Architect analytics pipeline

[MinhHaDuong]

Rsync distant reports/monitor-logs
Ingest, using Jupyter (Python) as the data analysis framework (Pandas should work fast enough, otherwise Polars or Dask)
Compute key metrics (see next comment)
Produce visualisation (Plotly, see next next comment)
write analysis

[MinhHaDuong]

To understand what people are using the RAG app for, here are the key metrics Claude would recommend computing:

Query Analysis & Intent:

Most frequent user queries/questions (from userinput events)
Query categories or topics (using keyword extraction or clustering)
Query complexity (length, question types, technical vs. casual language)
Follow-up question patterns within sessions

Content Engagement:

Which articles are most frequently retrieved and clicked (article + userclick events)
Article relevance scores (if logged in response events)
Time spent with articles visible (visibilitychange timing)
Articles that generate follow-up questions

User Behavior Patterns:

Session duration and depth (number of queries per session)
Common user journeys (sequence of queries and article interactions)
Abandonment points (where users stop engaging)
Return user behavior vs. new users

System Performance Impact:

Response times for different query types (request to response timing)
Queries that don't lead to article clicks (potential relevance issues)
Peak usage times and query volumes

Success Indicators:

Sessions with high engagement (multiple queries + article clicks)
Query-to-click conversion rates
Sessions ending with article engagement vs. abandonment

[MinhHaDuong]

Here's what each metric should be visualized as and what insights it reveals:
Query Analysis & Intent:
Most frequent user queries

Visualization: Horizontal bar chart (top 20-50 queries)
What it shows: Primary use cases, common problems users are trying to solve, repetitive needs that could be addressed with better UX

Query categories/topics

Visualization: Treemap or sunburst chart
What it shows: Distribution of subject areas, whether app serves diverse needs or is focused on specific domains

Query complexity

Visualization: Histogram of query length + box plot by complexity score
What it shows: User sophistication level, whether people ask simple vs. complex questions, optimal response strategies needed

Follow-up question patterns

Visualization: Sankey diagram (query → follow-up → follow-up)
What it shows: Whether initial responses satisfy users, common information gaps, iterative research patterns

Content Engagement:
Most retrieved/clicked articles

Visualization: Horizontal bar chart with dual axis (retrievals vs clicks)
What it shows: Content effectiveness, whether retrieved content actually gets consumed, content gaps

Article relevance vs engagement

Visualization: Scatter plot (relevance score x-axis, click rate y-axis, bubble size = frequency)
What it shows: Whether your RAG system's relevance scoring matches actual user interest

Time spent with articles

Visualization: Box plot by article category + histogram of engagement time
What it shows: Content quality, readability, whether articles actually help users vs just get clicked

Query-to-article relationships

Visualization: Network graph or chord diagram
What it shows: Content clusters, whether certain articles serve multiple query types, content organization insights

User Behavior Patterns:
Session duration and depth

Visualization: 2D histogram (duration x queries per session)
What it shows: User engagement levels, whether app supports quick answers vs deep research, session value distribution

User journey sequences

Visualization: Flow diagram or sequential sunburst
What it shows: Common usage patterns, drop-off points, successful vs unsuccessful session paths

Abandonment points

Visualization: Funnel chart with step-by-step conversion rates
What it shows: Where users get frustrated, UX friction points, content quality issues

System Performance Impact:
Response times by query type

Visualization: Box plot with query categories on x-axis
What it shows: Which queries are computationally expensive, performance bottlenecks, user experience impact

Queries without clicks

Visualization: Stacked bar chart (clicked vs not clicked) by query characteristics
What it shows: Relevance problems, content gaps, whether users find what they need

Success Indicators:
High engagement sessions

Visualization: Scatter plot (queries per session vs time spent, colored by outcome)
What it shows: What constitutes successful app usage, user satisfaction patterns

Query-to-click conversion

Visualization: Time series line chart with trend line
What it shows: Whether your system is improving over time, seasonal patterns, A/B test results

Each visualization directly answers "What are people using this app for?" by showing different facets of user intent, success, and satisfaction.

[MinhHaDuong]

src/analytics/
├── README.md # Setup instructions & usage guide
├── requirements.txt # Python dependencies (pandas, plotly, etc.)
├── config.py # Local paths, server configs
├── notebooks/ # Interactive Jupyter analysis
│ ├── 01_data_exploration.ipynb # Understand log structure
│ ├── 02_usage_patterns.ipynb # What people use the app for
│ ├── 03_content_analysis.ipynb # HAL publication engagement
│ └── 04_performance_insights.ipynb # System optimization
├── scripts/ # Utility scripts
│ ├── fetch_logs.py # Pull logs from production server
│ ├── clean_data.py # Basic preprocessing
│ └── generate_report.py # Quick summary script
├── src/ # Reusable analysis modules
│ ├── data_loader.py # Parse JSON logs
│ ├── metrics.py # Calculate key metrics
│ ├── visualizations.py # Chart functions
│ └── utils.py # Helper functions
└── data/ # Local data (gitignored)
├── raw/ # Downloaded logs
├── processed/ # Cleaned data
└── cache/ # Intermediate results