Surface NER routing provenance (ModelOrigin, NerModelsInvoked) through the pipeline and WPF harness so English/Tamil routing is observable during POC validation. Consolidate docs into solution-guide and add NER logs, topic filtering, and batch UI fixes in the test harness.
This commit is contained in:
Bilal Nazer Ali
@@ -1,445 +0,0 @@
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# PII Redaction POC — Solution Architecture
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## Purpose
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This document describes the architectural design of the **PII Redaction POC**, a .NET proof-of-concept that intercepts user prompts containing regulated personally identifiable information (PII), redacts sensitive values into stable placeholders, and transmits **only sanitized text** across the LLM trust boundary. The solution is structured for enterprise adoption: clear layer separation, interface-driven composition, dependency injection, and swappable infrastructure adapters (ONNX NER, `Microsoft.Extensions.AI` chat clients).
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The POC validates a compliance-oriented pattern suitable for financial and customer-service workloads where raw PII must not leave the application process when invoking external language models.
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---
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## Canonical Example
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The console application ships with a **sample catalog** (16 prompts). The canonical demo is sample `FullFinancialWithCustomer`. Tamil script, Tanglish, and mixed-script samples run in the **default** `dotnet run` batch (no `--interactive` required). The table below shows the exact strings produced by the production pipeline when the ONNX NER models are loaded (run `scripts/download-ner-model.ps1` and `scripts/download-tamil-ner-model.ps1` first).
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| Stage | Value |
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|-------|-------|
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| **Input** | `Customer Ravi Kumar with email ravi.kumar@gmail.com and phone 9876543210 has LoanNumber LN-456789 and PAN ABCDE1234F. Please summarize this customer issue.` |
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| **Sanitized Output** | `Customer <PERSON_1> with email <EMAIL_1> and phone <PHONE_1> has LoanNumber <LOAN_NUMBER_1> and PAN <PAN_1>. Please summarize this customer issue.` |
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| **Mock LLM Response** | `[Mock LLM Response] Received sanitized prompt (146 chars). No original PII was transmitted.` |
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Detected entities for this prompt:
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| Type | Value | Detection Source |
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|------|-------|------------------|
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| PERSON | Ravi Kumar | Ner |
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| EMAIL | ravi.kumar@gmail.com | Regex |
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| PHONE | 9876543210 | Regex |
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| LOAN_NUMBER | LN-456789 | Domain |
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| PAN | ABCDE1234F | Regex |
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The internal placeholder map (`<PERSON_1>` → `Ravi Kumar`, etc.) is retained in-process and is **not** included in the outbound LLM request.
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---
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## Console Sample Catalog
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Running `dotnet run --project src/PiiRedaction.ConsoleApp` executes all samples sequentially. Use `--list`, `--sample N`, or `--name SampleName` to filter.
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### NER / person-name samples
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These prompts exercise `OnnxNerPiiDetector` and `RoutingOnnxNerModelRunner`. Person names require ONNX models (`models/en/` for English, `models/ta/` for Tamil script). Without models, person spans are not detected. Legacy `models/ner-model.onnx` is still supported for English.
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| Sample | Input (excerpt) | Detected person | Sanitized (excerpt) |
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|--------|-----------------|-----------------|---------------------|
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| **CustomerNameOnly** | Customer Anita Sharma reported unauthorized… | Anita Sharma | Customer `<PERSON_1>` reported unauthorized… |
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| **MrTitlePerson** | Mr. John Smith called about a duplicate debit… | John Smith | `<PERSON_1>` called about a duplicate debit… |
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| **MrsTitlePerson** | Mrs. Lakshmi Reddy requested a callback regarding LN-112233. | Lakshmi Reddy | `<PERSON_1>` requested a callback regarding `<LOAN_NUMBER_1>`. |
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| **DrTitlePerson** | Dr. Jane Doe escalated a complaint… | Jane Doe | `<PERSON_1>` escalated a complaint… |
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| **TwoCustomersInOnePrompt** | Customer Ravi Kumar and Customer Priya Nair… | Ravi Kumar, Priya Nair | Customer `<PERSON_1>` and Customer `<PERSON_2>`… |
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| **PersonWithDomainIds** | Customer Meera Iyer holds CID-7070… | Meera Iyer | Customer `<PERSON_1>` holds `<CUSTOMER_ID_1>`… |
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| **PersonWithEmailNoPhone** | Customer Arjun Mehta wrote from arjun.mehta@company.in… | Arjun Mehta | Customer `<PERSON_1>` wrote from `<EMAIL_1>`… |
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### Tamil / Tanglish / mixed samples
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These prompts exercise `RoutingOnnxNerModelRunner` script routing. Tamil script uses `models/ta/`; Latin Tanglish uses `models/en/`. Mixed prompts may invoke both models.
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| Sample | Input (excerpt) | Detected person | Sanitized (excerpt) |
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|--------|-----------------|-----------------|---------------------|
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| **TamilCustomerNameOnly** | வாடிக்கையாளர் ராஜேஷ் குமார் சேமிப்பு… | ராஜேஷ் குமார் | வாடிக்கையாளர் `<PERSON_1>` சேமிப்பு… |
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| **TamilWithPhonePan** | வாடிக்கையாளர் ராஜேஷ் குமார் தொலைபேசி 9876543210 PAN… | ராஜேஷ் குமார் | `<PERSON_1>` … `<PHONE_1>` … `<PAN_1>` |
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| **TanglishCustomer** | Customer Senthil phone 9876543210… | Senthil | Customer `<PERSON_1>` phone `<PHONE_1>`… |
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| **MixedTamilEnglish** | வாடிக்கையாளர் Ravi Kumar phone 9876543210… | Ravi Kumar | வாடிக்கையாளர் `<PERSON_1>` phone `<PHONE_1>`… |
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| **TamilFullFinancial** | வாடிக்கையாளர் ராஜேஷ் குமார் மின்னஞ்சல் ravi.kumar@gmail.com… | ராஜேஷ் குமார் | Tamil canonical — all placeholder types |
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### Other sample categories
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| Category | Sample | Purpose |
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|----------|--------|---------|
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| NER + Regex + Domain | FullFinancialWithCustomer | End-to-end financial prompt (canonical) |
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| Regex only | AllRegexTypes | Email, phone, PAN, Aadhaar, credit card |
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| Domain only | AllDomainIds | Loan number, customer ID, account number |
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| Negative | NoPiiCleanTicket | Passthrough with no detected PII |
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Sample definitions live in [`SamplePromptCatalog.cs`](../src/PiiRedaction.ConsoleApp/Samples/SamplePromptCatalog.cs).
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---
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## High-Level Data Flow
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The diagram below traces the canonical example from console input through Core sanitization to the Infrastructure LLM adapter. Data labels reflect the canonical strings at each stage.
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```mermaid
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flowchart TB
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subgraph consoleApp [PiiRedaction.ConsoleApp]
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program["Program.cs"]
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di["ServiceRegistration"]
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end
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subgraph core [PiiRedaction.Core]
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sanitizer["PromptSanitizer"]
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composite["CompositePiiDetector"]
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regexDet["RegexPiiDetector"]
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domainDet["DomainRulePiiDetector"]
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onnxDet["OnnxNerPiiDetector"]
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redactor["PlaceholderPiiRedactor"]
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end
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subgraph infra [PiiRedaction.Infrastructure]
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onnxRunner["RoutingOnnxNerModelRunner"]
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enRunner["EnglishOnnxNerRunner"]
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taRunner["TamilOnnxNerRunner"]
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mockLlm["MockLlmPromptService"]
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mockChat["MockChatClient"]
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end
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rawPrompt["Raw prompt with PII"]
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sanitizedPrompt["Sanitized prompt with placeholders"]
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llmResponse["Mock LLM acknowledgment"]
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program -->|"Customer Ravi Kumar ... PAN ABCDE1234F"| sanitizer
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sanitizer --> composite
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composite --> domainDet
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composite --> regexDet
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composite --> onnxDet
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onnxDet --> onnxRunner
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sanitizer --> redactor
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redactor -->|"Customer PERSON_1 ... PAN PAN_1"| sanitizedPrompt
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program -->|"SanitizedPrompt only"| mockLlm
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mockLlm --> mockChat
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mockChat --> llmResponse
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rawPrompt -.-> program
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di -.-> sanitizer
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di -.-> mockLlm
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```
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`RoutingOnnxNerModelRunner` selects English and/or Tamil ONNX models based on script composition in the prompt. See [Dual-Model NER Routing (Tamil + English)](#dual-model-ner-routing-tamil--english) for the routing decision tree.
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---
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## Detection to Redaction Detail
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`PromptSanitizer` orchestrates a two-phase pipeline: **detect** then **redact**. `CompositePiiDetector` aggregates spans from all registered detectors, resolves overlaps by registration order and source priority, and returns a merged entity list. `PlaceholderPiiRedactor` replaces spans right-to-left to preserve indices, assigns stable per-type counters, and builds the in-process placeholder map.
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```mermaid
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flowchart LR
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inputText["Original prompt text"]
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subgraph detectPhase [Detection Phase]
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domainDet["DomainRulePiiDetector"]
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regexDet["RegexPiiDetector"]
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onnxDet["OnnxNerPiiDetector<br/>(RoutingOnnxNerModelRunner)"]
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composite["CompositePiiDetector"]
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merge["Overlap merge and source priority"]
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entityList["PiiEntity list"]
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end
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subgraph redactPhase [Redaction Phase]
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redactor["PlaceholderPiiRedactor"]
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replace["Right-to-left span replacement"]
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placeholderMap["Placeholder map in-process"]
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sanitizedText["Sanitized text"]
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end
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inputText --> domainDet
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inputText --> regexDet
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inputText --> onnxDet
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domainDet --> composite
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regexDet --> composite
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onnxDet --> composite
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composite --> merge
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merge --> entityList
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entityList --> redactor
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inputText --> redactor
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redactor --> replace
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replace --> sanitizedText
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replace --> placeholderMap
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```
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**Overlap resolution rules** (applied by `CompositePiiDetector`):
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1. Detectors run in registration order: **Domain → Regex → ONNX NER**.
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2. On overlapping spans, the first registered detector wins.
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3. Tie-breaking uses source priority: Domain (3) > Regex (2) > NER (1).
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The ONNX NER detector delegates to `RoutingOnnxNerModelRunner`, which routes inference to English and/or Tamil models by script composition. See [Dual-Model NER Routing (Tamil + English)](#dual-model-ner-routing-tamil--english).
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**Placeholder assignment** (applied by `PlaceholderPiiRedactor`):
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- Format: `<{TYPE}_{n}>` (e.g. `<EMAIL_1>`, `<PERSON_1>`).
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- Duplicate values of the same type reuse the same placeholder.
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- Replacement proceeds from highest `StartIndex` to lowest to avoid index drift.
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---
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## Dual-Model NER Routing (Tamil + English)
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Person-name detection uses two ONNX token-classifier models: **English** (`models/en/`, BERT WordPiece) and **Tamil** (`models/ta/`, SentencePiece or WordPiece). `OnnxNerPiiDetector` calls `RoutingOnnxNerModelRunner`, which classifies prompt script via `ScriptRouter` and dispatches to `EnglishOnnxNerRunner` and/or `TamilOnnxNerRunner`. Both runners share `OnnxTokenClassifierRunner` for BIO decoding; only **PERSON** spans are emitted.
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The diagram below expands the detection and NER branches summarized in [High-Level Data Flow](#high-level-data-flow) and [Detection to Redaction Detail](#detection-to-redaction-detail).
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### End-to-end pipeline (with NER branch)
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```mermaid
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flowchart TB
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subgraph Entry["Console entry"]
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A["Program.cs<br/>Host + AddPiiRedactionServices()"]
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B["PromptDemoRunner.RunAsync()"]
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A --> B
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end
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B --> C["SanitizationRequest(OriginalPrompt)"]
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C --> D["PromptSanitizer.Sanitize()"]
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subgraph Detect["CompositePiiDetector.Detect() — registration order"]
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direction TB
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E1["DomainRulePiiDetector<br/>LOAN_NUMBER, CUSTOMER_ID, ACCOUNT_NUMBER"]
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E2["RegexPiiDetector<br/>EMAIL, PHONE, AADHAAR, PAN, CREDIT_CARD"]
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E3["OnnxNerPiiDetector<br/>PERSON (via IOnnxNerModelRunner)"]
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E1 --> MERGE
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E2 --> MERGE
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E3 --> MERGE
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MERGE["Merge overlapping spans<br/>sort: StartIndex ↑, Length ↓, Source priority ↓<br/>(Domain=3, Regex=2, Ner=1)<br/>first candidate wins on overlap"]
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end
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D --> Detect
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MERGE --> F["IReadOnlyList<PiiEntity>"]
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F --> G["PlaceholderPiiRedactor.Redact()<br/>replace spans right-to-left<br/>dedupe by Type|Value → <TYPE_n>"]
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G --> H["SanitizationResult<br/>SanitizedPrompt, DetectedEntities, PlaceholderMap"]
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H --> I["MockLlmPromptService.SendPromptAsync(SanitizedPrompt)"]
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I --> J["Mock LLM response<br/>(sanitized text only)"]
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subgraph NerBranch["OnnxNerPiiDetector branch"]
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E3 --> N1{"RoutingOnnxNerModelRunner<br/>.IsModelAvailable?"}
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N1 -->|no| N2["return []"]
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N1 -->|yes| N3["RoutingOnnxNerModelRunner<br/>.PredictEntities()"]
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end
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```
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### RoutingOnnxNerModelRunner decision tree
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`ScriptRouter.GetComposition` scans each character once. Tamil letters (U+0B80–U+0BFF) and ASCII Latin letters (`char.IsAsciiLetter`) determine the route. When both scripts appear, classification is **Mixed** (early exit).
