What the Data for This Category Looks Like
The core data for this category originates from search ad backends, search term reports, bidding ranking data, and landing page data. The update cycle is most commonly daily or weekly. Documents produced and used daily include keyword lists, negative keyword tables, bid records, and search term reports. Fields include keyword text, matching mode, bid amount, click volume, conversion cost, and additional relevant metrics. These assets are maintained daily by SEM optimizers, agency operations staff, and brand marketing personnel.
Constraints Imposed on Reranking and Hybrid Retrieval
The data characteristics of this category impose clear constraints on the reranking and hybrid retrieval process. Given the daily or weekly data update frequency, retrieved documents must align with timeliness requirements, and old data exceeding the update cycle must be filtered. Most documents exist as structured lists. During reranking, core fields such as keyword text, matching mode, and bid amount must be prioritized for matching. Data sources are dispersed, so hybrid retrieval must integrate native keyword data from the ad backend and user search behavior data from search term reports. Invalid extended keywords or misjudged negative keywords must also be filtered first to mitigate risks including invalid clicks and brand term encroachment.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
usingReRank | Enabled | This category requires filtering invalid extended keywords and misjudged negative keywords. Reranking improves the accuracy of keyword matching and avoids invalid click risks |
similarity | 0.75–0.85 | Matches the similarity between user queries and keyword text or search terms, covers keywords with core matching modes, and prevents overly strict filtering of valid delivery keywords or overly loose inclusion of irrelevant entries |
limit | Top 8–12 entries | The keyword list scale handled daily for this category is moderate. Retaining an appropriate number of retrieved entries for reranking adapts to the information carrying capacity of a single Q&A session |
searchMode | Hybrid Retrieval | Requires integrating native keyword data from the search ad backend and user search data from search term reports. Hybrid retrieval can cover actively searched user queries and native delivery keywords |
Common Misconfiguration Scenarios
- Phenomenon: Reranking results return false during Q&A, and no valid reranking entries are displayed in the interface. Cause: The
usingReRankparameter is not configured correctly, or the reranker model call path is not adapted to the structured keyword documents of this category. - Phenomenon: An error occurs when calling the deployed reranker model, while other models run normally. Cause: The input format of the reranker model is not adapted to the structured keyword documents of this category, or the parameter configuration during model loading does not match the FastGPT reranking interface.
- Phenomenon: The retrieved entries from reranking have low relevance to the user's query, and fail to hit core keywords or search terms. Cause: The query type used during the retrieval phase is not clearly defined. The default uses a rewritten query, while this category requires prioritizing matching the keyword intent of the original user query.
How to Confirm Successful Configuration
- Access the FastGPT reranking configuration panel, confirm that the
usingReRankparameter is enabled. - Upload a keyword list document for this category, submit a test query, and verify that matching keyword entries appear in the retrieved results.
- Review system logs to confirm that there are no errors in reranker model call requests and returned results.
- Compare the original user query and the rewritten query, confirm that the query type used during the reranking link aligns with business requirements.
The values provided here are common starting points and should be evaluated against the samples relevant to the actual deployment scenario.
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.