Text Embedding
Generate 1024-dimensional E5-Large embeddings from text content for semantic search
Why do anything?
Text data needs vector representations for semantic search. Without embeddings, you're limited to keyword matching.
Why now?
Modern search expects semantic understanding. Users search by meaning, not exact words.
Why this feature?
E5-Large model produces high-quality 1024D embeddings optimized for retrieval. Supports chunking for long documents.
How It Works
Text extractor uses E5-Large model for high-quality text embeddings optimized for retrieval tasks.
Input Processing
Accept text directly or fetch from URL
Chunking
Split into chunks by sentences, paragraphs, or fixed size
Embedding
Generate 1024D E5-Large embeddings per chunk
Storage
Store in Qdrant with vector index
Why This Approach
E5-Large is a leading embedding model for retrieval. Chunking enables long document handling while maintaining semantic coherence.
Integration
client.collections.create(feature_extractor={"feature_extractor_name": "text_extractor", "version": "v1"})