What the data for this category looks like
Data sources include regional economic statistical monthly reports, regulatory agency quarterly bulletins, in-house credit approval ledgers, securities firm regional financial research reports, and peer operation benchmarking documents. Update frequencies vary by document type: regulatory documents are updated quarterly, credit documents are synced in real time as business occurs, regional statistical documents are updated monthly, and research reports are synced per their publication cycle. Document formats include structured reports, semi-structured research report paragraphs, and unstructured approval opinion documents. Fields include regional economic growth metrics, credit asset quality values, credit limits, customer rating levels, and similar attributes. Common units include ten thousand yuan, person-times, rating identifiers, and similar units.
What constraints do these characteristics impose on the knowledge base retrieval and recall link
Regional economic data has high real-time requirements. High-frequency incremental sync support is required. Retrieval must prioritize matching current cycle metrics, otherwise recall results will lack timeliness. Credit ledgers have many structured fields. Hybrid retrieval combining precise field matching and semantic retrieval must be supported, to avoid interference from unstructured documents. Peer benchmarking documents are mostly internal materials. Permission filter rules must be configured to only grant access to authorized users. Document lengths vary widely. Short reports are only tens of characters long, while long approval documents can reach thousands of characters. Adaptive chunking rules for different lengths are needed to avoid truncating critical information. Additionally, urban commercial bank investment research data mostly covers local segmented industries. Semantic relevance relies on region-specific vocabulary. Custom vocabulary lists must be configured to improve recall accuracy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
RECALL_TOP_N | Top 10-15 entries | Urban commercial bank investment research data covers multiple document types. A sufficient number of recall results are needed to filter valid items via re-ranking |
SIMILARITY_THRESHOLD | Adjust based on vector model type. Use 0.7-0.85 for general vector models, 0.65-0.8 for region-specific models | Adapts to similarity calculation logic of different models, avoids filtering results matched via region-specific semantics |
PARSE_CHUNK_SIZE | 800-1200 characters | Balances chunking integrity for both short reports and long approval documents in urban commercial bank investment research data |
API_PARSE_STATUS_SYNC | Enabled | Real-time parsing status can be obtained via API, facilitating automated monitoring of investment research knowledge base update progress |
FIELD_MATCH_WEIGHT | 0.3-0.5 | Balances priority between semantic retrieval and structured field matching, improves retrieval accuracy |
RE_RANK_TOP_N | Top 3-5 entries | Urban commercial bank investment research needs focus on regional and in-house business. A small number of precise results meet decision-making requirements |
The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common misconfigurations
- Phenomenon: After calling the API to create a knowledge base, the parsing in progress, ready, or parsing failed status fields cannot be obtained. Reason: The
API_PARSE_STATUS_SYNCconfiguration item is not enabled, so the API does not return parsing status synchronously. - Phenomenon: The number of retrieval results is far lower than expected, or a large number of results unrelated to regional industries appear. Reason: The
FIELD_MATCH_WEIGHTparameter is not adjusted based on the structured field characteristics of urban commercial bank investment research data, leading to unbalanced weight between semantic retrieval and field matching. - Phenomenon: After switching the vector model, retrieval filtering does not take effect, or the filtering threshold exceeds a reasonable range. Reason: The similarity calculation interval of the new vector model is not adapted, and the original 0-1 interval threshold setting is still used.
How to verify correct configuration
- Upload a regional economic statistical monthly report and a credit approval ledger document, verify that parsing status can be obtained in real time via the API.
- Initiate a retrieval request containing regional economic metrics, confirm that recall results include hybrid results of structured field matching and semantic matching.
- After switching the vector model, adjust the value range of
SIMILARITY_THRESHOLDto match the calculation logic of the current model. - Run the preset investment research retrieval workflow, verify that the process can be fully executed to the AI conversation link without interruptions mid-process.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.