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```mermaid
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flowchart TB
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IN["text"] --> SR["ScriptRouter.GetComposition(text)<br/>scan each char"]
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SR --> C1{"LatinOnly?"}
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SR --> C2{"TamilOnly?"}
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SR --> C3{"Mixed?"}
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SR --> C4{"NoLetters?"}
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C1 -->|yes| EN1{"EnglishOnnxNerRunner<br/>.IsModelAvailable?"}
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EN1 -->|yes| EN_RUN["EnglishOnnxNerRunner.PredictEntities(text)"]
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EN1 -->|no| SKIP1["skip English"]
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EN_RUN --> ACC
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SKIP1 --> ACC
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C2 -->|yes| TA_GATE{"EnableTamilNer<br/>&& TamilOnnxNerRunner<br/>.IsModelAvailable?"}
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TA_GATE -->|yes| TA_RUN["TamilOnnxNerRunner.PredictEntities(text)"]
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TA_GATE -->|no| SKIP2["skip Tamil"]
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TA_RUN --> ACC
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SKIP2 --> ACC
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C3 -->|yes| EN2{"English available?"}
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EN2 -->|yes| EN_MIX["EnglishOnnxNerRunner.PredictEntities(text)"]
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EN2 -->|no| SKIP3["skip English"]
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EN_MIX --> TA_GATE2{"EnableTamilNer<br/>&& Tamil available?"}
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SKIP3 --> TA_GATE2
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TA_GATE2 -->|yes| TA_MIX["TamilOnnxNerRunner.PredictEntities(text)"]
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TA_GATE2 -->|no| SKIP4["skip Tamil"]
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TA_MIX --> ACC
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SKIP4 --> ACC
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C4 -->|yes| EMPTY["no NER inference"]
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EMPTY --> OUT_EMPTY["return []"]
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subgraph EN_Pipeline["EnglishOnnxNerRunner"]
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EN_RUN --> EN_ENC["BertWordPieceEncoder<br/>(model dir vocab.txt)"]
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EN_ENC --> EN_OCR["OnnxTokenClassifierRunner<br/>NerLabelConfig.English<br/>B-PER / I-PER / B-PERSON / I-PERSON"]
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end
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subgraph TA_Pipeline["TamilOnnxNerRunner"]
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TA_RUN --> TA_ENC["TokenClassifierEncoderFactory.Create()<br/>vocab.txt → BertWordPieceEncoder<br/>else SentencePiece (*.bpe.model, spiece.model, tokenizer.model)"]
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TA_ENC --> TA_OCR["OnnxTokenClassifierRunner<br/>NerLabelConfig.Tamil<br/>label contains 'person' (case-insensitive)"]
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end
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subgraph SharedInference["OnnxTokenClassifierRunner (shared)"]
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ENC["Encode(text, max 128 tokens)"]
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ONNX["ONNX InferenceSession.Run<br/>input_ids + attention_mask [+ token_type_ids]"]
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ARGMAX["Per-token argmax over logits"]
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BIO["BIO decode → PiiEntityType.Person<br/>PiiDetectionSource.Ner"]
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ENC --> ONNX --> ARGMAX --> BIO
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end
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EN_OCR --> SharedInference
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TA_OCR --> SharedInference
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BIO --> ACC["accumulate entities"]
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ACC --> MERGE["MergePersonSpans()<br/>sort: Length ↓, StartIndex ↑<br/>drop overlapping spans<br/>(longer span wins)"]
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MERGE --> OUT["return merged PERSON entities"]
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```
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### Routing rules
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| Rule | Source | Behavior |
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|------|--------|----------|
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| **Script classification** | `ScriptRouter.GetComposition` | Single pass over characters. Tamil letter = U+0B80–U+0BFF. Latin letter = `char.IsAsciiLetter`. Both seen → `Mixed` (early exit). Neither → `NoLetters`. Tamil only → `TamilOnly`. Latin only → `LatinOnly`. |
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| **LatinOnly** | `RoutingOnnxNerModelRunner` | Run **English only** if `englishRunner.IsModelAvailable`. |
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| **TamilOnly** | `RoutingOnnxNerModelRunner` | Run **Tamil only** if `EnableTamilNer` (default `true` in `PiiRedactionOptions`) **and** `tamilRunner.IsModelAvailable`. |
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| **Mixed** | `RoutingOnnxNerModelRunner` | Run **both** models independently on the **full text** (English if available; Tamil if `EnableTamilNer` and available). |
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| **NoLetters** | `RoutingOnnxNerModelRunner` | No NER inference; returns `[]` from routing (before merge). |
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| **Model availability gate** | `OnnxNerPiiDetector` | If `RoutingOnnxNerModelRunner.IsModelAvailable` is false, NER detector returns `[]` (English OR Tamil available when Tamil enabled). |
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| **Post-route merge** | `MergePersonSpans` | After EN/TA results are concatenated, overlapping PERSON spans are deduped; **longer span wins**, then ordered by `StartIndex`. |
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| **Composite merge** | `CompositePiiDetector` | Domain → Regex → NER all run. Overlaps resolved globally: earlier registration order + longer span + higher source priority (Domain > Regex > Ner). |
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| **Encoder choice** | `EnglishOnnxNerRunner` vs `TamilOnnxNerRunner` | English always uses `BertWordPieceEncoder`. Tamil uses factory: `vocab.txt` → WordPiece; else first SentencePiece file found; fallback WordPiece with warning. |
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| **NER output scope** | `OnnxTokenClassifierRunner` | Only **PERSON** entities decoded from BIO tags; max sequence length **128** tokens. |
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---
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## Runtime Sequence
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```mermaid
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sequenceDiagram
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participant User
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participant Program as Program.cs
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participant DI as ServiceProvider
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participant Sanitizer as PromptSanitizer
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participant Detector as CompositePiiDetector
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participant Redactor as PlaceholderPiiRedactor
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participant LlmSvc as MockLlmPromptService
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participant Chat as MockChatClient
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User->>Program: Start application
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Program->>DI: Resolve IPromptSanitizer, ILlmPromptService
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DI-->>Program: Sanitizer, LlmService
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alt Interactive mode
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User->>Program: Enter prompt via console
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else Default mode
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Program->>Program: Load canonical sample prompt
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end
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Program->>Sanitizer: Sanitize(SanitizationRequest)
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Sanitizer->>Detector: Detect(originalPrompt)
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Detector-->>Sanitizer: IReadOnlyList PiiEntity
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Sanitizer->>Redactor: Redact(originalPrompt, entities)
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Redactor-->>Sanitizer: RedactionResult
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Sanitizer-->>Program: SanitizationResult
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Program->>Program: Display detected entities
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Program->>Program: Display sanitized prompt
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Program->>Program: Display placeholder map in-process
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Program->>LlmSvc: SendPromptAsync(sanitizedPrompt)
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Note over Program,LlmSvc: Placeholder map never passed
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LlmSvc->>Chat: GetResponseAsync(user message)
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Chat-->>LlmSvc: Assistant response
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LlmSvc-->>Program: Mock LLM response string
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Program->>User: Write LLM response to console
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```
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---
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## Trust Boundary
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|
||||
The LLM boundary is the point at which data leaves the application process via `ILlmPromptService` / `IChatClient`. Only the sanitized prompt crosses this boundary. Original PII values, detection metadata, and the placeholder-to-value map remain in-process.
|
||||
|
||||
```mermaid
|
||||
flowchart TB
|
||||
subgraph inProcess [In-Process Trust Zone]
|
||||
originalPrompt["Original prompt with raw PII"]
|
||||
detectedEntities["Detected PiiEntity list"]
|
||||
placeholderMap["Placeholder map"]
|
||||
sanitizationResult["SanitizationResult"]
|
||||
consoleDisplay["Console audit output"]
|
||||
end
|
||||
|
||||
subgraph llmBoundary [LLM Trust Boundary]
|
||||
sanitizedOnly["Sanitized prompt text only"]
|
||||
end
|
||||
|
||||
subgraph externalLlm [External LLM Provider]
|
||||
chatClient["IChatClient implementation"]
|
||||
modelInference["Model inference"]
|
||||
end
|
||||
|
||||
originalPrompt --> sanitizationResult
|
||||
detectedEntities --> sanitizationResult
|
||||
placeholderMap --> sanitizationResult
|
||||
sanitizationResult --> consoleDisplay
|
||||
sanitizationResult -->|"SendPromptAsync"| sanitizedOnly
|
||||
sanitizedOnly --> chatClient
|
||||
chatClient --> modelInference
|
||||
|
||||
originalPrompt -.-x|"Never transmitted"| chatClient
|
||||
placeholderMap -.-x|"Never transmitted"| chatClient
|
||||
detectedEntities -.-x|"Never transmitted"| chatClient
|
||||
```
|
||||
|
||||
In the POC, `MockChatClient` simulates the external provider without network I/O. Replacing it with Azure OpenAI or another `IChatClient` implementation does not change the trust model: `MockLlmPromptService` (or a future production adapter) continues to accept only the sanitized string.
|
||||
|
||||
---
|
||||
|
||||
## Project Responsibilities
|
||||
|
||||
| Project | Layer | Responsibility |
|
||||
|---------|-------|----------------|
|
||||
| `PiiRedaction.ConsoleApp` | Presentation | Application entry point; reads prompt (sample or interactive); bootstraps `IHost` and DI via `AddPiiRedactionServices`; orchestrates sanitization and LLM invocation; renders audit output (detected entities, sanitized text, placeholder map). |
|
||||
| `PiiRedaction.Core` | Domain / Application | Defines abstractions (`IPiiDetector`, `IPiiRedactor`, `IPromptSanitizer`, `ILlmPromptService`); implements detection strategies (`RegexPiiDetector`, `DomainRulePiiDetector`, `OnnxNerPiiDetector`, `CompositePiiDetector`); implements `PlaceholderPiiRedactor` and `PromptSanitizer`; owns domain models (`PiiEntity`, `SanitizationResult`, `RedactionResult`) and configuration (`PiiRedactionOptions`). Has no dependency on ONNX Runtime or LLM SDKs. |
|
||||
| `PiiRedaction.Infrastructure` | Infrastructure | Implements technical adapters: `RoutingOnnxNerModelRunner`, `EnglishOnnxNerRunner`, `TamilOnnxNerRunner` (ONNX Runtime inference), `MockChatClient` and `MockLlmPromptService` (`Microsoft.Extensions.AI`); depends on Core abstractions and is swappable without changing domain logic. |
|
||||
| `tests/PiiRedaction.Core.Tests` | Test | Unit and integration tests for detectors, redactor, sanitizer, overlap rules, golden prompt scenarios (`PromptScenarioCatalog`), and LLM boundary assertions. |
|
||||
| `tests/PiiRedaction.Infrastructure.Tests` | Test | Tests for mock LLM behavior and ONNX runner load semantics. |
|
||||
|
||||
**Dependency direction:** `ConsoleApp` → `Infrastructure` → `Core`. Core references no outer layers, preserving the Dependency Inversion Principle and enabling future hosts (ASP.NET Core API, worker services) to reuse the same Core and Infrastructure assemblies.
|
||||
|
||||
---
|
||||
|
||||
## Key Abstractions and Extension Points
|
||||
|
||||
| Abstraction | Defined In | Default Implementation | Extension |
|
||||
|-------------|------------|------------------------|-----------|
|
||||
| `IPiiDetector` | Core | `CompositePiiDetector` wrapping Domain, Regex, ONNX | Add new detector; register in composite order |
|
||||
| `IPiiRedactor` | Core | `PlaceholderPiiRedactor` | Replace with hashing, vault-backed tokens, etc. |
|
||||
| `IPromptSanitizer` | Core | `PromptSanitizer` | Unlikely to change; orchestrates detect + redact |
|
||||
| `ILlmPromptService` | Core | `MockLlmPromptService` | Production adapter with telemetry, retry, policy |
|
||||
| `IChatClient` | Microsoft.Extensions.AI | `MockChatClient` | Azure OpenAI, OpenAI, or other provider SDK |
|
||||
| `IOnnxNerModelRunner` | Core | `RoutingOnnxNerModelRunner` | Script-based routing to English (BERT WordPiece) and Tamil (SentencePiece) ONNX models |
|
||||
|
||||
---
|
||||
|
||||
## Configuration Surface
|
||||
|
||||
Runtime behavior is controlled via `appsettings.json` under the `PiiRedaction` section:
|
||||
|
||||
| Setting | Effect |
|
||||
|---------|--------|
|
||||
| `OnnxModelPath` | Legacy English model path (`models/ner-model.onnx`). Used as fallback when `models/en/` is absent. |
|
||||
| `EnglishOnnxModelPath` | Primary English ONNX model (`models/en/ner-model.onnx`). |
|
||||
| `TamilOnnxModelPath` | Tamil ONNX model (`models/ta/model.onnx`). |
|
||||
| `EnableTamilNer` | When `false`, routing uses English model only. Default `true`. |
|
||||
|
||||
Download model assets with `scripts/download-ner-model.ps1` and `scripts/download-tamil-ner-model.ps1`.
