This text shows how to create a wise comprehension system that is empowered by Anthropic's Claopic Models. This program also improves the efficiency of feedback and quality by separating users automatically and direct them to special authorities. The work flow is analyzing of the questions, determines their purpose, and is in line with appropriate pipes – no matter customer support, technical assistance, or other answers to the domain.
Step 1: Enter the required packages in Python
!pip install anthropic pandas scikit-learnStep 2: Import the required project libraries
import os
import json
import time
import pandas as pd
import numpy as np
from anthropic import Anthropic
from IPython.display import display, Markdown
from sklearn.metrics import classification_reportStep 3: Set up anthropic apple authentication by describing your API key and starts an anthropic client
ANTHROPIC_API_KEY = "{Your API KEY}"
client = Anthropic(api_key=ANTHROPIC_API_KEY)Step 4: Create a sample data sample data for associated training categories and testing system.
customer_queries = [
{"id": 1, "query": "What are your business hours?", "category": "General Question"},
{"id": 2, "query": "How do I reset my password?", "category": "Technical Support"},
{"id": 3, "query": "I want a refund for my purchase.", "category": "Refund Request"},
{"id": 4, "query": "Where can I find your privacy policy?", "category": "General Question"},
{"id": 5, "query": "The app keeps crashing when I try to upload photos.", "category": "Technical Support"},
{"id": 6, "query": "I ordered the wrong size, can I get my money back?", "category": "Refund Request"},
{"id": 7, "query": "Do you ship internationally?", "category": "General Question"},
{"id": 8, "query": "My account is showing incorrect information.", "category": "Technical Support"},
{"id": 9, "query": "I was charged twice for my order.", "category": "Refund Request"},
{"id": 10, "query": "What payment methods do you accept?", "category": "General Question"}
]
Step 5: Turn the customer list questions into the data of the cheat data and analysis. Then, show the Dutrame in the registry to visualize the training data structure.
df = pd.DataFrame(customer_queries)
display(df)Step 6: Describe the route of the route using Claude 3.7 Sonnet to separate customer questions in previously defined paragraphs.
def route_query(query, client):
"""
Route a customer query to the appropriate category using Claude 3.5 Haiku.
Args:
query (str): The customer query to classify
client: Anthropic client
Returns:
str: The classified category
"""
system_prompt = """
You are a query classifier for a customer service system.
Your job is to categorize customer queries into exactly one of these categories:
1. General Question - Basic inquiries about the company, products, policies, etc.
2. Refund Request - Any query related to refunds, returns, or billing issues
3. Technical Support - Questions about technical problems, bugs, or how to use products
Respond with ONLY the category name, nothing else.
"""
try:
response = client.messages.create(
model="claude-3-7-sonnet-20250219",
max_tokens=1024,
system=system_prompt,
messages=[{"role": "user", "content": query}]
)
category = response.content[0].text.strip()
valid_categories = ["General Question", "Refund Request", "Technical Support"]
for valid_cat in valid_categories:
if valid_cat.lower() in category.lower():
return valid_cat
return "General Question"
except Exception as e:
print(f"Error in routing: {e}")
return "General Question"Step 7: Describe three special activities of the Handler for each state section, each using Claude 3.5 Sonnet System System.
def handle_general_question(query, client):
"""Handle general inquiries using Claude 3.5 Haiku."""
system_prompt = """
You are a customer service representative answering general questions about our company.
Be helpful, concise, and friendly. Provide direct answers to customer queries.
"""
try:
response = client.messages.create(
model="claude-3-7-sonnet-20250219",
max_tokens=1024,
system=system_prompt,
messages=[{"role": "user", "content": query}]
)
return response.content[0].text.strip()
except Exception as e:
print(f"Error in general question handler: {e}")
return "I apologize, but I'm having trouble processing your request. Please try again later."def handle_refund_request(query, client):
"""Handle refund requests using Claude 3.5 Sonnet for more nuanced responses."""
system_prompt = """
You are a customer service representative specializing in refunds and billing issues.
Respond to refund requests professionally and helpfully.
For any refund request, explain the refund policy clearly and provide next steps.
Be empathetic but follow company policy.
"""
try:
response = client.messages.create(
model="claude-3-7-sonnet-20250219",
max_tokens=1024,
system=system_prompt,
messages=[{"role": "user", "content": query}]
)
return response.content[0].text.strip()
except Exception as e:
print(f"Error in refund request handler: {e}")
return "I apologize, but I'm having trouble processing your refund request. Please contact our support team directly."def handle_technical_support(query, client):
"""Handle technical support queries using Claude 3.5 Sonnet for more detailed technical responses."""
system_prompt = """
You are a technical support specialist.
