> For the complete documentation index, see [llms.txt](https://docs.fact-finder.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.fact-finder.com/integration/tracking-interface/shop-cachehit.md). # Shop Cachehit Log shop-side cache hits so FactFinder analytics and ASO stay accurate. If your shop uses its own cache and serves search or navigation requests from that cache without forwarding them to FactFinder, send a `log` event to notify FactFinder. This is important for correct analytics and for modules that depend on search volume, such as **ASO**. With this event, you send the most important search result data from your cache. The log event requires the event, `sid`, and `channel` parameters described in [General Parameters](/integration/tracking-interface/general-parameters.md). The session ID you send must belong to the request answered by your cache, not to the original request that created the cached result. You can then pass the following additional parameters: | Parameter | Required | Description | | ----------------- | --------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | query | essential | The search term for which the search was performed. | | page | essential | The number of the search result page. The first page is page 1. | | pageSize | essential | The number of products per search result page. | | hitCount | essential | The number of products found in the search result. | | searchTime | essential | The time needed to create the search result. | | bestSimi | essential | Similarity of the best product. | | minSimi | essential | Similarity of the last product. | | filter\[FELDNAME] | essential | Information about active filters in the search result. This parameter uses the `filter` prefix followed by the filtered field name, such as `filterManufacturer`. It can appear multiple times because multiple filters can be active across several fields. The value must use the same syntax as filters in your search requests, including the configured characters for `and`, `or`, and `not`. The log event does not distinguish between filters and substring filters, so send all active filters through this parameter. | | searchField | essential | The field in which the search was performed when the search was limited to a specific field. | | customSorting | essential | If the result did not use the default sorting, send `true`. If you omit this parameter, FactFinder assumes the default sorting was used. | | additionalInfo | essential | You can use this parameter to add information to the log entry, for example to make it easier to find later. Unlike other tracking events, `log` events are written into the search log of the relevant channel. To make these entries easy to identify, the `AdditionalInfo` area contains the value `ShopCacheHit`, for example `AdditionalInfo [ShopCacheHit \| sid=session1]`. Depending on whether the cached result comes from a search or navigation request, the entry is written into the search log or ASN log. | ## REST The REST API documentation is part of the search application and is available at \[your NG server]/fact-finder/swagger-ui.html. You can also access it here: Swagger Link: In addition to the parameter documentation, you can test requests and inspect the results directly. Because the documentation is built with Swagger, you can also use Swagger Codegen to generate a client. Shop Cachehit event documentation is available through the `log` method in the `tracking` resource. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. 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