|
||||
|
||||
---
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- [NER models](ner-models.md) — English/Tamil ONNX model IDs, assets, labels, and integration reference
|
||||
- [README](../README.md) — build, run, configuration, and testing instructions
|
||||
- [ServiceCollectionExtensions.cs](../src/PiiRedaction.ConsoleApp/DependencyInjection/ServiceCollectionExtensions.cs) — DI registration and detector ordering
|
||||
- [PromptScenarioCatalog.cs](../tests/PiiRedaction.Core.Tests/TestSupport/PromptScenarioCatalog.cs) — focused golden pipeline scenarios including the canonical example
|
||||
@@ -1,62 +0,0 @@
|
||||
# Git setup — Xenovex (xts.xenovex.com)
|
||||
|
||||
This repository is ready for push to your Xenovex Git server after you create a remote repository.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- Git 2.x (installed at `C:\Program Files\Git\bin\git.exe`)
|
||||
- Access to https://xts.xenovex.com/explore/repos
|
||||
- .NET 10 SDK for build/test
|
||||
|
||||
## 1. Create the remote repository
|
||||
|
||||
1. Sign in to **https://xts.xenovex.com**
|
||||
2. Open **Explore repos** (or **New repository**)
|
||||
3. Create a new empty repository, e.g. `llm-pii-poc`
|
||||
4. Copy the **HTTPS** or **SSH** clone URL (example shapes):
|
||||
- `https://xts.xenovex.com/<org-or-user>/llm-pii-poc.git`
|
||||
- `git@xts.xenovex.com:<org-or-user>/llm-pii-poc.git`
|
||||
|
||||
Do **not** initialize the remote with a README if you are pushing an existing local history.
|
||||
|
||||
## 2. Add remote and push (from repository root)
|
||||
|
||||
```powershell
|
||||
cd C:\Users\bilal.n\Projects\llm-pii-poc
|
||||
|
||||
# Use full path if git is not on PATH
|
||||
$git = "C:\Program Files\Git\bin\git.exe"
|
||||
|
||||
& $git remote add origin <YOUR_CLONE_URL>
|
||||
& $git branch -M main
|
||||
& $git push -u origin main
|
||||
```
|
||||
|
||||
If the remote already has commits (e.g. auto-generated README), either use an empty remote or:
|
||||
|
||||
```powershell
|
||||
& $git pull origin main --rebase
|
||||
& $git push -u origin main
|
||||
```
|
||||
|
||||
## 3. What is committed vs excluded
|
||||
|
||||
| Included | Excluded (`.gitignore`) |
|
||||
|----------|-------------------------|
|
||||
| Source (`src/`), tests, scripts, docs | `bin/`, `obj/`, `.vs/` |
|
||||
| `README.md`, `PiiRedaction.slnx` | `models/*.onnx`, `vocab.txt`, `ner-labels.txt` (~431MB model) |
|
||||
| `models/.gitkeep` (empty models folder) | `scratch/` |
|
||||
|
||||
After clone, download the NER model locally:
|
||||
|
||||
```powershell
|
||||
.\scripts\download-ner-model.ps1
|
||||
```
|
||||
|
||||
## 4. Verify after clone
|
||||
|
||||
```powershell
|
||||
dotnet build
|
||||
dotnet test
|
||||
dotnet run --project src/PiiRedaction.ConsoleApp -- --sample 0
|
||||
```
|
||||
@@ -1,548 +0,0 @@
|
||||
# NER Models for PII Redaction
|
||||
|
||||
This document describes the **Named Entity Recognition (NER)** ONNX models at the core of the PII Redaction POC. Person-name detection is the only NER responsibility in this solution; structured identifiers (email, phone, PAN, domain IDs) are handled by regex and domain-rule detectors.
|
||||
|
||||
For pipeline placement, trust boundaries, and routing diagrams, see [architecture.md](architecture.md). For the Tamil/Tanglish implementation plan and success metrics, see [tamil-tanglish-ner-plan.md](tamil-tanglish-ner-plan.md).
|
||||
|
||||
---
|
||||
|
||||
## 1. Executive Summary
|
||||
|
||||
The POC uses **dual-model ONNX NER routing** to redact **person-name PII** before prompts reach an LLM:
|
||||
|
||||
| Script in prompt | Model invoked | Typical use case |
|
||||
|------------------|---------------|------------------|
|
||||
| Latin only (`LatinOnly`) | English (`dslim/bert-base-NER`) | English names, Indian names in Roman script, **Tanglish** |
|
||||
| Tamil only (`TamilOnly`) | Tamil (`prachuryyaIITG/SampurNER_Tamil_IndicBERTv2`) | Tamil-script customer names |
|
||||
| Mixed (`Mixed`) | **Both** models on the full text; spans merged | Code-mixed Indian CS prompts |
|
||||
| No letters (`NoLetters`) | Neither | Digits-only or symbol-only text |
|
||||
|
||||
`RoutingOnnxNerModelRunner` classifies script via `ScriptRouter`, delegates to `EnglishOnnxNerRunner` and/or `TamilOnnxNerRunner`, and merges overlapping PERSON spans (longer span wins). Only **PERSON** entities are emitted to the redaction pipeline; all other NER labels are discarded.
|
||||
|
||||
---
|
||||
|
||||
## 2. English Model
|
||||
|
||||
### Hugging Face model ID
|
||||
|
||||
**[`dslim/bert-base-NER`](https://huggingface.co/dslim/bert-base-NER)**
|
||||
|
||||
### Architecture
|
||||
|
||||
| Property | Value |
|
||||
|----------|-------|
|
||||
| Base | BERT-base (uncased), ~110M parameters |
|
||||
| Task | Token classification (NER) |
|
||||
| Tokenizer | **WordPiece** via `vocab.txt` (`BertWordPieceEncoder`) |
|
||||
| Runtime | ONNX via Microsoft.ML.OnnxRuntime |
|
||||
| Export | Hugging Face Optimum (`ORTModelForTokenClassification`) or pre-exported ONNX from HF |
|
||||
|
||||
### Labels (BIO)
|
||||
|
||||
The English model uses standard CoNLL-style BIO tags. The POC maps only **person** labels to `PiiEntityType.Person`:
|
||||
|
||||
| Label | Mapped to PERSON |
|
||||
|-------|------------------|
|
||||
| `O` | No |
|
||||
| `B-PER`, `I-PER` | Yes |
|
||||
| `B-PERSON`, `I-PERSON` | Yes |
|
||||
| `B-ORG`, `I-ORG`, `B-LOC`, `I-LOC`, `B-MISC`, `I-MISC` | No |
|
||||
|
||||
Full label list is written to `ner-labels.txt` at download time from the model `config.json` `id2label` map (typically 9 labels for this model).
|
||||
|
||||
Label matching is implemented in `NerLabelConfig.English`:
|
||||
|
||||
```19:22:src/PiiRedaction.Infrastructure/Onnx/NerLabelConfig.cs
|
||||
private static bool IsEnglishPersonLabel(string label) =>
|
||||
label is "B-PER" or "I-PER" or "B-PERSON" or "I-PERSON"
|
||||
|| (label.EndsWith("-PER", StringComparison.Ordinal) &&
|
||||
(label.StartsWith("B-", StringComparison.Ordinal) || label.StartsWith("I-", StringComparison.Ordinal)));
|
||||
```
|
||||
|
||||
### Asset paths
|
||||
|
||||
| File | Primary path (`appsettings.json`) | Legacy fallback |
|
||||
|------|----------------------------------|-----------------|
|
||||
| ONNX model | `models/en/ner-model.onnx` | `models/ner-model.onnx` (`OnnxModelPath`) |
|
||||
| Vocabulary | `models/en/vocab.txt` | `models/vocab.txt` |
|
||||
| Labels | `models/en/ner-labels.txt` | `models/ner-labels.txt` |
|
||||
|
||||
`EnglishOnnxNerRunner` resolves the model path with primary + legacy fallback:
|
||||
|
||||
```15:22:src/PiiRedaction.Infrastructure/Onnx/EnglishOnnxNerRunner.cs
|
||||
var modelPath = OnnxAssetPathResolver.ResolveModelPath(
|
||||
options.Value.EnglishOnnxModelPath,
|
||||
options.Value.OnnxModelPath);
|
||||
|
||||
var modelDirectory = Path.GetDirectoryName(modelPath) ?? Environment.CurrentDirectory;
|
||||
var labels = OnnxAssetPathResolver.LoadLabels(modelDirectory);
|
||||
var encoder = new BertWordPieceEncoder(modelDirectory, logger);
|
||||
_runner = new OnnxTokenClassifierRunner(modelPath, encoder, NerLabelConfig.English, labels, logger);
|
||||
```
|
||||
|
||||
> **Note:** `scripts/download-ner-model.ps1` writes assets to `models/` (repository root). For the configured primary path, copy or move them into `models/en/`, or rely on the `OnnxModelPath` fallback.
|
||||
|
||||
### Download script
|
||||
|
||||
```powershell
|
||||
.\scripts\download-ner-model.ps1
|
||||
```
|
||||
|
||||
Or with Python directly:
|
||||
|
||||
```bash
|
||||
python scripts/download-ner-model.py
|
||||
```
|
||||
|
||||
**Behavior:**
|
||||
|
||||
1. If Python + Optimum are available → exports `dslim/bert-base-NER` to ONNX under `models/`.
|
||||
2. Otherwise → downloads pre-exported ONNX from `https://huggingface.co/dslim/bert-base-NER/resolve/main/onnx/` (`model.onnx`, `vocab.txt`, `config.json` → `ner-labels.txt`).
|
||||
|
||||
---
|
||||
|
||||
## 3. Tamil Model
|
||||
|
||||
### Hugging Face model ID
|
||||
|
||||
**[`prachuryyaIITG/SampurNER_Tamil_IndicBERTv2`](https://huggingface.co/prachuryyaIITG/SampurNER_Tamil_IndicBERTv2)**
|
||||
|
||||
(SampurNER Tamil IndicBERTv2 — fine-grained NER for Tamil script.)
|
||||
|
||||
### Why SampurNER IndicBERTv2 vs MuRIL
|
||||
|
||||
| Criterion | SampurNER Tamil IndicBERTv2 | MuRIL (fallback candidate) |
|
||||
|-----------|----------------------------|----------------------------|
|
||||
| Tamil NER training | Fine-grained SampurNER dataset (Tamil-specific labels) | General multilingual; NER requires separate fine-tune |
|
||||
| Model size | ~0.3B parameters (IndicBERTv2, ~278M base) | ~0.6B parameters |
|
||||
| POC fit | Lighter memory footprint; ONNX export path validated in this repo | Reserved for Phase 5 if Tamil recall is insufficient |
|
||||
| Indian financial context | Trained on Indian-language NER corpus; person subtypes map cleanly to PERSON | Heavier; eval-driven swap only |
|
||||
|
||||
See [tamil-tanglish-ner-plan.md](tamil-tanglish-ner-plan.md) §3 for the original selection rationale.
|
||||
|
||||
### Architecture
|
||||
|
||||
| Property | Value |
|
||||
|----------|-------|
|
||||
| Base | IndicBERTv2 (AI4Bharat), ~0.3B parameters |
|
||||
| Task | Fine-grained token classification |
|
||||
| Tokenizer | **WordPiece** when `vocab.txt` is present (this repo's export path); SentencePiece fallback if `sentencepiece.bpe.model` / `spiece.model` exists |
|
||||
| Runtime | Same shared `OnnxTokenClassifierRunner` as English |
|
||||
|
||||
### Tokenizer: WordPiece, not SentencePiece (in practice)
|
||||
|
||||
The Hugging Face repo for this model does **not** ship a SentencePiece model file. The download scripts extract **WordPiece** assets from `tokenizer.json` → `vocab.txt`. `TokenClassifierEncoderFactory` prefers `vocab.txt`:
|
||||
|
||||
```10:19:src/PiiRedaction.Infrastructure/Onnx/TokenClassifierEncoderFactory.cs
|
||||
public static ITokenClassifierEncoder Create(string modelDirectory, ILogger logger)
|
||||
{
|
||||
var vocabPath = OnnxAssetPathResolver.ResolveAssetPath(Path.Combine(modelDirectory, "vocab.txt"));
|
||||
if (File.Exists(vocabPath))
|
||||
{
|
||||
logger.LogInformation(
|
||||
"Using WordPiece tokenizer (vocab.txt) from {ModelDirectory}.",
|
||||
modelDirectory);
|
||||
return new BertWordPieceEncoder(modelDirectory, logger);
|
||||
}
|
||||
```
|
||||
|
||||
The PowerShell Tamil download script emits an explicit warning when WordPiece assets are saved instead of SentencePiece.
