A smart toolkit for HTML cleaning and pruning for RAG systems.
Project description
HtmlRAG Toolkit
A toolkit to apply HtmlRAG in your own RAG systems.
📦 Installation
Install the package using pip:
pip install htmlrag
Or install the package from source:
pip install -e .
📖 User Guide
🧹 HTML Cleaning
from htmlrag import clean_html
question = "When was the bellagio in las vegas built?"
html = """
<html>
<head>
<h1>Bellagio Hotel in Las</h1>
</head>
<body>
<p class="class0">The Bellagio is a luxury hotel and casino located on the Las Vegas Strip in Paradise, Nevada. It was built in 1998.</p>
</body>
<div>
<div>
<p>Some other text</p>
<p>Some other text</p>
</div>
</div>
<p class="class1"></p>
<!-- Some comment -->
<script type="text/javascript">
document.write("Hello World!");
</script>
</html>
"""
# . alternatively you can read html files and merge them
# html_files=["/path/to/html/file1.html", "/path/to/html/file2.html"]
# htmls=[open(file).read() for file in html_files]
# html = "\n".join(htmls)
simplified_html = clean_html(html)
print(simplified_html)
# <html>
# <h1>Bellagio Hotel in Las</h1>
# <p>The Bellagio is a luxury hotel and casino located on the Las Vegas Strip in Paradise, Nevada. It was built in 1998.</p>
# <div>
# <p>Some other text</p>
# <p>Some other text</p>
# </div>
# </html>
🔧 Configure Pruning Parameters
The example HTML document is rather a short one. Real-world HTML documents can be much longer and more complex. To handle such cases, we can configure the following parameters:
# Maximum number of words in a node when constructing the block tree for pruning with the embedding model
MAX_NODE_WORDS_EMBED = 10
# MAX_NODE_WORDS_EMBED = 256 # a recommended setting for real-world HTML documents
# Maximum number of tokens in the output HTML document pruned with the embedding model
MAX_CONTEXT_WINDOW_EMBED = 60
# MAX_CONTEXT_WINDOW_EMBED = 6144 # a recommended setting for real-world HTML documents
# Maximum number of words in a node when constructing the block tree for pruning with the generative model
MAX_NODE_WORDS_GEN = 5
# MAX_NODE_WORDS_GEN = 128 # a recommended setting for real-world HTML documents
# Maximum number of tokens in the output HTML document pruned with the generative model
MAX_CONTEXT_WINDOW_GEN = 32
# MAX_CONTEXT_WINDOW_GEN = 4096 # a recommended setting for real-world HTML documents
🌲 Build Block Tree
from htmlrag import build_block_tree
block_tree, simplified_html = build_block_tree(simplified_html, max_node_words=MAX_NODE_WORDS_EMBED)
# block_tree, simplified_html = build_block_tree(simplified_html, max_node_words=MAX_NODE_WORDS_GEN, zh_char=True) # for Chinese text
for block in block_tree:
print("Block Content: ", block[0])
print("Block Path: ", block[1])
print("Is Leaf: ", block[2])
print("")
# Block Content: <h1>Bellagio Hotel in Las</h1>
# Block Path: ['html', 'title']
# Is Leaf: True
#
# Block Content: <div>
# <p>Some other text</p>
# <p>Some other text</p>
# </div>
# Block Path: ['html', 'div']
# Is Leaf: True
#
# Block Content: <p>The Bellagio is a luxury hotel and casino located on the Las Vegas Strip in Paradise, Nevada. It was built in 1998.</p>
# Block Path: ['html', 'p']
# Is Leaf: True
✂️ Prune HTML Blocks with Embedding Model
from htmlrag import EmbedHTMLPruner
embed_model = "BAAI/bge-large-en"
query_instruction_for_retrieval = "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: "
embed_html_pruner = EmbedHTMLPruner(embed_model=embed_model, local_inference=True,
query_instruction_for_retrieval=query_instruction_for_retrieval)
# alternatively you can init a remote TEI model, refer to https://github.com/huggingface/text-embeddings-inference.
# tei_endpoint="http://YOUR_TEI_ENDPOINT"
# embed_html_pruner = EmbedHTMLPruner(embed_model=embed_model, local_inference=False, query_instruction_for_retrieval = query_instruction_for_retrieval, endpoint=tei_endpoint)
block_rankings = embed_html_pruner.calculate_block_rankings(question, simplified_html, block_tree)
print(block_rankings)
# [2, 0, 1]
#. alternatively you can use bm25 to rank the blocks
from htmlrag import BM25HTMLPruner
bm25_html_pruner = BM25HTMLPruner()
block_rankings = bm25_html_pruner.calculate_block_rankings(question, simplified_html, block_tree)
print(block_rankings)
# [2, 0, 1]
from transformers import AutoTokenizer
chat_tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-70B-Instruct")
pruned_html = embed_html_pruner.prune_HTML(simplified_html, block_tree, block_rankings, chat_tokenizer, MAX_CONTEXT_WINDOW_EMBED)
print(pruned_html)
# <html>
# <h1>Bellagio Hotel in Las</h1>
# <p>The Bellagio is a luxury hotel and casino located on the Las Vegas Strip in Paradise, Nevada. It was built in 1998.</p>
# </html>
✂️ Prune HTML Blocks with Generative Model
from htmlrag import GenHTMLPruner
import torch
# construct a finer block tree
block_tree, pruned_html = build_block_tree(pruned_html, max_node_words=MAX_NODE_WORDS_GEN)
# block_tree, pruned_html = build_block_tree(pruned_html, max_node_words=MAX_NODE_WORDS_GEN, zh_char=True) # for Chinese text
for block in block_tree:
print("Block Content: ", block[0])
print("Block Path: ", block[1])
print("Is Leaf: ", block[2])
print("")
# Block Content: <h1>Bellagio Hotel in Las</h1>
# Block Path: ['html', 'title']
# Is Leaf: True
#
# Block Content: <p>The Bellagio is a luxury hotel and casino located on the Las Vegas Strip in Paradise, Nevada. It was built in 1998.</p>
# Block Path: ['html', 'p']
# Is Leaf: True
ckpt_path = "zstanjj/HTML-Pruner-Phi-3.8B"
# ckpt_path = "zstanjj/HTML-Pruner-Llama-1B"
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
gen_html_pruner = GenHTMLPruner(gen_model=ckpt_path, max_node_words=MAX_NODE_WORDS_GEN, device=device)
block_rankings = gen_html_pruner.calculate_block_rankings(question, pruned_html, block_tree)
print(block_rankings)
# [1, 0]
pruned_html = gen_html_pruner.prune_HTML(pruned_html, block_tree, block_rankings, chat_tokenizer, MAX_CONTEXT_WINDOW_GEN)
print(pruned_html)
# <p>The Bellagio is a luxury hotel and casino located on the Las Vegas Strip in Paradise, Nevada. It was built in 1998.</p>
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