What the data for this category looks like
Baijiu investment research data primarily originates from public brokerage research reports, periodic financial reports of listed liquor enterprises, public statistics from industry associations, distributor circulation monitoring data, and official tasting documents. The update rhythm is tiered: listed company financial reports are updated quarterly and annually, brokerage research reports are updated in real time alongside industry dynamics, and circulation monitoring data is updated on a weekly cycle. Document structures fall into two categories: structured reports and unstructured reports. Structured reports include fields such as brand series, production region, suggested retail price per bottle, and channel shipment volume, with units mostly being kiloliters and ten thousand yuan. Unstructured reports cover content including production region terroir, brewing process details, and consumption scenario analysis.
Constraints on Context and Token Management
The tiered update and multi-structure characteristics of baijiu investment research data create multiple constraints for context and token management. First, multi-dimensional structured fields require precise matching with retrieval conditions. If the context recall range is too broad, irrelevant field data will be introduced, occupying token quotas. Second, individual unstructured reports have relatively long lengths. Splitting such reports risks breaking the logical coherence of process details and consumption scenario analysis, leading to fragmented recalled content. Additionally, real-time updated circulation monitoring data and quarterly financial report data have timeliness differences. Token priority allocation within the context window is required to prevent outdated data from occupying display space for critical information.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 12000–14000 token | The total token count of a single core baijiu investment research report plus financial report summary typically falls between 8000 and 12000 token. This range reserves sufficient space for recalled content and user queries |
chunkSize | 1500–2000 characters | Baijiu unstructured reports often contain long texts such as processes and consumption scenarios. This segment length preserves content coherence while avoiding single segments exceeding the model's single-segment processing limit |
similarityThreshold | 0.5–0.7 | Baijiu investment research data contains a large volume of similar content for cross-product comparison. A threshold that is too low will introduce irrelevant recalls, while a threshold that is too high will fail to cover precisely matched subdivided information |
retrieveTopK | Top 6–8 results | Baijiu investment research requires covering information across three dimensions: production capacity, channels, and consumption. Excessive recalls will occupy excessive token quotas |
outputMaxTokens | 12000–14000 token | Recalled content and analysis conclusions must be fully output, preventing key investment research conclusions from being lost due to output truncation |
rerankTopK | Top 3–4 results | Perform secondary filtering on recall results to filter redundant information and optimize token usage efficiency |
The parameter values provided on this page are conventional recommendations, serving as starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis. It is recommended to conduct tests on deployment-specific samples before finalizing settings.
Three Common Configuration Errors
- Issue: After setting
similarityThresholdto 0.4, the knowledge base has limited content but still fails to recall target documents. Cause: Baijiu investment research data contains a large number of similar descriptions for subdivided categories. A threshold that is too low will introduce a large volume of low-relevance competitor data, occupying token quotas for effective recalls. - Issue: When configuring
outputMaxTokensto 16384, the output content is truncated at 12288, and the interface displays that the reply limit is exceeded. Cause: ThemaxContextconfiguration was not adjusted synchronously. The sum of input context tokens and output tokens exceeds the total token limit supported by the deployed model. - Issue: Key process descriptions are lost after parsing a single long document into segments. Cause:
chunkSizeis set too small, and splitting breaks the logical coherence of process descriptions, leading to failure to match complete context during recall.
How to Verify Proper Configuration
- Submit test queries containing multi-dimensional baijiu investment research keywords, verify the number and relevance of recalled documents, and adjust corresponding configuration items to meet required ranges.
- Upload the longest single baijiu research report or financial report document, check the parsed segment results, and confirm that the logical coherence of the content is not disrupted.
- Trigger the complete investment research analysis process, check whether the output content is truncated, and adjust
maxContextandoutputMaxTokensto match the model's total token limit. - View the token statistics panel, confirm that token consumption across different application scenarios can be accurately recorded and distinguished.
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.