What the data for this category looks like
Dairy industry research report data mainly comes from domestic securities research institutes, public data from the Ministry of Agriculture and Rural Affairs’ Animal Husbandry and Veterinary Bureau, annual reports of dairy enterprises, and public documents from industry consulting agencies. Regular reports are released on a quarterly and monthly basis, with temporary supplementary documents issued after sudden industry events. Document structures typically include core indicator tables, market supply and demand analysis, competitive landscape chapters, and policy interpretation modules. Core fields include raw milk purchase price, terminal retail price, monthly sales volume, with corresponding units of yuan/kg, yuan/liter, ton.
What constraints these characteristics impose on deployment and upgrade
The large number of structured indicators, uneven update rhythm, and clear segmented tracks of dairy industry research reports impose multiple constraints on deployment and upgrade. First, coexistence of regular updates and temporary documents requires configuring an incremental synchronization mechanism to avoid resource waste from full synchronization. Second, a large number of structured tables require specialized parsing configuration, otherwise core indicator fields will be lost. Third, there are many segmented tracks, so the vector database needs to be sharded by track to reduce retrieval latency. Fourth, temporary documents have strong timeliness, so document expiration and elimination rules need to be adjusted during upgrades.
How to set the configurations
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
PARSE_TABLE_ENABLE | true | Dairy industry research reports contain a large number of structured indicator tables. Enabling this setting allows complete extraction of core fields and values |
VECTOR_DB_SHARD_COUNT | 4–6 | There are many segmented tracks for dairy industry research reports. Sharding by track reduces latency across-track retrieval |
UPLOAD_FILE_MAX_SIZE | 200 MB | Some large industry research report documents have lengthy content, so this setting adapts to large file upload requirements |
SYNC_INCREMENTAL_INTERVAL | 3600 seconds | Regular research reports are updated monthly, and temporary documents trigger synchronization on demand. Hourly incremental synchronization balances resource usage and timeliness |
RECALL_TOP_K | 8–12 | Dairy industry research reports have clear segmented tracks. A small number of recalls can cover core relevant content |
VECTOR_EMBEDDING_MODEL | text-embedding-3-large | Research report text contains professional terminology and structured data. This model better adapts to long text and professional vocabulary |
The parameter values provided on this page are conventional recommendations used as a starting point 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 test on your own samples before finalizing settings.
Three common mistakes
- Symptom: Container pull fails when deploying on ARM architecture servers, with an error indicating image architecture mismatch. Cause: The official ARM64 architecture image was not used, and pulling the x86 version image directly resulted in failure to run.
- Symptom: Milvus container fails to start, with logs showing
pgvector connection refusedormongo authentication failed. Cause: Database configuration placeholders in the compose file were not modified correctly. Some users directly used the default username and port from the example without matching local environment parameters. - Symptom: Some professional indicators are missing from parsed research report data, or field formats are chaotic. Cause: The officially recommended stable version
mineruimage was not used. Using unofficial or older versions of the parsing engine caused abnormal table parsing.
How to confirm the configuration is complete
- Upload a table document from a dairy industry research report, check the parsed field list, and confirm that core indicators such as raw milk purchase price and terminal sales volume are correctly extracted.
- Run an incremental synchronization task, check the synchronization logs, and confirm that only newly added research report documents are loaded, with no full synchronization triggered.
- Search for "2024 Liquid Milk Market Analysis", check the number and relevance of returned results, and adjust
RECALL_TOP_Kand the similarity threshold to a range that meets business requirements. - View the vector database sharding monitoring, confirm that shards divided by dairy industry segmented tracks are correctly created, with no redundant cross-track data.
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.