What this category of data looks like
Data for policy and regulatory basis Q&A for iron and steel smelting comes from public documents issued by ministries including the Ministry of Industry and Information Technology and the Ministry of Ecology and Environment, industry standards from the China Iron and Steel Industry Association, and internal compliance systems of relevant central state-owned enterprises. Documents include fields such as policy document number, issuing authority, effective date, applicable smelting processes (such as blast furnace ironmaking, converter steelmaking), and clause content. Units include ten thousand tons per year of production capacity, mg/m³ for pollutant emissions, and others. Most documents are in PDF, WORD, or web page formats, with update frequency aligned with new policy releases or regulatory revisions.
What constraints these characteristics impose on the vector model and indexing workflow
Documents from multiple sources and formats require the indexing system to support batch parsing of different file types, to avoid semantic loss caused by format parsing errors. Policy texts contain a large number of industrial technical terms, requiring the vector model to adapt to industrial domain semantic features and avoid vector mapping deviations for technical terms. Fields classified by applicable process require the index to support filtering recall results by business dimension, reducing interference from cross-process irrelevant policies. The irregular update rhythm requires the index to support incremental updates, avoiding resource consumption caused by full reindexing.
Configuration Recommendations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Single clauses in iron and steel smelting policy documents are mostly 500-1500 characters long; matching chunk length avoids semantic fragmentation |
| Overlap Length | 100–200 characters | Retains context for policy clauses across chunks, preventing key penalty or applicable clauses from being split |
| maxToken | 8192 | Adapts to the input length limit of general industrial term vector models, avoiding truncation of key policy content |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Filters low-correlation recall results from non-iron and steel smelting policy domains |
| Single Search Limit | Top 10 results | Matches the precise retrieval needs of policy Q&A, avoiding interference from redundant results |
| defaultToken | 1024 | Configures the default context window length, adapting to the information display needs of single-round policy Q&A |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Configuration Mistakes
- Phenomenon: A large number of non-iron and steel smelting policy texts appear in retrieval results. Cause: The index is not configured to filter by applicable process fields, leading to recall of cross-category policies.
- Phenomenon: Abnormally high semantic retrieval scores still occur after replacing the vector model. Cause: The similarity threshold is not adjusted to match the score distribution of the new model, and existing documents are not re-vectorized.
- Phenomenon: Multiple sets of matching vectors cannot be generated for the same set of policy data. Cause: Multi-vector indexing configuration is not enabled, or corresponding parameters are not set for different chunks of the same document.
How to Confirm the Configuration Is Set Correctly
- Upload a single iron and steel smelting policy document, check that the chunk results retain complete clause context without truncation of key content.
- Input a non-iron and steel smelting policy question, verify that retrieval results are filtered in line with the similarity threshold setting.
- After replacing the vector model, regenerate the full index, and verify that the semantic matching degree of retrieval results meets expectations.
- Upload multiple policy documents for different processes, check that the index is classified by applicable process fields and supports filtering retrieval by process.
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.