What this category of data looks like
Data sources for this category include State-owned Assets Supervision and Administration Commission investment supervision systems, enterprise ERP and project management systems, industry market data, audit and financial statements. The data update cycle is quarterly. Daily documents cover annual investment plans, project proposals, feasibility study reports, due diligence reports, asset appraisal reports, and post-investment evaluation reports. These documents include structured fields such as project number, investment amount, approval nodes, and evaluation conclusions, as well as large volumes of unstructured text for technical, market, and financial analysis.
What constraints these characteristics impose on vector models and indexing workflows
The quarterly data update rhythm requires indexes to support incremental synchronization, to avoid redundant computing resource consumption. Multi-source heterogeneous data from different systems needs unified vector mapping rules to ensure semantic consistency across documents from different sources. Long documents such as feasibility study reports and post-evaluation reports consume significant token counts, requiring reasonable chunking and context control. Coexistence of structured fields and unstructured analysis text requires combining metadata filtering and vector recall to narrow the retrieval scope, improve review efficiency and accuracy, and avoid state-owned asset losses or accountability caused by retrieval oversights.
Configuration Settings
| Configuration Setting | Recommended Value | Rationale for This Value |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to long text paragraphs in feasibility study reports and post-investment evaluation reports, preserves complete semantic units |
| Overlap Length | 100–150 characters | Connects context between adjacent chunks, prevents key decision-making information across paragraphs from being split |
| maxToken | 4096 | Matches the context window upper limit of mainstream large models, prevents long document analysis content from being truncated |
| Similarity Threshold | 0.72–0.80, calibrated via actual testing | Balances retrieval recall scope and accuracy, meets the strict requirements of state-owned asset project review |
| Single Search Limit | Top 8 results | Controls the scale of returned results, adapts to the scenario of parallel review of multiple documents for investment review committee materials |
| defaultToken | 2048 | Adapts to the text length of conventional declaration materials, meets the context requirements of basic retrieval |
Three Common Misconfigurations
- Symptom: The index model dropdown list is empty, and the target vector model cannot be selected. Cause: Local vector model environment dependencies have not been deployed, or the model mapping configuration for FastGPT v4.17.0 has not been fully synchronized and updated.
- Symptom: Retrieval results do not display specific paragraphs and file names of the corresponding source documents. Cause: The metadata binding switch for document traceability has not been enabled, or identification fields such as document path and file name were not retained during chunk processing.
- Symptom: Timeout errors occur in locally deployed vector retrieval tasks. Cause: Values for maxToken and defaultToken have not been adjusted based on local model performance, or the total document length for a single search exceeds the system's preset processing limit.
How to Verify Proper Configuration
- Upload a single typical document such as a standalone feasibility study report, run a retrieval test, and check that chunked text blocks retain complete semantics without obvious breaks.
- Submit a retrieval request containing core project parameters, verify that returned results are correctly associated with source documents and metadata, and that complete traceability information is displayed.
- Adjust the similarity threshold and single search limit, verify that the number and matching degree of retrieval results meet preset review requirements.
- Simulate quarterly incremental data synchronization, check that indexes support incremental updates without fully rebuilding existing index data.
The values provided are common starting points and should be measured against samples relevant to the specific deployment context.
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.