Document Embeddings
Meaning as coordinates — documents mapped into vector space where similar content sits close together.
Document embeddings are vector representations of document content — passages, pages, or whole files encoded as points in a high-dimensional space where geometric proximity tracks semantic similarity. A paragraph about "terminating the agreement for cause" and a query about "cancelling the contract when the vendor breaches" land near each other despite sharing few words; that property is what powers semantic search, similarity matching, clustering, deduplication, and the retrieval half of every RAG system. Embedding models — transformer encoders trained on similarity objectives — produce them; vector indexes store and search them.
The document-specific craft lies in what gets embedded and how. Chunk-level embeddings are the RAG workhorse, which makes embedding quality inseparable from parsing and chunking quality: an embedding of a mangled table or a mid-sentence fragment encodes the mangling. Content type matters — tables, dense with numbers and sparse with prose, embed poorly as raw text and often better as generated descriptions; multilingual corpora need models whose vector space aligns across languages; and domain vocabulary (legal, clinical, financial) rewards domain-tuned embedding models where general ones blur crucial distinctions. Metadata typically rides alongside rather than inside the vector: filters on document type, date, and jurisdiction constrain the search space before geometry ranks what remains.
Two operational facts deserve more attention than they get. Embeddings are content in recoverable form — inversion attacks reconstruct substantial text from vectors, so embedding stores inherit the source documents' confidentiality classification and residency constraints; "we only send the vectors out" is not an isolation argument. And embeddings are model-versioned: vectors from different models (or versions) are incomparable, so an embedding-model upgrade means re-embedding the corpus — a planned migration, with the index rebuilt and retrieval quality re-benchmarked, not a drop-in swap.
The retrieval operation itself — finding nearest neighbors in embedding space, and the algorithms that make it fast.
Search by meaning, not just matching words — finding the paraphrase that keyword search would miss.
Where you cut the document decides what the model can find — chunking is retrieval destiny.
Proof Perimeter runs document AI inside your own perimeter — with a provenance record on every field.
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