What data for this category looks like
Data sources for this category’s investment research data include store operation ledgers, district foot traffic monitoring data, ingredient supplier quotation sheets, and regional catering industry policy documents. Update schedules fall into three categories: real-time (same-day store revenue and foot traffic data), daily (ingredient purchase prices and waste statistics), and irregular (industry policies and district planning adjustment notices). Document structures include structured tables with fields such as per-store per-square-meter efficiency, average daily foot traffic, and ingredient purchase prices, unstructured operation review reports, and district research notes. Most fields relate to operational numerical values and time markers, with units including yuan, person-times, square meters, and other standard metrics.
What constraints do these characteristics impose on context and token handling
This category’s multi-source, high-frequency update data means retrieved context will include multiple sets of real-time operational values and time markers. The total volume of content retrieved in a single round can easily exceed the large model’s token limit. Uncompressed multi-field data in structured tables will take up extra token space. Long unstructured operation review reports can consume large amounts of context quota after parsing. Additionally, investment research scenarios require comparing operational data across different stores and time periods. Retrieved context will include multiple sets of comparison samples, further increasing token consumption and raising the risk of context overflow.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContextTokens | 8000–12000 tokens | Covers 3-5 sets of store/period operational comparison data required for hotel and catering investment research, avoids single-round retrieval overflow |
recallCount | Top 6–8 entries | Each retrieved entry for this category contains multiple operational fields; 6-8 entries cover core investment research dimensions while controlling total token usage |
chunkSize | 800–1200 characters | Adapts to the document structure of hotel and catering structured tables and short review reports, balances parsing accuracy and token consumption |
similarityThreshold | 0.75–0.85 | Filters low-relevance redundant data to reduce invalid token consumption |
rerankTopN | Top 3–5 entries | Retains the most relevant core content after reranking, avoids unnecessary information occupying context quota |
chunkOverlap | 50–100 characters | Maintains contextual continuity between document fragments, prevents loss of key associated information after splitting |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: After setting
recallCountto more than 12 entries, the context preview area in the knowledge base test chat interface shows no content, or the large model does not return matching results. Reason: The total token usage of single-round retrieval exceeds the large model’s supported context limit, and the system automatically truncates or discards context content. - Phenomenon: After uploading a single monthly operation review report, the large model prompts "input content is too long". Reason: No reasonable
chunkSizeis set, and the length of a single document segment exceeds the large model’s single-round input token limit. - Phenomenon: When
maxContextTokensis set to 3000, context cannot be sent normally to the large model. Reason: The 3000-token quota is insufficient to accommodate multiple sets of store or period comparison data required for hotel and catering investment research, and the system cannot complete effective context splicing.
How to confirm configurations are set correctly
- Access the knowledge base test chat interface, input an investment research question involving multi-store comparison, review the content in the context preview area, and adjust configuration items until the preview covers core investment research dimensions.
- Upload a single store operation ledger document, review the segment preview after parsing, and confirm that each segment’s length matches the preset configuration range.
- Review the large model’s request logs to confirm that the context token value does not exceed the maximum limit supported by the current large model.
- Adjust the similarity threshold, test retrieval results for the same question, and confirm that irrelevant content is effectively filtered out while core operational data is retained.
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.