What the data for this category looks like
This category of data primarily comes from government portals, document management systems, government information disclosure directories, policy document repositories, and press conference materials. Daily processed documents include policy documents, policy interpretation drafts, public disclosure directories, information release approval forms, press releases, and website column update records. Data is updated daily. Individual documents typically include fields such as public attribute tags, confidentiality levels, release time limit requirements, affiliated website columns, and approval status.
What constraints these characteristics impose on the vector models and indexing workflow
The daily update frequency requires indexes to support incremental synchronization. This avoids computing resource consumption caused by full index reconstruction. The range of document types is wide. It includes long-text policy documents and short-text approval forms and column update records. Adaptive chunking logic is needed for different text lengths. Metadata such as public attributes and confidentiality levels must be included with the main text in the index. This ensures retrieval can filter compliant content using metadata. Format differences across multiple data sources require unified metadata standardization before index construction. This prevents retrieval exceptions caused by missing fields.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the natural paragraph length of policy documents and interpretation drafts, avoiding chunk loss |
| Chunk Overlap Length | 100–200 characters | Retains cross-chunk context to ensure coherence in long-text retrieval |
| Similarity Threshold | Calibrated based on actual testing | Adjusted based on semantic similarity requirements for public content, filtering irrelevant results |
| Single Recall Limit | Top 10 entries | Controls retrieval return volume, adapting to the batch processing needs of ledger summary |
defaultToken | 4096 | Matches the context window of common embedding models, avoiding truncation of critical metadata |
maxToken | 8192 | Adapts to vectorization processing of long documents, covering complete policy document content |
Three common mistakes
- Indexing status remains stuck for an extended period after knowledge base upload: Incremental indexing mode is not enabled, and daily updated batch data triggers full index reconstruction, consuming large amounts of computing resources and causing progress to stall.
- Chunk loss occurs when chunk length is set to 3000 characters: The natural paragraph structure of policy documents and interpretation drafts is not accommodated, and truncation occurs during long-text chunking without proper alignment, resulting in partial content not being included in the index.
- Rate limit error occurs during vectorization: Concurrent thread count is not restricted, and batch processing of multi-source daily updated data exceeds the call limit of the embedding service, triggering rate limit errors.
How to confirm configuration is correct
- In FastGPT v4.17.0, upload a single typical policy document. Check whether retrieval results include complete content after indexing is complete, with no chunk loss.
- Adjust the chunk length and chunk overlap length parameters. Verify the contextual coherence of retrieval results, ensuring cross-chunk content can be correctly recalled.
- Batch upload documents updated on the current day. Observe whether indexing progress completes within a reasonable timeframe, with no extended stalled states.
- Simulate retrieval requests. Check whether returned results include content that meets metadata filtering conditions, with no irrelevant or confidential documents being recalled.
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.