Structured RAG for Unknown and Mixed Languages

Structured RAG for Unknown and Mixed Languages

How I stopped my multilingual activity stream from turning RAG into chaos

In the previous article (RAG with PostgreSQL and C# (pros and cons) | Joche Ojeda) I explained how naïve RAG breaks when you run it over an activity stream.

Same UI language.
Totally unpredictable content language.
Spanish, Russian, Italian… sometimes all in the same message.

Humans handle that fine.
Vector retrieval… not so much.

This is the “silent failure” scenario: retrieval looks plausible, the LLM sounds confident, and you ship nonsense.

So I had to change the game.

The Idea: Structured RAG

Structured RAG means you don’t embed raw text and pray.

You add a step before retrieval:

  • Extract a structured representation from each activity record
  • Store it as metadata (JSON)
  • Use that metadata to filter, route, and rank
  • Then do vector similarity on a cleaner, more stable representation

Think of it like this:

Unstructured text is what users write.
Structured metadata is what your RAG system can trust.

Why This Fix Works for Mixed Languages

The core problem with activity streams is not “language”.

The core problem is: you have no stable shape.

When the shape is missing, everything becomes fuzzy:

  • Who is speaking?
  • What is this about?
  • Which entities are involved?
  • Is this a reply, a reaction, a mention, a task update?
  • What language(s) are in here?

Structured RAG forces you to answer those questions once, at write-time, and save the answers.

PostgreSQL: Add a JSONB Column (and Keep pgvector)

We keep the previous approach (pgvector) but we add a JSONB column for structured metadata.

ALTER TABLE activities
ADD COLUMN rag_meta jsonb NOT NULL DEFAULT '{}'::jsonb;

-- Optional: if you store embeddings per activity/chunk
-- you keep your existing embedding column(s) or chunk table.

Then index it.

CREATE INDEX activities_rag_meta_gin
ON activities
USING gin (rag_meta);

Now you can filter with JSON queries before you ever touch vector similarity.

A Proposed Schema (JSON Shape You Control)

The exact schema depends on your product, but for activity streams I want at least:

  • language: detected languages + confidence
  • actors: who did it
  • subjects: what object is involved (ticket, order, user, document)
  • topics: normalized tags
  • relationships: reply-to, mentions, references
  • summary: short canonical summary (ideally in one pivot language)
  • signals: sentiment/intent/type if you need it

Example JSON for one activity record:

{
  "schemaVersion": 1,
  "languages": [
    { "code": "es", "confidence": 0.92 },
    { "code": "ru", "confidence": 0.41 }
  ],
  "actor": {
    "id": "user:42",
    "displayName": "Joche"
  },
  "subjects": [
    { "type": "ticket", "id": "ticket:9831" }
  ],
  "topics": ["billing", "invoice", "error"],
  "relationships": {
    "replyTo": "activity:9912001",
    "mentions": ["user:7", "user:13"]
  },
  "intent": "support_request",
  "summary": {
    "pivotLanguage": "en",
    "text": "User reports an invoice calculation error and asks for help."
  }
}

Notice what happened here: the raw multilingual chaos got converted into a stable structure.

Write-Time Pipeline (The Part That Feels Expensive, But Saves You)

Structured RAG shifts work to ingestion time.

Yes, it costs tokens.
Yes, it adds steps.

But it gives you something you never had before: predictable retrieval.

Here’s the pipeline I recommend:

  1. Store raw activity (as-is, don’t lose the original)
  2. Detect language(s) (fast heuristic + LLM confirmation if needed)
  3. Extract structured metadata into your JSON schema
  4. Generate a canonical “summary” in a pivot language (often English)
  5. Embed the summary + key fields (not the raw messy text)
  6. Save JSON + embedding

The key decision: embed the stable representation, not the raw stream text.

C# Conceptual Implementation

I’m going to keep the code focused on the architecture. Provider details are swappable.