|
||||
|
||||
### Labels (fine-grained person tags)
|
||||
|
||||
SampurNER uses fine-grained BIO tags (e.g. `B-person-politician`, `I-person-artist`, `B-location`, `O`). The POC treats **any label containing `person`** (case-insensitive) as a person span:
|
||||
|
||||
```24:25:src/PiiRedaction.Infrastructure/Onnx/NerLabelConfig.cs
|
||||
private static bool IsTamilPersonLabel(string label) =>
|
||||
label.Contains("person", StringComparison.OrdinalIgnoreCase);
|
||||
```
|
||||
|
||||
Unit tests lock this behavior:
|
||||
|
||||
```20:27:tests/PiiRedaction.Infrastructure.Tests/Onnx/NerLabelConfigTests.cs
|
||||
[TestCase("B-person-politician", true)]
|
||||
[TestCase("I-person-artist", true)]
|
||||
[TestCase("B-location", false)]
|
||||
[TestCase("O", false)]
|
||||
public void Tamil_IsPersonLabel_MatchesFineGrainedTags(string label, bool expected)
|
||||
{
|
||||
NerLabelConfig.Tamil.IsPersonLabel(label).Should().Be(expected);
|
||||
}
|
||||
```
|
||||
|
||||
### Asset paths
|
||||
|
||||
| File | Path |
|
||||
|------|------|
|
||||
| ONNX model | `models/ta/model.onnx` |
|
||||
| Tokenizer | `models/ta/vocab.txt` (WordPiece, preferred) **or** `models/ta/sentencepiece.bpe.model` |
|
||||
| Labels | `models/ta/ner-labels.txt` |
|
||||
| Optional | `models/ta/tokenizer.json` (intermediate export artifact) |
|
||||
|
||||
### Download script
|
||||
|
||||
```powershell
|
||||
.\scripts\download-tamil-ner-model.ps1
|
||||
```
|
||||
|
||||
Or with Python directly:
|
||||
|
||||
```bash
|
||||
python scripts/download-tamil-ner-model.py
|
||||
```
|
||||
|
||||
**Behavior:**
|
||||
|
||||
1. Python + Optimum → full export to `models/ta/` including ONNX, labels, and tokenizer assets.
|
||||
2. PowerShell fallback → downloads `onnx/model.onnx` from Hugging Face when published; otherwise requires Python export (pre-exported ONNX may return 404).
|
||||
|
||||
`TamilOnnxNerRunner` wiring:
|
||||
|
||||
```15:19:src/PiiRedaction.Infrastructure/Onnx/TamilOnnxNerRunner.cs
|
||||
var modelPath = OnnxAssetPathResolver.ResolveModelPath(options.Value.TamilOnnxModelPath);
|
||||
var modelDirectory = Path.GetDirectoryName(modelPath) ?? Environment.CurrentDirectory;
|
||||
var labels = OnnxAssetPathResolver.LoadLabels(modelDirectory);
|
||||
var encoder = TokenClassifierEncoderFactory.Create(modelDirectory, logger);
|
||||
_runner = new OnnxTokenClassifierRunner(modelPath, encoder, NerLabelConfig.Tamil, labels, logger);
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 4. Why These Models
|
||||
|
||||
Evidence-based rationale for this Indian financial POC:
|
||||
|
||||
| Requirement | Decision |
|
||||
|-------------|----------|
|
||||
| **English + Indian Latin names** | `dslim/bert-base-NER` is industry-standard, pre-integrated, and handles many Indian names in Roman script (e.g. `Ravi Kumar`, `Anita Sharma`) |
|
||||
| **Tamil script names** | English BERT is out-of-vocabulary for Tamil letters (U+0B80–U+0BFF); a Tamil-trained NER model is required |
|
||||
| **Tanglish (Roman-script Tamil)** | Routed to the **English** model only (`ScriptComposition.LatinOnly`); no Tamil ONNX on Latin-only text |
|
||||
| **Code-mixed prompts** | Both models run on the full prompt; `MergePersonSpans` deduplicates overlaps |
|
||||
| **Deployable size** | English ~431 MB ONNX (FP32); Tamil IndicBERTv2 ~0.3B params — smaller than MuRIL ~0.6B |
|
||||
| **ONNX export path** | Both models export via Hugging Face Optimum; English has pre-exported ONNX on HF; Tamil may require local Python export |
|
||||
| **PERSON-only scope** | POC redacts person names via NER; org/location/misc labels are intentionally ignored to limit false positives |
|
||||
|
||||
---
|
||||
|
||||
## 5. How They Integrate
|
||||
|
||||
### Configuration (`appsettings.json`)
|
||||
|
||||
```1:8:src/PiiRedaction.ConsoleApp/appsettings.json
|
||||
{
|
||||
"PiiRedaction": {
|
||||
"OnnxModelPath": "models/ner-model.onnx",
|
||||
"EnglishOnnxModelPath": "models/en/ner-model.onnx",
|
||||
"TamilOnnxModelPath": "models/ta/model.onnx",
|
||||
"EnableTamilNer": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Options type:
|
||||
|
||||
```3:14:src/PiiRedaction.Core/Configuration/PiiRedactionOptions.cs
|
||||
public sealed class PiiRedactionOptions
|
||||
{
|
||||
public const string SectionName = "PiiRedaction";
|
||||
|
||||
public string OnnxModelPath { get; set; } = "models/ner-model.onnx";
|
||||
|
||||
public string EnglishOnnxModelPath { get; set; } = "models/en/ner-model.onnx";
|
||||
|
||||
public string TamilOnnxModelPath { get; set; } = "models/ta/model.onnx";
|
||||
|
||||
public bool EnableTamilNer { get; set; } = true;
|
||||
}
|
||||
```
|
||||
|
||||
Set `EnableTamilNer` to `false` for English-only routing.
|
||||
|
||||
### Dependency injection
|
||||
|
||||
```34:36:src/PiiRedaction.ConsoleApp/DependencyInjection/ServiceCollectionExtensions.cs
|
||||
services.AddSingleton<EnglishOnnxNerRunner>();
|
||||
services.AddSingleton<TamilOnnxNerRunner>();
|
||||
services.AddSingleton<IOnnxNerModelRunner, RoutingOnnxNerModelRunner>();
|
||||
```
|
||||
|
||||
`OnnxNerPiiDetector` consumes `IOnnxNerModelRunner` (the router) and returns `[]` when no model is available — **fail-open** for person detection.
|
||||
|
||||
### Script routing (`ScriptRouter`)
|
||||
|
||||
```11:41:src/PiiRedaction.Core/Detection/ScriptRouter.cs
|
||||
public ScriptComposition GetComposition(string text)
|
||||
{
|
||||
ArgumentNullException.ThrowIfNull(text);
|
||||
|
||||
var hasLatin = false;
|
||||
var hasTamil = false;
|
||||
|
||||
foreach (var character in text)
|
||||
{
|
||||
if (IsTamilLetter(character))
|
||||
{
|
||||
hasTamil = true;
|
||||
}
|
||||
else if (char.IsAsciiLetter(character))
|
||||
{
|
||||
hasLatin = true;
|
||||
}
|
||||
|
||||
if (hasLatin && hasTamil)
|
||||
{
|
||||
return ScriptComposition.Mixed;
|
||||
}
|
||||
}
|
||||
// ...
|
||||
return hasTamil ? ScriptComposition.TamilOnly : ScriptComposition.LatinOnly;
|
||||
}
|
||||
```
|
||||
|
||||
### Routing runner (`RoutingOnnxNerModelRunner`)
|
||||
|
||||
```39:79:src/PiiRedaction.Infrastructure/Onnx/RoutingOnnxNerModelRunner.cs
|
||||
public IReadOnlyList<PiiEntity> PredictEntities(string text)
|
||||
{
|
||||
var composition = _scriptRouter.GetComposition(text);
|
||||
var entities = new List<PiiEntity>();
|
||||
|
||||
switch (composition)
|
||||
{
|
||||
case ScriptComposition.LatinOnly:
|
||||
if (_englishRunner.IsModelAvailable)
|
||||
{
|
||||
entities.AddRange(_englishRunner.PredictEntities(text));
|
||||
}
|
||||
break;
|
||||
case ScriptComposition.TamilOnly:
|
||||
if (_enableTamilNer && _tamilRunner.IsModelAvailable)
|
||||
{
|
||||
entities.AddRange(_tamilRunner.PredictEntities(text));
|
||||
}
|
||||
break;
|
||||
case ScriptComposition.Mixed:
|
||||
if (_englishRunner.IsModelAvailable)
|
||||
{
|
||||
entities.AddRange(_englishRunner.PredictEntities(text));
|
||||
}
|
||||
if (_enableTamilNer && _tamilRunner.IsModelAvailable)
|
||||
{
|
||||
entities.AddRange(_tamilRunner.PredictEntities(text));
|
||||
}
|
||||
break;
|
||||
case ScriptComposition.NoLetters:
|
||||
break;
|
||||
}
|
||||
|
||||
return MergePersonSpans(entities);
|
||||
}
|
||||
```
|
||||
|
||||
### Shared inference (`OnnxTokenClassifierRunner`)
|
||||
|
||||
Both runners share:
|
||||
|
||||
- **Encode** → `input_ids`, `attention_mask`, optional `token_type_ids`
|
||||
- **Argmax** over per-token logits
|
||||
- **BIO decode** → `PiiEntityType.Person` with `PiiDetectionSource.Ner`
|
||||
- **Max sequence length: 128 tokens**
|
||||
|
||||
```13:13:src/PiiRedaction.Infrastructure/Onnx/OnnxTokenClassifierRunner.cs
|
||||
private const int MaxSequenceLength = 128;
|
||||
```
|
||||
|
||||
### End-to-end flow
|
||||
|
||||
```
|
||||
Prompt → CompositePiiDetector → OnnxNerPiiDetector
|
||||
→ RoutingOnnxNerModelRunner → ScriptRouter
|
||||
→ EnglishOnnxNerRunner / TamilOnnxNerRunner
|
||||
→ OnnxTokenClassifierRunner → PERSON entities
|
||||
→ PlaceholderPiiRedactor → <PERSON_n>
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 6. Evidence from Codebase
|
||||
|
||||
### Real-model tests (`Category=RealModel`)
|
||||
|
||||
English direct inference — `RealNerModelRunnerTests`:
|
||||
|
||||
```15:47:tests/PiiRedaction.Infrastructure.Tests/Onnx/RealNerModelRunnerTests.cs
|
||||
[Category("RealModel")]
|
||||
public sealed class RealNerModelRunnerTests : RealNerModelFixture
|
||||
{
|
||||
[TestCase("Customer Ravi Kumar called about billing.", "Ravi", "Ravi Kumar")]
|
||||
[TestCase("Mr. John Smith called about a duplicate debit.", "John", "John Smith")]
|
||||
public void PredictEntities_DetectsPersonWithCorrectSpan(...)
|
||||
```
|
||||
|
||||
English pipeline — `RealNerPipelineTests` (`Category=RealModel`): canonical `FullFinancialWithCustomer`, multi-person, clean-ticket negative.
|
||||
|
||||
Fixture skips when model missing:
|
||||
|
||||
```5:6:tests/TestSupport.Shared/RealNerModelPaths.cs
|
||||
public const string ModelMissingMessage =
|
||||
"ONNX model not found. Run scripts/download-ner-model.ps1 from the repository root.";
|
||||
```
|
||||
|
||||
Resolves `models/en/ner-model.onnx` then `models/ner-model.onnx`.
|
||||
|
||||
### Tamil tests (`Category=TamilNer`)
|
||||
|
||||
Direct Tamil runner — `RealTamilNerModelRunnerTests`:
|
||||
|
||||
```9:69:tests/PiiRedaction.Infrastructure.Tests/Onnx/RealTamilNerModelRunnerTests.cs
|
||||
[Category("TamilNer")]
|
||||
public sealed class RealTamilNerModelRunnerTests : RealTamilNerModelFixture
|
||||
{
|
||||
[TestCase("வாடிக்கையாளர் ராஜேஷ் குமார் அழைத்தார்.", "ராஜேஷ்", "ராஜேஷ் குமார்")]
|
||||
public void PredictEntities_TamilScript_DetectsPersonEntity(...)
|
||||
// ...
|
||||
[TestCase("Customer Senthil phone 9876543210", "Senthil")]
|
||||
public void PredictEntities_TanglishLatinScript_DoesNotInvokeTamilRunner(...)
|
||||
```
|
||||
|
||||
Routed pipeline — `RealTamilPipelineTests` covers Tamil-only, Tanglish (English path), mixed, full financial, and clean Tamil negative.
