{"slug": "embeddings-as-encodings", "title": "Embeddings as Encodings", "summary": "HASH engineers have determined that embeddings in knowledge graphs should be treated as encodings of entities rather than metadata, requiring content-grade access control and metadata-grade lifecycle management. This framework ensures system reliability under model changes, scale, and security constraints.", "body_md": "Correctly conceptualizing and handling vectorization in knowledge graphs\n\nJanuary 26th, 2026\n\nEmbeddings are now a default building block in modern data services, powering semantic search, retrieval-augmented generative AI (RAG), clustering, deduplication, recommendations, anomaly detection, and more.\n\nIn fact, if you’re building AI-native products, you’re almost certainly storing vectors somewhere... but the *how* and *where* vary wildly.\n\nThe moment embeddings sit alongside a knowledge graph, a deceptively simple question becomes operationally important: **are embeddings part of an entity, or are they metadata about an entity?**\n\nThis arose internally at HASH in the way such questions often do: a few engineers, a few competing intuitions, and some spirited debate. We've since converged on a framework that’s less about word choice and more about building systems that remain reliable under model changes, scale, and security constraints.\n\nThe resulting best practice is straightforward: **Embeddings are encodings of entities** (derived *representations* of them). Embeddings therefore require **content-grade access control**, with the added consideration of **metadata-grade lifecycle management**. These principles resolve most downstream design debates.\n\nA common argument goes: embeddings are computed from entity content, therefore they’re metadata.\n\n**However, that’s not quite right.** Embeddings *represent* the content. Not in a way that may be intelligible to you or me, but in a way that tells us far more than the `created_at`\n\ndate of a million entities of different types.\n\nPlenty of things are derived from “original data” or “content”, yet are simply different encodings of it:\n\nThese are all different **representations** of information — often lossy, often dependent on an algorithm/codec/model, but fundamentally still encodings of the same underlying content.\n\nEmbeddings fit better in this bucket than as descriptive “metadata”, at least as most people (including developers) intuitively use the term (for example, to refer to file authors, timestamps, tags, etc.)\n\nSo why think about embeddings as “metadata” at all? The value isn’t, of course, in the label, but in the required *systems discipline* that typically comes with handling metadata: provenance, versioning, refresh policies, and explicit separation from canonical truth.\n\nIn knowledge graphs the term “entity” usually means more than a blob of text. It’s a stable identifier plus a set of claims (attributes + relationships), ideally with provenance. In HASH, an entity is [even more than this](https://hash.ai/guide/entities).\n\nA durable way to structure this world consists of *at least* three layers:\n\nThe graph’s explicit record of what is asserted:\n\n`Company: HASH`\n\n`hasWebsite(hash.dev)`\n\n`employs(Person: …)`\n\nThis is the layer you can audit, reason over, and reconcile.\n\nAlternate encodings used for consumption or computation:\n\nThese are often lossy, often recomputable, and usually dependent on a specific model/codec. They are valuable, but they are not the canonical record.\n\nThe lifecycle and provenance data that keeps everything sane:\n\nThis is “data about the data”—and it’s what prevents the system from devolving into unexplainable artifacts.\n\nBecause embeddings are computed representations of canonical claims they live in layer 2, instead of layer 3 (alongside the metadata about claims).\n\nIn HASH, **an embedding is a machine-oriented encoding of an entity, or some projection of entity attributes, into a vector space optimized for similarity operations.**\n\nTwo properties matter:\n\nFor either one, never mind *both* of these reasons, embeddings should not be treated as canonical truth about an entity. Embeddings provide powerful computational indices, not a ground-truth semantic substrate.\n\nFor operational scalability, it's vital to ensure that embeddings are derived artifacts **you can regenerate**, and those generated by different models/occupying incompatible vector spaces are not mixed.\n\nIn practice, this looks like storing embeddings with enough context to make them explainable and safe.\n\nEmbeddings ought to be **versioned**, able to be migrated **without mutating entities**, and their origin(s) should be **tracked** (in other words, the model used to compute them is known and remains accessible).\n\nNeeds will vary between systems, and for many designs the following will be overkill, but appropriate base provenance for embeddings may include:\n\nIf those fields aren’t present, or can't be looked up, it can become difficult to answer questions that matter in production:\n\nThis is what we mean by “metadata-grade lifecycle management”: embeddings are treated as first-class derived representations with lineage, not as mysterious numbers floating in a vector database.