What the data for this category looks like
Daily documents for this category include property registration forms, asset appraisal reports, property rights transfer plans, on-site transaction announcements, asset disposal approval slips, and similar items. Data mainly comes from state-owned assets supervision information systems, enterprise asset management systems, property rights exchange systems, appraisal agency reports, and financial statements. The update frequency is monthly. Each individual document has fields such as ownership identification, asset details, and approval process nodes. Some documents contain structured numerical values and unstructured descriptive content. The data volume changes with the scale of stock assets, and there is no fixed unified format template.
What constraints these characteristics impose on the vector models and indexing link
The monthly updated data rhythm requires index configuration to adapt to batch incremental updates, avoiding resource consumption caused by frequent full index builds. Differences in document formats from multiple sources require indexing to support vectorization processing of mixed-type content, ensuring that both structured fields and unstructured descriptions can be correctly encoded. The length of individual documents varies widely, ranging from short approval slips to long appraisal reports. The vector model’s token processing capability must adapt to texts of different lengths. Additionally, since errors may lead to risks such as state-owned asset loss, the recall accuracy of the index must meet the strict requirements of business audits, avoiding omission of key ownership information.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of documents in this category, avoiding overly fragmented single chunks or exceeding the model’s processing limit |
| Overlap Length | 100–200 characters | Ensures key cross-chunk information such as asset numbers and ownership statements is not truncated |
maxToken | 8192 token | Adapts to content encoding for long asset appraisal reports, avoiding content loss due to token limit exceedance |
defaultToken | 4096 token | Matches conventional text processing needs in most scenarios, balancing computing resources and recall effectiveness |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance results, ensuring recalled content matches core requirements related to property rights and assets |
| Single Search Limit | Top 10 results | Controls the number of returned results, avoiding redundant information interfering with audit processes |
Three common errors
- Issue: After updating FastGPT, the original indexed knowledge base cannot support vector retrieval, and searches return no results. Cause: The index building logic has been adjusted in the new version, and the vector encoding format of the original index is incompatible with the new version. Indexes must be rebuilt, and old data cannot be reused directly.
- Issue: Chunking errors occur for the same CSV file after a version upgrade. Cause: The new version has adjusted the field parsing rules for CSV files. The original file contains unprocessed special characters or empty fields, causing the chunking process to interrupt.
- Issue: Retrieval results fail to hit key content related to asset ownership. Cause: The similarity threshold is set too high, filtering some low-scoring but critical matching results, or the chunk length is set too short, causing key information to be split and lost.
How to confirm the configuration is correct
- Import a single typical document (such as a property rights transfer plan), check if the chunking preview interface fully retains core fields and content, with no obvious truncation or loss.
- Initiate a search for an asset ownership number, confirm that the recalled results include relevant fragments of the corresponding document, and the quantity matches the single search limit setting.
- Adjust the similarity threshold and initiate a search, observe whether the number of results changes as expected, and verify the threshold’s filtering effect.
- Perform an incremental index update, check if the system can normally recognize newly added monthly data, with no errors or omissions.
The values given 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.