|
||||
|
||||
### Console samples (`SamplePromptCatalog`)
|
||||
|
||||
Tamil/Tanglish/mixed samples (indices 10–14):
|
||||
|
||||
| Sample | Category | Input excerpt |
|
||||
|--------|----------|---------------|
|
||||
| `TamilCustomerNameOnly` | NER (Tamil) | `வாடிக்கையாளர் ராஜேஷ் குமார் …` |
|
||||
| `TamilWithPhonePan` | NER (Tamil) + Regex | Tamil person + phone + PAN |
|
||||
| `TanglishCustomer` | NER (English/Tanglish) | `Customer Senthil phone 9876543210 …` |
|
||||
| `MixedTamilEnglish` | NER (Mixed) | `வாடிக்கையாளர் Ravi Kumar phone …` |
|
||||
| `TamilFullFinancial` | NER (Tamil) + Regex + Domain | Tamil canonical demo |
|
||||
|
||||
Run all samples (including Tamil) with no flags:
|
||||
|
||||
```bash
|
||||
dotnet run --project src/PiiRedaction.ConsoleApp
|
||||
```
|
||||
|
||||
Or a single Tamil sample:
|
||||
|
||||
```bash
|
||||
dotnet run --project src/PiiRedaction.ConsoleApp -- --name TamilCustomerNameOnly
|
||||
```
|
||||
|
||||
### Expected console output (English canonical)
|
||||
|
||||
When models are loaded, person names appear as `[PERSON]` with source `Ner`:
|
||||
|
||||
```
|
||||
Detected PII:
|
||||
[PERSON ] Ravi Kumar (Ner)
|
||||
[EMAIL ] ravi.kumar@gmail.com (Regex)
|
||||
...
|
||||
|
||||
Sanitized Prompt:
|
||||
Customer <PERSON_1> with email <EMAIL_1> ...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 7. Model Assets Table
|
||||
|
||||
All model binaries are **gitignored**; only `.gitkeep` placeholders are committed.
|
||||
|
||||
| Directory | File | Approx. size | Gitignored | Purpose |
|
||||
|-----------|------|--------------|------------|---------|
|
||||
| `models/` or `models/en/` | `ner-model.onnx` | ~431 MB | Yes | English BERT NER (FP32 ONNX from HF) |
|
||||
| `models/` or `models/en/` | `vocab.txt` | ~213 KB | Yes | WordPiece vocabulary |
|
||||
| `models/` or `models/en/` | `ner-labels.txt` | < 1 KB | Yes | BIO label index (one per line) |
|
||||
| `models/ta/` | `model.onnx` | ~1.2 GB (FP32 export, varies) | Yes | Tamil IndicBERT NER |
|
||||
| `models/ta/` | `vocab.txt` | varies | Yes | WordPiece vocab (preferred tokenizer) |
|
||||
| `models/ta/` | `tokenizer.json` | varies | Yes | HF tokenizer export (optional) |
|
||||
| `models/ta/` | `sentencepiece.bpe.model` | varies | Yes | SentencePiece (if present instead of vocab) |
|
||||
| `models/ta/` | `ner-labels.txt` | few KB | Yes | Fine-grained SampurNER labels |
|
||||
|
||||
`.gitignore` entries:
|
||||
|
||||
```
|
||||
models/*.onnx
|
||||
models/vocab.txt
|
||||
models/ner-labels.txt
|
||||
models/*.json
|
||||
models/en/*
|
||||
models/ta/*
|
||||
```
|
||||
|
||||
English size reference: [docs/git-xenovex-setup.md](git-xenovex-setup.md) notes ~431 MB for the English ONNX file.
|
||||
|
||||
---
|
||||
|
||||
## 8. Limitations
|
||||
|
||||
| Limitation | Detail |
|
||||
|------------|--------|
|
||||
| **Tanglish on English model only** | Roman-script Tanglish (`Customer Senthil`) is classified `LatinOnly` and handled by English BERT. Recall is best-effort and inconsistent for non-standard spellings. Tamil ONNX is **not** invoked on Latin-only text. |
|
||||
| **Fail-open if model missing** | `OnnxNerPiiDetector` and routing runners return `[]` when models are unavailable. Person names are **not** redacted; regex/domain layers still run. No regex fallback for names. |
|
||||
| **128 token limit** | `OnnxTokenClassifierRunner` truncates encoding at 128 tokens. Very long prompts may miss person names beyond the window. |
|
||||
| **PERSON-only NER scope** | Organization, location, and misc NER labels are ignored. Only person spans become `<PERSON_n>`. |
|
||||
| **Mixed-script merge** | When both models run, overlapping spans are deduped by length; shorter overlapping spans are dropped. |
|
||||
| **Tamil ONNX availability** | Pre-exported Tamil ONNX may not exist on Hugging Face; local Python export is often required. |
|
||||
| **No fail-closed mode** | Missing NER does not block sanitization or LLM calls (optional Phase 5 enhancement). |
|
||||
|
||||
---
|
||||
|
||||
## 9. How to Reproduce
|
||||
|
||||
### Download models
|
||||
|
||||
From the repository root:
|
||||
|
||||
```powershell
|
||||
.\scripts\download-ner-model.ps1
|
||||
.\scripts\download-tamil-ner-model.ps1
|
||||
```
|
||||
|
||||
If Tamil PowerShell download fails with a 404 on `onnx/model.onnx`, install Python 3.12+ and re-run:
|
||||
|
||||
```powershell
|
||||
winget install Python.Python.3.12 --accept-package-agreements --accept-source-agreements
|
||||
.\scripts\download-tamil-ner-model.ps1 -Python "$env:LOCALAPPDATA\Programs\Python\Python312\python.exe"
|
||||
```
|
||||
|
||||
Optional: copy English assets from `models/` to `models/en/` to match `EnglishOnnxModelPath`.
|
||||
|
||||
### Verify with tests
|
||||
|
||||
```bash
|
||||
dotnet build
|
||||
dotnet test
|
||||
dotnet test --filter "Category=RealModel"
|
||||
dotnet test --filter "Category=TamilNer"
|
||||
dotnet test --logger "console;verbosity=detailed"
|
||||
```
|
||||
|
||||
Tests skip gracefully when the corresponding ONNX files are absent.
|
||||
|
||||
### Verify with console
|
||||
|
||||
```bash
|
||||
dotnet run --project src/PiiRedaction.ConsoleApp -- --name CustomerNameOnly
|
||||
dotnet run --project src/PiiRedaction.ConsoleApp -- --name TamilCustomerNameOnly
|
||||
dotnet run --project src/PiiRedaction.ConsoleApp -- --name TanglishCustomer
|
||||
```
|
||||
|
||||
### Disable Tamil routing (English-only)
|
||||
|
||||
Set in `appsettings.json`:
|
||||
|
||||
```json
|
||||
"EnableTamilNer": false
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- [README.md](../README.md) — build, run, and test overview
|
||||
- [architecture.md](architecture.md) — dual-model routing diagrams and trust boundary
|
||||
- [tamil-tanglish-ner-plan.md](tamil-tanglish-ner-plan.md) — implementation phases and Tanglish expectations
|
||||
557
docs/solution-guide.md
Normal file
557
docs/solution-guide.md
Normal file
@@ -0,0 +1,557 @@
|
||||
# PII Redaction POC — Solution Guide
|
||||
|
||||
Single reference for architecture, NER models, routing, operations, Tamil/Tanglish support, Git setup, and the post-POC improvement backlog.
|
||||
|
||||
---
|
||||
|
||||
## Table of contents
|
||||
|
||||
1. [Purpose](#1-purpose)
|
||||
2. [Canonical example](#2-canonical-example)
|
||||
3. [Detection strategies](#3-detection-strategies)
|
||||
4. [Solution architecture](#4-solution-architecture)
|
||||
5. [Trust boundary](#5-trust-boundary)
|
||||
6. [NER models](#6-ner-models)
|
||||
7. [NER routing — English vs Tamil](#7-ner-routing--english-vs-tamil)
|
||||
8. [Configuration and dependency injection](#8-configuration-and-dependency-injection)
|
||||
9. [Running and testing](#9-running-and-testing)
|
||||
10. [Tamil and Tanglish support](#10-tamil-and-tanglish-support)
|
||||
11. [Git remote setup (Xenovex)](#11-git-remote-setup-xenovex)
|
||||
12. [Improvement roadmap](#12-improvement-roadmap)
|
||||
13. [Key source files](#13-key-source-files)
|
||||
|
||||
---
|
||||
|
||||
## 1. Purpose
|
||||
|
||||
This .NET proof-of-concept intercepts user prompts containing regulated personally identifiable information (PII), redacts sensitive values into stable placeholders, and transmits **only sanitized text** across the LLM trust boundary.
|
||||
|
||||
The solution uses:
|
||||
|
||||
- Clear layer separation (Core / Infrastructure / hosts)
|
||||
- Interface-driven composition and dependency injection
|
||||
- Swappable ONNX NER adapters and `Microsoft.Extensions.AI` chat clients
|
||||
|
||||
It targets financial and customer-service workloads where raw PII must not leave the application process when invoking external language models.
|
||||
|
||||
---
|
||||
|
||||
## 2. Canonical example
|
||||
|
||||
With English and Tamil ONNX models loaded (`scripts/download-ner-model.ps1`, `scripts/download-tamil-ner-model.ps1`):
|
||||
|
||||
| Stage | Value |
|
||||
|-------|-------|
|
||||
| **Input** | `Customer Ravi Kumar with email ravi.kumar@gmail.com and phone 9876543210 has LoanNumber LN-456789 and PAN ABCDE1234F. Please summarize this customer issue.` |
|
||||
| **Sanitized output** | `Customer <PERSON_1> with email <EMAIL_1> and phone <PHONE_1> has LoanNumber <LOAN_NUMBER_1> and PAN <PAN_1>. Please summarize this customer issue.` |
|
||||
| **Mock LLM response** | `[Mock LLM Response] Received sanitized prompt (146 chars). No original PII was transmitted.` |
|
||||
|
||||
| Type | Value | Source |
|
||||
|------|-------|--------|
|
||||
| PERSON | Ravi Kumar | NER |
|
||||
| EMAIL | ravi.kumar@gmail.com | Regex |
|
||||
| PHONE | 9876543210 | Regex |
|
||||
| LOAN_NUMBER | LN-456789 | Domain |
|
||||
| PAN | ABCDE1234F | Regex |
|
||||
|
||||
The placeholder map (`<PERSON_1>` → `Ravi Kumar`, etc.) stays **in-process** and is never sent to the LLM.
|
||||
|
||||
---
|
||||
|
||||
## 3. Detection strategies
|
||||
|
||||
| Strategy | Detects | Rationale |
|
||||
|----------|---------|-----------|
|
||||
| **Regex** | Email, phone, PAN, Aadhaar, credit card | Deterministic, format-bound, auditable |
|
||||
| **ONNX NER** | Person names | Contextual; no rigid format |
|
||||
| **Domain rules** | Loan number (`LN-`), customer ID (`CID-`), account (`ACC-`) | Business-specific identifiers |
|
||||
|
||||
**Overlap resolution** (`CompositePiiDetector`): detectors run in order **Domain → Regex → NER**. On overlapping spans, candidates are sorted by start index, length, and source priority (**Domain 3 > Regex 2 > NER 1**); the first non-overlapping candidate wins.
|
||||
|
||||
**Redaction** (`PlaceholderPiiRedactor`): format `<{TYPE}_{n}>`; duplicate values reuse placeholders; replacement is right-to-left to preserve indices.
|
||||
|
||||
---
|
||||
|
||||
## 4. Solution architecture
|
||||
|
||||
### Project structure
|
||||
|
||||
```
|
||||
src/
|
||||
├── PiiRedaction.ConsoleApp/ # Console demo, DI bootstrap
|
||||
├── PiiRedaction.TestHarness.Wpf/ # WPF MVVM manual test harness
|
||||
├── PiiRedaction.Core/ # Detection, redaction, abstractions
|
||||
└── PiiRedaction.Infrastructure/ # ONNX runners, mock LLM
|
||||
models/ # ONNX assets (gitignored)
|
||||
tests/ # NUnit unit and integration tests
|
||||
```
|
||||
|
||||
| Project | Layer | Responsibility |
|
||||
|---------|-------|----------------|
|
||||
| `PiiRedaction.ConsoleApp` | Presentation | Samples, interactive mode, audit output |
|
||||
| `PiiRedaction.TestHarness.Wpf` | Presentation | Category-filtered prompts, redact UI, batch runner |
|
||||
| `PiiRedaction.Core` | Domain | `IPiiDetector`, `IPromptSanitizer`, detectors, models |
|
||||
| `PiiRedaction.Infrastructure` | Infrastructure | `RoutingOnnxNerModelRunner`, `MockLlmPromptService` |
|
||||
| `tests/*` | Test | ~112 tests; `RealModel` and `TamilNer` categories |
|
||||
|
||||
**Dependency direction:** `ConsoleApp` / `Wpf` → `Infrastructure` → `Core`. Core has no ONNX or LLM SDK references.
|
||||
|
||||
### Key abstractions
|
||||
|
||||
| Abstraction | Default implementation | Extension |
|
||||
|-------------|------------------------|-----------|
|
||||
| `IPiiDetector` | `CompositePiiDetector` | Add detector; register in composite order |
|
||||
| `IPiiRedactor` | `PlaceholderPiiRedactor` | Hashing, vault tokens |
|
||||
| `IPromptSanitizer` | `PromptSanitizer` | Orchestrates detect + redact |
|
||||
| `IOnnxNerModelRunner` | `RoutingOnnxNerModelRunner` | Script-based EN/TA routing |
|
||||
| `ILlmPromptService` | `MockLlmPromptService` | Production adapter |
|
||||
| `IChatClient` | `MockChatClient` | Azure OpenAI, etc. |
|
||||
|
||||
### Data flow
|
||||
|
||||
```mermaid
|
||||
flowchart TB
|
||||
subgraph hosts [Hosts]
|
||||
console[ConsoleApp / WpfHarness]
|
||||
end
|
||||
|
||||
subgraph core [PiiRedaction.Core]
|
||||
sanitizer[PromptSanitizer]
|
||||
composite[CompositePiiDetector]
|
||||
domain[DomainRulePiiDetector]
|
||||
regex[RegexPiiDetector]
|
||||
onnxDet[OnnxNerPiiDetector]
|
||||
redactor[PlaceholderPiiRedactor]
|
||||
end
|
||||
|
||||
subgraph infra [PiiRedaction.Infrastructure]
|
||||
router[RoutingOnnxNerModelRunner]
|
||||
en[EnglishOnnxNerRunner]
|
||||
ta[TamilOnnxNerRunner]
|
||||
llm[MockLlmPromptService]
|
||||
end
|
||||
|
||||
console --> sanitizer
|
||||
sanitizer --> composite
|
||||
composite --> domain
|
||||
composite --> regex
|
||||
composite --> onnxDet
|
||||
onnxDet --> router
|
||||
router --> en
|
||||
router --> ta
|
||||
sanitizer --> redactor
|
||||
console -->|"sanitized text only"| llm
|
||||
```
|
||||
|
||||
**Pipeline:** `Sanitize` → `Detect` (all detectors) → `Redact` → `SanitizationResult`. Optional: `SendPromptAsync(sanitizedPrompt)` to LLM.
