EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·25d ago·5 min read
Better Vector Search for Long Documents: Chunking Inside Manticore Search
TL;DR
Manticore Search now automates document chunking for vector search, allowing long documents to be effectively indexed and searched without losing content. This enhancement significantly improves retrieval accuracy, increasing recall rates from 55.1% to 83.3%.
✦ Why It Matters
Implement Manticore's chunking feature to improve search accuracy in your long documents today.
Key Takeaways
- Use chunk_strategy to optimize document embedding.
- Choose 'sentence' for better retrieval of long documents.
- Test the new feature on your internal documentation.
- Monitor recall rates to assess search improvements.
How It Works
Manticore Search's new chunking feature allows users to specify how documents are divided for embedding. By using the chunk_strategy parameter, documents can be split into smaller segments based on sentences or other criteria, ensuring that all relevant content is captured and indexed.
This approach allows each chunk to compete individually in search queries, improving the chances of retrieving the most relevant information.