What the 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 relevant specifications of the Government Information Disclosure Regulations. Daily documents include government information disclosure application forms, supplementary notice letters, third-party opinion solicitation letters, reply letters, service receipts, administrative reconsideration response materials, and more. Data updates have no fixed cycle, and change dynamically with business processing and archive filing. Most documents are a mix of structured and semi-structured content, with clear fields including applicant information, application content, reply basis, relief channels, and others. There is no unified standard format, but core fields are fixed.
What constraints these characteristics impose on the vector models and indexes link
Data for this category originates from scattered sources with large format differences. This requires adapting vector extraction logic for multi-source heterogeneous content. The irregular update feature requires indexes to support incremental updates, avoiding resource consumption from full reconstruction. Core fields are fixed, but document formats are not unified. Chunking must retain key context to maintain semantic relevance. Single documents are mostly case-related content. It is necessary to balance chunk length and semantic integrity, avoiding loss of key associated information due to overly fragmented chunks, or reduced retrieval accuracy due to overly large chunks.
Reference only; verify against the target FastGPT release and deployment documentation.
How to set the configurations
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the semantic integrity of case documents for this category, avoiding overly fragmented or overly large chunks |
| Overlap Length | 100–200 characters | Retains semantic relevance between chunks, preventing key information from being truncated |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Matches the semantic matching degree between case applications and documents, balancing recall rate and precision |
| Single Search Limit | Top 10 entries | Adapts to the limited context requirements of internal office work, avoiding interference from excessive irrelevant content |
defaultToken | 8192 | Adapts to the total length requirements of documents in this category, ensuring complete loading of reply-related materials |
maxToken | 16384 | Reserves sufficient context space to accommodate retrieval results from multiple associated documents |
Three common mistakes
- Phenomenon: Indexes generated from manually inserted associated reply materials disappear automatically after several hours. Cause: Persistent storage configuration for indexes is not enabled, and temporary indexes are not correctly written to persistent storage media.
- Phenomenon: The number of recall results for multilingual applications drops sharply after replacing the embedding model. Cause: A full re-embedding operation is not performed on existing knowledge base documents, and old vectors do not match the semantic space of the new model.
- Phenomenon: No results are returned when searching for associated documents for a specific application. Cause: The chunk length is set too large, causing the core semantics of the application request to not be fully extracted, making it impossible to match the vector index.
How to confirm the configuration is correct
- Upload a typical government information disclosure application form and associated reply letter, check whether the chunked content retains the key semantics of applicant information, application content, and reply basis.
- Perform a simulated application search, verify whether the number of returned results meets the configuration requirements of the single search limit.
- After replacing the embedding model, perform a full re-embedding operation on existing documents, verify the recall effect of multilingual applications under the new semantic space.
- View the system index storage logs, confirm that incrementally updated indexes do not experience automatic disappearance.
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.