|
||||
|
||||
---
|
||||
|
||||
## 5. Trust boundary
|
||||
|
||||
Only the **sanitized prompt string** crosses `ILlmPromptService` / `IChatClient`. Original PII, entity metadata, and the placeholder map remain in-process.
|
||||
|
||||
```mermaid
|
||||
flowchart LR
|
||||
subgraph inProcess [In-Process]
|
||||
raw[Original prompt]
|
||||
entities[Detected entities]
|
||||
map[Placeholder map]
|
||||
audit[Console / WPF display]
|
||||
end
|
||||
|
||||
subgraph boundary [LLM boundary]
|
||||
sanitized[Sanitized prompt only]
|
||||
end
|
||||
|
||||
subgraph external [External LLM]
|
||||
chat[IChatClient]
|
||||
end
|
||||
|
||||
raw --> audit
|
||||
entities --> audit
|
||||
map --> audit
|
||||
sanitized --> chat
|
||||
raw -.->|never sent| chat
|
||||
map -.->|never sent| chat
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 6. NER models
|
||||
|
||||
Person-name detection is the **only** NER responsibility. Structured PII uses regex and domain rules.
|
||||
|
||||
### Routing summary
|
||||
|
||||
| Script in prompt | Model(s) | Typical use |
|
||||
|------------------|----------|-------------|
|
||||
| `LatinOnly` | English | English names, Indian names in Roman script, **Tanglish** |
|
||||
| `TamilOnly` | Tamil | Tamil-script names |
|
||||
| `Mixed` | Both; merge spans | Code-mixed prompts |
|
||||
| `NoLetters` | Neither | Digits/symbols only |
|
||||
|
||||
### English model
|
||||
|
||||
| Property | Value |
|
||||
|----------|-------|
|
||||
| Hugging Face ID | [`dslim/bert-base-NER`](https://huggingface.co/dslim/bert-base-NER) |
|
||||
| Tokenizer | WordPiece (`vocab.txt`, `BertWordPieceEncoder`) |
|
||||
| Person labels | `B-PER`, `I-PER`, `B-PERSON`, `I-PERSON` |
|
||||
| Primary path | `models/en/ner-model.onnx` |
|
||||
| Legacy fallback | `models/ner-model.onnx` |
|
||||
| Download | `.\scripts\download-ner-model.ps1` |
|
||||
|
||||
### Tamil model
|
||||
|
||||
| Property | Value |
|
||||
|----------|-------|
|
||||
| Hugging Face ID | [`prachuryyaIITG/SampurNER_Tamil_IndicBERTv2`](https://huggingface.co/prachuryyaIITG/SampurNER_Tamil_IndicBERTv2) |
|
||||
| Tokenizer | WordPiece when `vocab.txt` present (export path); SentencePiece fallback |
|
||||
| Person labels | Any BIO tag containing `person` (case-insensitive) |
|
||||
| Path | `models/ta/model.onnx` |
|
||||
| Download | `.\scripts\download-tamil-ner-model.ps1` |
|
||||
|
||||
**Why SampurNER over MuRIL:** Tamil-specific NER training, smaller footprint (~0.3B vs ~0.6B), validated ONNX export in this repo. MuRIL reserved as eval-driven fallback.
|
||||
|
||||
### Model assets (gitignored)
|
||||
|
||||
| Directory | Key files | Approx. size |
|
||||
|-----------|-----------|--------------|
|
||||
| `models/en/` | `ner-model.onnx`, `vocab.txt`, `ner-labels.txt` | ~431 MB ONNX |
|
||||
| `models/ta/` | `model.onnx`, `vocab.txt`, `ner-labels.txt` | ~1 GB ONNX |
|
||||
|
||||
Only `models/en/.gitkeep` and `models/ta/.gitkeep` are committed.
|
||||
|
||||
### Shared inference
|
||||
|
||||
Both runners use `OnnxTokenClassifierRunner`:
|
||||
|
||||
- Max sequence length: **128 tokens** (long prompts truncate silently)
|
||||
- BIO decode → `PiiEntityType.Person` only
|
||||
- Missing model → fail-open: `[]` from NER (person names not redacted)
|
||||
|
||||
---
|
||||
|
||||
## 7. NER routing — English vs Tamil
|
||||
|
||||
### Call chain
|
||||
|
||||
```
|
||||
PromptSanitizer → CompositePiiDetector → OnnxNerPiiDetector
|
||||
→ RoutingOnnxNerModelRunner.PredictEntities()
|
||||
→ ScriptRouter.GetComposition(text)
|
||||
→ switch (composition) { English / Tamil / both }
|
||||
→ MergePersonSpans()
|
||||
```
|
||||
|
||||
### Step 1: `ScriptRouter` (Core)
|
||||
|
||||
**File:** `src/PiiRedaction.Core/Detection/ScriptRouter.cs`
|
||||
|
||||
Single pass over characters:
|
||||
|
||||
- Tamil letter: Unicode **U+0B80 – U+0BFF**
|
||||
- Latin letter: `char.IsAsciiLetter`
|
||||
- Both seen → `Mixed` (early exit)
|
||||
- Neither → `NoLetters`
|
||||
- Otherwise → `TamilOnly` or `LatinOnly`
|
||||
|
||||
```csharp
|
||||
// ScriptComposition enum: LatinOnly, TamilOnly, Mixed, NoLetters
|
||||
public ScriptComposition GetComposition(string text) { /* scan chars */ }
|
||||
```
|
||||
|
||||
**Tanglish** in Roman script → `LatinOnly` → **English model only**.
|
||||
|
||||
### Step 2: `RoutingOnnxNerModelRunner` (Infrastructure)
|
||||
|
||||
**File:** `src/PiiRedaction.Infrastructure/Onnx/RoutingOnnxNerModelRunner.cs`
|
||||
|
||||
This is the **branch decision**:
|
||||
|
||||
| `ScriptComposition` | English | Tamil |
|
||||
|---------------------|---------|-------|
|
||||
| `LatinOnly` | Yes if available | No |
|
||||
| `TamilOnly` | No | Yes if `EnableTamilNer` and available |
|
||||
| `Mixed` | Yes if available | Yes if `EnableTamilNer` and available |
|
||||
| `NoLetters` | No | No |
|
||||
|
||||
Both models run on the **full prompt text** for `Mixed` (no script segmentation).
|
||||
|
||||
### Step 3: Merge
|
||||
|
||||
`MergePersonSpans`: overlapping PERSON spans → **longer span wins**; ordered by `StartIndex`.
|
||||
|
||||
### Routing diagram
|
||||
|
||||
```mermaid
|
||||
flowchart TD
|
||||
text[Prompt text] --> sr[ScriptRouter]
|
||||
sr --> latin[LatinOnly]
|
||||
sr --> tamil[TamilOnly]
|
||||
sr --> mixed[Mixed]
|
||||
sr --> none[NoLetters]
|
||||
latin --> en[EnglishOnnxNerRunner]
|
||||
tamil --> ta[TamilOnnxNerRunner]
|
||||
mixed --> en
|
||||
mixed --> ta
|
||||
en --> merge[MergePersonSpans]
|
||||
ta --> merge
|
||||
none --> empty[No NER]
|
||||
```
|
||||
|
||||
### Worked examples
|
||||
|
||||
| Input style | Composition | Models |
|
||||
|-------------|---------------|--------|
|
||||
| `Customer Ravi Kumar…` | `LatinOnly` | English |
|
||||
| `வாடிக்கையாளர் ராஜேஷ் குமார்…` | `TamilOnly` | Tamil |
|
||||
| `Naan Suresh, phone 9003789456…` | `LatinOnly` | English (Tanglish) |
|
||||
| `வாடிக்கையாளர் Ravi Kumar phone…` | `Mixed` | Both |
|
||||
| `Callback on 9123456780…` | `NoLetters` | Neither (phone via Regex) |
|
||||
|
||||
---
|
||||
|
||||
## 8. Configuration and dependency injection
|
||||
|
||||
### `appsettings.json`
|
||||
|
||||
```json
|
||||
{
|
||||
"PiiRedaction": {
|
||||
"OnnxModelPath": "models/ner-model.onnx",
|
||||
"EnglishOnnxModelPath": "models/en/ner-model.onnx",
|
||||
"TamilOnnxModelPath": "models/ta/model.onnx",
|
||||
"EnableTamilNer": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
| Setting | Effect |
|
||||
|---------|--------|
|
||||
| `EnglishOnnxModelPath` | Primary English model |
|
||||
| `OnnxModelPath` | Legacy English fallback |
|
||||
| `TamilOnnxModelPath` | Tamil model |
|
||||
| `EnableTamilNer` | `false` = English-only routing |
|
||||
|
||||
### DI registration
|
||||
|
||||
```csharp
|
||||
services.AddSingleton<EnglishOnnxNerRunner>();
|
||||
services.AddSingleton<TamilOnnxNerRunner>();
|
||||
services.AddSingleton<IOnnxNerModelRunner, RoutingOnnxNerModelRunner>();
|
||||
// OnnxNerPiiDetector receives IOnnxNerModelRunner (the router)
|
||||
```
|
||||
|
||||
Same pattern in `PiiRedaction.ConsoleApp` and `PiiRedaction.TestHarness.Wpf` `ServiceCollectionExtensions.cs`.
|
||||
|
||||
Detector registration order in composite: **Domain → Regex → ONNX NER**.
|
||||
|
||||
---
|
||||
|
||||
## 9. Running and testing
|
||||
|
||||
### Build
|
||||
|
||||
```bash
|
||||
dotnet restore
|
||||
dotnet build
|
||||
```
|
||||
|
||||
### Console app
|
||||
|
||||
```bash
|
||||
# All 16 samples (English + Tamil/Tanglish/mixed)
|
||||
dotnet run --project src/PiiRedaction.ConsoleApp
|
||||
|
||||
dotnet run --project src/PiiRedaction.ConsoleApp -- --list
|
||||
dotnet run --project src/PiiRedaction.ConsoleApp -- --name TamilCustomerNameOnly
|
||||
dotnet run --project src/PiiRedaction.ConsoleApp -- --interactive
|
||||
```
|
||||
|
||||
Samples: `src/PiiRedaction.ConsoleApp/Samples/SamplePromptCatalog.cs`
|
||||
|
||||
### WPF test harness (Windows)
|
||||
|
||||
```bash
|
||||
dotnet run --project src/PiiRedaction.TestHarness.Wpf
|
||||
```
|
||||
|
||||
- **Category dropdown:** Career Guidance, Banking & Financial, Negative, Edge & Harness
|
||||
- Click prompt → loads input; **Redact** runs pipeline
|
||||
- **Run All** executes scenarios in the selected category
|
||||
- Script badge shows predicted routing (`LatinOnly`, `TamilOnly`, `Mixed`)
|
||||
|
||||
### Download models
|
||||
|
||||
```powershell
|
||||
.\scripts\download-ner-model.ps1
|
||||
.\scripts\download-tamil-ner-model.ps1
|
||||
```
|
||||
|
||||
If Tamil PowerShell download 404s, use Python 3.12+:
|
||||
|
||||
```powershell
|
||||
.\scripts\download-tamil-ner-model.ps1 -Python "$env:LOCALAPPDATA\Programs\Python\Python312\python.exe"
|
||||
```
|
||||
|
||||
### Tests
|
||||
|
||||
```bash
|
||||
dotnet test
|
||||
dotnet test --filter "Category=RealModel"
|
||||
dotnet test --filter "Category=TamilNer"
|
||||
dotnet test --filter "FullyQualifiedName~ScriptRouterTests|FullyQualifiedName~RoutingOnnxNerModelRunnerTests"
|
||||
```
|
||||
|
||||
Tests skip gracefully when ONNX files are absent.
