What the data for this category looks like
Data for this category comes from government information disclosure platforms, official archive systems, document management systems, and departmental business ledgers. Update frequency is irregular, with new or updated entries added as application processing progresses. Document types include government information disclosure application forms, supplementary notice letters, third-party opinion solicitation letters, response letters, service receipts, administrative reconsideration response materials, and more. Single-document fields include application number, applicant information, application reason, response content, disclosure attribute, processing status, and others. Most content is semi-structured text, and some documents include electronic signatures and unique identification numbers.
What constraints these characteristics impose on the embedding model and indexing link
The data characteristics of this category impose multiple constraints on the embedding model and indexing process. Multi-source and heterogeneous data sources require indexing to support cross-system data access and unified format conversion. The irregular update feature requires configuring an incremental indexing mechanism to avoid resource consumption caused by full reindexing. Semi-structured document types require a chunking strategy that balances semantic integrity and indexing granularity, to avoid splitting that breaks the association between core fields such as application reason and response content. The business logic centered on individual case requests requires similarity judgment to accurately match application content, to avoid irrelevant documents interfering with search results. At the same time, the rigor of government documents requires the indexing process to retain metadata such as original document numbers and processing status, providing a reliable basis for subsequent ledger summary and report generation.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the semantic units of government semi-structured documents, avoiding splitting that breaks the association between core fields such as application reason and response content |
| Similarity Threshold | 0.75–0.85 | Matches the semantic similarity judgment standards for government documents, filters irrelevant search results while covering core relevant documents |
maxToken | 4096 | Adapts to the input length limits of mainstream embedding models such as m3e, ensuring complete transmission of document content |
| Single Search Limit | Top 5 entries | Meets the retrieval needs of individual case applications, avoiding returning too many irrelevant documents that increase the workload of ledger summary |
defaultToken | 1500 | Meets the context splicing needs during ledger summary, ensuring the complete association logic between retrieval results and application content |
| Vector Database Storage Space | Calibrated based on actual measurements | Fluctuates with application processing volume, avoiding waste from over-provisioning resources |
Three common misconfigurations
- Replacing the embedding model results in abnormally high similarity values. The cause is failure to adjust the similarity threshold to adapt to the output range of the new model, causing the original threshold to fail to correctly filter low-relevance results.
- The knowledge base shows no indexing status after upload. The cause is failure to configure the calling channel for the corresponding embedding model, or the uploaded document format does not meet the system-supported type requirements.
- The number of search return results does not match expectations. The cause is that the single search limit is set too low, failing to cover all relevant business ledgers and documents involved in the application.
How to confirm proper configuration
- Upload a single government information disclosure application form, check whether the system indexing status shows completed with no error prompts.
- Enter a simulated application reason, check whether the returned search results include matching document content and metadata fields.
- Check the embedding model call logs to confirm there are no error messages such as timeouts, insufficient permissions, or format errors.
- Adjust the similarity threshold, verify that the relevance of search results changes as expected, with no obvious misjudgments or missed judgments.
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 2026-09-28.