Provide clear, step-by-step solutions to technical problems.
If you need more information to resolve an issue, specify what information you need.
Prioritize simple solutions first before suggesting complex troubleshooting.
"""
try:
response = client.messages.create(
model="claude-3-7-sonnet-20250219",
max_tokens=1024,
system=system_prompt,
messages=[{"role": "user", "content": query}]
)
return response.content[0].text.strip()
except Exception as e:
print(f"Error in technical support handler: {e}")
return "I apologize, but I'm having trouble processing your technical support request. Please try our knowledge base or contact our support team."Step 8: Create the primary function of the transaction that organizes the entire route process. This work begins with the first question, metric tracks metric, directing to the appropriate party based on the stage, and returns the complete dictionary of the implementation results.
def process_customer_query(query, client):
"""
Process a customer query through the complete routing workflow.
Args:
query (str): The customer query
client: Anthropic client
Returns:
dict: Information about the query processing, including category and response
"""
start_time = time.time()
category = route_query(query, client)
routing_time = time.time() - start_time
start_time = time.time()
if category == "General Question":
response = handle_general_question(query, client)
model_used = "claude-3-5-haiku-20240307"
elif category == "Refund Request":
response = handle_refund_request(query, client)
model_used = "claude-3-5-sonnet-20240620"
elif category == "Technical Support":
response = handle_technical_support(query, client)
model_used = "claude-3-5-sonnet-20240620"
else:
response = handle_general_question(query, client)
model_used = "claude-3-5-haiku-20240307"
handling_time = time.time() - start_time
total_time = routing_time + handling_time
return {
"query": query,
"routed_category": category,
"response": response,
"model_used": model_used,
"routing_time": routing_time,
"handling_time": handling_time,
"total_time": total_time
}Step 9: Processing each question in the sample database through the transaction movement in the route travel, collecting results in the actual phases of vs.
results = []
for _, row in df.iterrows():
query = row['query']
result = process_customer_query(query, client)
result["actual_category"] = row['category']
results.append(result)
results_df = pd.DataFrame(results)
display(results_df[["query", "actual_category", "routed_category", "model_used", "total_time"]])
accuracy = (results_df["actual_category"] == results_df["routed_category"]).mean()
print(f"Routing Accuracy: {accuracy:.2%}")
from sklearn.metrics import classification_report
print(classification_report(results_df["actual_category"], results_df["routed_category"]))Step 10: The results made.
simulated_results = []
for _, row in df.iterrows():
query = row['query']
actual_category = row['category']
if "hours" in query.lower() or "policy" in query.lower() or "ship" in query.lower() or "payment" in query.lower():
routed_category = "General Question"
model_used = "claude-3-5-haiku-20240307"
elif "refund" in query.lower() or "money back" in query.lower() or "charged" in query.lower():
routed_category = "Refund Request"
model_used = "claude-3-5-sonnet-20240620"
else:
routed_category = "Technical Support"
model_used = "claude-3-5-sonnet-20240620"
simulated_results.append({
"query": query,
"actual_category": actual_category,
"routed_category": routed_category,
"model_used": model_used,
"routing_time": np.random.uniform(0.2, 0.5),
"handling_time": np.random.uniform(0.5, 2.0)
})
simulated_df = pd.DataFrame(simulated_results)
simulated_df["total_time"] = simulated_df["routing_time"] + simulated_df["handling_time"]
display(simulated_df[["query", "actual_category", "routed_category", "model_used", "total_time"]])
Step 11: Count and show the accuracy of the route system made by comparing the stages foretold in real categories.
accuracy = (simulated_df["actual_category"] == simulated_df["routed_category"]).mean()
print(f"Simulated Routing Accuracy: {accuracy:.2%}")
print(classification_report(simulated_df["actual_category"], simulated_df["routed_category"]))Step 12: Create an active demo interface using ipython widgets.
from IPython.display import HTML, display, clear_output
from ipywidgets import widgets
def create_demo_interface():
query_input = widgets.Textarea(
value="",
placeholder="Enter your customer service query here...",
description='Query:',
disabled=False,
layout=widgets.Layout(width="80%", height="100px")
)
output = widgets.Output()
button = widgets.Button(
description='Process Query',
disabled=False,
button_style="primary",
tooltip='Click to process the query',
icon='check'
)
def on_button_clicked(b):
with output:
clear_output()
query = query_input.value
if not query.strip():
print("Please enter a query.")
