What Data for This Category Looks Like
Data for this category comes from government information disclosure platforms, official archive systems, document management systems, departmental business ledgers, and standard documents related to the Government Information Disclosure Regulations. Daily processed documents include government information disclosure application forms, supplementary notice letters, third-party opinion solicitation letters, response letters, delivery receipts, and administrative reconsideration response materials. Each document typically includes fixed fields such as application number, application content, response document number, and third-party feedback. Data updates have no fixed cycle. New or revised entries are added irregularly following individual case applications and archiving processes.
What Constraints Do These Characteristics Impose on Vector Models and Indexing
Standardized documents have fixed fields, but single-document content lengths vary significantly. Vector chunking must accommodate text ranging from hundreds to thousands of characters, to avoid damaging the integrity of core information such as application content and response basis. Data from multiple sources has format differences. Unified standardized preprocessing is required before index construction, to eliminate format deviations from documents exported by different systems. Data updates have no fixed cycle. Incremental indexing and batch re-embedding operations must be supported, to accommodate sudden model changes or data revisions. Individual case handling has timeliness requirements. This limits the maximum single vector retrieval time, requiring a balance between recall accuracy and response speed.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average content length of standardized documents, avoids splitting core information |
| Overlap Length | 100–200 characters | Preserves contextual connections between chunks, ensures semantic coherence of application content |
maxToken | 4096 token | Adapts to the upper context window limit of mainstream embedding models, avoids text truncation |
defaultToken | 512 token | Adapts to the default token output length of embedding models, matches the basic text length of government documents |
| Similarity Threshold | 0.72–0.85, calibrated via actual testing | Matches the semantic similarity judgment standard for government documents, balances recall accuracy and recall volume |
| Single Search Limit | Top 10 results | Controls the number of retrieval results, adapts to the information screening needs of individual case review |
| Maximum Context | 8000 characters | Limits the total length of recalled content, adapts to the context length requirements for drafting response letters |
Three Common Mistakes
- Batch re-embedding of the knowledge base fails to produce a noticeable improvement in recall rate. The cause is failure to adjust domain adaptation parameters of the embedding model for professional terms in government documents, or direct execution of re-embedding without first cleaning the old index.
- Indexes created for manually inserted documents disappear automatically several hours later. The cause is failure to enable persistent configuration for incremental indexing, or the document format does not meet the platform's standardized verification rules, leading to automatic cleaning of abnormal indexes by the system.
- After connecting a specified embedding model, files remain stuck in the "indexing" state for a long time. The cause is failure to configure access timeout parameters for the model, or the
defaultTokensetting exceeds the window limit supported by the model, causing embedding tasks to fail to complete normally.
How to Confirm Proper Configuration
- Upload a standard government information disclosure application form, verify that the chunked text fragments retain the core semantics of applicant information and application content.
- Execute a batch re-embedding operation, check for error prompts related to
maxTokenexceeding limits in the task log, and confirm that the parameters are compatible with the selected embedding model. - Manually insert a response letter document, wait for index construction to complete, retrieve the application number keyword of the document, and confirm that the retrieval result includes the document and the index does not disappear automatically.
- Adjust the similarity threshold to the lower bound of the range, check whether the number of retrieval results meets the business screening needs, then adjust to the upper bound to confirm whether the accuracy meets requirements.
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.