|
||||
|
||||
### Console sample categories
|
||||
|
||||
| Category | Examples |
|
||||
|----------|----------|
|
||||
| NER (English) | `CustomerNameOnly`, `MrTitlePerson`, `TwoCustomersInOnePrompt` |
|
||||
| NER (Tamil) | `TamilCustomerNameOnly`, `TamilFullFinancial` |
|
||||
| Tanglish / Mixed | `TanglishCustomer`, `MixedTamilEnglish` |
|
||||
| Regex / Domain | `AllRegexTypes`, `AllDomainIds` |
|
||||
| Negative | `NoPiiCleanTicket` |
|
||||
|
||||
---
|
||||
|
||||
## 10. Tamil and Tanglish support
|
||||
|
||||
### Implementation status
|
||||
|
||||
| Phase | Status | Scope |
|
||||
|-------|--------|-------|
|
||||
| **1** Generic ONNX token classifier | Done | `OnnxTokenClassifierRunner`, encoders, `NerLabelConfig` |
|
||||
| **2** Tamil download + config | Done | Scripts, dual paths, `EnableTamilNer` |
|
||||
| **3** Script routing + DI | Done | `ScriptRouter`, `RoutingOnnxNerModelRunner` |
|
||||
| **4** Tests, samples, docs | Partial | Tests/samples done; eval metrics open |
|
||||
| **5** Optional enhancements | Not started | See below |
|
||||
|
||||
### Remaining gaps (Phase 4–5)
|
||||
|
||||
| Gap | Impact |
|
||||
|-----|--------|
|
||||
| Tamil numeral normalization (௦–௯ → 0–9) | May miss phone/Aadhaar in Tamil script |
|
||||
| Tanglish heuristics (`peru`, `enga peru`) | Better Latin-name recall in Tamil context |
|
||||
| Label-aware regex cues | Contextual name detection |
|
||||
| Fail-closed when NER missing | Compliance hardening |
|
||||
| MuRIL model swap | If Tamil recall insufficient |
|
||||
|
||||
### Tanglish expectations
|
||||
|
||||
| Input | Handler | Expected recall |
|
||||
|-------|---------|-----------------|
|
||||
| Tamil script names | Tamil ONNX | High (with tuning) |
|
||||
| Latin Indian names (`Ravi Kumar`) | English ONNX | High |
|
||||
| Tanglish spellings (`Senthil`) | English NER + optional heuristics | Medium |
|
||||
| Code-mixed prompts | Both models + merge | Medium–high for IDs; names variable |
|
||||
|
||||
### Success metrics (eval set target)
|
||||
|
||||
| Metric | MVP target |
|
||||
|--------|------------|
|
||||
| Tamil script person recall | ≥ 85% |
|
||||
| Tanglish person recall | ≥ 70% |
|
||||
| False positives on clean prompts | ≤ 5% |
|
||||
| Structured PII in Tamil prompts (regex) | ≥ 95% |
|
||||
| English canonical regression | 100% |
|
||||
|
||||
---
|
||||
|
||||
## 11. Git remote setup (Xenovex)
|
||||
|
||||
Remote: `https://xts.xenovex.com/Bilal-Nazer-Ali/llm-pii-poc.git`
|
||||
|
||||
### Create and push
|
||||
|
||||
```powershell
|
||||
cd C:\Users\bilal.n\Projects\llm-pii-poc
|
||||
$git = "C:\Program Files\Git\bin\git.exe"
|
||||
|
||||
& $git remote add origin <YOUR_CLONE_URL>
|
||||
& $git branch -M main
|
||||
& $git push -u origin main
|
||||
```
|
||||
|
||||
Use an **empty** remote repository (no README) when pushing existing history.
|
||||
|
||||
### Committed vs gitignored
|
||||
|
||||
| Committed | Gitignored |
|
||||
|-----------|------------|
|
||||
| `src/`, `tests/`, `scripts/`, `docs/` | `bin/`, `obj/`, `.vs/` |
|
||||
| `README.md`, `PiiRedaction.slnx` | `models/*.onnx`, `vocab.txt`, `models/en/*`, `models/ta/*` |
|
||||
| `models/en/.gitkeep`, `models/ta/.gitkeep` | `scratch/` |
|
||||
|
||||
After clone, run model download scripts locally.
|
||||
|
||||
---
|
||||
|
||||
## 12. Improvement roadmap
|
||||
|
||||
Post-POC items **not yet implemented**. Current maturity: strong architecture and tests; production needs fail-closed policy, API host, and observability.
|
||||
|
||||
### P0 — Security and correctness
|
||||
|
||||
| Item | Proposal |
|
||||
|------|----------|
|
||||
| Fail-closed when NER unavailable | `RequireNerOnStartup`, `BlockLlmWhenNerUnavailable` options |
|
||||
| Outbound LLM guard | Verify sanitized text before `IChatClient` |
|
||||
| Truncation warning | Surface 128-token limit in `SanitizationResult` |
|
||||
| NER confidence | Populate `PiiEntity.Confidence` or hide UI column |
|
||||
| WPF leak check | Use placeholder map, not naive `Contains` |
|
||||
|
||||
### P1 — Platform
|
||||
|
||||
| Item | Proposal |
|
||||
|------|----------|
|
||||
| `PiiRedaction.Composition` | Shared `AddPiiRedactionServices` (Console + WPF duplicate today) |
|
||||
| Unified prompt catalog | Single source for console, WPF, golden tests |
|
||||
| Async `IPromptSanitizer` | Replace WPF `Task.Run` wrapper |
|
||||
| `PiiRedaction.Application` | Shared orchestration for API/WPF |
|
||||
| Minimal API + health checks | `POST /v1/prompts/sanitize`, model readiness |
|
||||
| Tamil Phase 4 | Numeral normalization, Tanglish heuristics |
|
||||
|
||||
### P2 — Operations
|
||||
|
||||
| Item | Proposal |
|
||||
|------|----------|
|
||||
| Placeholder audit store | TTL, encryption, correlation ID |
|
||||
| Observability | Metrics, OpenTelemetry traces |
|
||||
| ONNX session pool | Concurrency strategy for API load |
|
||||
| CI pipeline | Fast tests without models; nightly real-model job |
|
||||
|
||||
### Suggested phases
|
||||
|
||||
1. **Production hardening** — fail-closed, composition root, API, truncation metadata (~1–2 weeks)
|
||||
2. **Detection quality** — normalizer, Tamil Phase 4, regex hardening (~1 week)
|
||||
3. **Operations** — audit store, metrics, session pool (~1–2 weeks)
|
||||
|
||||
---
|
||||
|
||||
## 13. Key source files
|
||||
|
||||
| Topic | Path |
|
||||
|-------|------|
|
||||
| Script classification | `src/PiiRedaction.Core/Detection/ScriptRouter.cs` |
|
||||
| **Model branch decision** | `src/PiiRedaction.Infrastructure/Onnx/RoutingOnnxNerModelRunner.cs` |
|
||||
| English NER | `src/PiiRedaction.Infrastructure/Onnx/EnglishOnnxNerRunner.cs` |
|
||||
| Tamil NER | `src/PiiRedaction.Infrastructure/Onnx/TamilOnnxNerRunner.cs` |
|
||||
| Shared ONNX inference | `src/PiiRedaction.Infrastructure/Onnx/OnnxTokenClassifierRunner.cs` |
|
||||
| NER detector | `src/PiiRedaction.Core/Detection/OnnxNerPiiDetector.cs` |
|
||||
| Sanitizer | `src/PiiRedaction.Core/Sanitization/PromptSanitizer.cs` |
|
||||
| Composite merge | `src/PiiRedaction.Core/Detection/CompositePiiDetector.cs` |
|
||||
| Options | `src/PiiRedaction.Core/Configuration/PiiRedactionOptions.cs` |
|
||||
| DI | `src/PiiRedaction.ConsoleApp/DependencyInjection/ServiceCollectionExtensions.cs` |
|
||||
| Console samples | `src/PiiRedaction.ConsoleApp/Samples/SamplePromptCatalog.cs` |
|
||||
| WPF catalog | `src/PiiRedaction.TestHarness.Wpf/Services/TestPromptCatalog.cs` |
|
||||
| Routing tests | `tests/PiiRedaction.Infrastructure.Tests/Onnx/RoutingOnnxNerModelRunnerTests.cs` |
|
||||
| Script tests | `tests/PiiRedaction.Core.Tests/Detection/ScriptRouterTests.cs` |
|
||||
|
||||
---
|
||||
|
||||
*Last consolidated: July 2026. Replaces separate architecture, NER models, routing reference, Tamil plan, improvement roadmap, and Git setup documents.*
|
||||
@@ -1,307 +0,0 @@
|
||||
# Tamil / Tanglish NER — Implementation Plan
|
||||
|
||||
**Goal:** Raise language coverage from ~15% to production-viable for Tamil script and Tanglish (Roman-script Tamil-English) customer prompts, without changing the secure LLM boundary pattern.
|
||||
|
||||
**Status:** Phase 1–3 implemented
|
||||
**Approach:** Dual-model ONNX NER routing (English + Tamil) + lightweight text normalization + optional Tanglish heuristics
|
||||
**Estimated effort:** 4–6 engineering days across 4 phases
|
||||
|
||||
---
|
||||
|
||||
## 1. Current State vs Gap
|
||||
|
||||
| Capability | Today | Tamil script | Tanglish (Latin) |
|
||||
|------------|-------|--------------|------------------|
|
||||
| Phone, PAN, Aadhaar, email, domain IDs | Regex + domain rules | Works (ASCII digits) | Works |
|
||||
| Person names | `dslim/bert-base-NER` (English BERT) | **Fails** — out of vocabulary | **Partial** — inconsistent |
|
||||
| Script / language routing | None | N/A | N/A |
|
||||
| Tamil numerals (௦–௯) | Not normalized | **May miss** phone/Aadhaar | N/A |
|
||||
| Label-aware cues (`பெயர்`, `peru`, `enga peru`) | None | **Misses** contextual names | **Misses** |
|
||||
|
||||
**Root cause:** Person detection is a single English-only ONNX model behind `IOnnxNerModelRunner` → `OnnxNerPiiDetector`. Regex/domain layers are already language-agnostic.
|
||||
|
||||
---
|
||||
|
||||
## 2. Target Architecture
|
||||
|
||||
No change to the trust boundary: `PromptSanitizer` → `CompositePiiDetector` → `PlaceholderPiiRedactor` → sanitized text only to LLM.
|
||||
|
||||
Only the **NER adapter** expands:
|
||||
|
||||
```mermaid
|
||||
flowchart TD
|
||||
text["Prompt text"]
|
||||
router["ScriptRouter (Core)"]
|
||||
routing["RoutingOnnxNerModelRunner"]
|
||||
en["EnglishOnnxRunner\nBERT WordPiece"]
|
||||
ta["TamilOnnxRunner\nIndicBERT SentencePiece"]
|
||||
nerDet["OnnxNerPiiDetector"]
|
||||
composite["CompositePiiDetector"]
|
||||
|
||||
text --> composite
|
||||
text --> nerDet
|
||||
nerDet --> routing
|
||||
routing --> router
|
||||
router -->|"LatinOnly / Mixed"| en
|
||||
router -->|"TamilOnly / Mixed"| ta
|
||||
en --> routing
|
||||
ta --> routing
|
||||
```
|
||||
|
||||
### Routing rules
|
||||
|
||||
| `ScriptComposition` | Models invoked | Tanglish note |
|
||||
|---------------------|----------------|---------------|
|
||||
| `LatinOnly` | English NER only | Tanglish names in Roman script |
|
||||
| `TamilOnly` | Tamil NER only | Tamil script names |
|
||||
| `Mixed` | **Both**, merge person spans | Common in Indian CS prompts |
|
||||
| `NoLetters` | Neither (or English fallback off) | Digits-only prompts |
|
||||
|
||||
**Merge inside `RoutingOnnxNerModelRunner`:** dedupe overlapping person spans (prefer longer span; tie-break Tamil vs English by start index order).
