What the data for this category looks like
Livestock and poultry farming-related data mainly comes from production ledgers, feed purchase records, disease prevention and control logs of breeding entities, as well as industry market announcement platforms. Production data is updated daily or weekly, while market data is synchronized daily. The documents include two categories: structured ledgers and unstructured text. Structured fields include inventory volume, slaughter volume, and feed consumption, with units of head/animal, kilogram, and yuan. Unstructured text mostly consists of daily breeding records, epidemic prevention disposal instructions, and some include breeding scene images shot on site.
What constraints these characteristics impose on the "model integration and configuration" link
The fixed fields and units of structured ledgers require field mapping rules to be configured during model integration, to avoid misidentifying the unit of inventory volume as ton and ensure the recognition result uses head/animal. The length of unstructured breeding records varies widely, ranging from a few dozen characters to hundreds of characters, so the text segmentation threshold of the model needs to be adjusted. The high-frequency updates of industry market data require configuring a timed synchronization data source trigger mechanism, to avoid generating marketing content using static cached data. In addition, there are many exclusive terms for breeding scenarios, so the industry term adaptation switch needs to be enabled in the model configuration to improve the accuracy of content generation.
How to set the configurations
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the mixed text length of livestock and poultry farming records and market briefings, avoids truncating critical farming data |
embeddingModel | Baidu embedding-v1 | Adapts to field semantic matching of structured farming ledgers, improves recall accuracy |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Farming documents often include multi-page ledgers and images, so parsing timeout needs to be extended |
Recall count | Top 6–8 entries | Farming marketing content needs to cover multiple dimensions of inventory, feed, and market data, increasing the number of recalled entries to ensure comprehensive information |
Similarity threshold | 0.72–0.78 | Filters low-relevance farming historical records, avoids irrelevant content mixing into marketing materials |
Chunk size | 500–800 characters | Adapts to natural segmentation of unstructured farming logs, improves the model's accuracy in understanding single-segment content |
The parameter values provided on this page are all common recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to conduct actual tests on your own samples before finalizing the settings.
Three common mistakes
- Phenomenon: A 404 error is returned when configuring Baidu embedding-v1 as the embedding model. Cause: The API key and calling domain name of Baidu Cloud are not correctly filled in the model integration configuration, or the embedding model version name does not match the official definition.
- Phenomenon: A 404 conversation error occurs when accessing the bge-large-zh-v1.5 model deployed with ollama. Cause: The local calling port and model alias are not correctly configured in FastGPT's model configuration, resulting in an incorrect request path.
- Phenomenon: Large differences in classification results, with inconsistent classification results across different calling links. Cause: A unified classification model and similarity threshold are not configured, or the used model parameters do not adapt to the short-text classification requirements of farming scenarios.
How to confirm that the configuration is complete
- Upload a livestock and poultry farming ledger document, check whether the parsed structured fields completely match the table headers and units of the original document.
- Initiate a marketing content generation request, check whether the returned content includes exclusive terms for breeding scenarios and has no unit identification errors.
- View the model call logs, confirm that the call status codes of both the embedding model and the large language model are 200, with no 404 or timeout errors.
- Compare the classification results of two identical inputs, confirm that the classification logic is consistent and the results meet the expectations of the farming business scenario.
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.