\n\nOne of the most obvious conclusions that stems from using embeddings is the need to treat them with the same level of security and protection as entities in their ordinary form.\n\n**If someone shouldn’t have access to an entity’s underlying data, they should not have access to embeddings (or any encodings) derived from it.**\n\nEmbeddings carry content signal. In fact, that's the entire reason vector-driven semantic search works.\n\nEven though embeddings aren’t human-readable, they can still leak **membership** (whether a record was part of an indexed corpus), **attribute inference** (whether something resembles a sensitive category), **clustering and correlation information** (which entities are “near” each other)... and may at least pose proximate **reconstruction risks** in certain threat models (e.g. [vec2text](https://github.com/vec2text/vec2text) enabling original meaning to be roughly reverse-engineered, albeit without certainty).\n\nThe correct approach is therefore *not* to treat “vector store access” as a separate, looser security domain, but to gate embedding read access behind the **same ACLs** as the underlying entity attributes; and if permissions are field-level, bind embeddings to the **specific projection** of fields embedded.\n\nIn practice, this often means storing multiple embeddings per entity. A description embedding might power semantic search, while a name-based embedding serves deduplication. Each projection carries its own permission boundary. HASH supports multiple embeddings per entity for this reason.\n\nThis is easier said than done. Many architectures separate their vector store (Pinecone, Weaviate, Qdrant, etc.) from their primary datastore, creating a sync problem: when entity permissions change, vector access must update accordingly. When embeddings are regenerated, old vectors must be invalidated atomically. When field-level permissions differ, you may need separate vector collections per permission boundary.\n\nHASH sidesteps this by treating embeddings as first-class graph artifacts subject to the same query-time access control as entities themselves — no separate vector ACL layer to keep in sync.\n\nMeanwhile many RAG solutions marketed as commercially-ready today do not handle this, leading to potential data leakage and confidential information exposure.\n\nEven classic metadata *can be* sensitive depending on context (e.g. relationships, timestamps, communication graphs, and associations). Risk is measured by the ability to *infer things* that weren't intended to be disclosed, rather than any label. But universally we can say embeddings are *high inference potential*, and should therefore be protected with the same care as underlying data directly.\n\nIn HASH, and in general within graph-backed AI systems, we recommend letting **embeddings propose** and **graphs decide**.\n\nThis separation prevents “soft similarity” from silently being mistaken for “hard truth”.\n\nEmbeddings are excellent at generating candidates:\n\nBut turning candidates into durable relationships or claims should flow through graph-native checks:\n\nHASH provides all of these things natively.\n\nIn HASH deployments, embeddings are treated as:\n\nHosted environments may choose to keep only one active embedding set at a time for cost reasons, but the architecture assumes embeddings are replaceable artifacts and avoids baking a single vector space into the identity of an entity.\n\nTeams get stuck when they try to force embeddings into a binary: “content” or “metadata”.\n\nThe operationally correct framing is more nuanced, and more useful:\n\n**Embeddings are representations (encodings) of entity data.** They are **not canonical claims** in the knowledge graph, but they combine a need for **content-grade security** (same permissions as underlying data) with **metadata-grade lifecycle management** (provenance, versioning, refresh). Their best role is to **propose candidates**, with the graph providing the auditable backbone for what becomes truth.\n\nIf you build with embeddings like this, model migrations stop being existential, retrieval becomes explainable, and security doesn’t depend on hoping vectors are “just metadata.”\n\n*With thanks to hashist Tim Diekmann and community contributor Bilal Mahmoud for helping develop the key insights within this post.*\n\nGet notified when new long-reads and articles go live. Follow along as we dive deep into new tech, and share our experiences. **No sales stuff.**", "url": "https://wpnews.pro/news/embeddings-as-encodings", "canonical_source": "https://hash.dev/blog/embeddings-as-encodings", "published_at": "2026-06-20 14:19:05+00:00", "updated_at": "2026-06-20 14:38:30.287567+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "ai-infrastructure"], "entities": ["HASH"], "alternates": {"html": "https://wpnews.pro/news/embeddings-as-encodings", "markdown": "https://wpnews.pro/news/embeddings-as-encodings.md", "text": "https://wpnews.pro/news/embeddings-as-encodings.txt", "jsonld": "https://wpnews.pro/news/embeddings-as-encodings.jsonld"}}