What the data for this category looks like
Baijiu investment research data sources include China Alcoholic Drinks Association public statistics, listed liquor companies' quarterly/annual financial reports, brokerage food and beverage team research reports, e-commerce platform sales monitoring data, offline channel survey records, professional tasting reports, and more. Update frequencies are split into real-time (e-commerce sales, channel updates), daily (public opinion monitoring), monthly (industry association data), and quarterly/annual (financial reports, research report updates). Document structures include long-form research reports (up to dozens of pages per piece), structured tables (with fields including alcohol content, production capacity, sales price, etc.), short-form tasting notes, and policy notices. Fields include alcohol content (unit: %vol), single-bottle volume (unit: ml), terminal sales price (unit: yuan per bottle), base liquor vintage, brewing process, and others.
What constraints these characteristics impose on model access and configuration
The multi-source nature and varying update frequencies of baijiu investment research data require configuring switching logic between incremental synchronization and full updates to avoid repeated parsing of historical data. The high proportion of structured data requires configuring field extraction and structured parsing parameters during model access to ensure precise matching of table-like content during retrieval. Dense professional terminology (such as kunsha, fansha, Feitian series, etc.) requires configuring terminology enhancement functions to improve the model's semantic recognition ability for industry-specific vocabulary. The high proportion of long-form text requires adapting the model's context window to large sections of content to avoid truncation of key information.
How to set configurations
| Configuration Item | Recommended Value Range/Method | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Single baijiu research reports often contain content exceeding 5000 characters; adapting to a large context window avoids truncation of core data |
chunkSize | 800–1200 characters | Baijiu data contains a large number of professional terms and structured tables; appropriate segment length preserves term integrity and reduces retrieval errors |
embeddingModel | Select an embedding model optimized for the food and beverage/finance sector | General-purpose embedding models have insufficient semantic matching accuracy for baijiu-specific terminology such as kunsha and base liquor grade |
enableCustomTerm | Enable and import the baijiu industry terminology library | Improves the model's recognition accuracy for professional terminology and avoids missing relevant content during retrieval |
syncSchedule | Incremental synchronization at 2:00 AM daily | Adapts to the update rhythm of baijiu e-commerce sales data (updated daily) and industry association data (updated monthly), reducing computing costs |
similarityThreshold | 0.75–0.85 | The high proportion of structured data in baijiu data means increasing the threshold filters low-relevance unstructured content and improves retrieval accuracy |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Phenomenon: The list of text understanding models is empty when creating a knowledge base. Cause: No custom large model API key has been added on the platform's model management page, or the key permissions are insufficient to call the target model.
- Phenomenon: A 500 error is returned when calling a custom large model. Cause: The API address and request headers of the third-party large model have not been correctly configured, making it impossible to connect to the purchased external model service.
- Phenomenon: A 400 error or timeout is returned when calling the TTS model. Cause: The address of the privately deployed TTS model has not been configured, or the correct API key has not been filled in.
How to Verify Successful Configuration
- Access the platform's model management page, review the list of added custom large models and TTS models, and confirm the target model has been successfully imported.
- Upload a baijiu annual research report, run segment parsing, and check the parsed text for complete professional terminology and structured fields.
- Initiate a retrieval test, input "2024 high-end baijiu terminal sales prices", and verify the number and similarity of returned results meet expectations.
- Input a section of baijiu tasting notes, call the configured TTS model, and confirm speech synthesis operates normally without stuttering.
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.