What the data for this category looks like
Internal office data for urban investment platforms primarily comes from OA system document approval, project management ledgers, employee handbooks, special urban investment documents published via government disclosure channels, financial reimbursement standards, and similar sources. Update frequency falls into three categories:
- Project ledgers are updated in real time as projects are initiated or their progress is adjusted.
- Official documents are updated on a monthly cycle.
- Employee handbooks and benefit policies are updated annually or when major changes occur.
Document structure includes both structured fields and unstructured content. Structured fields include project number, investment amount (unit: ten thousand yuan), document number, and responsible department. Unstructured content includes project feasibility study reports, policy interpretation texts, and job responsibility descriptions.
Constraints on Knowledge Base Retrieval and Recall
The document characteristics of urban investment platforms create multiple constraints for retrieval and recall. The high volume of structured fields requires retrieval to support precise field matching, to avoid retrieving irrelevant non-target documents. Long documents account for a large share of the total. For example, project feasibility study reports can reach tens of thousands of characters, so chunking parameters must be set appropriately to avoid semantic fragmentation. Update frequencies vary widely, so a synchronization strategy combining incremental and full updates must be supported. Many professional terms and abbreviations are used, such as "urban investment bonds" and "shantytown renovation projects". Retrieval must support extended queries to cover these abbreviated expressions. These constraints require retrieval configurations to balance precision and coverage, while adapting to processing needs for different document types.
How to Set the Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.65–0.75 | Urban investment documents contain a large number of professional terms and structured fields. A threshold that is too low will introduce irrelevant government disclosure documents, while a threshold that is too high will miss relevant internal project documents |
limit | Top 8–12 entries | Internal office documents related to urban investment platforms typically fall into three categories: projects, policies, and personnel. Too many results will exceed the context window limit and reduce the response efficiency for employee queries |
searchMode | vector+keyword | Balances precise matching of structured fields and semantic recall of unstructured content, adapting to the mixed characteristics of structured ledgers and unstructured reports in urban investment documents |
usingReRank | Enabled | Urban investment project documents are numerous in volume. Re-ranking can filter out low-quality results that are semantically relevant but do not match field requirements, improving the relevance of retrieved results |
datasetSearchUsingExtensionQuery | Enabled | Urban investment employees often use abbreviations in queries, such as "shantytown renovation" instead of "shantytown renovation projects". Extended query support can cover these abbreviated expressions and improve retrieval coverage |
collectionIds | Specify dedicated collection IDs for internal office and employee services | Avoid retrieving documents from other business lines, ensuring the retrieval scope only covers datasets related to internal office and employee services |
Three Common Misconfigurations
- Symptom: In the FastGPT v4.17.0 private deployment version, the add entry for image datasets cannot be found in the knowledge base management page. Cause: The image embedding plugin is not enabled by default. Manually enable the corresponding function in the configuration file before attempting to add.
- Symptom: Historical call logs for individual knowledge base documents cannot be viewed. Cause: The log collection switch is not enabled, or the log retention period is set to less than 7 days. This causes historical call records to be automatically cleared.
- Symptom: The error "The dollar ($) prefixed field '$schema'" appears when importing structured data. Cause: The imported JSON or CSV file contains the $schema field. FastGPT's import logic intercepts fields prefixed with $. Remove this field before importing the file.
How to Verify Proper Configuration
- Submit test queries containing urban investment professional terms, such as "investment amount of the XX project in 2024", and check if the retrieved results include the corresponding project ledger documents.
- View the knowledge base retrieval logs and confirm that the
similarityscore of the retrieved results falls within the 0.65–0.75 range. - Test queries using abbreviations, such as "shantytown renovation", and check if relevant documents for shantytown renovation projects are retrieved.
- Check the
collectionIdsconfiguration of the knowledge base collection, confirm that only datasets related to internal office and employee services are included, and no documents from other business lines are included.
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 2026-09-28.