Data Indexing

The system processes and encodes data into synonym-aware vectors and builds indexes so that you can search quickly and accurately in natural language across your entire database.

Data indexing is performed through the following steps:

Step 1. Ingestion and normalization: The system extracts clean text from multiple sources (URL, PDF, DOC, XLS), removes noise, and attaches metadata (source, location) to support precise citation.
Step 2. Logical segmentation (Chunking): Documents are split into information blocks of optimal size, preserving context and speeding up processing for AI models.
Step 3. Multi-layer vector encoding: Each information block is simultaneously converted into three representations: Dense (semantic understanding), Sparse (keyword recognition), and Token-level (deep contextual analysis) to maximize search accuracy.
Step 4. Storage and indexing: All original content, metadata, and vector layers are synchronized into the Qdrant database, forming a multi-dimensional index system ready for intelligent retrieval.

Data Indexing

The system processes and encodes data into synonym-aware vectors and builds indexes so that you can search quickly and accurately in natural language across your entire database.

Data indexing is performed through the following steps:

Step 1. Ingestion and normalization: The system extracts clean text from multiple sources (URL, PDF, DOC, XLS), removes noise, and attaches metadata (source, location) to support precise citation.
Step 2. Logical segmentation (Chunking): Documents are split into information blocks of optimal size, preserving context and speeding up processing for AI models.
Step 3. Multi-layer vector encoding: Each information block is simultaneously converted into three representations: Dense (semantic understanding), Sparse (keyword recognition), and Token-level (deep contextual analysis) to maximize search accuracy.
Step 4. Storage and indexing: All original content, metadata, and vector layers are synchronized into the Qdrant database, forming a multi-dimensional index system ready for intelligent retrieval.