Protocol5 Net Architecture: JustAnIota IOTA 1 Bidirectional Semantic Converter

Status: planning record for the Protocol5.com mathematical experiment.

Metadata

Field	Value
Source site	ɩ.com / JustAnIota.com
Source URL	https://justaniota.com/
Canonical AIWikis URL	https://aiwikis.org/justaniota/uai-system/files/raw-system-archives-justaniota-protocol5-experiment-2026-05-03-protocol5-94bfaea4/
Source reference	raw/system-archives/justaniota/protocol5-experiment/2026-05-03-protocol5-bidirectional-semantic-converter-rules/PROTOCOL5_IOTA1_DOTNET_ARCHITECTURE.md
File type	md
Content category	memory-file
Last fetched	2026-05-15T00:23:56.0837262Z
Last changed	2026-05-04T15:29:04.2217963Z
Content hash	sha256:94bfaea4d2d8fa1888b179a049b6c4e38828a9c05af70b6c864febd2de95c912
Import status	unchanged
Raw source layer	data/sources/justaniota/raw-system-archives-justaniota-protocol5-experiment-2026-05-03-protocol5-bidirectional-semantic-94bfaea4d2d8.md
Normalized source layer	data/normalized/justaniota/raw-system-archives-justaniota-protocol5-experiment-2026-05-03-protocol5-bidirectional-semantic-94bfaea4d2d8.txt

Current File Content

Structure Preview

• Protocol5 .NET Architecture: JustAnIota IOTA-1 Bidirectional Semantic Converter
• Operating Rule
• Product Split
• Enterprise Layers
• Facade
• Application / Logic Layer
• Domain Layer
• Infrastructure Layer
• SQL Server 2025 Data Model
• Proposed Project Layout
• Pipelines
• Population Pipeline
• English To IOTA-1
• IOTA-1 To English
• Round Trip
• Testing Rules
• References

Raw Version

# Protocol5 .NET Architecture: JustAnIota IOTA-1 Bidirectional Semantic Converter

Status: planning record for the Protocol5.com mathematical experiment.

## Operating Rule

The JustAnIota Converter is not an exact translator. It is an approximate semantic-comparison system. Objections that only say exact bidirectional translation is impossible are non-blocking because exactness is not the target. The useful target is a ranked "jist" of meaning from embedding-space similarity between ISO/IEC 10646 characters/symbols and English words or phrases.

The Protocol5 version must not collapse into a hidden dictionary or a versioned private-use profile. Its point is to iterate through transparent Unicode / ISO/IEC 10646 character inventory, generate or store embeddings, compare those embeddings against English embeddings, and expose the approximate relationship.

## Product Split

- JustAnIota.com: WordPress implementation, plugin/REST workbench, public demonstration surface.
- Protocol5.com: .NET / C# experiment surface, SQL Server 2025 AI database, ADO.NET repositories, local LM Studio adapter.
- Shared product name: JustAnIota IOTA-1 Bidirectional Semantic Converter.
- Short label: JustAnIota Converter.

## Enterprise Layers

### Facade

The Facade is the public consumption boundary for other projects and tests. It hides orchestration details and exposes stable async methods:

```csharp
public interface IJustAnIotaConverter
{
    Task<ConversionResult> EnglishToIotaAsync(EnglishInput input, CancellationToken cancellationToken = default);
    Task<ConversionResult> IotaToEnglishAsync(IotaInput input, CancellationToken cancellationToken = default);
    Task<RoundTripResult> RoundTripAsync(RoundTripInput input, CancellationToken cancellationToken = default);
    Task<IReadOnlyList<SemanticCandidate>> SearchMeaningAsync(SemanticQuery query, CancellationToken cancellationToken = default);
    Task<ExplanationResult> ExplainAsync(ExplanationRequest request, CancellationToken cancellationToken = default);
}
```

### Application / Logic Layer

- Normalize input and select query mode.
- Build English word/phrase vectors from stored terms when AI is disabled.
- Build ISO/IEC 10646 symbol vectors from stored symbol records.
- Aggregate token vectors into weighted query vectors.
- Rank candidates by vector distance and confidence rules.
- Produce traceable explanations: source terms, candidate symbols, distances, weights, and model snapshot.

### Domain Layer

Core domain objects:

- `IsoSymbol`: code point, glyph, block, name, aliases, category, source metadata.
- `EnglishTerm`: normalized term, language, phrase class, source metadata.
- `EmbeddingVector`: vector value, dimensions, model ID, created timestamp.
- `SemanticCandidate`: candidate ID, score, distance, confidence, explanation.
- `RoundTripTrace`: source, intermediate candidates, final candidates, loss notes.

### Infrastructure Layer

- `SqlIotaRepository`: ADO.NET repository for symbol, term, vector, and similarity queries.
- `LmStudioEmbeddingClient`: local LM Studio HTTP adapter for generating embeddings during population or enrichment.
- `EmbeddingCache`: avoids repeated local model calls.
- `ExperimentRunStore`: records reproducible run metadata.
- `BackgroundIndexer`: populates and refreshes embeddings and similarity edges.

## SQL Server 2025 Data Model

Use SQL Server 2025 vector features for native vector storage and search where available.

Recommended tables:

- `IotaSymbol`: `SymbolId`, `CodePoint`, `Glyph`, `UnicodeName`, `BlockName`, `GeneralCategory`, `DescriptionText`.
- `EnglishTerm`: `TermId`, `TermText`, `NormalizedText`, `LanguageTag`, `SourceKind`.
- `EmbeddingModel`: `ModelId`, `Provider`, `ModelName`, `Dimensions`, `DistanceMetric`, `VersionHash`.
- `SymbolEmbedding`: `SymbolId`, `ModelId`, `Embedding VECTOR(...)`, `Weight`, `CreatedAtUtc`.
- `EnglishEmbedding`: `TermId`, `ModelId`, `Embedding VECTOR(...)`, `Weight`, `CreatedAtUtc`.
- `SimilarityEdge`: `FromKind`, `FromId`, `ToKind`, `ToId`, `Distance`, `Score`, `ModelId`, `ComputedAtUtc`.
- `ExperimentRun`: inputs, selected model, parameters, candidate output, and explanation JSON.

