What data for this category looks like
Data related to business processes for state-owned asset management entities comes primarily from internal business process manuals, asset registration ledgers, state-owned asset supervision approval templates, and cross-department collaboration notices. Updates follow state-owned asset supervision policies or internal process optimizations, and typically occur every quarter or semi-annually. Most documents use structured nodes, and include fields such as asset number, approval level, handling time limit (unit: working days), submitted material list, and responsible entity. The length of individual documents ranges from 2000 to 5000 characters.
Constraints imposed by these characteristics on vector models and indexing
The presence of structured fields and units requires vector indexes to associate metadata. This prevents confusion between different asset types or time limit requirements during matching. Moderate update frequency with full or incremental sync requirements means indexes must support lightweight incremental construction. Wide variation in document length and coherent process nodes require avoiding disruption to the logical integrity of approval steps during text chunking. Some fields are enumeration values, such as approval status. These must be used separately for metadata filtering. FastGPT v4.17.0’s indexing module must adapt to these structured metadata association rules. This ensures recall results are accurately bound to business fields.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Core descriptions of single segments of state-owned asset management business processes typically range from 500–1000 characters. This setting avoids splitting that would disrupt approval node integrity. |
| Overlap Length | 100–150 characters | This preserves contextual connections between adjacent process nodes. It prevents loss of cross-block step transitions. |
| Similarity Threshold | 0.72–0.80 | Business process keywords have high distinctiveness. A threshold that is too low may match irrelevant processes, while a threshold that is too high may lead to insufficient recall. |
| Single Search Limit | Top 6 results | Business handling processes for state-owned asset management entities typically include 3–5 core nodes. Too many recall results cause redundant context. |
maxToken | 8000 characters | Context concatenation after matching must accommodate complete approval processes and form requirements. This prevents truncation of critical information. |
defaultToken | Calibrated via actual testing | Token processing limits vary across different internal network deployment environments. Adjustments must be made based on hardware resources. |
Three Common Configuration Errors
- Issue: Connection timeout error occurs when integrating an indexing model after Docker deployment. Cause: Internal network environment lacks mirror acceleration configuration, or port mapping is incorrect. This leads to failure when pulling or calling the model.
- Issue: Exported dataset.csv only includes the index field and no content field. Cause: The "retain original text content" option was not checked when importing the knowledge base, or the chunk configuration did not associate the source document’s text field.
- Issue: No matching results appear after importing text when using the bge-large model. Cause: Chunk length was not adjusted to fit long process documents. This causes vector matching to fail after key steps are split.
How to Confirm Correct Configuration
- Uploading a standard business process document allows verification that the chunk preview interface retains complete approval nodes, with no critical steps truncated across chunks.
- Running a process guidance test allows verification that recalled results are correctly associated with structured fields such as asset number and handling time limit.
- Exporting the knowledge base’s dataset.csv file allows verification that the file includes both index and content fields, with content matching the source document.
- Checking hardware resource monitoring allows verification that GPU or CPU resource usage during vector model calls aligns with expectations, with no abnormal peaks.
The values provided here are common starting points. They 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.