What Data for This Category Looks Like
Financial report data for the automated equipment category comes from public periodic financial reports of listed entities, segmented operation statistics released by industry associations, and official product documentation from equipment suppliers. Update cadence follows this schedule: quarterly financial reports are updated every 3 months, annual financial reports are updated once per year, industry statistics are updated quarterly, and product documents are updated irregularly alongside model iterations. Most documents use a combined format of structured tables and paragraph text. They include fields such as equipment model, cumulative units delivered, per-unit output value, cumulative operating hours, core component cost amount, and maintenance cycle. Units include units, hours, yuan, megawatts, and others.
Constraints on Knowledge Base Retrieval and Recall
High proportions of structured fields require retrieval systems to support both precise field matching and full-text semantic retrieval. This prevents irrelevant equipment models or output value data from being returned by full-text search alone. Fixed and concentrated data update cycles require knowledge bases to configure incremental sync tasks aligned with quarterly or annual financial report update rhythms. This reduces resource consumption from full reindexing. Long document length for equipment sections within single financial reports requires retaining contextual association between adjacent fields during segmented retrieval. This avoids losing the correspondence between equipment models and output values after document splitting. Strong binding between field units and product category requires automatically associating matching weights for unit keywords during retrieval. This improves recall accuracy for same-category data.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
RECALL_TOP_K | Top 8-12 entries | Automated equipment financial report data has concentrated fields, this value balances recall coverage and excessive context load |
RERANK_MODEL_ENABLED | Enabled | Financial report data contains mixed structured and unstructured content, reranking models filter semantically mismatched recall results |
CHUNK_SIZE | 800-1200 characters | Core information density is high in single equipment financial report sections, this chunk length retains complete association between model and output value |
MAX_CONTEXT_LENGTH | 3000-4000 characters | Concatenated multi-segment recall context must fit large model input limits, while retaining sufficient financial report details |
STRUCTURED_FIELD_RECALL_ENABLE | Enabled | Automated equipment financial reports contain large numbers of structured fields, enabling this allows precise matching of highly relevant fields such as equipment model and delivery quantity |
SYNC_INTERVAL | Every 7 days | Matches update cycles for industry data and semi-annual financial reports, balances data freshness and index resource usage |
The parameter values provided on this page are general recommendations used as starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: Full-text and semantic retrieval returns a large number of irrelevant results, and cannot match target equipment models or output value data. Cause: Structured field recall is not enabled, and only full-text retrieval is used, which cannot precisely match highly relevant fields such as equipment model and delivery quantity.
- Phenomenon: The reranking model is not triggered during online recall testing, and returned results are consistent with those when reranking is disabled. Cause: The reranking model switch is only enabled in the retrieval phase, but reranking preprocessing is not enabled in the knowledge base index configuration, so the reranking logic does not take effect.
- Phenomenon: When
MAX_CONTEXT_LENGTHis configured to more than 3000 characters, the large model cannot receive the recalled context content. Cause: For FastGPT 4.9.0 version knowledge base configurations, large model context window adaptation parameters are not adjusted, exceeding the default input truncation threshold, causing the context to be automatically filtered.
How to Confirm Successful Configuration
- Upload a sample automated equipment financial report, run a retrieval test, and verify whether the returned results include the target equipment model and corresponding business data.
- View the knowledge base index logs to confirm that the reranking model preprocessing task has executed normally, with no error messages.
- Adjust the recall count parameter, observe changes in the number of retrieval results, and confirm that the parameter configuration has taken effect synchronously.
- Test filtering retrieval by report time, and confirm that financial report data within the specified cycle can be accurately recalled.
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.