Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Policy and regulatory basis data for warehousing and logistics comes from official transportation authorities, internal warehousing operation management systems of state-owned and public enterprises, and public service logistics policy documents. Update frequency aligns with the release schedule of national and local policies. Document structure is mostly chapter-based, including fields such as policy document number, issuing authority, effective date, clause number, applicable scenario, and implementation requirements. The length of individual documents varies widely: some special policies are concise, while comprehensive policies contain more content.
What constraints these characteristics impose on the vector model and indexing link
Structured fields in warehousing logistics policy data require that key metadata be retained during chunking, to avoid breaking the associative properties of policy clauses. Otherwise, retrieval results cannot accurately match the applicable scenarios of the corresponding policy. Policy updates do not follow a fixed cycle, so the index must support incremental updates to reduce operational costs of full reconstruction. The length of different policies varies significantly. Chunk length must adapt to documents of different lengths, to prevent excessive splitting of long policies that causes semantic breaks, or short policies failing to cover complete implementation requirements. Most policy clauses are highly restrictive content, which has higher requirements for retrieval accuracy. The introduction of irrelevant results must be strictly controlled.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the length span of warehousing logistics policy documents, avoiding overly fragmented or overly long single chunks that affect semantic coherence |
| Overlap Length | 100–200 characters | Retains semantic association between adjacent chunks, ensuring contextual integrity of policy clauses |
| Similarity Threshold | 0.75–0.85 | Filters low-match irrelevant policies while retaining compliance clauses for similar scenarios |
| Single Search Limit | Top 8 results | Applicable scenarios of warehousing logistics policies are relatively concentrated, and too many retrieval results will increase the burden of context processing |
maxToken | 4000–6000 tokens | Adapts to the total length of policy documents, ensuring that retrieved context can be fully passed to the large model |
defaultToken | Calibrated via actual testing | Token limits vary across different deployment environments, and adjustments must be made based on actual invocation conditions |
Three common mistakes
- Symptom: The large model chat function works normally, but the vector index task has no progress, and logs show no embedding call records. Cause: The embedding model configuration for vector indexing is not enabled correctly, or the configured model does not support the current document chunking format.
- Symptom: Retrieval results contain a large number of cross-industry irrelevant policies, and matching degrees do not meet expectations. Cause: The similarity threshold is set too low, failing to filter low-match results, or applicable scenario metadata of policies is not retained during chunking.
- Symptom: After the vector index is built, retrieval results show a large number of duplicate policy clauses. Cause: The overlap length is set unreasonably, leading to a large amount of duplicate content during chunking, and duplicate hits of the same clauses occur during retrieval.
How to confirm configurations are set correctly
- Upload a special warehousing and logistics policy document, manually trigger index construction, check the interface progress status and system logs to confirm there are no timeout or error records.
- Input a query targeting warehousing operation compliance, verify whether the retrieval results cover policy clauses for the corresponding scenario, with no obvious irrelevant content.
- Check the vector model configuration parameters, confirm that settings such as chunk length and overlap length match the current policy document structure.
- Export the current application configuration and import it to a test instance, confirm that configuration items related to vector indexing are fully retained.
The values provided here 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.