Reference only; verify against the target FastGPT release and deployment documentation.
What data in this category looks like
Data for this category mainly comes from state-owned assets supervision information systems, enterprise asset management systems, property right exchange systems, valuation firm reports and financial statements. Daily processed documents include property registration forms, asset valuation reports, property right transfer plans, on-site transaction announcements and asset disposal approval documents. The data update frequency is monthly. A single document usually contains fixed fields such as ownership number, original asset value, appraisal value and transaction base price. Some bulk asset data is stored in categorized packages. Field units include ten thousand yuan, square meter, unit and others.
What constraints these characteristics impose on the vector models and indexing link
The monthly update frequency means the indexing link does not require high-frequency real-time synchronization. Timed full refresh can be used to reduce computing overhead. Document types include structured tables and long-form text. Mixed-format content requires indexing to adapt to chunking logic, preventing structured fields from being split and their associations broken. Data from multiple sources has field differences. The indexing link needs unified metadata mapping to avoid field matching deviations during retrieval. Bulk packaged asset data has long content per chunk. Chunking parameters need adjustment to fit different document lengths, while ensuring vector integrity of core fields such as ownership and transaction base price.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the semantic integrity of long texts such as asset valuation reports and property right transfer plans, while avoiding vector calculation overflow caused by overly long single chunks |
| Overlap Length | 100–200 characters | Ensure semantic association between adjacent chunks, prevent core ownership information across chunks from being split |
defaultToken | 1024 | Adapt to basic vector input requirements for conventional short documents, avoid missing vector encoding for short texts |
| Similarity Threshold | 0.75–0.85 | Filter low-match asset data, avoid mixing irrelevant historical archives in retrieval results |
| Single Search Limit | Top 10 entries | Adapt to batch verification needs in internal office scenarios, while controlling computing load per round of retrieval |
maxToken | 8192 | Adapt to context input requirements for long valuation reports, avoid content truncation and loss of key transaction information |
| Reranking | Enabled, take top 3 reranked results | Optimize relevance ranking of retrieval results, prioritize matching core business fields such as property ownership and transaction conditions |
Three common mistakes
- Retrieval response time exceeds expectations, and the interface shows loading timeout. Cause: Chunking parameters are not adjusted for bulk packaged asset data, overly long single chunks increase vector calculation time, and the timed full refresh strategy is not adopted, leading to accumulation of redundant retrieval tasks.
- Indexing tasks remain in the "Indexing" state for a long time and cannot become ready. Cause: The configured chunk length exceeds the semantic carrying capacity of a single document, or metadata mapping for multi-source data is not unified, causing indexing process blocking.
- Multiple duplicate index entries appear automatically in the dataset. Cause: The automatic incremental sync switch is not turned off, old index data is not cleaned during monthly updates, leading to repeated index generation for the same asset archive.
How to confirm the configuration is correct
- A typical asset valuation report may be uploaded, and the chunked text can be verified to retain complete ownership information and transaction terms, with no obvious truncation or splitting errors.
- A retrieval test may be initiated using core business keywords. The field matching degree and ranking rationality of returned results can be verified to confirm the similarity threshold and reranking configuration take effect.
- Indexing task logs may be reviewed to confirm that metadata from multi-source data has completed unified mapping, with no field missing or format errors.
- The monthly update task may be allowed to complete, and the number of index entries in the dataset can be checked to confirm it matches the pre-update expectation, with no duplicate entries generated.
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.