What data for this category looks like
Daily documents for content recommendation operations primarily include recommendation strategy records, content pool lists, experiment reports, and operation reviews. Data sources include content tag libraries, user behavior logs, experiment platform data, and recommendation backend configuration records. Update rhythm is mostly daily or weekly. Each document contains fields such as content pool ID, tag weight, experiment group, and traffic share. There are no unified fixed units. Some fields require matching based on business scenarios. The top three time-consuming tasks in daily work are content pool filtering, experiment indicator interpretation, and abnormal traffic troubleshooting. Knowledge base retrieval must cover business details of these high-frequency scenarios.
Constraints for Knowledge Base Retrieval and Recall
Multi-source data such as content tags and user behavior logs have different update frequencies. Knowledge base recall must adapt to synchronization of data sources with varying cycles. Documents like content pool lists and experiment reports have diverse structures. Mixed-format retrieval must be supported. Recall results must match the association logic of business fields. High-frequency time-consuming retrieval demands require recall speed to meet real-time on-call scenarios. Low-relevant historical experiment data must be filtered to avoid interfering with abnormal troubleshooting. Fields such as content pool ID and tag weight use non-standard formats. Retrieval configuration must support custom field matching. Configurations that only support fixed field retrieval cannot meet requirements.
How to Set Configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the association degree of business fields such as content tags and experiment indicators, filters low-relevant historical data |
limit | Top 8 entries | Adapts to high-frequency retrieval needs for content pool filtering and abnormal troubleshooting, controls the number of results returned per round |
usingReRank | Enabled | Optimizes recall ranking for long documents such as experiment reports and operation reviews, improves the accuracy of indicator interpretation |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands the retrieval scope of business keywords such as content pool ID and tag weight, covers multi-source data |
collectionIds | Configured by business group | Binds exclusive knowledge base collections such as recommendation strategies and content pools, avoids interference from cross-category data |
Three Common Misconfigurations
- Attempting to create a knowledge base and being unable to select a tabular dataset. The symptom is that only text and image format options appear in the dataset list, with no tabular dataset entry. The cause is that the tabular dataset permission switch is not enabled, or the uploaded table does not meet the format specifications supported by FastGPT.
- Using variable references in knowledge base search cards without matching the variable naming rules for business fields such as content pool ID and tag weight. This leads to empty retrieval results. The cause is that the variable reference does not align with the actual field names in the document.
- Not configuring incremental synchronization rules, leading to failure to update daily updated content pool and experiment data in a timely manner, resulting in lagging retrieval results. The cause is that the synchronization mechanism triggered by update frequency is not enabled.
How to Confirm Configurations Are Properly Set
- Input a business question related to content pool filtering. Check whether returned results include exclusive business fields such as content pool lists and tag weight.
- Trigger a knowledge base synchronization task. Check whether synchronization logs show successful update status for daily and weekly updated data sources.
- Configure variable references in the search card. Input test variable values. Check whether retrieval results match business data corresponding to the variables.
- Adjust the value of the
similarityconfiguration item. Check whether the relevance of retrieval results meets business expectations.
The values provided here are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.