Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
The primary data for this category originates from government offices, government information disclosure offices, legal affairs divisions, and archives. Commonly processed documents include government information disclosure application forms, supplementary notice letters, third-party opinion solicitation letters, reply letters, service receipts, and administrative reconsideration response materials. Data sources include government information disclosure platforms, official archive systems, document management systems, departmental business ledgers, and relevant specifications of the Government Information Disclosure Regulations. Data updates occur irregularly, with no fixed batch update cycle. Document structures vary significantly by type. Some documents include applicant identity information and application item descriptions. Others include public attribute judgment basis and relief pathway explanations.
What constraints these characteristics impose on the vector models and indexing link
Scattered, diverse data sources require flexible adaptation to document formats exported from different systems, creating requirements for the index preprocessing link. Irregular data updates eliminate reliance on fixed-cycle full indexing. Incremental indexing must be supported to avoid repeated processing of historical documents. Multiple document types including applications, replies, and reconsideration materials have large semantic boundary differences. The chunking strategy must balance individual chunk semantic integrity and cross-chunk context coherence. The retrieval link must accurately match application items to existing information. High similarity matching accuracy is required to prevent irrelevant documents from interfering with business judgments.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the typical length of various business documents in this category, avoiding semantic fragmentation |
| Overlap Length | 100–200 characters | Retains cross-chunk context information to ensure complete matching of application items |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Balances retrieval recall rate and accuracy to meet the business judgment needs of government affairs processing |
| Single Search Limit | Top 10 results | Controls the scale of returned content, aligning with the limited context requirements of government affairs processing |
maxToken | 4096 token | Matches the input limits of mainstream open-source embedding models, avoiding truncation of critical business information |
defaultToken | 1024 token | Serves as the input threshold for default embedding models, adapting to short text fields |
| Reranking | Enabled | Optimizes the sorting logic of retrieval results to improve matching accuracy with application items |
Three Common Misconfigurations
- Phenomenon: Indexing tasks take more than 30 minutes to complete, with the interface showing an "indexing" status with no updates. Cause: No incremental indexing strategy is configured, and full indexing repeatedly processes historical documents, leading to excessive time consumption.
- Phenomenon: Retrieval results return a large number of irrelevant archive materials with extremely low matching degrees to the application items. Cause: The similarity threshold is set too low, recalling non-target documents outside the business scope.
- Phenomenon: Embedding model calls return errors indicating input length exceeds limits. Cause: No reasonable
maxTokenparameter is set, and no pre-truncation processing is performed for ultra-long documents.
How to Verify Successful Configuration
- View the indexing task logs to confirm that only newly added or updated documents are processed, with no repeated full indexing records.
- Submit a simulated government information disclosure application and verify that the retrieval results include corresponding types of business documents.
- Test application texts of different lengths to confirm that embedding model calls do not produce input length overrun errors.
- Adjust the similarity threshold to verify that changes in the matching degree of retrieval results align with business expectations.
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.