What this category of data looks like
This category of data mainly comes from state-owned assets investment supervision systems, enterprise ERP and project management systems, industry market data, audit and financial statements. Updates occur quarterly. Documents processed daily include annual investment plans, project proposals, feasibility study reports, due diligence reports, asset appraisal reports, and post-investment evaluation reports. Most documents contain structured and semi-structured fields such as project investment, progress milestones, risk control clauses, and post-evaluation indicators. There is no unified fixed single-document length standard.
What constraints do these characteristics impose on the vector model and index link
Data sources are scattered and have fixed update cycles. The index link must support incremental synchronization of cross-system data and regular full updates. Document types cover multiple categories of professional reports. Some reports are lengthy. Adapt chunking rules for different documents to avoid truncating critical business information. Data includes professional fields such as project investment, risk control, and post-evaluation. Vector recall must prioritize matching core business-related content. Index stability must also be ensured to avoid subsequent decision bias caused by data errors.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph length of most professional reports in this category, avoids splitting critical business statements |
| Overlap Length | 100–150 characters | Ensures contextual coherence between adjacent chunks, covers cross-chunk project milestone descriptions |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Balances recall precision and recall range for professional content, meets rigorous requirements related to state-owned assets supervision |
| Reranking | Enabled, top 3–5 results | Filters irrelevant results for multi-field professional queries, focuses on core investment and post-evaluation content |
| maxToken | 8000 | Adapts to the contextual length requirements of most professional reports, avoids truncating important risk control and financial information |
| defaultToken | 1000 | Controls the return length of single-round Q&A, complies with information output specifications for party building learning Q&A |
Three common configuration mistakes
- Phenomenon: After enabling the reranking configuration, the number of results returned by online recall tests does not match the configured reranking quantity. Cause: The reranking switch was not enabled synchronously in the knowledge base index link; only configuring it in the Q&A link does not take effect.
- Phenomenon: After uploading chart attachments from professional reports, index results do not include chart-related text or metadata. Cause: The local deployment version does not enable the image indexing model, and this function requires corresponding plugin support.
- Phenomenon: Connection timeout errors occur during knowledge base indexing, and OneAPI returns a connection failure status code. Cause: The calling address and key of the Embedding model are not configured correctly, leading to failure to connect to external services normally during the vector generation phase.
How to confirm configurations are correctly set
- Check the knowledge base index logs of FastGPT v4.17.0, confirm that the number of chunked documents matches the number of original documents, with no obvious truncation errors.
- Execute a single professional query, verify that recall results include relevant content of core fields such as project investment and risk control.
- After enabling the reranking configuration, compare the recall result rankings before and after enabling, confirm that the result order has changed.
- Check the connection status of the Embedding model, confirm there are no consecutive connection failure logs.
Note: The configuration values listed above are common starting points. Adjustments should be made based on the reader's own specific samples and use cases.
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.