Query modes:

- DB-only known-term mode: tokenize input, resolve known English terms or IOTA symbols, aggregate stored vectors, and search nearest neighbors.
- AI-assisted mode: use LM Studio to embed out-of-vocabulary phrases or generate explanation text, then store the resulting vector and metadata.

## Proposed Project Layout

```text
Protocol5.JustAnIotaConverter.Abstractions
Protocol5.JustAnIotaConverter.Facade
Protocol5.JustAnIotaConverter.Application
Protocol5.JustAnIotaConverter.Domain
Protocol5.JustAnIotaConverter.Infrastructure.Sql
Protocol5.JustAnIotaConverter.Infrastructure.LmStudio
Protocol5.JustAnIotaConverter.Worker.Indexer
Protocol5.JustAnIotaConverter.Web
Protocol5.JustAnIotaConverter.Tests
Protocol5.JustAnIotaConverter.TestHarness
```

## Pipelines

### Population Pipeline

1. Load ISO/IEC 10646 / Unicode character inventory and metadata.
2. Select candidate classes such as CJK ideographs, emoji, symbols, arrows, math, and other idea-bearing blocks.
3. Generate descriptive text for each symbol without making it a secret dictionary.
4. Generate embeddings through LM Studio or SQL-managed AI model features when configured.
5. Store vectors, model metadata, and repeatability hashes.
6. Precompute similarity edges for common English terms and high-value symbols.

### English To IOTA-1

1. Normalize English input.
2. Resolve known terms/phrases from the database.
3. Aggregate weighted vectors.
4. Search nearest ISO/IEC 10646 symbol vectors.
5. Return ranked IOTA-1 candidates with scores and explanation.

### IOTA-1 To English

1. Parse symbol/code-point input.
2. Resolve symbol vectors.
3. Aggregate weighted vectors.
4. Search nearest English term vectors.
5. Return ranked English gist candidates with scores and explanation.

### Round Trip

Round trips must show loss. A successful result is not exact reconstruction; it is a traceable approximate semantic path with scores.

## Testing Rules

- Unit-test vector aggregation, scoring, and normalization independently from SQL and LM Studio.
- Integration-test ADO.NET repositories against a disposable SQL Server test database.
- Contract-test the Facade so other projects can consume the converter without knowing storage details.
- Golden-test known examples, but assert ranges and ordering, not exact English reconstruction.
- Regression-test that exact-translation objections do not remove the approximate experiment path.

## References

- Microsoft Learn: SQL Server 2025 vector data type: https://learn.microsoft.com/sql/t-sql/data-types/vector-data-type
- Microsoft Learn: SQL Server 2025 vector functions and `VECTOR_DISTANCE`: https://learn.microsoft.com/sql/t-sql/functions/vector-distance-transact-sql
- Microsoft Learn: SQL Server 2025 vector search and vector indexes: https://learn.microsoft.com/sql/relational-databases/vectors/vectors-sql-server
- Microsoft Learn: ADO.NET vector data type support in SqlClient: https://learn.microsoft.com/sql/connect/ado-net/sql/vector-data-sql-server
- Microsoft Learn: SQL Server 2025 AI functions such as `AI_GENERATE_EMBEDDINGS`: https://learn.microsoft.com/sql/t-sql/functions/ai-functions-transact-sql

Why This File Exists

This is a memory-system evidence file from ɩ.com / JustAnIota.com. It is shown here because AIWikis.org is demonstrating the real source files that make the UAIX / LLM Wiki memory system work, not only summarizing those systems after the fact.

Role

This file is memory-system evidence. It records source history, archive transfer, intake disposition, or another piece of provenance that should be retrievable without becoming an unsupported public claim.

Structure

The file is structured around these visible headings: Protocol5 .NET Architecture: JustAnIota IOTA-1 Bidirectional Semantic Converter; Operating Rule; Product Split; Enterprise Layers; Facade; Application / Logic Layer; Domain Layer; Infrastructure Layer. Those headings are retrieval anchors: a crawler or LLM can decide whether the file is relevant before reading every line.

Prompt-Size And Retrieval Benefit

Keeping this material in a separate file reduces prompt pressure because an agent can load this exact unit only when its role, source site, category, or hash is relevant. The surrounding index pages point to it, while this page preserves the full content for audit and exact recall.

How To Use It

• Humans should read the metadata first, then inspect the raw content when they need exact wording or provenance.
• LLMs and agents should use the source site, category, hash, headings, and related files to decide whether this file belongs in the active prompt.
• Crawlers should treat the AIWikis page as transparent evidence and follow the source URL/source reference for authority boundaries.
• Future maintainers should regenerate this page whenever the source hash changes, then review the explanation if the role or structure changed.

Update Requirements

When this source file changes, update the raw source layer, normalized source layer, hash history, this rendered page, generated explanation, source-file inventory, changed-files report, and any source-section index that links to it.

Related Pages

• Source overview
• UAI system index
• Source provenance
• Global File Index
• Reports

Provenance And History

• Current observation: 2026-05-15T00:23:56.0837262Z
• Source origin: current-source-workspace
• Retrieval method: local-source-workspace
• Duplicate group: sfg-460 (primary)
• Historical hash records are stored in data/hashes/source-file-history.jsonl.

Machine-Readable Metadata

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