|
||||
|
||||
---
|
||||
|
||||
## 3. Model Selection
|
||||
|
||||
| Role | Model | Rationale |
|
||||
|------|-------|-----------|
|
||||
| English / Tanglish (Latin) | **Keep** `dslim/bert-base-NER` | Already integrated; works for many Indian names in Latin script |
|
||||
| Tamil script | **`prachuryyaIITG/SampurNER_Tamil_IndicBERTv2`** | Tamil NER; lighter than MuRIL (~0.6B) |
|
||||
| Fallback (optional Phase 5) | MuRIL Tamil NER | Only if IndicBERT recall is insufficient on eval set |
|
||||
|
||||
### Asset layout
|
||||
|
||||
```
|
||||
models/
|
||||
en/
|
||||
ner-model.onnx # or model.onnx (BERT export)
|
||||
vocab.txt
|
||||
ner-labels.txt
|
||||
ta/
|
||||
model.onnx
|
||||
sentencepiece.bpe.model # or tokenizer.json from HF export
|
||||
ner-labels.txt
|
||||
ner-model.onnx # legacy path — keep for backward compatibility
|
||||
```
|
||||
|
||||
### Label mapping (Tamil fine-grained NER)
|
||||
|
||||
SampurNER uses fine-grained tags (e.g. `B-person-politician`, `I-person-artist`). Map **any label containing `person`** (case-insensitive) → `PiiEntityType.Person`.
|
||||
|
||||
English labels remain: `B-PER`, `I-PER`, `B-PERSON`, `I-PERSON`.
|
||||
|
||||
---
|
||||
|
||||
## 4. Implementation Phases
|
||||
|
||||
### Phase 1 — Generic ONNX token classifier (1–2 days)
|
||||
|
||||
**Objective:** Refactor `OnnxNerModelRunner` so BERT and SentencePiece are pluggable.
|
||||
|
||||
| Action | Location |
|
||||
|--------|----------|
|
||||
| Add `ITokenClassifierEncoder` + `EncodedSequence` | `Infrastructure/Onnx/` |
|
||||
| `BertWordPieceEncoder` — extract from current runner | Infrastructure |
|
||||
| `SentencePieceEncoder` — IndicBERT tokenizer | Infrastructure |
|
||||
| `OnnxTokenClassifierRunner` — shared inference + BIO decode | Infrastructure |
|
||||
| `NerLabelConfig` — English vs Tamil person label predicates | Infrastructure |
|
||||
| `OnnxAssetPathResolver` — resolve model dir from repo root | Infrastructure |
|
||||
| Thin wrappers: `EnglishOnnxNerRunner`, `TamilOnnxNerRunner` | Infrastructure |
|
||||
|
||||
**Backward compat:** If `models/en/` missing, fall back to `OnnxModelPath` (`models/ner-model.onnx`).
|
||||
|
||||
**No behavior change** until Phase 3 wiring — existing tests must pass.
|
||||
|
||||
---
|
||||
|
||||
### Phase 2 — Tamil model download + config (0.5–1 day)
|
||||
|
||||
| Action | Details |
|
||||
|--------|---------|
|
||||
| `scripts/download-tamil-ner-model.ps1` + `.py` | Mirror `download-ner-model.ps1`; export via `optimum-cli export onnx --task token-classification` |
|
||||
| Optional: `scripts/download-all-ner-models.ps1` | Calls English + Tamil scripts |
|
||||
| Extend `PiiRedactionOptions` | `EnglishOnnxModelPath`, `TamilOnnxModelPath`, `EnableTamilNer` (default `true`) |
|
||||
| Update `appsettings.json` | New paths under `PiiRedaction` section |
|
||||
| `.gitignore` | `models/ta/*`, `models/en/*` (same as today for onnx/vocab) |
|
||||
|
||||
---
|
||||
|
||||
### Phase 3 — Script routing + DI (0.5–1 day)
|
||||
|
||||
| Action | Location |
|
||||
|--------|----------|
|
||||
| `ScriptRouter` + `ScriptComposition` enum | `Core/Detection/` |
|
||||
| `RoutingOnnxNerModelRunner` implements `IOnnxNerModelRunner` | Infrastructure |
|
||||
| DI registration | `ServiceCollectionExtensions.cs` |
|
||||
|
||||
```csharp
|
||||
services.AddSingleton<EnglishOnnxNerRunner>();
|
||||
services.AddSingleton<TamilOnnxNerRunner>();
|
||||
services.AddSingleton<IOnnxNerModelRunner, RoutingOnnxNerModelRunner>();
|
||||
```
|
||||
|
||||
`OnnxNerPiiDetector` and `CompositePiiDetector` **unchanged**.
|
||||
|
||||
---
|
||||
|
||||
### Phase 4 — Tests, samples, docs (1 day)
|
||||
|
||||
#### Unit tests
|
||||
|
||||
| Test class | Coverage |
|
||||
|------------|----------|
|
||||
| `ScriptRouterTests` | Tamil-only, Latin-only, mixed, no-letters, boundary chars U+0B80/U+0BFF |
|
||||
| `RoutingOnnxNerModelRunnerTests` | Fake EN/TA runners; mixed script merges both |
|
||||
| `NerLabelConfigTests` | Tamil fine-grained person labels map correctly |
|
||||
|
||||
#### Real-model tests (`Category=RealModel` or `Category=TamilNer`)
|
||||
|
||||
| Scenario | Input example | Assert |
|
||||
|----------|---------------|--------|
|
||||
| Tamil name | `வாடிக்கையாளர் ராஜேஷ் தொலைபேசி 9876543210` | `<PERSON_1>`, phone redacted |
|
||||
| Tanglish name | `Customer Senthil phone 9876543210` | person + phone (best-effort) |
|
||||
| Mixed | `Rajesh மற்றும் Priya` | both persons redacted |
|
||||
| Clean Tamil | `பணத்தை திரும்பப் பெறுவது எப்படி?` | no false positives |
|
||||
| Canonical English | existing golden tests | no regression |
|
||||
|
||||
Skip gracefully when `models/ta/model.onnx` missing (mirror `RealNerModelFixture`).
|
||||
|
||||
#### Console samples
|
||||
|
||||
Add to `SamplePromptCatalog.cs`:
|
||||
|
||||
- `TamilCustomerName` — Tamil script person
|
||||
- `TanglishCustomerName` — `enga peru Rajesh` / `Customer Senthil`
|
||||
- `MixedTamilEnglish` — code-mixed prompt
|
||||
|
||||
#### Docs
|
||||
|
||||
- Update `README.md` ONNX setup (dual models)
|
||||
- Update `docs/architecture.md` NER section
|
||||
- Link this plan from README
|
||||
|
||||
---
|
||||
|
||||
### Phase 5 — Optional enhancements (post-MVP)
|
||||
|
||||
| Enhancement | Benefit | Effort |
|
||||
|-------------|---------|--------|
|
||||
| **Unicode digit normalization** pre-pass | Tamil numerals → ASCII for regex | 0.5 day |
|
||||
| **Label-based regex** (`பெயர்`, `peru`, `peyar`, `enga peru`) | Tanglish recall without ML | 0.5 day |
|
||||
| **Tamil name gazetteer** `IPiiDetector` | High precision for top names | 1 day |
|
||||
| **Fail-closed policy** when NER unavailable | Compliance option | 0.5 day |
|
||||
| MuRIL model swap | Higher Tamil recall | eval-driven |
|
||||
|
||||
---
|
||||
|
||||
## 5. Tanglish — Realistic Expectations
|
||||
|
||||
| Input type | Primary handler | Expected recall |
|
||||
|------------|-----------------|-----------------|
|
||||
| Tamil script names | Tamil ONNX NER | High (with eval tuning) |
|
||||
| Standard Latin Indian names (`Ravi Kumar`) | English ONNX NER | High (already works) |
|
||||
| Tanglish spellings (`Senthil`, `senthil`, `Centhil`) | English NER + optional gazetteer | Medium |
|
||||
| Code-mixed (`Rajesh oda account ACC-123456`) | English NER + domain regex | Medium–high for IDs; name variable |
|
||||
|
||||
**MVP target:** Tamil script person names reliably redacted; Tanglish improved but not 100% without Phase 5 heuristics.
|
||||
|
||||
---
|
||||
|
||||
## 6. Success Metrics
|
||||
|
||||
Before marking language gap closed, run an **eval set of 20–30 real prompts** (anonymized production samples):
|
||||
|
||||
| Metric | MVP target |
|
||||
|--------|------------|
|
||||
| Tamil script person-name recall | ≥ 85% |
|
||||
| Tanglish person-name recall | ≥ 70% (with English model + optional heuristics) |
|
||||
| False positive rate (clean prompts) | ≤ 5% |
|
||||
| Structured PII (phone/PAN/domain) in Tamil prompts | ≥ 95% (regex layer) |
|
||||
| Regression on English canonical demo | 100% (existing golden tests) |
|
||||
|
||||
---
|
||||
|
||||
## 7. Files to Create / Modify (checklist)
|
||||
|
||||
### New files
|
||||
|
||||
- `src/PiiRedaction.Core/Detection/ScriptRouter.cs`
|
||||
- `src/PiiRedaction.Core/Detection/ScriptComposition.cs`
|
||||
- `src/PiiRedaction.Infrastructure/Onnx/OnnxTokenClassifierRunner.cs`
|
||||
- `src/PiiRedaction.Infrastructure/Onnx/BertWordPieceEncoder.cs`
|
||||
- `src/PiiRedaction.Infrastructure/Onnx/SentencePieceEncoder.cs`
|
||||
- `src/PiiRedaction.Infrastructure/Onnx/ITokenClassifierEncoder.cs`
|
||||
- `src/PiiRedaction.Infrastructure/Onnx/NerLabelConfig.cs`
|
||||
- `src/PiiRedaction.Infrastructure/Onnx/OnnxAssetPathResolver.cs`
|
||||
- `src/PiiRedaction.Infrastructure/Onnx/EnglishOnnxNerRunner.cs`
|
||||
- `src/PiiRedaction.Infrastructure/Onnx/TamilOnnxNerRunner.cs`
|
||||
- `src/PiiRedaction.Infrastructure/Onnx/RoutingOnnxNerModelRunner.cs`
|
||||
- `scripts/download-tamil-ner-model.ps1` / `.py`
|
||||
- `tests/.../ScriptRouterTests.cs`
|
||||
- `tests/.../RoutingOnnxNerModelRunnerTests.cs`
|
||||
- `tests/.../RealTamilNerPipelineTests.cs`
|
||||
- `tests/TestSupport.Shared/RealTamilModelFixture.cs`
|
||||
|
||||
### Modified files
|
||||
|
||||
- `PiiRedactionOptions.cs` — dual model paths
|
||||
- `ServiceCollectionExtensions.cs` — routing DI
|
||||
- `appsettings.json` — config
|
||||
- `SamplePromptCatalog.cs` — Tamil/Tanglish demos
|
||||
- `ProductionPipelineFactory.cs` — `CreateWithRoutingRealModel()` for tests
|
||||
- `RealNerModelFixture.cs` / paths — support EN + TA
|
||||
- `README.md`, `docs/architecture.md`
|
||||
- `.gitignore` — `models/en/`, `models/ta/`
|
||||
|
||||
### Unchanged (by design)
|
||||
|
||||
- `PromptSanitizer`, `PlaceholderPiiRedactor`, `CompositePiiDetector`
|
||||
- `RegexPiiDetector`, `DomainRulePiiDetector`
|
||||
- `MockLlmPromptService` / LLM boundary
|
||||
|
||||
---
|
||||
|
||||
## 8. Rollout & Risk
|
||||
|
||||
| Risk | Mitigation |
|
||||
|------|------------|
|
||||
| Tamil model export fails on Windows | PowerShell fallback downloads pre-exported ONNX from Hugging Face |
|
||||
| Larger memory (two models) | Lazy-load Tamil runner only when `EnableTamilNer` and Tamil script detected |
|
||||
| Fine-grained label mismatch | Load labels from `ner-labels.txt`; unit test label config |
|
||||
| Tanglish disappointment | Set stakeholder expectation in README; Phase 5 heuristics |
|
||||
| CI without models | Fast tests use fakes; `Category=TamilNer` skips like `RealModel` |
|
||||
|
||||
**Feature flag:** `EnableTamilNer=false` reverts to English-only behavior for gradual rollout.
|
||||
|
||||
---
|
||||
|
||||
## 9. Command Reference (after implementation)
|
||||
|
||||
```powershell
|
||||
# Download both models
|
||||
.\scripts\download-ner-model.ps1
|
||||
.\scripts\download-tamil-ner-model.ps1
|
||||
|
||||
# Run Tamil-focused console sample
|
||||
dotnet run --project src/PiiRedaction.ConsoleApp -- --name TamilCustomerName
|
||||
|
||||
# Tests
|
||||
dotnet test
|
||||
dotnet test --filter "Category=TamilNer"
|
||||
dotnet test --filter "Category=RealModel"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 10. Approval Checklist
|
||||
|
||||
- [ ] Stakeholder sign-off on dual-model approach (vs single multilingual model)
|
||||
- [ ] Tamil eval prompt set collected (20–30 samples)
|
||||
- [ ] Xenovex CI policy: models downloaded in pipeline or tests skip
|
||||
- [x] Phase 1–3 implementation PR
|
||||
- [ ] Phase 4 eval metrics met
|
||||
- [ ] Optional Phase 5 for Tanglish heuristics if recall < 70%
|
||||
|
||||
---
|
||||
|
||||
**Next step:** Implement Phase 1 in a feature branch (`feature/tamil-tanglish-ner`), open PR to `main` on `xts.xenovex.com/Bilal-Nazer-Ali/llm-pii-poc`.
|
||||
Reference in New Issue
Block a user