Entities

public sealed class Activity
{
    public long Id { get; set; }
    public string RawText { get; set; } = "";
    public string UiLanguage { get; set; } = "en";

    // JSONB column in Postgres
    public string RagMetaJson { get; set; } = "{}";

    // Vector (pgvector) - store via your pgvector mapping or raw SQL
    public float[] RagEmbedding { get; set; } = Array.Empty<float>();

    public DateTimeOffset CreatedAt { get; set; }
}

Metadata Contract (Strongly Typed in Code, Stored as JSONB)

public sealed class RagMeta
{
    public int SchemaVersion { get; set; } = 1;
    public List<DetectedLanguage> Languages { get; set; } = new();
    public ActorMeta Actor { get; set; } = new();
    public List<SubjectMeta> Subjects { get; set; } = new();
    public List<string> Topics { get; set; } = new();
    public RelationshipMeta Relationships { get; set; } = new();
    public string Intent { get; set; } = "unknown";
    public SummaryMeta Summary { get; set; } = new();
}

public sealed class DetectedLanguage
{
    public string Code { get; set; } = "und";
    public double Confidence { get; set; }
}

public sealed class ActorMeta
{
    public string Id { get; set; } = "";
    public string DisplayName { get; set; } = "";
}

public sealed class SubjectMeta
{
    public string Type { get; set; } = "";
    public string Id { get; set; } = "";
}

public sealed class RelationshipMeta
{
    public string? ReplyTo { get; set; }
    public List<string> Mentions { get; set; } = new();
}

public sealed class SummaryMeta
{
    public string PivotLanguage { get; set; } = "en";
    public string Text { get; set; } = "";
}

Extractor + Embeddings

You need two services:

  • Metadata extraction (LLM fills the schema)
  • Embeddings (Microsoft.Extensions.AI) for the stable text
public interface IRagMetaExtractor
{
    Task<RagMeta> ExtractAsync(Activity activity, CancellationToken ct);
}

Then the ingestion pipeline:

using System.Text.Json;
using Microsoft.Extensions.AI;

public sealed class StructuredRagIngestor
{
    private readonly IRagMetaExtractor _extractor;
    private readonly IEmbeddingGenerator<string, Embedding<float>> _embeddings;

    public StructuredRagIngestor(
        IRagMetaExtractor extractor,
        IEmbeddingGenerator<string, Embedding<float>> embeddings)
    {
        _extractor = extractor;
        _embeddings = embeddings;
    }

    public async Task ProcessAsync(Activity activity, CancellationToken ct)
    {
        // 1) Extract structured JSON
        RagMeta meta = await _extractor.ExtractAsync(activity, ct);

        // 2) Create stable text for embeddings (summary + keywords)
        string stableText =
            $"{meta.Summary.Text}\n" +
            $"Topics: {string.Join(", ", meta.Topics)}\n" +
            $"Intent: {meta.Intent}";

        // 3) Embed stable text
        var emb = await _embeddings.GenerateAsync(new[] { stableText }, ct);
        float[] vector = emb.First().Vector.ToArray();

        // 4) Save into activity record
        activity.RagMetaJson = JsonSerializer.Serialize(meta);
        activity.RagEmbedding = vector;

        // db.SaveChangesAsync(ct) happens outside (unit of work)
    }
}

This is the core move: you stop embedding chaos and start embedding structure.

Query Pipeline: JSON First, Vectors Second

When querying, you don’t jump into similarity search immediately.

You do:

  1. Parse the user question
  2. Decide filters (actor, subject type, topic)
  3. Filter with JSONB (fast narrowing)
  4. Then do vector similarity on the remaining set

Example: filter by topic + intent using JSONB:

SELECT id, raw_text
FROM activities
WHERE rag_meta @> '{"intent":"support_request"}'::jsonb
  AND rag_meta->'topics' ? 'invoice'
ORDER BY rag_embedding <=> @query_embedding
LIMIT 20;

That “JSON first” step is what keeps multilingual streams from poisoning your retrieval.

Tradeoffs (Because Nothing Is Free)

Structured RAG costs more at write-time:

  • more tokens
  • more latency
  • more moving parts

But it saves you at query-time:

  • less noise
  • better precision
  • more predictable answers
  • debuggable failures (because you can inspect metadata)

In real systems, I’ll take predictable and debuggable over “cheap but random” every day.

Final Thought

RAG over activity streams is hard because activity streams are messy by design.

If you want RAG to behave, you need structure.

Structured RAG is how you make retrieval boring again.
And boring retrieval is exactly what you want.

