What the data for this category looks like
Livestock and poultry farming financial report data comes primarily from periodic reports publicly disclosed by listed entities, publicly available statistical documents from industry associations, and internal enterprise breeding ledgers. The core update cycle is quarterly. Annual reports must be publicly disclosed by the end of April of the following year. Monthly breeding operation data is updated at a higher frequency. Document structures include dedicated breeding business sections, consolidated financial statements, and detailed operational schedule appendices. Fields include breeding stock inventory, slaughter volume, feed consumption, unit breeding cost, and more. Common units are head, ten thousand heads, yuan/head, and ten thousand yuan.
Constraints on vector models and indexing workflows
The layered structure, high-frequency update rhythm, and specialized business fields of livestock and poultry farming financial reports impose multiple constraints on vector models and indexing workflows. Breeding business sections are mixed with general financial sections, so precise identification of breeding-related paragraphs is required to avoid non-business text interfering with recall accuracy. Monthly operational data is updated at high frequency, so an incremental indexing update mechanism is needed to avoid performance losses from full index rebuilding. Specialized fields such as breeding stock inventory and unit cost are tied to specific units. Vector models must adapt to the semantic association between breeding terminology and units, otherwise errors will occur where semantic matching is correct but unit requirements are not met. The text length of individual breeding section paragraphs varies widely, so a flexible segmentation strategy is needed to avoid loss of critical information due to long text truncation.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | bge-m3 (local deployment) or text-embedding-v3 (cloud call) | Covers agricultural professional terminology and long text encoding requirements, adapts to the semantic characteristics of financial report text |
segment_length | 800–1200 characters | Balances semantic completeness and encoding efficiency, adapts to the text length of breeding business sections in livestock and poultry farming financial reports |
recall_count | Top 10–15 results | Covers multi-dimensional business data requirements, avoids missing critical information due to too few results or increasing processing overhead due to too many results |
incremental_update_interval | Every 1 hour | Adapts to the high-frequency update rhythm of monthly breeding data, ensures timeliness of index data |
embedding_batch_size | 16–32 | Balances batch encoding efficiency and memory usage, adapts to processing requirements for single-batch financial report documents |
similarity_threshold | 0.72–0.78 | Distinguishes breeding business-related text from general financial text, filters irrelevant recall content |
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 configuration errors
- Symptom: The interface displays "No available vector model channel" with error code
400 Bad Request. Cause: The API address or local deployment path of the corresponding vector model is not configured in FastGPT's model management. For example, bge-m3 deployed via Ollama does not have the local port address filled in. - Symptom: The number of retrieval results is far lower than expected, and includes a large amount of non-breeding related financial text. Cause: The
similarity_thresholdis set too high, or thesegment_lengthis set too short, causing critical business paragraphs to not be properly encoded and recalled. - Symptom: Retrieval response times out, taking longer than
30 seconds. Cause: Therecall_countis set too large and re-ranking filtering is not enabled, or theembedding_batch_sizeis set too high, causing memory usage to exceed the limit and blocking the retrieval link.
How to verify successful configuration
- Access the FastGPT model management page, check whether the configuration parameters of
embedding_modelinclude the correct API key or local deployment address, and confirm that the model status shows "Connected". - Upload a sample livestock and poultry farming financial report, view the parsed segmentation results, confirm that the segment length matches the preset configuration, and that breeding business paragraphs are not excessively truncated.
- Initiate a financial report keyword search, verify the number and relevance of recall results, and adjust the
similarity_thresholdorrecall_countto a range that meets business requirements. - Simulate a high-frequency data update scenario, check the index update logs, and confirm that the incremental update task executes normally at the preset interval, with no full index rebuild trigger records.
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.