What This Category of Data Looks Like
Typical data sources for this category include state-owned asset supervision information systems, enterprise asset management systems, property rights exchange systems, third-party evaluation reports, and internal financial statements. Daily processed documents include property rights registration forms, asset evaluation reports, property rights transfer plans, on-site transaction announcements, and asset disposal approval documents. Document fields cover core information such as asset ownership number, category, book value, evaluation base date, and transaction status. Data is updated monthly. Each update includes newly added or adjusted stock assets and transaction approval materials for the current period. Supported vectorizable file types match the above document formats, including various office documents, spreadsheets, and structured reports.
Constraints on Vector Models and Indexing
Monthly updated bulk data requires index construction to support batch splitting. This avoids single-batch task timeouts. Multiple sources and types of documents require compatibility with different text and table formats. The chunking strategy must balance the integrity of long paragraphs and short fields. Ledger summary and report generation require association of precise information such as asset ownership and transaction status. Vector retrieval recall accuracy must meet strict requirements. This prevents incorrect recall of non-matching assets. Errors in state-owned asset management are directly linked to compliance risks. Index stability and traceability must be ensured.
Configuration Settings
The following configurations are adapted to the business requirements of this category for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the typical paragraph length of property rights registration forms and asset evaluation reports, avoids splitting core fields such as ownership numbers and evaluation values |
| Overlap Length | 100–150 characters | Retains core identifying information across chunks, prevents gaps in critical information during retrieval |
| Similarity Threshold | 0.72–0.85 | Matches the precision requirements for state-owned asset ownership, reduces the probability of incorrect recall of non-target assets |
| Single Search Limit | Top 8 results | Balances the comprehensiveness of ledger summary and retrieval response speed, avoids excessive redundant results |
defaultToken | 4096 | Adapts to the context carrying requirements of single long documents, supports reference to multiple segments for report generation |
maxToken | 8192 | Covers the total token count after merging multiple documents, supports complete ledger summary and generation |
Three Common Configuration Mistakes
- Symptom: After building and running a Docker image, all vector retrieval scores are identical. Cause: Vector model API request parameters for cross-container environments were not configured correctly. All requests reuse locally cached default embedding results.
- Symptom: Index construction tasks remain stuck on processing the final set of documents. Cause: No batch splitting rules were set for index construction tasks for monthly bulk updates. Full data was not split by document type. Single-batch tasks exceed processing limits.
- Symptom: Hybrid retrieval response time exceeds 10 seconds. Cause: Unnecessary cross-system data source retrieval was not disabled. The single search limit was set beyond actual business requirements. This creates additional vector computing overhead.
How to Verify Correct Configuration
- Import a single property rights registration form and asset evaluation report. Check that chunked text fully retains core fields such as ownership number and evaluation base date. No forced splitting of critical information occurs.
- Submit a ledger summary request. Verify that the number of returned retrieval results matches the set single search limit. The score distribution aligns with the precision expectations of the business scenario.
- View the vector model API call logs. Confirm that the
defaultTokenandmaxTokenconfigurations are correctly applied. No token overflow errors appear. - Deploy the Docker image. Run a small bulk indexing test. Confirm that tasks complete normally. No abnormal retrieval results with identical scores occur.
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.