What the data for this category looks like
The core data for this category comes from the regulatory and rule database, higher-level law database, departmental policy document database, and administrative normative document filing system. Document types include normative document drafts, drafting explanations, legality review opinions, solicitation opinion summaries, and filing reports. Data updates have no fixed cycle, and are dynamically generated alongside policy formulation, review, and filing workflows. A single document contains fields such as drafting background, article content, solicitation opinion details, review basis, etc. There is no unified fixed format, and some documents are multi-page compiled materials.
What constraints do these characteristics impose on vector models and indexes
Dispersed data sources with no unified format require the indexing system to support automatic adaptive parsing of multi-source heterogeneous data, preventing indexing failures caused by format incompatibility. Irregular update frequency requires configuring an incremental index synchronization mechanism to avoid resource consumption and time delays caused by full index reconstruction. Most documents are long policy texts, so the vector chunking logic for long segments must be adapted to avoid splitting key legal basis or policy articles. Legality review requires precise matching of higher-level legal basis, so the relevance threshold for index recall must be higher than general scenarios to ensure no core legal basis is missed. Multiple associated documents must maintain index consistency to avoid conflicting basis during review.
How to set configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph length of normative document articles, avoid splitting key legal basis |
| Overlap Length | 100–150 characters | Retain contextual association between adjacent chunks, prevent logical breaks caused by article splitting |
| Similarity Threshold | 0.85–0.9, calibrated via actual testing | Ensure recalled higher-level legal basis has high relevance, meets the rigor requirements of legality review |
| Single Search Limit | Top 10 results | Balance recall efficiency and the coverage range of basis required for review |
maxToken | 16384 | Adapt to the context window requirements of long-text review, avoid truncating core review basis |
defaultToken | 2048 | Match the encoding length of single-block vectors, ensure vector consistency of chunked data |
Three common mistakes
- Index tasks remain in the "indexing" state for a long time with no progress updates. The cause is failure to configure the timeout mechanism for incremental indexing, or blocking caused by the vector model exceeding the
maxTokenlimit when encoding long texts. - Index entries in the dataset automatically increase repeatedly. The cause is failure to enable the duplicate data deduplication switch, or failure to perform incremental judgment based on file hashes during incremental synchronization, resulting in multiple sets of indexes being generated for repeatedly uploaded documents.
- The language model associated with the configured index model cannot be called normally. The cause is failure to correctly bind the permission configuration between the index model and the language model, or a mismatch between the encoding parameters of the index model and the context requirements of the language model.
How to confirm correct configuration
Reference only; verify against the target FastGPT release and deployment documentation.
- Manually trigger an incremental synchronization, check that the number of index entries in the dataset only updates with newly added documents, and no duplicate entries appear.
- Enter a legality review retrieval request, check that the similarity scores of the recall results all fall within the preset threshold range.
- View the vector model call logs, confirm that the
defaultTokenandmaxTokenconfiguration parameters are correctly loaded, with no parameter conflict prompts.
The values provided here 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.