Reference only; verify against the target FastGPT release and deployment documentation.
What Data for This Category Looks Like
Data for this category comes primarily from state-owned assets supervision information systems, enterprise asset management systems, property right exchange systems, valuation firm reports, and financial statements. Daily documents include property registration forms, asset valuation reports, property transfer plans, on-site transaction announcements, and asset disposal approval forms. These documents contain fields such as ownership number, original asset value, assessed value, and transaction base price. The update frequency for this data is monthly. Most data is a mix of structured forms and semi-structured reports. Individual document length varies widely. Some valuation reports have significant length.
Constraints Imposed on Vector Models and Indexing
Decentralized data sources with multi-system heterogeneous content require support for vector ingestion and associated indexing across sources, to avoid missing field encoding. The monthly update frequency requires indexing builds to support incremental update modes, reducing resource consumption and time costs from full reconstruction. Documents include structured fields and semi-structured reports. Differentiated chunking rules must be configured for different document types, to preserve the integrity and association of business fields. Wide variation in individual document length requires limiting the total token count per chunked document, to avoid exceeding the model’s context processing limits. Key business fields such as ownership number and transaction base price require separate index weight configuration, to improve retrieval accuracy for core information.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the mixed content of semi-structured reports and structured forms in this category, to avoid damaging business field integrity |
| Overlap Length | 100–200 characters | Ensures continuity of cross-chunk business logic, avoids splitting clauses in property transfer plans |
| Similarity Threshold | 0.72–0.85 | Meets the strong matching requirements for asset ownership information, filters low-relevance search results |
| Single Search Limit | Top 10 entries | Matches the small set of core asset information that needs verification during business processing, avoids interference from redundant results |
maxToken | 8192 | Adapts to the context limits of mainstream open-source vector models, ensures normal vector generation for chunked long documents |
defaultToken | 1024 | Configures the default token length for individual vectors, balances retrieval accuracy and storage overhead |
Three Common Misconfigurations
- Phenomenon: An error occurs when adding an index after deploying a vector model locally. Cause: No field extraction rules are configured for the structured forms in this category, leading to incorrect encoding of some business fields.
- Phenomenon: A 404 error is returned when configuring Baidu Embedding-v1. Cause: The authentication key and request path for the model interface are not filled correctly, preventing the platform from sending valid requests.
- Phenomenon: The m3e vector model cannot be added to the platform after deployment without a GPU environment. Cause: The model service port inside the container is not mapped to a locally accessible port, preventing the platform from connecting to the model service.
How to Confirm Configurations Are Complete
- Import a single property registration form document, check if all configured business field encoding records are included in the vector generation log.
- Submit a search request that includes an asset ownership number, verify that the similarity scores of returned results fall within the preset range.
- Run an incremental indexing task, check that only documents updated in the current month undergo vector ingestion, and no full reconstruction is triggered.
- Check the storage space usage of the vector database, confirm that the token length of individual vectors matches the
defaultTokenconfiguration value.
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.