertools.islice(it, batch_size))
|