What the Data for This Category Looks Like
This category’s data originates from State-owned Assets Supervision and Administration Commission investment supervision systems, enterprise ERP and project management systems, industry market data, audit and financial statements, with a quarterly update frequency. Daily documents include annual investment plans, project proposals, feasibility study reports, due diligence reports, asset evaluation reports, and post-investment evaluation reports. Documents contain structured fields such as project number, investment amount, approval nodes, and evaluation indicators, alongside lengthy policy citations, technical analysis, and project review text. Data volume accumulates gradually as individual investment projects advance.
Constraints Imposed on Vector Models and Indexing
Cross-source, multi-system data sources require indexes to support unified vector mapping and retrieval across multiple data sources. Quarterly update frequency requires indexes to adapt to low-frequency batch incremental updates, avoiding resource consumption from full reconstruction. Documents include both structured metadata and unstructured policy text, requiring dual support for metadata field retrieval and semantic vector matching. Investment projects are tied to state-owned asset supervision requirements, so indexes must retain complete policy citation traceability information while ensuring access permission controls for sensitive data. Document volume grows gradually as projects progress, so index sharding storage and query performance must adapt to expanding data scales.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text length of policy citations and technical analysis in documents of this category, avoiding semantic fragmentation |
| Overlap Length | 100–200 characters | Ensures semantic coherence for cross-chunk content such as policy clauses and project nodes |
| Similarity Threshold | 0.65–0.75, calibrated via actual testing | Adapts to semantic similarity judgment for state-owned asset policy text, avoiding missed or false judgments |
| Maximum Search Results per Query | Top 10 entries | Balances retrieval efficiency and coverage of policy basis |
maxToken | 8000 | Adapts to the context splicing length of policy text and project documents, aligns with the default limit of FastGPT v4.17.0 |
defaultToken | 200 | Controls the token length of returned results for a single retrieval, ensuring response speed |
Three Common Misconfigurations
- Phenomenon: Semantic similarity values in retrieval results fall outside the 0-1 range, for example showing values over 10000. Cause: The value range for the similarity threshold was not configured correctly, and non-standardized vector similarity calculation results were directly used for filtering.
- Phenomenon: After importing a dataset, the dataset status remains stuck in "Indexing" for an extended period. Cause: An incremental indexing strategy adapted to quarterly updated data was not configured, and full indexing timed out while processing large batches of cross-source documents.
- Phenomenon: After configuring a custom vector model, actual requests still call the large language model interface. Cause: The "Only for Vector Generation" option was not correctly checked in the vector model configuration page, and the system forwarded requests to the large language model by default.
How to Verify Proper Configuration
- Upload a single post-investment evaluation report, check whether the index generation progress completes within a reasonable time frame, and confirm that the chunk length and overlap length settings match the actual segmentation of the document.
- Initiate a retrieval targeting approval policies, verify the semantic similarity values of returned results, and confirm that threshold settings should be adjusted based on business scenarios, rather than using fixed values.
- Review system call logs, confirm that vector model requests are only used for vector generation, with no redundant calls to the large language model.
- Simulate concurrent multi-user retrievals, check whether response speeds meet business requirements, and confirm that index sharding configurations adapt to data scale.
The values listed above are common starting points, and should be evaluated against the reader’s own specific 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.