What this category of data looks like
Ledger data for this category primarily comes from supervision data pushed by the state-owned assets supervision platform, data from the financial accounting systems of the group headquarters and its subsidiary companies, and reported data from the project investment management module. The update rhythm follows: subsidiary companies submit data in monthly batches, the group performs monthly summary updates, and real-time updates are triggered by major investment project milestones. Each individual ledger document includes fields such as project unique identifier, total investment amount, investing entity, implementing unit, cumulative investment amount, stage node, and reporting date. The unit for monetary amounts is ten thousand yuan. Time fields use the YYYY-MM-DD format.
What constraints do these characteristics impose on the knowledge base retrieval and recall process
The multi-source reporting, monthly updates, and multi-field structure of this category impose multiple constraints on the knowledge base retrieval and recall process. Multi-source heterogeneous ledger data comes from different subsidiary systems. Extended queries must be used to adapt field mapping rules, to avoid retrieving invalid content with inconsistent formats. The monthly update rhythm requires the knowledge base synchronization cycle to match the subsidiary reporting frequency, to prevent retrieving outdated data that affects report accuracy. Each individual ledger includes multi-dimensional fields. Retrieval results for core business fields must be prioritized, while the length of individual results must be controlled to stay within the large model context window. Real-time updates for major projects require trigger-based retrieval response times to meet business milestone requirements.
How to configure the settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.85 | Matches the precise field requirements of state-owned capital ledgers, filters irrelevant entries with low similarity scores |
limit | Top 12 entries | Covers multi-project ledgers from subsidiary companies, while controlling context length to stay within large model processing limits |
searchMode | vector+keyword | Balances semantic matching and precise field matching, adapting to the retrieval needs of multi-dimensional ledgers |
usingReRank | Enabled | Performs secondary ranking on initial retrieval results, prioritizing entries with higher matching scores for core ledger fields |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands user ledger queries into precise queries that include field names, adapting to the multi-field structure |
collectionIds | Specify the dedicated collection ID for state-owned investment ledgers | Isolates content from other business knowledge bases, avoiding retrieval of irrelevant non-ledger data |
Three common mistakes
- Symptom: The retrieval node returns empty results, and the model response indicates that no knowledge base content was retrieved. Cause: The global variable was not correctly bound to the
collectionIdsparameter of the retrieval node, or the variable transfer path was configured incorrectly. - Symptom: A large number of low-matching irrelevant documents appear in retrieval results. Cause: The
similaritythreshold was set too low, failing to filter out irrelevant non-ledger data. - Symptom: Retrieval results do not include matching content for core project fields. Cause:
datasetSearchUsingExtensionQuerywas not enabled, so queries were not automatically expanded into precise searches that include field names.
How to confirm that the configuration is correct
- Check if the
collectionIdsof the retrieval node is bound to the dedicated state-owned investment ledger knowledge base collection, confirming that no content from other business knowledge bases is included. - Verify the
similaritythreshold setting. Run test queries that match core field entries, and adjust to the matching degree interval that meets business requirements. - After enabling
datasetSearchUsingExtensionQuery, run a test query that includes field names, confirming that retrieval results prioritize ledger entries that match core fields. - Check if
usingReRankis enabled, confirming that the results after secondary ranking meet business priority requirements.
The values provided here are common starting points. They 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 2026-09-28.