What Data for This Category Looks Like
Data for this category primarily comes from creator backends, enterprise credit information systems, account history databases, and contract management systems. Daily processed documents include onboarding application forms, scanned identity qualification materials, certification audit records, cooperation agreement texts, and similar items. Data update timing is triggered by application workflows. The full update frequency for a single application’s end-to-end process is daily or per submitted application. Core fields include unique account identifiers, qualification verification status, audit node records, cooperation clause summaries, and similar items. There are no unified fixed unitized statistical fields; data only flows along with business processes.
What Constraints Do These Characteristics Impose on Knowledge Base Retrieval and Recall?
Data for this category is scattered across multiple independent systems, with updates triggered per individual application. This requires knowledge base retrieval to support targeted recall across collections, avoiding redundant matching across data sources. Documents include two types of content: structured forms and unstructured qualification materials. Retrieval logic must adapt to both semantic search and keyword matching hybrid workflows. Single-application process data has high timeliness requirements. The retrieval stage must prioritize recalling the most recently updated audit records and qualification files, avoiding calls to expired data. Additionally, retrieval results directly relate to account access and identity verification. The relevance threshold for recall results must be strictly controlled to prevent irrelevant content from interfering with verification judgments.
How to Configure Settings
Configurations on this page are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches semantic relevance between qualification materials and audit records, balancing recall completeness and result accuracy |
limit | Top 6 entries | Adapts to the multi-document associated audit scenario for single applications, avoiding excessive redundant content exceeding the context window |
searchMode | hybrid | Covers retrieval needs for both structured form fields and unstructured qualification text |
usingReRank | Enabled | Re-ranks recalled results to correct local biases in semantic retrieval and improve matching precision for qualification verification |
datasetSearchUsingExtensionQuery | Enabled | Expands relevant keywords based on user queries to cover synonymous phrases common in onboarding applications, such as "certification" and "qualification audit" |
collectionIds | Configured by business collection grouping | Targetedly recalls knowledge base collections corresponding to creator backends, enterprise credit databases, and contract systems, avoiding interference from cross-business data |
Three Common Configuration Mistakes
- Symptom: Semantic retrieval recalls multiple matching results, but the model returns "No relevant answers found". Cause: The
similaritythreshold for recall results is set too low, orusingReRankis not enabled, causing highly relevant results to be overwritten by less relevant ones and exceeding the valid range of the model’s context window. - Symptom: The total token count passed to the knowledge base retrieval in a single request exceeds the preset limit, causing query timeouts. Cause: The number of recalled entries from the
limitparameter is not restricted, or the expanded query content is not trimmed for length, leading to excessive token consumption. - Symptom: Markdown-formatted image links in the knowledge base cannot be properly retrieved and matched, or the AI returned results do not match the original document content in the knowledge base. Cause: Retrieval index rules for markdown resources are not configured, only text content is indexed while ignoring metadata associated with images; or the hybrid mode of
searchModeis not enabled, only semantic retrieval is used, leading to failure to match structured form fields.
How to Confirm Configurations Are Correctly Applied
- Access the retrieval configuration page in the FastGPT knowledge base backend, confirm that
collectionIdsis bound to the knowledge base collections corresponding to the target business data sources, and no unrelated business datasets are included in the retrieval scope. - Submit query text for a simulated onboarding application, verify that retrieval results cover core business documents such as qualification materials and audit records, and adjust the
similaritythreshold to meet business verification requirements. - Trigger a simulated application workflow, check that the number of results returned by the retrieval stage matches the
limitconfiguration, with no excessive redundant results interfering with subsequent processing. - View retrieval logs, confirm that
usingReRankanddatasetSearchUsingExtensionQueryare functioning as configured, with no abnormal parameter error messages.
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.