What the data for this category looks like
Data sources for this category include government portals, document management systems, government information disclosure directories, policy document repositories, and press conference materials. Daily documents cover policy documents, policy interpretation drafts, public disclosure directories, information release approval forms, press releases, and website column update records. The data update frequency is daily. Documents include fields such as public attribution, confidentiality level, and publishing column affiliation. Document structures vary significantly: policy files are mostly long text paragraphs, while approval forms use short form formats.
What constraints these characteristics impose on vector models and indexing
Daily updated data requires indexes to support incremental synchronization, to avoid resource consumption from full reindexing. Varied document structures require chunking strategies adapted to different text lengths, to prevent long text fragmentation or short text over-segmentation. Dispersed data sources require unified preprocessing workflows, to ensure compatibility with document formats exported from different systems. Fields for public attribution and confidentiality level require indexes to associate metadata, to ensure retrieval results only include content meeting public disclosure requirements, and prevent accidental recall of sensitive information.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the long text structure of policy documents, avoid semantic chunk fragmentation |
| Overlap Length | 50–100 characters | Retain contextual connection between adjacent chunks, improve retrieval coherence |
| Similarity Threshold | 0.75–0.85 | Filter low-relevance retrieval results, meet the precise matching needs of public documents |
| Single Retrieval Count | Top 5 results | Adapt to the limited candidate set of government public disclosure documents, avoid redundant content being passed to the context window |
maxToken | 8000 token | Aligns with the general model context limit of FastGPT v4.17.0, ensures complete retrieval content is passed in |
defaultToken | Set based on actual testing | Adapt to chunk processing for short documents such as approval forms, balance accuracy and resource consumption |
Three common configuration mistakes
- Symptom: Incremental synchronization tasks for indexes show failures, with logs indicating missing metadata fields. Cause: Extraction rules for metadata such as public attribution and confidentiality level are not configured, leading to failure to identify valid data during incremental synchronization.
- Symptom: The large language model does not use retrieved policy document content when answering questions, and directly calls general knowledge. Cause: The
maxTokensetting is too small, preventing the complete retrieved policy text from being fully passed into the context window. - Symptom: Retrieval results include undisclosed confidential documents. Cause: No filtering is performed using public attribution metadata, leading to recall of sensitive content.
How to confirm correct configuration
- Log into the FastGPT v4.17.0 admin backend, view the index task list, confirm that the daily incremental synchronization task has a successful execution status.
- Enter a query related to policy interpretation, verify that the retrieval results include entries from the corresponding public disclosure directory, and do not include confidential or undisclosed documents.
- Adjust the value range of the similarity threshold, verify that the relevance of retrieval results changes as expected with the threshold.
- Check the context window configuration, confirm that the length of retrieved content does not exceed the
maxTokenlimit, with no truncation prompts.
The values provided here are common starting points, and should be evaluated against the user'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.