What the data for this category looks like
Data for this category typically comes from national public resource trading platforms, natural resource trading systems, property right exchange systems, and state-owned asset supervision systems. The update frequency is daily. Daily documents include transfer announcements, trading guidelines, bid application materials, margin vouchers, transaction confirmation letters, listing result announcements, property right transfer descriptions, and more. Document structures vary significantly: transfer announcements include target location and ownership descriptions, while property right transfer descriptions include target valuation and trading terms. Fields cover target ID, ownership status, bid requirements, margin standards, and other related items.
What constraints do these characteristics impose on the vector models and indexing workflow
The daily updated data requires indexes to support incremental updates, to avoid resource consumption and delays caused by full reconstruction. The significant differences in document structures require chunking to adapt to the content logic of different documents. For example, location descriptions in transfer announcements and trading terms in property right transfer descriptions should be split by semantic blocks separately, to avoid semantic breaks across blocks. Professional fields such as ownership status and bid requirements must be accurately captured by vector models, so the models must be adapted to the professional terminology of the public resource trading domain. At the same time, the recall accuracy of key information directly affects subsequent business processes. The relevance of recall results must be strictly controlled, to avoid transaction process disruptions caused by incorrect matching.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average content density of documents in this category, avoids including too many unrelated professional terms in a single chunk, and ensures semantic integrity |
| Overlap Length | 100–200 characters | Compensates for semantic breaks during chunking, ensures key cross-chunk information such as ownership restriction clauses is not split |
| Similarity Threshold | 0.75–0.85 | Filters low-correlation results, avoids irrelevant transaction announcements interfering with the recall of policy basis |
| Single Search Limit | Top 10 results | Balances recall scope and response speed, covers relevant policy and transaction documents in most scenarios |
maxToken | 4000 token | Limits the context length of a single round of Q&A, adapts to the model call limit of FastGPT v4.17.0, avoids timeout errors |
defaultToken | 2000 token | Sets the default vector generation length, balances semantic integrity of professional terms and computational efficiency |
Three Common Misconfigurations
- Phenomenon: Key ownership restriction clauses are missing from vector recall results, and the number of returned results is far lower than expected. Cause: The chunk length is set too large, and documents containing multiple independent clauses are split into too few chunks, leading to truncation of key information.
- Phenomenon: Timeout errors occur when calling vector models, with a 504 status code displayed in the interface. Cause: The
maxTokensetting exceeds the model call limit of FastGPT v4.17.0, or the single search limit is set too high, leading to exhaustion of computational resources. - Phenomenon: Attempts to connect a custom vector model fail, with an incompatibility prompt displayed. Cause: Model call parameters are not configured according to platform requirements, or different types of vector models and indexing rules are mixed, with unified preprocessing logic not implemented.
How to Confirm Successful Configuration
- Upload a single property right transfer description document, test the chunking results, and confirm that each chunk has complete semantic content with no cross-clause splitting.
- Submit a test query involving ownership condition checks, verify that the similarity scores of the recall results fall within the preset threshold range, with no significantly low-correlation results.
- Check the index update log, confirm that the daily incremental update task runs normally with no failed records.
- Switch between different vector models, test whether calls work normally, and confirm that the parameter configuration takes effect.
The values provided here are common starting points, and should be evaluated against the samples relevant to the specific use case.
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.