Ever struggled with splitting texts on your RAG system? Meet Chonkie โ your new greatest buddy for textual content chunking that simply works!
๐ฅ Why Everybodyโs Speaking About It
- ๐ฆ Set up and go:
pip set up chonkie - ๐ป One-liner chunking that really works
- ๐โโ๏ธ Blazing quick โ course of 1000’s of docs in seconds
- ๐งฉ Excellent for LangChain, LlamaIndex, or your customized RAG
๐ ๏ธ Select Your Chunking Type:
- ๐ฏ TokenChunker
from chonkie import TokenChunker
chunks = TokenChunker(chunk_size=512).break up(textual content)
2. ๐ค WordChunker
from chonkie import WordChunker
chunks = WordChunker(words_per_chunk=100).break up(textual content)
```
3. ๐ง SemanticChunker
from chonkie import SemanticChunker
chunks = SemanticChunker(mannequin="openai").break up(textual content)
๏ฟฝ Superior Options:
- ๐ Overlap management for higher context
- ๐ Versatile chunk sizing
- ๐จ Customized tokenizer help
- ๐ Metadata preservation
๐ก Actual-World Efficiency:
- ๐ 1M tokens โ 60 seconds
- ๐ฏ 99.9% chunking accuracy
- ๐พ Minimal reminiscence footprint
- ๐ CPU-friendly processing
๐ฎ Fast Begin:
# The best technique to chunk
from chonkie import SentenceChunker
chunker = SentenceChunker()
chunks = chunker.break up("Your lengthy textual content right here")
# Every chunk maintains context
for chunk in chunks:
print(f"Chunk measurement: {len(chunk)}")
๐ฎ Coming Quickly:
- ๐ฑ Cell optimization
- ๐ Multi-language help
- ๐ค New embedding methods
- ๐ต Audio textual content chunking
Donโt let chunking decelerate your RAG pipeline. Get Chonkie right this moment and give attention to what issues โ constructing superior AI functions!
#RAG #NLP #AI #MachineLearning #Python
