What data looks like for this category
Data for this category originates primarily from analytics tracking systems, data warehouses, CRM systems, and ticket systems. Update cycles follow daily or weekly schedules. Daily produced documents include usage analysis reports, tracking plans, indicator definition tables, and dashboard requirement forms. Data fields cover function usage frequency, customer behavior tags, indicator definition specifications, ticket flow status, and similar metrics. Common units include counts, people, percentages, and days. No unified fixed format exists, so field definitions must align with specific business scenarios.
Constraints imposed by these characteristics on knowledge base retrieval and recall
The data characteristics of this category create multiple constraints for knowledge base retrieval and recall. Multi-source data sources require retrieval to support cross-dataset associated recall, covering content from analytics tracking systems, data warehouses, CRM systems, and ticket systems. Daily or weekly update cycles require the knowledge base to use a scheduled synchronization mechanism to maintain data timeliness. Diverse document formats, including structured and unstructured content such as indicator definition tables and tracking plans, require adaptive content splitting and semantic matching. The uniqueness of indicator definitions requires retrieval results to strictly match specification definitions to avoid ambiguous recall. Real-time requirements from operations on-call scenarios require retrieval response times to be controlled within a reasonable duration to ensure timely internal ticket querying.
How to set configurations
This guide is based on FastGPT v4.17.0. All listed configurations are officially supported standard settings:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the semantic precision of product usage data. This range filters low-relevance indicator definition or tracking definition content to ensure retrieval result relevance |
limit | Top 8 entries | Adapts to multi-source data retrieval volume, avoids excessive redundant content exceeding the context window, and covers multi-dimensional data such as customer segments and ticket flow |
searchMode | hybrid | Combines keyword and semantic retrieval, adapts to precise matching of indicator definition tables and semantic recall of tracking data, balancing accuracy and recall rate |
usingReRank | Enabled | Re-ranks initial recall results, optimizes ranking of long document content such as usage analysis reports, and improves accuracy |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands retrieval queries, supplements synonyms or aliases for indicator definitions, and resolves issues where different teams use different names for the same indicator |
collectionIds | Fill in according to actual associated datasets | Limits the retrieval scope to datasets corresponding to analytics tracking systems, data warehouses, CRM systems, and ticket systems, avoiding interference from irrelevant content |
Three common misconfigurations
- An API authorization failure prompt appears in the interface, but a valid API key has been configured. Cause: The API key is not bound to the dataset associated with the current retrieval task, or dataset permissions are not granted to the corresponding application.
- After increasing the
limitparameter to 2000, the large language model does not reference any recall results. Cause: The initial recall results are too many and exceed the context window, or valid content is not included in the context range afterusingReRankis configured. - After attempting to import WeChat Official Account content, retrieval results do not include Official Account-related data. Cause: The import adapter for Official Account content is not configured, or the Official Account dataset is not added to the
collectionIdsconfiguration.
How to confirm successful configuration
- Submit a retrieval request containing specific indicator definition keywords, check whether the dataset sources of the returned results include the configured analytics tracking systems, data warehouses, CRM systems, and ticket systems.
- Adjust the
similaritythreshold to 0.8, verify that the retrieval results do not contain significantly low-matching irrelevant content, and that core indicator definition content is recalled first. - After enabling
usingReRank, compare the result ranking before and after re-ranking, confirm that highly relevant usage analysis reports or tracking plan content is displayed first. - Submit a test ticket-related retrieval query, check whether the retrieval results include content from the ticket system dataset, and that the response duration meets the real-time requirements of operations on-call scenarios.
The values provided are common starting points and should be evaluated 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.