mistral-data-handling

Mistral Data Handling

Overview

Manage data flows through Mistral AI APIs with PII redaction, audit logging, fine-tuning dataset sanitization, and conversation retention policies. Mistral's data policy: API requests on La Plateforme are not used for training by default. Self-deployed models give full data sovereignty.

Prerequisites

• Mistral API key configured
• Understanding of data classification (PII, PHI, PCI)
• Logging infrastructure for audit trails

Instructions

Step 1: PII Redaction Before API Calls

interface RedactionRule {
  pattern: RegExp;
  replacement: string;
  type: string;
}

const PII_RULES: RedactionRule[] = [
  { pattern: /\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b/gi, replacement: '[EMAIL]', type: 'email' },
  { pattern: /\b\d{3}[-.]?\d{3}[-.]?\d{4}\b/g, replacement: '[PHONE]', type: 'phone' },
  { pattern: /\b\d{3}-\d{2}-\d{4}\b/g, replacement: '[SSN]', type: 'ssn' },
  { pattern: /\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b/g, replacement: '[CARD]', type: 'credit_card' },
  { pattern: /\b\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\b/g, replacement: '[IP]', type: 'ip_address' },
];

function redactPII(text: string): { cleaned: string; redactions: string[] } {
  const redactions: string[] = [];
  let cleaned = text;

  for (const rule of PII_RULES) {
    const matches = cleaned.match(rule.pattern);
    if (matches) {
      redactions.push(...matches.map(m => `${rule.type}: ${m.slice(0, 4)}***`));
      cleaned = cleaned.replace(rule.pattern, rule.replacement);
    }
  }
  return { cleaned, redactions };
}

Step 2: Safe Mistral API Wrapper

import { Mistral } from '@mistralai/mistralai';

const client = new Mistral({ apiKey: process.env.MISTRAL_API_KEY });

async function safeChatCompletion(
  messages: Array<{ role: string; content: string }>,
  options: { redactPII?: boolean; model?: string; auditLog?: boolean } = {},
) {
  const processed = messages.map(msg => {
    if (options.redactPII !== false) {
      const { cleaned, redactions } = redactPII(msg.content);
      if (redactions.length > 0 && options.auditLog) {
        console.warn(`Redacted ${redactions.length} PII items from ${msg.role} message`);
      }
      return { ...msg, content: cleaned };
    }
    return msg;
  });

  const response = await client.chat.complete({
    model: options.model ?? 'mistral-small-latest',
    messages: processed,
  });

  // Optionally redact PII in output too
  const output = response.choices?.[0]?.message?.content ?? '';
  if (options.redactPII !== false) {
    const { cleaned } = redactPII(output);
    if (response.choices?.[0]?.message) {
      response.choices[0].message.content = cleaned;
    }
  }

  return response;
}

Step 3: Fine-Tuning Dataset Sanitization

Mistral fine-tuning requires JSONL files. Sanitize before uploading:

import { createReadStream, createWriteStream } from 'fs';
import { createInterface } from 'readline';

async function sanitizeTrainingData(inputPath: string, outputPath: string) {
  const rl = createInterface({ input: createReadStream(inputPath) });
  const out = createWriteStream(outputPath);
  let lines = 0, redacted = 0;

  for await (const line of rl) {
    const record = JSON.parse(line);
    const sanitized = record.messages.map((msg: any) => {
      const { cleaned, redactions } = redactPII(msg.content);
      if (redactions.length > 0) redacted++;
      return { ...msg, content: cleaned };
    });

    out.write(JSON.stringify({ messages: sanitized }) + '\n');
    lines++;
  }

  out.end();
  console.log(`Processed ${lines} training examples, redacted PII in ${redacted}`);
  return { lines, redacted };
}

Step 4: Conversation History with TTL

class ConversationStore {
  private store = new Map<string, { messages: any[]; createdAt: number }>();
  private maxAgeMins: number;
  private maxMessages: number;

  constructor(maxAgeMins = 60, maxMessages = 100) {
    this.maxAgeMins = maxAgeMins;
    this.maxMessages = maxMessages;
  }

  get(sessionId: string): any[] {
    const entry = this.store.get(sessionId);
    if (!entry) return [];

    // Auto-expire
    if (Date.now() - entry.createdAt > this.maxAgeMins * 60_000) {
      this.store.delete(sessionId);
      return [];
    }

    return entry.messages;
  }

  append(sessionId: string, message: any): void {
    const entry = this.store.get(sessionId) ?? { messages: [], createdAt: Date.now() };
    entry.messages.push(message);

    // Cap message count
    if (entry.messages.length > this.maxMessages) {
      const system = entry.messages[0]?.role === 'system' ? [entry.messages[0]] : [];
      entry.messages = [...system, ...entry.messages.slice(-this.maxMessages)];
    }

    this.store.set(sessionId, entry);
  }

  destroy(sessionId: string): void {
    this.store.delete(sessionId);
  }

  // GDPR right-to-erasure
  eraseUser(userId: string): number {
    let count = 0;
    for (const [key] of this.store) {
      if (key.startsWith(userId)) {
        this.store.delete(key);
        count++;
      }
    }
    return count;
  }
}

Step 5: Audit Logging

interface AuditEntry {
  timestamp: string;
  sessionId: string;
  model: string;
  inputChars: number;
  outputChars: number;
  piiRedacted: number;
  tokensUsed: { prompt: number; completion: number };
}

function logAudit(entry: AuditEntry): void {
  // Log metadata only — never log actual message content
  console.log(JSON.stringify({
    ...entry,
    // Intentionally exclude message content for compliance
  }));
}

Error Handling

Issue	Cause	Solution
PII leak to API	Regex missed pattern	Add domain-specific rules (e.g., patient IDs)
Fine-tune rejected	Unsanitized data in JSONL	Run sanitization before client.files.upload()
Conversation too long	No retention policy	Set max age and message count limits
GDPR request	Right to erasure	Implement eraseUser() across all stores

Examples

Safe Embedding Generation

async function safeEmbed(texts: string[]) {
  const cleaned = texts.map(t => redactPII(t).cleaned);
  return client.embeddings.create({
    model: 'mistral-embed',
    inputs: cleaned,
  });
}

Batch API with PII Redaction

import json

def sanitize_batch_file(input_path: str, output_path: str):
    """Sanitize a Mistral batch JSONL file before submission."""
    with open(input_path) as f_in, open(output_path, "w") as f_out:
        for line in f_in:
            record = json.loads(line)
            for msg in record["body"]["messages"]:
                msg["content"] = redact_pii(msg["content"])
            f_out.write(json.dumps(record) + "\n")

Resources

• Mistral Data Policy
• Fine-Tuning Guide
• Batch Inference
• GDPR Compliance

Output

• PII redaction layer for all API calls
• Safe chat wrapper with audit logging
• Fine-tuning dataset sanitization pipeline
• Conversation store with TTL and GDPR erasure