Advanced

Enhancements & Best Practices

Add multi-language support, real-time sentiment detection, proactive support suggestions, and explore advanced patterns. Includes a comprehensive FAQ.

Enhancement 1: Multi-Language

# Detect language and respond accordingly
from langdetect import detect

def multilingual_respond(engine, session_id, message):
    lang = detect(message)
    if lang != "en":
        # Translate to English for retrieval, respond in user's language
        system_addition = f"The user is writing in {lang}. Respond in the same language."
    result = engine.respond(session_id, message)
    return result

Enhancement 2: Proactive Support

# Detect user behavior patterns and offer help proactively
class ProactiveSupport:
    def analyze_page_time(self, page, time_seconds):
        if page == "/checkout" and time_seconds > 120:
            return "It looks like you might need help with checkout. Can I assist?"
        if page == "/pricing" and time_seconds > 60:
            return "Would you like help choosing the right plan?"
        return None

Frequently Asked Questions

How do I improve the bot's accuracy?

Focus on the knowledge base: add more FAQ pairs, improve documentation clarity, and review conversations where the bot escalated to identify missing content. Quality of the knowledge base is the single biggest factor in bot accuracy.

How do I handle sensitive customer data?

Never store PII in conversation logs. Redact emails, phone numbers, and account numbers before logging. Use encrypted connections for all channels. Comply with GDPR/CCPA by implementing data retention policies and deletion requests.

What resolution rate should I target?

Start with 50-60% for initial deployment. Well-tuned bots achieve 70-80% resolution rate. Above 80% is excellent. The remaining cases genuinely require human judgment. Do not sacrifice quality to chase higher resolution rates.

What You Built

Step	What You Built	Key Files
1. Setup	Project, FastAPI, config	app/main.py, config.py
2. Knowledge	Ingestion, vector store	knowledge/ingester.py, retriever.py
3. Conversation	RAG engine, context	conversation/engine.py, context.py
4. Escalation	Detection, agent routing	escalation/detector.py, router.py
5. Channels	Web, Slack, email	channels/web.py, slack.py, email.py
6. Analytics	Metrics, cost savings	analytics/tracker.py
7. Extras	Multi-lang, proactive	Various