return
if "hours" in query.lower() or "policy" in query.lower() or "ship" in query.lower() or "payment" in query.lower():
category = "General Question"
model = "claude-3-5-haiku-20240307"
response = "Our standard business hours are Monday through Friday, 9 AM to 6 PM Eastern Time. Our customer service team is available during these hours to assist you."
elif "refund" in query.lower() or "money back" in query.lower() or "charged" in query.lower():
category = "Refund Request"
model = "claude-3-5-sonnet-20240620"
response = "I understand you're looking for a refund. Our refund policy allows returns within 30 days of purchase with a valid receipt. To initiate your refund, please provide your order number and the reason for the return."
else:
category = "Technical Support"
model = "claude-3-5-sonnet-20240620"
response = "I'm sorry to hear you're experiencing technical issues. Let's troubleshoot this step by step. First, try restarting the application. If that doesn't work, please check if the app is updated to the latest version."
print(f"Routed to: {category}")
print(f"Using model: {model}")
print("nResponse:")
print(response)
button.on_click(on_button_clicked)
return widgets.VBox([query_input, button, output])
Step 13: Use the Advanced Routing work that is not only separating questions but also provides guidelines for conviction and consultation with each separation.
def advanced_route_query(query, client):
"""
An advanced routing function that includes confidence scores and fallback mechanisms.
Args:
query (str): The customer query to classify
client: Anthropic client
Returns:
dict: Classification result with category and confidence
"""
system_prompt = """
You are a query classifier for a customer service system.
Your job is to categorize customer queries into exactly one of these categories:
1. General Question - Basic inquiries about the company, products, policies, etc.
2. Refund Request - Any query related to refunds, returns, or billing issues
3. Technical Support - Questions about technical problems, bugs, or how to use products
Respond in JSON format with:
1. "category": The most likely category
2. "confidence": A confidence score between 0 and 1
3. "reasoning": A brief explanation of your classification
Example response:
{
"category": "General Question",
"confidence": 0.85,
"reasoning": "The query asks about business hours, which is basic company information."
}
"""
try:
response = client.messages.create(
model="claude-3-5-sonnet-20240620",
max_tokens=150,
system=system_prompt,
messages=[{"role": "user", "content": query}]
)
response_text = response.content[0].text.strip()
try:
result = json.loads(response_text)
if "category" not in result or "confidence" not in result:
raise ValueError("Incomplete classification result")
return result
except json.JSONDecodeError:
print("Failed to parse JSON response. Using simple classification.")
if "general" in response_text.lower():
return {"category": "General Question", "confidence": 0.6, "reasoning": "Fallback classification"}
elif "refund" in response_text.lower():
return {"category": "Refund Request", "confidence": 0.6, "reasoning": "Fallback classification"}
else:
return {"category": "Technical Support", "confidence": 0.6, "reasoning": "Fallback classification"}
except Exception as e:
print(f"Error in advanced routing: {e}")
return {"category": "General Question", "confidence": 0.3, "reasoning": "Error fallback"}Step 14: Create a flexibility of improved interruptions based on reliability based on relation to low confidence questions in specializing, which is made for displayed purposes.
def advanced_process_customer_query(query, client, confidence_threshold=0.7):
"""
Process a customer query with confidence-based routing.
Args:
query (str): The customer query
client: Anthropic client
confidence_threshold (float): Minimum confidence score for automated routing
Returns:
dict: Information about the query processing
"""
start_time = time.time()
if "hours" in query.lower() or "policy" in query.lower() or "ship" in query.lower() or "payment" in query.lower():
classification = {
"category": "General Question",
"confidence": np.random.uniform(0.7, 0.95),
"reasoning": "Query related to business information"
}
elif "refund" in query.lower() or "money back" in query.lower() or "charged" in query.lower():
classification = {
"category": "Refund Request",
"confidence": np.random.uniform(0.7, 0.95),
"reasoning": "Query mentions refunds or billing issues"
}
elif "password" in query.lower() or "crash" in query.lower() or "account" in query.lower():
classification = {
"category": "Technical Support",
"confidence": np.random.uniform(0.7, 0.95),
"reasoning": "Query mentions technical problems"
}
else:
categories = ["General Question", "Refund Request", "Technical Support"]
classification = {
"category": np.random.choice(categories),
"confidence": np.random.uniform(0.4, 0.65),
"reasoning": "Uncertain classification"
}
routing_time = time.time() - start_time
start_time = time.time()
if classification["confidence"] >= confidence_threshold:
category = classification["category"]
if category == "General Question":
response = "SIMULATED GENERAL QUESTION RESPONSE: I'd be happy to help with your question about our business."
