|
|
import pandas as pd |
|
|
from datetime import datetime |
|
|
import json |
|
|
from typing import List, Dict |
|
|
from dataclasses import dataclass, asdict |
|
|
|
|
|
@dataclass |
|
|
class ChatAnalysis: |
|
|
"""Class for storing analysis results of a single chat interaction""" |
|
|
timestamp: str |
|
|
user_input: str |
|
|
bot_response: str |
|
|
context: str |
|
|
kb_version: str |
|
|
response_time: float |
|
|
tokens_used: int |
|
|
context_relevance_score: float |
|
|
|
|
|
class ChatAnalyzer: |
|
|
"""Class for analyzing chat interactions""" |
|
|
def __init__(self): |
|
|
self.analyses: List[ChatAnalysis] = [] |
|
|
|
|
|
def load_logs(self, log_file_path: str) -> List[Dict]: |
|
|
"""Load and parse chat logs from JSON file""" |
|
|
logs = [] |
|
|
with open(log_file_path, 'r', encoding='utf-8') as f: |
|
|
for line in f: |
|
|
try: |
|
|
logs.append(json.loads(line.strip())) |
|
|
except json.JSONDecodeError: |
|
|
continue |
|
|
return logs |
|
|
|
|
|
def analyze_interaction(self, log_entry: Dict) -> ChatAnalysis: |
|
|
"""Analyze a single chat interaction""" |
|
|
timestamp = datetime.fromisoformat(log_entry["timestamp"]) |
|
|
response_time = len(log_entry["bot_response"]) * 0.01 |
|
|
tokens_used = len(log_entry["bot_response"].split()) + len(log_entry["user_input"].split()) |
|
|
|
|
|
context_relevance = self._calculate_context_relevance( |
|
|
log_entry["user_input"], |
|
|
log_entry["context"], |
|
|
log_entry["bot_response"] |
|
|
) |
|
|
|
|
|
return ChatAnalysis( |
|
|
timestamp=timestamp.isoformat(), |
|
|
user_input=log_entry["user_input"], |
|
|
bot_response=log_entry["bot_response"], |
|
|
context=log_entry["context"], |
|
|
kb_version=log_entry["kb_version"], |
|
|
response_time=response_time, |
|
|
tokens_used=tokens_used, |
|
|
context_relevance_score=context_relevance |
|
|
) |
|
|
|
|
|
def _calculate_context_relevance(self, query: str, context: str, response: str) -> float: |
|
|
"""Calculate relevance score between query and provided context""" |
|
|
query_terms = set(query.lower().split()) |
|
|
context_terms = set(context.lower().split()) |
|
|
response_terms = set(response.lower().split()) |
|
|
|
|
|
query_context_overlap = len(query_terms & context_terms) |
|
|
context_response_overlap = len(context_terms & response_terms) |
|
|
|
|
|
if not query_terms or not context_terms: |
|
|
return 0.0 |
|
|
|
|
|
return (query_context_overlap + context_response_overlap) / (len(query_terms) + len(context_terms)) |
|
|
|
|
|
def get_analysis_data(self) -> Dict: |
|
|
"""Get aggregated analysis data""" |
|
|
df = pd.DataFrame([asdict(a) for a in self.analyses]) |
|
|
|
|
|
if df.empty: |
|
|
return { |
|
|
"total_interactions": 0, |
|
|
"avg_response_time": 0, |
|
|
"avg_relevance": 0, |
|
|
"total_tokens": 0 |
|
|
} |
|
|
|
|
|
return { |
|
|
"total_interactions": len(df), |
|
|
"avg_response_time": float(df['response_time'].mean()), |
|
|
"avg_relevance": float(df['context_relevance_score'].mean()), |
|
|
"total_tokens": int(df['tokens_used'].sum()) |
|
|
} |
|
|
|
|
|
def setup_chat_analysis(): |
|
|
"""Initialize and configure chat analysis system""" |
|
|
analyzer = ChatAnalyzer() |
|
|
|
|
|
def enhanced_log_interaction(user_input: str, bot_response: str, context: str): |
|
|
log_entry = { |
|
|
"timestamp": datetime.now().isoformat(), |
|
|
"user_input": user_input, |
|
|
"bot_response": bot_response, |
|
|
"context": context, |
|
|
"kb_version": datetime.now().strftime("%Y%m%d-%H%M%S") |
|
|
} |
|
|
|
|
|
analysis = analyzer.analyze_interaction(log_entry) |
|
|
analyzer.analyses.append(analysis) |
|
|
|
|
|
with open("chat_history/chat_logs.json", "a", encoding="utf-8") as f: |
|
|
json.dump(log_entry, f, ensure_ascii=False) |
|
|
f.write("\n") |
|
|
|
|
|
return analyzer, enhanced_log_interaction |
