What Data for This Category Looks Like
Service subjects for this category include government offices, departmental government information disclosure offices, legal affairs divisions, and archives. Daily processed documents cover 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, internal document systems, departmental business ledgers, and government information disclosure related specifications. Data update frequency is irregular, changing with specific application cases and archive filing progress. Document structures include fixed fields such as applicant information, application content, disclosure attribute judgment, and relief pathway descriptions. Single document lengths vary widely, ranging from hundreds of characters to thousands of characters.
Constraints Imposed on Vector Models and Indexes
Dispersed data sources, mostly internal systems, require indexes to support unified access and vectorization of multi-source heterogeneous data. Significant differences in document lengths require adapting chunking rules for short paragraphs and long texts to avoid semantic fragmentation. The core business is matching application content with existing archives. This requires the semantic matching accuracy of vector models to align with the rigor of government documents, without excessively loosening or tightening matching thresholds. Irregularly updated data requires incremental index support to avoid resource consumption from full reconstruction. The mixed structure of fixed fields and variable content requires indexes to retain field-level semantic associations. This ensures that the matching degree between retrieval results and application content meets government service handling standards.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the structure of government documents with both short paragraphs (such as application forms) and long texts (such as reply letters), avoiding semantic fragmentation |
| Overlap Length | 100–150 characters | Retains contextual connections across chunks, ensuring complete semantic matching of application content |
maxToken | 4096 token | Matches the input limit of mainstream open-source vector models, adapting to vectorization processing of long documents |
| Similarity Threshold | 0.72–0.80 | Filters low-matching irrelevant archives, aligning with the rigorous semantic judgment standards for government documents |
| Single Search Limit | Top 10 entries | Balances retrieval efficiency and recall completeness, avoiding excessive redundant data interfering with reply drafting |
defaultToken | 200 token | Adapts to vectorization requests for short documents, avoiding token resource waste |
Three Common Misconfigurations
- After connecting a local m3e vector model, the interface always displays "indexing". The symptom is that the index status remains unchanged for an extended period. The cause is failure to configure the model's request timeout parameter, resulting in chunk vectorization requests failing to complete responses within the preset time.
- Improper use of
defaultTokencauses vectorization errors. The symptom is a token limit exceeded error prompt. The cause is failure to adjust thedefaultTokenvalue based on the actual input limit of the used vector model, exceeding the model's carrying capacity. - Custom index configurations produce no expected effects. The symptom is that retrieval results are unrelated to the custom description. The cause is failure to embed the disclosure attribute judgment logic for government information application disclosure into the index's semantic matching rules, resulting in retrieval results that do not align with business requirements.
How to Confirm Configurations Are Correct
- Upload a single government information disclosure application form. Check that there are no token limit exceeded or connection timeout errors in the index generation logs in the FastGPT backend.
- Input simulated application content. Confirm that the retrieved results include key fields of the corresponding archive, and the number of results matches the single search limit configuration.
- Adjust the similarity threshold. Verify that the matching degree of retrieval results changes as expected, with high-matching results displayed first.
- Enable the incremental index function. Upload new archived files, confirm that the index can complete updates in a short time without abnormal errors.
The values listed above are common starting points, and should be evaluated against the reader's own sample data.
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.