Data model
Words made simple
A few useful meanings before you read the explanation.
- Embedding
- A list of numbers that represents the meaning of some content. Similar meanings can produce nearby lists of numbers.
- Vector
- An ordered list of numbers. An embedding is a vector used to represent content for tasks such as similarity search.
- Agent
- An application that uses a model to choose its next action within defined limits. It can use tools and continue through several steps.
More words from this lesson
- State
- The data describing where a workflow is now, what it already knows, and what still needs to happen.
- Checkpoint
- A saved snapshot of workflow progress that can help it continue later.
- Durable
- Able to retain progress and recover across interruptions. Durable workflows require appropriate storage and retry behavior.
- Tenant
- A customer or organization whose data must stay separate from other customers in a shared application.
- Chunking
- Dividing a long document into smaller pieces so relevant parts can be found and sent to the model.
| Record | Essential fields and purpose |
|---|---|
customers | Tenant, customer identity, permitted contact/profile fields; maintained by business service |
policies | Tenant/customer, product, region, validity, document version, endorsements; canonical applicability |
claims | Tenant/customer/policy, status, receipt, required documents; canonical claim state |
documents | Tenant, source ID/hash, MIME/size, ACL, version, parser state, publication status, timestamps |
chunks | Tenant/document/version, section/page, text, embedding model/dimension, vector, eligible metadata |
conversations/messages | Tenant/user/session, bounded content and roles, tool relationships, retention category |
agent_runs | Tenant/user, graph version, task, phase, deadline/budget, checkpoint reference, outcome |
claim_drafts | Draft ID/version, structured fields, source refs, content hash, creator, validation results |
approvals | Draft ID/hash, action, reviewer, scope, timestamp, expiry, decision; authenticated record |
operation_receipts | Tenant/action/key, payload hash, pending/completed/unknown state, external reference |
outbox | Business-linked event ID, payload, dispatch status; reliable async publication |
user_memories | Tenant/user, approved fact/preference, source, timestamp, expiry; separate from claim truth |
audit_events | Actor, action, object/version, decision, outcome, run/receipt reference; restricted immutable history |
- Enforce unique operation keys and optimistic draft versions.
- Keep large original files in protected object storage.
- Keep model-visible projections small.
- A document index and conversation cache are rebuildable derived data; claim and approval records need stronger durability.
Can you explain it without the notes?
Data model
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Enforce unique operation keys and optimistic draft versions. Keep large original files in protected object storage. Keep model-visible projections small. A document index and conversation cache are rebuildable derived data; claim and approval records need…
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Practice this conceptAdapted from 13_AI.MD · “Data model”. Examples are study fragments; framework behavior can depend on your version.