In the next article, I’ll go deeper into the exact pipeline details: language routing, mixed-language detection, pivot summaries, chunk policies, and how I made this production-friendly without turning it into a token-burning machine.

Let the year begin 🚀

“`

Setting Up Hostnames for Multi-Tenant Sites in Oqtane

Setting Up Hostnames for Multi-Tenant Sites in Oqtane

OK, so it’s time for a new article about Oqtane!

In my previous post (or “mental note,” as I like to call them), I covered how to set up
multi-tenancy in Oqtane. Today, I got a really nice surprise —
Shaun Walker just posted an excellent video explaining how multi-tenancy works,
along with its advantages and possible drawbacks.

From my point of view, the advantages clearly outweigh the disadvantages,
although it depends on your specific scenario.


Extending the Previous Example

I wanted to improve my previous example a bit. So, I created a new GitHub repository using the same base code,
but this time I added hostnames for each tenant.

A hostname is basically the domain that points to one of your tenants in Oqtane.
In a typical setup, you use DNS records for this.
The simplest case is an A record that points to a specific IP address.

When a request arrives, the server reads the hostname from the request and routes it to the correct tenant.

This part isn’t specific to Oqtane — it’s how web servers work in general.
The concept exists in IIS, Apache, and NGINX,
and it’s part of basic networking theory. If you want to learn more,
there are countless articles about how DNS works.


A Small Story from the Past

This actually takes me back — one of the first things I learned as a teenager was how to configure DNS
and run my own Apache web server.

I even started offering web hosting from my home using an old 486 computer (yes, really).
Eventually, my internet provider noticed what I was doing, blocked my connection, and called my parents.
Let’s just say… that Christmas was canceled for me. 😅
Anyway, that’s a story for another time.


Setting Up Local Domains for Tenants

For today’s example, I’m using the same structure as before:

  • One host site
  • Two tenant sites: MyCompany1 and MyCompany2

I want to show you how to assign domain names to each of them.

If you’re running everything locally (for example, through Visual Studio or VS Code),
you can’t use real domain names — but you can simulate them using the Windows hosts file.

If you’ve ever wondered how your computer resolves localhost to 127.0.0.1,
the answer lies in that file. It’s located inside the Windows system folder,
and it maps domain names to IP addresses.

Here’s the cool part: you can add your own domains there, pointing them to any IP you like.
It’s a great trick for local testing.

Below, you’ll see a screenshot of my hosts file.
I’ve mapped my fake domains to my local IP address,
so when I open them in the browser, the requests go straight to my
Kestrel server, which then routes them to the correct tenant.


How to Edit the Windows Hosts File

Editing the hosts file in Windows is simple, but you need administrative permissions.
Here’s how you can do it safely:

  1. Press Start, type Notepad, then right-click it and select
    Run as administrator.
  2. Once Notepad opens, go to File → Open and browse to:
    C:\Windows\System32\drivers\etc\hosts
  3. In the open dialog, change the filter from “Text Documents (*.txt)” to “All Files (*.*)”
    so you can see the hosts file.
  4. Add your entries at the bottom of the file. For example:
    127.0.0.1   mycompany1.xyz
    127.0.0.1   mycompany2.xyz
    

    Each line maps an IP address to a domain name.

  5. Save the file and close Notepad.
  6. Open your browser and visit http://mycompany1.xyz:44398
    (or the port your Oqtane app is running on).
    You should see the tenant corresponding to that domain.

⚠️ Important: If you edit the file without admin rights,
you won’t be able to save it. Also, be careful — if you modify or delete system entries by accident,
your network resolution might stop working.

Here is how my host file actually looks at the moment

 

Set siteURL for :Company 1

Set siteURL for :Company 2

 

Testing with Real Domains

Of course, this same logic applies to real domains too — as long as your Oqtane instance is publicly accessible.

In one of the next parts (maybe part 3 or 4), I’ll show how to configure it using a web server like
Apache. I know that NGINX is more popular these days,
but I’ve used Apache since my teenage years, so I’m more comfortable with it.

Still, I’ll probably demonstrate both.

Most developers today use cloud providers like AWS or Azure,
but honestly, I still prefer spinning up a simple Ubuntu server and doing everything manually.
The best tool is the one you know best — and for me, that’s Apache on Ubuntu.


