Show Your Work: Provenance as a Query, Not a Forensic Reconstruction
Explore the ideas and implementation context behind this capability.
Explore the models / 8DB
Documents & text
Keep the source behind the answer.
Preserve passages and document structure so retrieval can lead back to the material that supports a result.
Structure · search · trace
Model / DocumentsConceptual diagram
Implementation snapshot / Reviewed 18 September 2026
Use stored knowledge to explain a match.
Documented native search regressionA documented regression indexes “automobile” and stores its lexical relationship to “car”. Exact-token search finds the first term; enabling stored synonym expansion lets the second retrieve the entity. The application uses the native Rust search API.
See the stored synonym example →01 / QUERYcar
The term the application asks for.
02 / STORED RELATIONSHIPcar ↔ automobile
Present in the store; expansion is disabled for this query.
03 / INDEXED ENTITYautomobile
No exact-token match for “car”.
Expected fixture resultNo match
The indexed token is “automobile”. The stored relationship is not used while expansion is off.
Browser illustration of the documented native-search regression at 7daa7908. This control does not execute 8DB or generate an answer. A stored synonym connection is distinct from embedding similarity; input parsing, permissions and answer generation require their own qualification.
Download the example and evaluation checklist ↓API, release scope and evidence limits
unified_search_with_store with a populated store and typed request. Example pinned to native_search_lexical_scope.rs at 7daa7908; later integration status is tracked separately.
This is a scoped search fixture, not qualification of every document parser, access-control policy or assistant workflow. The reviewed search records retain open integration and broader release gates. Confirm the evaluated build and input formats.
Start with the operations
your question needs.
- Document and hierarchical representations
- Native lexical retrieval
- Links between passages, claims and source documents
Documented product scope, reviewed 17 September 2026. The underlying build and deployment must be qualified for your application.
Parsing coverage depends on the input format and decomposer. Extracting a claim, generating an embedding or interpreting a document can require an external model; storage does not establish correctness of those derived claims.
Connected native models
Keep the next question within reach.
Document retrieval can be the start of a source-backed investigation. 8DB keeps document structure and relationships available alongside vectors, records and graph dependencies.
Give a similar result its context.
Compare · rank · retrieve
Explore model ↗Follow the relationship to a better answer.
Connect · traverse · follow
Explore model ↗Familiar rows. More context within reach.
Filter · sort · project
Explore model ↗These connections show the models involved in the application design. Compare the supported combination and full workflow with a realistic alternative.
Input → usable value / interactive design example
Answer an engineering question with inspectable sources
Receive and parse a supported report
A document is represented through its sections and passages, with a route back to the source.
Next: Keep the source
Conceptual application workflow. Qualify the supported operations, external tools, protection and persistence for the complete path. These diagrams are explanatory, not measured results.
Read the complete workflow and comparison scope
- Receive and parse a supported report
- Preserve document sections and source identity
- Retrieve relevant passages and semantic candidates
- Resolve related equipment and recorded observations
- Assemble an answer with traceable supporting passages
A useful answer includes parsing, indexing, retrieval, evidence assembly and any model call. Compare the quality and cost of that whole path; count synchronization if source documents and indexes live separately.
Compare a realistic alternative.
Multimodel products such as SurrealDB provide document and related data-model entry points. The choice requires comparing concrete source-preservation and query behaviour, not just counting supported labels.
SurrealDB data modelsCompare equivalent outputs and required guarantees, including the simplest single-product alternative where one exists. A specialist may remain the better fit when its operators, ecosystem or operational maturity are decisive. Combining native models becomes valuable when your actual question crosses them.
Modality performance / Evidence reviewed 18 September 2026
Define the useful result. Measure the complete job.
A modality-specific timing is not yet linked to this guide.
There may be supporting engineering measurements in the internal archive. We are reconciling their workload, result quality and comparison conditions before presenting a public chart. A benchmark for an adjacent model does not establish this model’s performance.
01Retrieval quality
02Parse-to-searchable time
03Permission & update visibility
Include memory, storage, energy and hardware writes
Record build-time and serving memory separately, original and derived stored bytes, measured joules per completed job, and physical writes including indexing, compaction and recovery. A smaller representation does not by itself prove lower energy or longer device life.
Match correctness, freshness, durability and protection across alternatives. Encryption, authorization, adapters, network setup and output delivery belong in the complete-job comparison wherever required. No universal zero-overhead claim follows from these results.
Choose the path that fits your deployment.
Verify the supported API/SDK, data format, persistence and indexing behaviour for the release you plan to use. Match the operation-by-modality protection profile and device qualification to the workload.
Related articles
Take the next step into the thinking.
Give Your AI the Evidence Behind the Answer
Explore the ideas and implementation context behind this capability.
Reference / Reviewed 17 September 2026
Readable, shareable and traceable.
This reference explains 8DB’s public modality description with an explicit workload and selection criteria. It is a documentary review, not fresh execution of every operator or platform.
Public sources: 8DB modality scope; SurrealDB data models; the related articles above. Internal source leads and outstanding release checks remain in the editorial record.
Download this referenceEvaluate the question
your application needs to answer.
Bring the data shapes, input state, expected output and the systems you use today. Define a comparison around that complete job.
Discuss your workload