What this category of data looks like
This category of data originates from government portals, document management systems, government information disclosure directories, policy document repositories, and press conference materials. Commonly processed documents include policy documents, policy interpretation drafts, public directories, information release approval forms, press releases, and website column update records. Data updates occur daily. Involved entities include government offices, departmental offices, policy and legal affairs departments, and website operation and maintenance units. Documents typically include fields such as title, body text, release document number, release date, and public attribute label. Some approval-related documents also include audit process records.
What constraints do these characteristics impose on the vector model and indexing stage
The daily update frequency of this category of data requires vector indexes to support daily incremental synchronization or fast full updates, to avoid affecting publication timelines. Documents include approval process records and public attribute labels, so the indexing stage must associate these corresponding fields to ensure vectors are only generated for content that meets public disclosure requirements. Different document types have large length differences: policy documents have long main text, while policy interpretation drafts are shorter. Chunking rules must adapt to the length characteristics of multiple document types to avoid damaging semantic integrity. Additionally, indexing results must strictly match public directory classifications, to facilitate accurate recall of corresponding public information in subsequent service consultation and response stages.
How to configure 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 common semantic unit length of policy documents and interpretation drafts, avoiding damage to contextual connections |
| Overlap Length | 100–150 characters | Retains semantic coherence between adjacent chunks, reducing information fragmentation |
maxToken | 4096 token | Matches the context window limit of mainstream large models, avoiding truncation of key policy content |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance results, ensuring recalled content meets the relevance requirements of public information |
| Single Search Limit | Top 5 entries | Controls the number of returned results, adapting to the conciseness requirements of service consultation responses |
defaultToken | 1024 token | Sets the default token limit for chunks, balancing semantic integrity and indexing efficiency |
Three common configuration errors
- Long-term "Indexing in progress" status after importing data. The cause is failure to adapt to the batch data scale of daily updates for this category, and failure to enable incremental index configuration, resulting in excessively long full index processing time.
- Abnormally high and identical vector calculation scores. The cause is failure to perform pre-filtering on the public attribute field, resulting in classified or non-public documents being included in the vector generation scope, leading to disordered semantic feature matching logic.
- Slow vectorization task execution. The cause is chunk length settings exceeding the reasonable range of document semantic units, or failure to enable segmented vectorization optimization for long policy documents, resulting in increased processing time per data entry.
How to confirm correct configuration
- Check the dataset's indexing progress logs to confirm that incremental synchronization tasks are triggered at the daily update frequency.
- Randomly select policy documents and policy interpretation drafts to verify the semantic integrity of chunked content, with no obvious contextual fragmentation.
- Test retrieval of policy content under the public directory to confirm that the similarity values of recalled results fall within the preset threshold range.
- Verify that classified or non-public documents are not included in the public index library, and that retrieved results contain no sensitive content.
The values given 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.