Demo

As you can see there is a little bit of a different behavior if is a default site or not If it’s a default site it will redirect to that URL if not it’s going to redirect to the default site URL

 

Resources

  • 🧩 GitHub Repository — This project is based on the previous example
    but adds hostname configuration and uses SQLite for simplicity.

egarim/DomainNames

  • 🎥 Shaun Walker’s Video on Multi-Tenancy
    Highly recommended if you want a solid overview of how it all works:


 

From Weasel to Sequel to “Speckified”: How Developers Twist Acronyms

From Weasel to Sequel to “Speckified”: How Developers Twist Acronyms

If you hang out around developers long enough, you’ll notice we don’t just use tools — we nickname them, mispronounce them, and sometimes turn them into full-blown mascots. Here are three favorites: WSL, SQL, and GitHub Copilot’s Spec Kit.


WSL → “Weasel”

English reality: WSL stands for Windows Subsystem for Linux.

Nickname: Said quickly as “double-u S L,” it echoes weasel, so the meme stuck.

Spanish (El Salvador / Latin America): In El Salvador and many Latin American countries, the letter W is read as “doble be” (not doble u). So WSL is pronounced “doble be, ese, ele.”

SQL → “Sequel”

English reality: SQL stands for Structured Query Language.

Pronunciation: Both “S-Q-L” and “sequel” are used in English.

Spanish (LatAm): Most developers say it letter by letter: “ese cu e ele.” Bilingual teams sometimes mix in “sequel.”

Spec Kit → “Speckified” (Spooky Spell)

English reality: GitHub Copilot’s Spec Kit helps scaffold code from specs.

Community fun: Projects get “speckified,” a word that mischievously echoes “spookified.” Our playful mascot idea is a wizard enchanting a codebase: You have been Speckified!

Spanish (LatAm): Phonetically, SPEC is “ese, pe, e, ce.” In casual talk many devs just say “espec” (es-pek) to keep the pun alive.

Quick Reference (Latin American / El Salvador Spanish)

Acronym English Pronunciation Spanish (LatAm / El Salvador) Phonetics Nickname / Mascot
WSL “double-u S L” (sounds like weasel) “doble be, ese, ele” Weasel
SQL “S-Q-L” or “sequel” “ese cu e ele” Sequel Robot
SPEC “spec” → “speckified” “ese, pe, e, ce” (or “espec”) Spec Wizard (spell)

Why This Matters

These playful twists — weasel, sequel robot, speckified wizard — show how dev culture works:

  • Acronyms turn into characters.
  • English vs. Spanish pronunciations add layers of humor.
  • Memes make otherwise dry tools easier to talk about.

Next time someone says their project is fully speckified on WSL with SQL, you might be hearing about a weasel, a robot, and a wizard casting spooky spec spells.

Related Links

VS Code – Let it Cook – Introducing Spec Kit for Spec-Driven Development! – Episode 13

 

 


Building a Comprehensive Accounting System Integration Test – Day 5

Building a Comprehensive Accounting System Integration Test – Day 5

Integration testing is a critical phase in software development where individual modules are combined and tested as a group. In our accounting system, we’ve created a robust integration test that demonstrates how the Document module and Chart of Accounts module interact to form a functional accounting system. In this post, I’ll explain the components and workflow of our integration test.

The Architecture of Our Integration Test

Our integration test simulates a small retail business’s accounting operations. Let’s break down the key components:

Test Fixture Setup

The AccountingIntegrationTests class contains all our test methods and is decorated with the [TestFixture] attribute to identify it as a NUnit test fixture. The Setup method initializes our services and data structures:

[SetUp]
public async Task Setup()
{
    // Initialize services
    _auditService = new AuditService();
    _documentService = new DocumentService(_auditService);
    _transactionService = new TransactionService();
    _accountValidator = new AccountValidator();
    _accountBalanceCalculator = new AccountBalanceCalculator();
    
    // Initialize storage
    _accounts = new Dictionary<string, AccountDto>();
    _documents = new Dictionary<string, IDocument>();
    _transactions = new Dictionary<string, ITransaction>();
    
    // Create Chart of Accounts
    await SetupChartOfAccounts();
}

This method:

  1. Creates instances of our services
  2. Sets up in-memory storage for our entities
  3. Calls SetupChartOfAccounts() to create our initial chart of accounts

