What This Category of Data Looks Like
Data for this category originates from government information disclosure platforms, official archive systems, document management systems, departmental business ledgers, and relevant normative documents. Daily processed documents include government information disclosure application forms, supplementary notice letters, third-party opinion solicitation letters, response letters, service receipts, and administrative reconsideration response materials. These documents are generated and maintained by entities such as government offices and government affairs disclosure offices. Data updates follow no fixed cycle, and are synchronized with application processing progress, file archiving, or policy adjustments. Each individual document includes clear fields for applicant subject, matter description, processing flow, and response result.
Constraints for Vector Models and Indexes
Decentralized data sources and non-fixed update cycles require indexing systems to support cross-source incremental synchronization. This prevents duplicate indexing and missed updates to the latest documents. Diverse document types with distinct structural differences require adaptation to each document type’s field characteristics. A uniform chunking rule cannot be used. The core requirement of individual case handling means retrieval must accurately match the specific description of the application matter. This places high demands on the semantic understanding accuracy of vector models. Errors directly impact administrative procedure compliance. Retrieval recall accuracy and completeness must be guaranteed to avoid missing key supporting materials.
Configuration Settings
The following configurations are optimized for FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the typical length of government service documents for this category. Ensures a single chunk covers complete application matters or response key points, avoiding semantic fragmentation |
| Overlap Length | 100–150 characters | Retains logical connection information between document paragraphs. Prevents loss of critical contextual associations after chunking |
| Similarity Threshold | 0.72–0.80 | Differentiates subtle differences between similar application matters. Balances recall rate and precision, avoiding false recall of unrelated documents |
defaultToken | 4096 | Meets the vector generation length requirements of most government service documents. Ensures complete basic context for single-round retrieval |
maxToken | 8192 | Accommodates concatenated content from multiple relevant documents. Supports complete information required for drafting response letters |
| Single Search Limit | Top 8 results | Matches the typical number of relevant documents for individual case applications. Avoids excessive redundant content increasing model processing load |
Three Common Misconfigurations
- Issue: Relevant files are retrieved, but the large language model outputs "no relevant content found". Cause: Incorrect similarity threshold setup causes retrieved documents to have overly low semantic matching, or context concatenation exceeds the
maxTokenlimit, leading to critical content being truncated. - Issue: Index tasks throw timeout errors. Cause: Chunking parameters are not adjusted for government service document lengths. Excessively long single chunks increase vector generation time, exceeding system processing thresholds.
- Issue: Recall results include large volumes of proactively disclosed information unrelated to the current application. Cause: No association with the specific description field of the application matter, based on the individual case nature of application-based disclosure. This leads to generalized recall of unrelated content.
How to Verify Proper Configuration
- Upload a single typical government service document, such as an official response letter. Use the chunk verification feature in FastGPT v4.17.0 to check the completeness of chunked content. Confirm that no critical logical nodes are split between chunks.
- Enter a simulated application-based disclosure matter description. Review the number and matching degree of recall results. Confirm that the similarity threshold aligns with the business scenario requirements.
- Test the concatenated length of multiple relevant documents. Confirm that the
maxTokenlimit is not exceeded, to prevent automatic truncation of critical content. - View index task running logs. Confirm that there are no error reports for vector generation failures, timeouts, or duplicate indexing.
The configuration values provided here are common starting points, and should be evaluated against samples from the target deployment environment.
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.