What Data for This Category Looks Like
Livestock and poultry farming investment research data comes from four main sources: official monitoring data from the Ministry of Agriculture and Rural Affairs, weekly reports from industry associations, operational data submitted by large-scale farms, and feed raw material quotes from third-party market platforms. The data update rhythm has clear tiers: bulk feed raw material prices are updated daily, inventory and slaughter data from large-scale farms are submitted weekly, and regional livestock and poultry disease monitoring data is released per individual monitoring cycle.
The data includes two categories: structured reports and unstructured analysis documents. Structured fields include livestock and poultry breed, statistical cycle, total inventory, slaughter volume, average feed price, disease type, and number of cases. Corresponding units are head, day/week/month, head, head, yuan/kg, none, and head.
Constraints Imposed on Database and Operations by These Characteristics
The tiered update rhythm of livestock and poultry farming investment research data demands that the database support flexible switching between incremental and full synchronization. This avoids repeatedly loading frequently updated market data, while adapting to batch import of low-frequency submitted inventory and slaughter data.
The need to store multiple types of data together requires the database to support mixed indexing of structured and unstructured data, to accommodate both reports and analysis documents. Field units are strongly tied to categories, so unified field semantics must be enforced at the database level to prevent cross-category data confusion.
Frequently updated feed price data creates continuous write pressure, so a sharding strategy must be configured to distribute load. Sudden disease monitoring data requires the database to support rapid scaling to handle temporary bulk write requests. Additionally, differences in data formats from multiple sources increase the complexity of pre-ingestion validation.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
milvus.collection.shards | 2–4 shards | Livestock and poultry farming investment research data has low write concurrency, but must support simultaneous vector recall for multiple users. The shard count adapts to daily workload |
database.upload.max_size | 1000 MB | Single livestock and poultry farming industry analysis reports typically do not exceed 800 MB. Setting this value prevents upload failures for overly large files |
rag.retrieve.top_k | Top 8–12 results | Valid recall results for livestock and poultry farming investment research typically cluster around 10 entries. Excessive results increase context redundancy |
database.sync.interval | 15 minutes | Frequently updated feed price data requires synchronization every 15 minutes. Low-frequency inventory data can be synchronized weekly |
tool.database.connection.timeout | 30 seconds | Queries for livestock and poultry farming structured data typically take little time. This timeout setting avoids unnecessary waiting |
The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material format, 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: Unable to establish a connection when using Attu to connect to the Milvus database. The interface prompts connection timeout or authentication failure. Cause: Public network access permissions for Milvus are not configured, or the Attu connection port does not match the Milvus service port.
- Symptom: A
400 Bad Requesterror is returned when calling the database query plugin. The log showsMessages with role 'content truncated. Cause: Fields returned by database queries are not escaped, leading to abnormal JSON formatting. - Symptom: Some requests time out when multiple FastGPT applications access concurrently. The backend shows that the database connection pool is exhausted. Cause: The maximum number of connections in the database connection pool was not adjusted based on the query frequency of livestock and poultry farming data, leading to concurrent overload.
How to Confirm Proper Configuration
- Run an incremental synchronization task for feed price data, check if there are any field verification failure prompts in the synchronization log, and confirm that the synchronization rules match the data fields.
- Call the vector recall interface, check if the number of returned results matches the preset recall configuration, and confirm that the vector database index is correctly associated with the data fields.
- Simulate multi-user concurrent queries, observe the usage of the database connection pool, and confirm that the connection count configuration covers daily concurrent requirements.
- Use the Attu tool to attempt connecting to the Milvus service, verify the availability of the database connection, and confirm that the authentication information and port configuration are correct.
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.