model_used = "claude-3-5-haiku-20240307"
elif category == "Refund Request":
response = "SIMULATED REFUND REQUEST RESPONSE: I understand you're looking for a refund. Let me help you with that process."
model_used = "claude-3-5-sonnet-20240620"
elif category == "Technical Support":
response = "SIMULATED TECHNICAL SUPPORT RESPONSE: I see you're having a technical issue. Let's troubleshoot this together."
model_used = "claude-3-5-sonnet-20240620"
else:
response = "I apologize, but I'm not sure how to categorize your request."
model_used = "claude-3-5-sonnet-20240620"
else:
response = "SIMULATED ESCALATION RESPONSE: Your query requires special attention. I'll have our advanced support system help you with this complex request."
model_used = "claude-3-5-sonnet-20240620"
category = "Escalated (Low Confidence)"
handling_time = time.time() - start_time
total_time = routing_time + handling_time
return {
"query": query,
"routed_category": classification["category"],
"confidence": classification["confidence"],
"reasoning": classification["reasoning"],
"final_category": category,
"response": response,
"model_used": model_used,
"routing_time": routing_time,
"handling_time": handling_time,
"total_time": total_time
}Step 15: Examine the advanced route system with a variety of sample questions.
test_queries = [
"What are your business hours?",
"I need a refund for my order #12345",
"My app keeps crashing when I try to save photos",
"I received the wrong item in my shipment",
"How do I change my shipping address?",
"I'm not sure if my payment went through",
"The product description was misleading"
]
advanced_results = []
for query in test_queries:
result = advanced_process_customer_query(query, None, 0.7)
advanced_results.append(result)
advanced_df = pd.DataFrame(advanced_results)
display(advanced_df[["query", "routed_category", "confidence", "final_category", "model_used"]])
print("nRouting Distribution:")
print(advanced_df["final_category"].value_counts())
print(f"nAverage Confidence: {advanced_df['confidence'].mean():.2f}")
escalated = (advanced_df["final_category"] == "Escalated (Low Confidence)").sum()
print(f"Escalated Queries: {escalated} ({escalated/len(advanced_df):.1%})")
Step 16: Describe the work of using the key metric count of the route program, including processing periods, confidence levels, increase prices, and paragraph statistics.
def calculate_routing_metrics(results_df):
"""
Calculate key metrics for routing performance.
Args:
results_df (DataFrame): Results of routing tests
Returns:
dict: Key performance metrics
"""
metrics = {
"total_queries": len(results_df),
"avg_routing_time": results_df["routing_time"].mean(),
"avg_handling_time": results_df["handling_time"].mean(),
"avg_total_time": results_df["total_time"].mean(),
"avg_confidence": results_df["confidence"].mean(),
"escalation_rate": (results_df["final_category"] == "Escalated (Low Confidence)").mean(),
}
category_distribution = results_df["routed_category"].value_counts(normalize=True).to_dict()
metrics["category_distribution"] = category_distribution
return metricsStep 17: Produce and indicate the complete performance report by route program.
metrics = calculate_routing_metrics(advanced_df)
print("Routing System Performance Metrics:")
print(f"Total Queries: {metrics['total_queries']}")
print(f"Average Routing Time: {metrics['avg_routing_time']:.3f} seconds")
print(f"Average Handling Time: {metrics['avg_handling_time']:.3f} seconds")
print(f"Average Total Time: {metrics['avg_total_time']:.3f} seconds")
print(f"Average Confidence: {metrics['avg_confidence']:.2f}")
print(f"Escalation Rate: {metrics['escalation_rate']:.1%}")
print("nCategory Distribution:")
for category, percentage in metrics["category_distribution"].items():
print(f" {category}: {percentage:.1%}")
This The Intelligent Assistance Program Ask shows how Claude models It can clearly distinguish and manage various customer questions. By using specialized categories for the optional model options, the system introduces related responses to the maintenance of high accuracy. Faith-based line in ways of enhancing complex questions Find special attention, creating a powerful customer solution.
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