What the data for this category looks like
Baijiu investment research data comes from multiple sources. These include public industry research reports, listed distilleries’ periodic financial reports, industry association statistics, e-commerce platform sales monitoring data, and reviews from professional tasting institutions. Update frequencies vary significantly. E-commerce sales data is updated daily. Distillery financial reports are updated quarterly or annually. Industry research reports update in real time alongside market trends. Document structures fall into three categories. These are structured reports such as tables for production capacity, revenue, and single-product prices, long-form research reports, and short-form tasting notes. Fields include professional metrics with clear units. Examples are alcohol content (%vol), net content (ml), ex-factory price (yuan/bottle), and revenue (10,000 yuan).
What constraints do these characteristics impose on database and operations workflows?
Multi-source heterogeneous data types require the database to support structured table storage, unstructured text indexing, and time-series data queries. This increases storage architecture complexity. Data sources with different update frequencies need matching incremental synchronization mechanisms. This avoids full synchronization consuming excessive resources. Professional fields with clear units require field validation rules. This prevents incorrect unit values from interfering with subsequent analysis. The mixed document structure of long-form financial reports and short-form tasting notes requires indexing and segmentation strategies that adapt to different text lengths. This avoids losing contextual association from over-segmentation, or poor recall accuracy from overly long segments.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
POSTGRES_MAX_CONNECTIONS | 200–300 | Baijiu investment research data includes large volumes of time-series sales queries and multi-dimensional associated queries. This connection count range supports concurrent business requirements |
VECTOR_SEGMENT_LENGTH | 800–1200 characters | Adapts to the mixed structure of long-form financial reports and short-form tasting notes in baijiu research reports. Balances contextual completeness and retrieval efficiency |
TEXT_INDEX_CONFIG | pg_catalog.zhparser | Adapts to Chinese professional terminology in baijiu data. Improves token matching accuracy |
UPLOAD_FILE_MAX_SIZE | 500 MB | Covers upload requirements for single large industry datasets or complete financial reports |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Meets parsing time requirements for large financial report documents. Prevents unexpected interruptions |
RECALL_TOP_K | Top 10–15 results | Covers multi-dimensional associated information required for baijiu investment research. Prevents omission of critical data |
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 targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: After upgrading Docker to version 4.8.20, a
text index required for $text queryerror occurs during knowledge base queries. Cause: No dedicated text index was configured for Chinese professional terminology. The default English tokenization rule was used, which cannot match Chinese query keywords for the baijiu industry. - Phenomenon: After upgrading from version 4.8.9 to the latest version, existing knowledge base data is lost. Cause: Persistent storage configuration for the PostgreSQL database was not enabled. Long-term accumulated baijiu investment research data was not persistently saved.
- Phenomenon: After orchestrating database queries, the number of returned results does not match expectations. Only a small number of single-product data entries are returned. Cause: The
RECALL_TOP_Kconfiguration was not adjusted. The default recall count is too low to cover the multi-dimensional associated information required for baijiu investment research.
How to Verify Proper Configuration
- Connect to the target PostgreSQL database, run
SELECT * FROM pg_indexes WHERE tablename = 'document_chunks', and confirm there are index entries matching Chinese tokenization. - Upload a baijiu financial report PDF, wait for parsing to complete, then check system logs. Confirm that parsing time does not exceed the set value of
PARSE_FILE_TIMEOUT_SECONDS. - Initiate a query related to baijiu production capacity, check the number of returned knowledge base recall results, and adjust
RECALL_TOP_Kto a quantity that meets business requirements. - Back up the corresponding database storage directory before performing a database version upgrade. Confirm that existing knowledge base entries are not lost after the upgrade.
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.