What This Category of Data Looks Like
Livestock and poultry farming research report data mainly comes from agricultural and rural affairs monitoring platforms, public reports from industry associations, special research reports from securities firms on agriculture, forestry, animal husbandry and fishery, and publicly disclosed information from breeding entities. The update rhythm falls into three categories: core operating data such as monthly inventory and slaughter volume is updated monthly; quarterly in-depth industry analysis reports are released quarterly; content related to sudden epidemics and policy adjustments is updated at any time. Most documents contain three types of content: structured data tables, policy interpretations, and epidemic dynamics. Core fields include inventory volume, slaughter volume, feed cost, and epidemic monitoring status, with corresponding units mostly being ten thousand heads, yuan/ton, and similar units.
Constraints Imposed During Model Integration and Configuration
The multi-type update rhythm of livestock and poultry farming research reports requires configuring the trigger interval for incremental synchronization during model integration, to adapt to the different update frequencies of monthly, quarterly, and sudden content. The high proportion of structured data requires configuring segmentation rules that retain table structures when accessing vector models, to avoid destroying data relevance through improper splitting. The unit differences in core fields require configuring unified field standardization processing parameters before embedding, to ensure semantic consistency during retrieval. The real-time requirements for sudden content require adjusting the timeliness weight parameters of the recall model, to prioritize matching newly released research report content.
Recommended Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_batch_size | 32–64 items/batch | The structured paragraphs of livestock and poultry farming research reports are moderately sized, and this batch size balances embedding efficiency and memory usage |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Large in-depth research reports contain multi-page tables and long text, requiring sufficient time to complete parsing |
recall_top_k | Top 8–12 items | Research report content has strong relevance; too many recall results will introduce irrelevant information, while too few will miss valid data |
similarity_threshold | 0.72–0.78 | Terminology in the livestock and poultry farming segment is highly professional, requiring a relatively high threshold to filter low-relevance retrieval results |
maxContext | 8000–12000 characters | The core argumentative paragraphs of research reports are relatively long, requiring sufficient context to support accurate question answering |
ollama_api_url | http://localhost:11434/v1 | Locally deployed Ollama models use this address by default, which complies with OpenAI-compatible format requirements |
The parameter values provided on this page are common recommended 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
- Symptom: When configuring the text-embedding-v3 model, the interface prompts "No available channels under the current default group". Cause: Access permissions for the API key are not configured in the corresponding group, or the key is not bound to the correct service region.
- Symptom: When configuring a BGE-M3 vector model deployed via Ollama, the interface prompts integration failure, or empty results are returned after retrieval. Cause: The Ollama OpenAI-compatible interface is not enabled, or the filled
ollama_api_urldoes not point to the v1 version API endpoint. - Symptom: After importing and parsing research reports, the field units of structured tables become inconsistent. Cause: No field standardization processing rules are configured, resulting in inconsistent units of research reports from different sources, which affects embedding and retrieval effects.
How to Verify Successful Configuration
- Upload a dedicated livestock and poultry farming research report, wait for parsing to complete, and check if the parsed text retains the complete table structure and field information.
- Initiate a retrieval request for core breeding data, and verify that the number of returned results matches the configured recall quantity requirement.
- Call the model for question-and-answer testing, and check if returned results reference professional terms and specific data from the research report, with no irrelevant content included.
- View system operation logs, confirm that there are no errors in logs related to vector embedding and large model calls, and that update frequency complies with configured synchronization rules.
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.