What the data for this use case looks like
Data for internal office and employee services at state-owned asset management companies primarily comes from state-owned asset supervision policy documents, internal asset management rules, employee handbooks, asset ledgers, office approval workflows, and employee training materials. Data update cadence falls into three categories: state-owned asset policies are updated quarterly or annually, asset ledgers are updated in real time or daily as asset changes occur, and internal office documents are updated on demand. Documents include structured asset cards (with fields such as asset ID, book value, using department, and others), unstructured rule texts and approval forms, with fixed fields and units such as ten thousand yuan, square meters, units/sets.
What constraints these characteristics impose on vector models and indexing
Mixed structured asset cards and unstructured rule texts require vector models and indexing to adapt to chunking rules for different document formats, to avoid splitting and losing structured fields. Real-time update requirements for asset ledgers require indexing to support incremental synchronization configuration, reducing resource consumption from full reindexing. Fixed fields and units require retaining field association information during retrieval to ensure accurate matching results. Long documents such as state-owned asset policy files require reasonable control of chunk length to avoid exceeding context window limits.
How to set configurations
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the content length of state-owned asset policy files and asset ledgers, avoids excessively long single chunks exceeding model limits |
| Overlap Length | 100–150 characters | Retains contextual association between chunks, prevents key information from being split across two chunks |
| Similarity Threshold | 0.7–0.8 | Filters low-match retrieval results, ensures returned content is highly relevant to query needs |
| Single Search Limit | Top 10 results | Controls the total context volume sent to the large language model, avoids exceeding maxToken limits |
| maxToken | 8000–12000 token | Adapts to the average length of documents for state-owned asset management companies, ensures complete recall of key content |
| defaultToken | 4000 token | Limits the default context quota for a single retrieval, balances retrieval effectiveness and system performance |
Reference only; verify against the target FastGPT release and deployment documentation.
Three common misconfigurations
- Phenomenon: Retrieval returns matching asset ledgers or policy files, but the large language model prompts that no relevant content was found when generating a reply. Cause: Retrieved document metadata and the context window are not correctly associated, or the
maxTokensetting is too small, resulting in truncation of the core content of matching documents. - Phenomenon: Knowledge base query response times out, or the system prompts that the context token limit is exceeded. Cause: The context token sent to the large language model in a single search is not restricted by
defaultToken, exceeding the system's carrying limit and leading to excessive processing time for the large language model. - Phenomenon: After uploading updated asset images or documents, the latest content cannot be returned during retrieval. Cause: Incremental index synchronization rules are not configured, or the real-time update switch is not enabled, resulting in old data not being replaced.
How to confirm the configuration is properly set
- Upload a typical state-owned asset supervision file and asset ledger, perform a retrieval test, and verify that the matching accuracy of returned results meets the threshold standards preset by the business.
- View system retrieval logs to confirm that the context token sent to the large language model does not exceed the preset reasonable range, and no truncation occurs.
- Check execution records of incremental index tasks to confirm that updated data from asset ledgers or policy files has been correctly synchronized to the index library.
- Test retrieval recall rates under different chunk lengths, and adjust configurations to a recall level that meets business needs.
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.