What this category of data looks like
Data for policy and regulatory basis Q&A for state-owned asset management companies comes from three main sources: normative documents issued by state-owned asset supervision departments at all levels, industry regulatory requirements, and internal management rules for asset operation, valuation, disposal and other work. Data updates follow no fixed schedule. Updates are synchronized when new policies are issued or internal systems are revised. Most documents include standard fields: uniform document numbers, issuing units, and effective dates. Content is divided by clauses. Some documents add fields such as asset category and evaluation value. Common units are ten thousand yuan and hundred million yuan.
Constraints on Vector Models and Indexing
Data sources for policy and regulatory basis are scattered, including external regulatory documents and internal management rules. Differentiated metadata indexing rules must be configured for different sources. Documents include fields such as effective dates and asset categories. Recall results must support filtering by release time and asset type. Document lengths vary significantly. Some notices are hundreds of words long, while some management rules span tens of thousands of words. Different chunking strategies must be used to avoid context breaks. Data updates follow no fixed schedule. Incremental indexing must be supported to reduce redundant computing overhead.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Aligns with the clause length of policy documents, avoids single chunks covering cross-topic regulatory requirements, and keeps single-chunk token count within the maxToken limit |
| Overlap Length | 100–200 characters | Preserves contextual connections between adjacent chunks, prevents policy clauses from being split across chunks which would harm recall accuracy |
| Similarity Threshold | 0.75–0.85 | Distinguishes relevance levels of policy documents, filters low-match irrelevant regulatory content |
| Single Search Limit | Top 8 results | Matches the coverage scope of state-owned asset management policies, ensures recall results cover key regulatory clauses and internal management rules |
defaultToken | 4096 | Adapts to the context window of mainstream large models, accommodates chunked policy content and user questions |
maxToken | 8192 | Supports complete chunk processing of long documents, prevents indexing failures caused by token count exceeding limits |
Three Common Misconfigurations
- Imported data remains in the "creating index" state for a long time: This occurs when chunk length is set too large, causing single-chunk token count to exceed the
maxTokenlimit, leading to repeated failed retries of the indexing process. - Vector calculation scores are abnormal and consistent across results: This occurs when filtering rules for metadata such as document numbers and effective dates of policy documents are not configured. Recall results fail to distinguish active policies from expired documents.
- Vectorization processing takes too long: This occurs when too many documents are loaded in a single batch, and incremental indexing mode is not enabled. Full indexing repeats calculations for already processed historical data.
How to Verify Proper Configuration
- Upload a single typical policy document, check the chunk preview results, confirm single-chunk length falls within the preset range, and there are no unreasonable chunk splits across clauses.
- Test retrieval of active asset disposal policies, verify that recall results match filtering requirements for release time and asset category.
- Check indexing logs in FastGPT v4.17.0, confirm there are no
maxTokenover-limit error messages, and that incremental indexing only processes newly added or updated data. - Run comparative tests on recall results across different similarity thresholds, confirm the threshold settings effectively filter irrelevant content.
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.