What the data for this category looks like
The core data for this category comes from centralized treasury payment systems, unified budget management systems, electronic invoice platforms, and contract management systems. It covers daily documents including payment applications, fund usage plans, invoices, contracts, acceptance receipts, reimbursement approval slips, and payment vouchers. Data is updated daily, and is maintained by entities such as budget unit finance staff, fiscal treasury payment center staff, and department finance review positions. This data focuses on the compliance review link for single fund payments, and includes core fields such as budget indicators, economic classifications, and document chains.
Constraints on vector models and indexing
Varied document formats across multiple systems require unified field mapping before entering the indexing link, to avoid deviations in vector representations. Daily updated data requires indexing to support incremental synchronization, reducing resource consumption and time costs from full reindexing. The characteristic that a single payment is linked to multiple document types requires retaining cross-document associated context during chunking, preventing loss of key information from the review chain. Compliance requirements for core fields demand precise vector recall, limiting interference from irrelevant recall results to ensure the accuracy and authority of review basis.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Documents in this category contain multi-field associated content. Excessively long chunks lead to redundant vector representations, while excessively short chunks lose associated information from the review chain |
| Overlap Length | 100–200 characters | Cross-document or cross-paragraph review chains require contextual continuity. Overlap length ensures associated information is not split apart |
maxToken | 4096 token | Adapts to the text length of policy documents, and complies with input limits of mainstream embedding models |
defaultToken | 2048 token | Matches the context length requirement for single queries in daily policy Q&A |
| Single Search Limit | Top 8 results | This category requires precise matching of policy basis. Too many results increase the screening burden for review positions |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Ensures accuracy of core field matching, avoiding interference from low-relevance results for compliance reviews |
Three common configuration errors
- Symptom: An immediate error occurs after clicking test after enabling the
defaultTokenconfigured indexing model, filling in a custom request address and apikey. Cause: Failure to confirm that the custom interface's return format matches the vector output format required by FastGPT, or that the apikey permission does not allow calling the corresponding model. - Symptom: "No available indexing model detected" still displays after the knowledge base is refreshed. Cause: The configured indexing model was not saved and activated, or the model associated with the knowledge base does not match the globally set model.
- Symptom: Retrieval results cannot match the corresponding policy basis, and chunk records show the document is split into multiple independent segments. Cause: The chunk length is set too small, splitting associated review chain documents into multiple chunks, which prevents complete policy basis from being associated during vector recall.
How to confirm correct configuration
- Access the knowledge base settings page of FastGPT v4.17.0, check whether the indexing model's enable status is set to enabled, and confirm that the configured model matches the actually called model.
- Upload a single test document of this category, perform chunking and indexing operations, and check whether the chunking results conform to the preset length and overlap rules.
- Initiate a test query targeting policy basis, check whether the retrieval results include core document fields of this category, and confirm that the recall logic is working properly.
- Simulate the daily incremental data upload process, check whether the index updates normally with no error prompts.
The values provided here 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.