What the Data for This Category Looks Like
The data for this category mainly comes from state-owned assets supervision information systems, enterprise asset management systems, property rights exchange systems, evaluation agency reports and financial statements. The update frequency is once per month. Routine documents include property registration forms, asset evaluation reports, property rights transfer plans, on-site transaction announcements, and asset disposal approval documents. Data fields include asset number, ownership unit, evaluation value, transaction status, and others. Individual documents contain mixed structured tables and unstructured text. Some special-purpose documents have a relatively large length span.
Constraints Imposed on Vector Models and Indexing by These Characteristics
Multi-source heterogeneous data sources require indexing to support unified import and vectorization processing of multiple data formats. The monthly update frequency requires the incremental indexing scheduling cycle to match the business update rhythm, to prevent index content from lagging behind actual business data. The mixed structured and unstructured document structure requires a chunking strategy that balances the integrity of key fields such as asset numbers and evaluation values, to avoid splitting that breaks business associations. The precise business field requirements require vector models to distinguish core business identifiers from general text, improving retrieval accuracy in party building Q&A scenarios.
Recommended Configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the mixed-structure business documents of this category, avoid truncating key fields such as asset numbers and evaluation values |
| Overlap Length | 50–80 characters | Retain contextual association between chunks, avoid losing document logical coherence after splitting |
maxToken | 4096 token | Match the input upper limit of mainstream vector models, adapt to the vectorization requirements of long documents in this category |
defaultToken | 2048 token | Adapt to the default input truncation threshold of vector models, avoid over-truncating long text |
| Similarity Threshold | Calibrated based on actual testing | Adapt to the semantic similarity judgment standard for state-owned asset business documents, adjust in combination with the party building Q&A scenario |
| Single Search Limit | Top 10 results | Balance retrieval efficiency and result coverage, meet the requirements of precise business retrieval |
| Reranking | Enabled | Optimize the sorting logic of semantic retrieval results, improve content relevance in party building Q&A scenarios |
Three Common Configuration Mistakes
- Phenomenon: Semantic retrieval returns results with generally high scores, and contains a large number of asset transaction details unrelated to party building Q&A. Cause: The similarity threshold is not adjusted for the precise business fields of this category, and default parameters from general scenarios are directly used, resulting in low-relevance content being misjudged as high-match results.
- Phenomenon: Uploaded party building learning auxiliary materials do not participate in retrieval matching. Cause: The vectorization configuration for auxiliary data is not enabled, resulting in auxiliary materials not being included in the index library and unable to be recalled by semantic retrieval.
- Phenomenon: After generating multiple sets of vectors for a single property rights transfer document, duplicate fragments appear in the retrieval results. Cause: The chunk length is set too short and the overlap length is not configured, resulting in the same business logic being split into multiple independent fragments and generating multiple sets of duplicate vectors.
How to Verify Proper Configuration
- Upload a typical asset evaluation report, perform a retrieval test that includes an asset number, and check whether relevant documents are prioritized in the results.
- View the update log in the index management interface, confirm that the monthly automatic incremental indexing task has executed normally.
- After adjusting the chunk length parameter, re-vectorize a single long document, and check that the number of generated vectors matches the expected number of chunks.
- After importing party building learning materials, perform a retrieval related to party building topics, and confirm that the material content is correctly recalled.
The values provided 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.