What this category of data looks like
The data for this category primarily comes from contract systems, copyright registration databases, internal content repositories, and complaint inboxes. Daily processed documents include authorization contracts, copyright ledgers, take-down notices, and infringement complaint materials. Data updates are tied to individual business processes: updates occur once a single authorization or complaint is resolved, and routine ledgers are synchronized monthly. Document fields include rights holder information, authorization scope, validity period, infringing subject identifiers, and more. There is no unified fixed format, and some key information must be extracted from unstructured text.
What constraints do these characteristics impose on the knowledge base retrieval and recall link
The data characteristics of this category impose multiple constraints on the retrieval and recall process. Decentralized, multi-source data requires the retrieval system to adapt to different document formats, to avoid failure to extract key fields due to format differences. Core retrieval conditions such as authorization scope and validity period require precise matching, so recall results must strictly correspond to the query conditions in the ticket. The high update frequency requires the knowledge base to synchronize the latest contract and complaint data regularly; otherwise, recall results will lag behind actual business status. Unstructured infringement complaint materials also require the retrieval model to have semantic extraction capabilities to locate key relevant information from vague text.
How to set configurations
The following configuration items apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.85 | Core retrieval for this category requires precise matching of fields such as authorization scope and validity period. This interval is calibrated based on actual testing to filter irrelevant documents with low similarity |
limit | Top 8–12 results | The number of authorization-related documents recalled per batch must be controlled within a reasonable range to avoid context overload, while covering multiple associated contracts or ledgers |
searchMode | vector | This category requires semantic matching of authorization scope and infringement-related information; vector retrieval adapts to the semantic recall needs of unstructured text |
usingReRank | Enabled | Most infringement complaint materials are unstructured text; re-ranking can improve the ranking priority of highly relevant results and optimize retrieval accuracy |
datasetSearchUsingExtensionQuery | Enabled | Rights holder names mentioned in tickets may have abbreviations or aliases; extended queries can cover documents with synonymous expressions |
collectionIds | Configured according to the exclusive dataset for authorization documents | Isolate the exclusive knowledge base for copyright and authorization management to avoid recalling irrelevant content from other business modules |
Three common mistakes
- A large number of irrelevant non-copyright documents are returned in retrieval results. The cause is failure to limit the exclusive dataset via
collectionIds, which recalls knowledge base content from other business modules. - When using the
bge-m3vector model, semantic retrieval matching is too broad, returning a large number of low-relevance contract texts. The cause is failure to properly set thesimilaritythreshold and filter non-precisely matched results. - No matching results are returned when querying authorization scope via a ticket. The cause is failure to enable
datasetSearchUsingExtensionQuery, which cannot identify the association between abbreviated rights holder names in the ticket and full names in documents.
How to confirm configurations are correctly set
- Check the environment variable configuration in the FastGPT App/Admin server, confirm that
AIPROXY_API_ENDPOINT(AI Proxy service root address, format example: http://aiproxy:3000, do not append /v1 or model service path) andAIPROXY_API_TOKEN(AI Proxy administrator Token, must match the AI Proxy server configuration and not be empty) are correctly configured with no empty values. - Initiate a test ticket, enter a query statement containing a known authorization scope, and verify whether the retrieval results include the corresponding authorization contract or ledger document.
- Adjust the
similaritythreshold, observe changes in the relevance of retrieval results, and confirm that the threshold value meets business requirements. - View the knowledge base synchronization logs, confirm that the exclusive knowledge base for copyright and authorization management has completed synchronization according to the update frequency, with no abnormal errors.
The values provided 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.