Admin panel → Analytics — usage, cost, and traffic for your knowledge base. This section is available only to the Admin and Owner roles and is split into five tabs: Dashboard, Search History, Tokens, Traffic, Feedback.
Period selection (Today / 7 days / 30 days / 90 days) is available on the Dashboard, Tokens, and Feedback tabs. Search History uses an arbitrary date range, and Traffic uses a fixed 30 days.
#Dashboard
Three charts for the selected period:
- Queries and quality — how many search queries users submitted during the period (via the search portal, widget, API, or AI search mode), and the average quality score of the answers produced.
- Quality distribution — a pie chart: a breakdown of the "Quality" metric from the first chart — how answers were distributed across quality levels.
- Answer sources — a pie chart: what the answers were built from (the types are in the table below).
The legend is interactive. Clicking a legend item under any chart turns that data series on or off in the chart — for example, to hide cache and view only generated answers.
#Answer types (source)
| Type | What it means |
|---|---|
| AI-generated (LLM) | A fresh answer generated by AI based on your articles. |
| Cache (CACHE_HIT) | The answer was served from cache — this or a similar question has been asked before and the answer was saved. It's returned instantly and doesn't spend tokens. |
| Technical answer (fallback) | The AI could not produce a quality answer from your articles: either no relevant information was found, or the AI service was temporarily unavailable. Instead of generating one, the system returned a prepared technical answer. |
| Error | The query could not be processed for a technical reason: the base is not yet indexed or indexing is in progress, an index error occurred, or the query was blocked by a filter (meaningless input). AI unavailability does not fall here — that case goes to "Technical answer". |
#Search History
A chronological list of all queries. Filters: search by question text, date range (from / to), language, answer type. The CSV button — export to a file.
Table columns:
| Column | What it shows |
|---|---|
| Question | The text of the user's query |
| Language | The query's language |
| Type | Answer type: AI-generated / CACHE_HIT / Technical / Error |
| Cache | HIT — answer from cache, MISS — generated anew |
| Quality | The answer's quality score in % (for cached answers — a dash) |
| Tokens | How many tokens the answer consumed (for cache — a dash) |
| Latency | The time to produce the answer, ms |
| Country | The query's country (by geolocation) |
| Date | When the query was submitted |
#Tokens
AI token consumption for the period. Six metrics: Total queries, Total tokens, Tokens (input), Tokens (output), Indexing tokens (embedding when indexing articles), Avg. per query.
What tokens are. A token is the unit AI models use to measure the amount of text (roughly ¾ of a word, or ~4 characters). LLM consumption is counted in tokens: for the input (your question + the article fragments passed to the model) and for the output (the generated answer). Indexing tokens are spent once — when an article is created or changed and converted into a vector representation (embedding) for search. Your plan includes a monthly token limit, and this tab shows exactly that consumption.
The "Token consumption" chart has three series: Tokens/day (bars), Cumulative total (line), and Plan limit (dashed). The legend is interactive — a series can be hidden by clicking. Below are two breakdowns:
- By result type — AI-generated / CACHE_HIT / Technical.
- By AI type — search, editor, generation, chat, system, indexing (embedding).
#Traffic
Data for 30 days. Three metrics: Total queries, Countries, Languages. Below are lists with shares:
- By country — distribution of queries by geography.
- By language — distribution by query language.
- By result type — AI-generated / Technical answer / CACHE_HIT.
- Popular queries — the most frequent questions for the period with the number of hits (for example, "how to create a new article · 4×").
#Feedback
"Was this article helpful?" ratings from users for the period. Three metrics: Total responses, Positive (%), Negative. Below are a chart by day, a breakdown of the reasons for negative feedback, and a list of articles with negative feedback (the link leads to the article editor). Until there are any ratings, the section shows "No feedback yet".