What Data Looks Like for This Category
The data for this category comes from regulatory and rule repositories, higher-level law databases, departmental policy document repositories, and administrative normative document filing systems. Daily processed documents include normative document drafts, drafting explanatory notes, legality review opinions, solicited comment summaries, filing reports, and some documents include multi-page attachments. Data updates have no fixed cycle, and are triggered by policy formulation or filing processes. Fields for individual documents include document number, drafting unit, release date, article content, and other details. Some file data includes association identifiers for multiple related documents.
Constraints Imposed on Vector Models and Indexes
Decentralized data sources with no fixed update cycle require indexes to support multi-data-source synchronization and incremental updates, to avoid resource consumption from full reindexing. Documents include long articles and multi-attachment combinations, with large differences in the length of individual text blocks. This requires adapting to variable-length chunking logic. The retrieval target is precise matching of higher-level law articles and policy authority basis, with high requirements for vector recall relevance. The volume of irrelevant recalled results must be limited. Files contain multiple related documents, so indexes must support batch retrieval based on association identifiers to meet full-file associated query needs during review.
How to Set Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph length of normative document articles, avoiding chunk loss issues |
| Overlap Length | 100–200 characters | Ensure semantic coherence between adjacent chunks, covering cross-chunk logic for long articles |
| Similarity Threshold | 0.75–0.85 | Meet the precision requirements for matching higher-level law articles, filtering irrelevant retrieval results |
| Recalled Results per Request | Top 10 | Meet the full basis retrieval needs of legality review, avoiding missed associated basis |
maxToken | 4096 | Adapt to the context carrying needs of long-text review, complying with input limits of mainstream vector models |
defaultToken | Calibrated via actual testing | Adapt to the average token proportion of documents in this category, balancing retrieval accuracy and performance |
Three Common Mistakes
- Phenomenon: The knowledge base remains in the indexing state for an extended period after upload, with no progress updates. Cause: Incremental indexing trigger rules are not configured, or the chunk length is set too large, causing single-block processing timeouts.
- Phenomenon: Embedding rate limit errors occur during vectorization. Cause: The number of vectorization threads is not limited, or reasonable request interval parameters are not configured.
- Phenomenon: Text block loss occurs when chunk length is set to 3000 characters. Cause: The line break logic for long articles in normative documents is not adapted, and chunking truncates unclosed legal article structures.
How to Confirm Proper Configuration
- Upload a single normative document draft, view the chunk preview, confirm that each block's length falls within the preset chunk length range, with no obviously truncated article fragments.
- Initiate a higher-level law article retrieval, verify the relevance of recalled results, and adjust the similarity threshold until it meets the matching accuracy requirements for review.
- View vectorization logs, confirm that embedding request rates do not trigger limit errors, and that thread count settings match available system resources.
- Verify multi-document associated retrieval, enter the file association identifier, and confirm that all associated review materials and basis documents can be recalled.
The configuration values provided 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.