Chart of Accounts Setup

The SetupChartOfAccounts method creates a basic chart of accounts for our retail business:

private async Task SetupChartOfAccounts()
{
    // Clear accounts dictionary in case this method is called multiple times
    _accounts.Clear();
    
    // Assets (1xxxx)
    await CreateAccount("Cash", "10100", AccountType.Asset, "Cash on hand and in banks");
    await CreateAccount("Accounts Receivable", "11000", AccountType.Asset, "Amounts owed by customers");
    // ... more accounts
    
    // Verify all accounts are valid
    foreach (var account in _accounts.Values)
    {
        bool isValid = _accountValidator.ValidateAccount(account);
        Assert.That(isValid, Is.True, $"Account {account.AccountName} validation failed");
    }
    
    // Verify expected number of accounts
    Assert.That(_accounts.Count, Is.EqualTo(17), "Expected 17 accounts in chart of accounts");
}

This method:

  1. Creates accounts for each category (Assets, Liabilities, Equity, Revenue, and Expenses)
  2. Validates each account using our AccountValidator
  3. Ensures we have the expected number of accounts

Individual Transaction Tests

We have separate test methods for specific transaction types:

Purchase of Inventory

CanRecordPurchaseOfInventory demonstrates recording a supplier invoice:

[Test]
public async Task CanRecordPurchaseOfInventory()
{
    // Arrange - Create document
    var document = new DocumentDto { /* properties */ };
    
    // Act - Create document, transaction, and entries
    var createdDocument = await _documentService.CreateDocumentAsync(document, TEST_USER);
    // ... create transaction and entries
    
    // Validate transaction
    var isValid = await _transactionService.ValidateTransactionAsync(
        createdTransaction.Id, ledgerEntries);
        
    // Assert
    Assert.That(isValid, Is.True, "Transaction should be balanced");
}

This test:

  1. Creates a document for our inventory purchase
  2. Creates a transaction linked to that document
  3. Creates ledger entries (debiting Inventory, crediting Accounts Payable)
  4. Validates that the transaction is balanced (debits = credits)

Sale to Customer

CanRecordSaleToCustomer demonstrates recording a customer sale:

[Test]
public async Task CanRecordSaleToCustomer()
{
    // Similar pattern to inventory purchase, but with sale-specific entries
    // ...
    
    // Create ledger entries - a more complex transaction with multiple entries
    var ledgerEntries = new List<ILedgerEntry>
    {
        // Cash received
        // Sales revenue
        // Cost of goods sold
        // Reduce inventory
    };
    
    // Validate transaction
    // ...
}

This test is more complex, recording both the revenue side (debit Cash, credit Sales Revenue) and the cost side (debit Cost of Goods Sold, credit Inventory) of a sale.

Full Accounting Cycle Test

The CanExecuteFullAccountingCycle method ties everything together:

[Test]
public async Task CanExecuteFullAccountingCycle()
{
    // Run these in a defined order, with clean account setup first
    _accounts.Clear();
    _documents.Clear();
    _transactions.Clear();
    
    await SetupChartOfAccounts();
    
    // 1. Record inventory purchase
    await RecordPurchaseOfInventory();
    
    // 2. Record sale to customer
    await RecordSaleToCustomer();
    
    // 3. Record utility expense
    await RecordBusinessExpense();
    
    // 4. Create a payment to supplier
    await RecordPaymentToSupplier();
    
    // 5. Verify account balances
    await VerifyAccountBalances();
}

This test:

  1. Starts with a clean state
  2. Records a sequence of business operations
  3. Verifies the final account balances

Mock Account Balance Calculator

The MockAccountBalanceCalculator is a crucial part of our test that simulates how a real database would work:

public class MockAccountBalanceCalculator : AccountBalanceCalculator
{
    private readonly Dictionary<string, AccountDto> _accounts;
    private readonly Dictionary<Guid, List<LedgerEntryDto>> _ledgerEntriesByTransaction = new();
    private readonly Dictionary<Guid, decimal> _accountBalances = new();

    public MockAccountBalanceCalculator(
        Dictionary<string, AccountDto> accounts,
        Dictionary<string, ITransaction> transactions)
    {
        _accounts = accounts;
        
