What the Data for This Category Looks Like
Data comes mainly from official policy documents released by the Ministry of Transport, provincial transportation authorities, and internal management systems of local expressway operation entities. New policy releases and annual system revisions trigger updates. Most documents present structured text with document numbers, effective dates, and applicable road sections. Some documents include attachments such as toll standards and maintenance specifications. Fields include policy number, issuing authority, effective period, enforcement clauses, and others. Units include yuan per vehicle kilometer, maintained mileage, and similar units.
Constraints Imposed on Vector Models and Indexing
Indexing must retain associated fields for structured metadata from policy documents, such as effective date and applicable road section. This enables subsequent filtering of search results by region and time. Long text attachments may contain continuous enforcement clauses. Chunking processes must avoid cutting off critical logic. Policy updates have no fixed cycle. Deploy incremental indexing to reduce reconstruction overhead. Some policies involve numerical values with units such as amount and mileage. The vector model must correctly associate these numerical values with contextual semantics.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Most policy documents are structured long texts, this avoids cutting off continuous enforcement clauses |
| Overlap Length | 100–200 characters | Retains contextual connections across chunks, ensures complete clause semantics |
| Similarity Threshold | 0.75–0.85 | Policy Q&A requires strict semantic matching, filters low-relevance results |
| Single Search Limit | Top 10 results | The number of policy documents is relatively fixed, too many results increase context pressure |
| maxToken | 8000 | Accommodates long text fragments from policy document attachments, meets retrieval requirements |
| defaultToken | 200 | Controls token consumption per vector, balances retrieval accuracy and resource usage |
Three Common Mistakes
- Phenomenon: Truncated policy enforcement clauses appear in search results. Cause: Chunk length is set too small, causing continuous clause content to be split across different chunks.
- Phenomenon: No image indexing model option appears when creating a new knowledge base in a locally deployed FastGPT v4.17.0. Cause: This feature is exclusive to the commercial version, and the local deployment version does not include the corresponding module.
- Phenomenon: Cannot filter search results by policy effective date or applicable road section. Cause: Associated metadata fields of the policy were not included during indexing, so the filtering logic does not work.
How to Verify Proper Configuration
- Upload a single policy document, check that the chunked text retains all key clauses completely, with no forced truncation.
- Enter search terms that include the applicable road section or effective date of a policy, verify that results within the corresponding range can be filtered out.
- After adjusting retrieval-related parameters, preview the adjusted retrieval effect without re-uploading documents.
- View the indexing build log, confirm that the metadata fields of the policy have been correctly included in the indexing scope.
The 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.