What the data for this category looks like
This category’s daily processed documents include five core types: regulatory document drafts, drafting explanations, legality review opinions, solicited opinion summaries, and filing reports. Data sources include regulatory and rule databases, higher-level law databases, departmental policy document libraries, and administrative regulatory document filing systems. Most documents have structured paragraphs paired with policy provisions; some files include summary entries from public solicitation of opinions. There are no unified fixed fields, and content must be adjusted based on the issuing unit and review stage. Data updates have no fixed cycle, with new additions or revisions synchronized alongside policy development and filing process progress.
What constraints these characteristics impose on the "vector models and indexing" link
Heterogeneous data from multiple sources requires adaptation to different text splitting formats, to avoid splitting deviations caused by differences in text structure between higher-level laws and drafts. Irregularly updated data requires indexes to support incremental synchronization, to avoid resource consumption from full reconstruction. Individual documents have large content spans, with obvious differences in text length from drafting explanations to filing reports, so variable-length vector extraction must be supported. The review scenario requires precise matching of provision-based references, so the accuracy requirement for vector recall is higher than that of general scenarios. The recall range must be limited to higher-level law entries related to policy-making authority, to avoid irrelevant information interfering with review judgments.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph length of regulatory document drafts and higher-level law provisions, avoiding single chunks containing cross-level policy content |
| Overlap Length | 100–200 characters | Retains contextual association between adjacent chunks, preventing splitting from destroying the complete logic of provisions |
maxToken | 8192 token | Matches the maximum input length of Qwen3-Embedding-8B, adapting to the vector extraction needs of long texts |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Filters low-match irrelevant regulatory entries, ensuring recall results focus on authority and provision references required for review |
| Single Search Limit | Top 10 entries | Controls the number of returned results, avoiding review personnel being distracted by redundant information |
defaultToken | 2048 token | Adapts to the vector extraction length of conventional review documents, balancing computing resources and recall accuracy |
Three common mistakes
- Connection failure errors occur when connecting to a locally deployed VLLM version of Qwen3-Embedding-8B. The cause is failure to correctly fill in the local API address and port of the model, or failure to configure cross-origin access rules.
- The total disk usage of the local FastGPT knowledge base cannot be accurately counted. The cause is failure to distinguish the storage directories of original files, split text chunks, and embedded vectors; the three types of data occupy different disk spaces respectively.
- Retrieval result relevance does not improve after configuring text splitting and data indexing. The cause is failure to adjust the recall range of the index based on the regulatory document review scenario, and the default indexing rules do not filter irrelevant policy entries.
How to confirm the configuration is properly set
- Upload a local regulatory document draft and the corresponding higher-level law document, perform retrieval verification, and check whether the returned recall results include relevant higher-level law provisions.
- View the vector model call logs, confirm that the
defaultTokenandmaxTokenparameters match the configured value ranges, and there are no errors exceeding model limits. - Use the system disk analysis tool to separately view the storage directories of original files, split text chunks, and embedded vectors, confirming that the occupied space of the three types of data meets expectations.
- Adjust the similarity threshold configuration, perform multiple retrieval verifications, and observe whether the number of recall results shows reasonable fluctuations as the threshold changes.
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.