        // Create mock ledger entries for each transaction
        InitializeLedgerEntries(transactions);
        
        // Calculate account balances based on ledger entries
        CalculateAllBalances();
    }

    // Methods to initialize and calculate
    // ...
}

This class:

  1. Takes our accounts and transactions as inputs
  2. Creates a collection of ledger entries for each transaction
  3. Calculates account balances based on these entries
  4. Provides methods to query account balances and ledger entries

The InitializeLedgerEntries method creates a collection of ledger entries for each transaction:

private void InitializeLedgerEntries(Dictionary<string, ITransaction> transactions)
{
    // For inventory purchase
    if (transactions.TryGetValue("InventoryPurchase", out var inventoryPurchase))
    {
        var entries = new List<LedgerEntryDto>
        {
            // Create entries for this transaction
            // ...
        };
        _ledgerEntriesByTransaction[inventoryPurchase.Id] = entries;
    }
    
    // For other transactions
    // ...
}

The CalculateAllBalances method processes these entries to calculate account balances:

private void CalculateAllBalances()
{
    // Initialize all account balances to zero
    foreach (var account in _accounts.Values)
    {
        _accountBalances[account.Id] = 0m;
    }
    
    // Process each transaction's ledger entries
    foreach (var entries in _ledgerEntriesByTransaction.Values)
    {
        foreach (var entry in entries)
        {
            if (entry.EntryType == EntryType.Debit)
            {
                _accountBalances[entry.AccountId] += entry.Amount;
            }
            else // Credit
            {
                _accountBalances[entry.AccountId] -= entry.Amount;
            }
        }
    }
}

This approach closely mirrors how a real accounting system would work with a database:

  1. Ledger entries are stored in collections (similar to database tables)
  2. Account balances are calculated by processing all relevant entries
  3. The calculator provides methods to query this data (similar to a repository)

Balance Verification

The VerifyAccountBalances method uses our mock calculator to verify account balances:

private async Task VerifyAccountBalances()
{
    // Create mock balance calculator
    var mockBalanceCalculator = new MockAccountBalanceCalculator(_accounts, _transactions);
    
    // Verify individual account balances
    decimal cashBalance = mockBalanceCalculator.CalculateAccountBalance(
        _accounts["Cash"].Id, 
        _testDate.AddDays(15)
    );
    Assert.That(cashBalance, Is.EqualTo(-2750m), "Cash balance is incorrect");
    
    // ... verify other account balances
    
    // Also verify the accounting equation
    // ...
}

The Benefits of Our Collection-Based Approach

Our redesigned MockAccountBalanceCalculator offers several advantages:

  1. Data-Driven: All calculations are based on collections of data, not hardcoded values.
  2. Flexible: New transactions can be added easily without changing calculation logic.
  3. Maintainable: If transaction amounts change, we only need to update them in one place.
  4. Realistic: This approach closely mirrors how a real database-backed accounting system would work.
  5. Extensible: We can add support for more complex queries like filtering by date range.

The Goals of Our Integration Test

Our integration test serves several important purposes:

  1. Verify Module Integration: Ensures that the Document module and Chart of Accounts module work correctly together.
  2. Validate Business Workflows: Confirms that standard accounting workflows (purchasing, sales, expenses, payments) function as expected.
  3. Ensure Data Integrity: Verifies that all transactions maintain balance (debits = credits) and that account balances are accurate.
  4. Test Double-Entry Accounting: Confirms that our system properly implements double-entry accounting principles where every transaction affects at least two accounts.
  5. Validate Accounting Equation: Ensures that the fundamental accounting equation (Assets = Liabilities + Equity + (Revenues – Expenses)) remains balanced.

Conclusion

This integration test demonstrates the core functionality of our accounting system using a data-driven approach that closely mimics a real database. By simulating a retail business’s transactions and storing them in collections, we’ve created a realistic test environment for our double-entry accounting system.

The collection-based approach in our MockAccountBalanceCalculator allows us to test complex accounting logic without an actual database, while still ensuring that our calculations are accurate and our accounting principles are sound.

While this test uses in-memory collections rather than a database, it provides a strong foundation for testing the business logic of our accounting system in a way that would translate easily to a real-world implementation.

Repo

egarim/SivarErp: Open Source ERP

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