What the data for this category looks like
Data sources for shipping port financing daily reports include internal financial systems of port enterprises, ship financing ledgers, and daily loan receipts from cooperating financial institutions. Updates follow a daily T+1 cadence, aggregating all financing transaction data from the previous trading day. Each document primarily uses structured tables, paired with a small number of business remark fields. Core fields include financing entity, credit limit, actual loan amount, financing term, loan date, repayment due date, financing purpose, and cooperating financial institutions. Amount fields use ten thousand yuan as their unit. Term fields use natural days or natural months as their unit. No redundant unstructured content is present.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
The daily incremental update feature requires the knowledge base to support efficient incremental parsing and synchronization, to avoid resource waste from full re-scanning. The primarily structured document structure requires retrieval to balance exact field matching and semantic association, to avoid field confusion caused by relying solely on semantic recall. Clear unit fields require the retrieval link to support unit filtering, to prevent confusion between monthly term records for ship leasing and annual term records for port construction. Moderate-length structured fields require controlling the context length of recall results, to avoid excessive irrelevant fields interfering with the context purity of AI conversations.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| `PARSEINCREMENTALENABLE | Enabled | Shipping port financing daily reports are updated incrementally daily. Incremental parsing reduces repeated computing resource consumption |
| `RECALLTOPK | Top 8 | Financing daily reports have highly correlated fields. A small number of recall results can cover core financing information, avoiding excessive redundant interference |
| `SIMILARITY_THRESHOLD | 0.72–0.78 | Semantic matching for structured financing fields requires balancing accuracy and recall completeness, to avoid missing financing records for the same entity with different terms |
| `PARSEFILETIMEOUT_SECONDS | 900 seconds | Single monthly summary financing daily report documents may contain multiple pages of structured tables. A longer timeout prevents parsing interruptions |
| `MAXCHUNKSIZE | 800–1200 characters | Financing daily reports mostly have short text fields paired with long remarks. Chunk length adapts to the semantic units of structured fields |
| `ENABLEFIELDUNIT_FILTER | Enabled | Financing daily reports include clearly unit-based amount and term fields. Enabling field unit filtering excludes invalid recall results with mismatched units |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require tailored analysis. It is recommended to test on your own samples before finalizing the configuration.
Three Common Mistakes
- Symptom: After creating a knowledge base via API and importing a financing daily report dataset, parsing status fields cannot be retrieved, and it is impossible to determine whether the knowledge base is ready or failed. Cause: The
return_parse_statusparameter is not included in the API request. Parsing progress-related fields are not returned by default. - Symptom: After switching the embedding model, the similarity scores returned by knowledge base retrieval exceed 10000+, falling outside the normal range. Cause: The configuration threshold for
SIMILARITY_THRESHOLDis not updated synchronously. The score calculation logic of the new model is inconsistent with the original model, and the new score range is not adapted. - Symptom: The workflow terminates directly after reaching the "knowledge base search" step, and cannot proceed to subsequent AI conversation steps. Cause: The number of recall entries is set to 0 or too low. The number of matching documents for the financing daily report does not meet the recall quantity requirements, causing the workflow to interrupt due to no context input.
How to Verify Successful Configuration
- Call the knowledge base parsing API, check whether the
parse_statusfield is included in the returned results, confirm that the status values "parsing", "ready", "parsing failed" can be obtained. - Import a single small financing daily report document, trigger retrieval, and check the returned similarity score range, confirm that the scores fall within the reasonable range adapted to the current embedding model.
- Run a test workflow, input a query containing specific financing entities and amounts, confirm that the workflow can fully execute to the AI conversation step without mid-run termination.
- Check the knowledge base incremental synchronization logs, confirm that daily T+1 financing daily report documents can automatically trigger incremental parsing without manual full upload.
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.