What the Data for This Category Looks Like
Financial report data for agrochemical-related operating entities mainly comes from periodic reports and temporary announcements disclosed by stock exchanges, as well as production and sales monitoring data released by industry associations. Update rhythms are divided into regular and irregular: annual reports are updated once per year, semi-annual reports and quarterly reports are updated every six months and every quarter respectively. Temporary announcements such as production capacity adjustments and new product approvals are released as needed. Most documents are in PDF format, with structures including financial statements, discussion and analysis of operating conditions, core product production and sales data, financial notes and other sections. Fields cover operating income, net profit attributable to shareholders of the listed company, pesticide formulation output, raw material purchase amount and others. Common units include RMB yuan, ten thousand tons, and tons.
Constraints Imposed on Vector Models and Indexing
The long text structure and multi-field characteristics of agrochemical financial reports impose multiple constraints on the vector models and indexing process. Individual financial report texts are lengthy, so chunking parameters must be set reasonably to avoid semantic fragmentation. The data mixes structured financial data and unstructured business analysis text, so vector mapping for multiple field types must be supported. Update frequencies vary and temporary announcements have no fixed cycle, so flexible trigger logic for incremental indexing must be adapted. Field units have different expressions such as tons and ten thousand tons, so standardization must be completed during the preprocessing stage. Otherwise, the accuracy of vector similarity calculation will be affected.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the typical length of business analysis paragraphs in agrochemical financial reports, avoids damaging the semantic integrity of technical terms and business logic during chunking |
overlap_ratio | 15%–20% | Retains contextual connections between adjacent chunks, prevents cross-paragraph product revenue and production capacity analysis information in financial reports from being fragmented |
embedding_model | text-embedding-3-large | Agrochemical financial reports contain technical terms such as herbicides and active ingredient content, and this model provides more stable vector representation for professional texts |
incremental_index_enable | Enabled | Supports generating vector indexes only for newly added or modified documents, no need to fully re-import the uploaded knowledge base |
rerank_top_k | Top 8–12 results | Balances the comprehensiveness of agrochemical financial report retrieval and computational efficiency, avoids excessive recall results increasing the burden on the reranking module |
similarity_threshold | 0.72–0.80 | Distinguishes similar product revenue data and business descriptions in financial reports, reduces false recall probability |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and testing on local samples is recommended before finalizing settings.
Three Common Misconfigurations
- Excessive time spent when calling the knowledge base interface to generate indexes, with status code 408 or timeout returned. Cause: Failed to adapt to the long text characteristics of agrochemical financial reports, and the set
chunk_sizeis too large, leading to increased embedding calculation volume for a single text. - Recall results of the imported knowledge base deviate after replacing the
embedding_model. Cause: Did not perform a reindexing operation for uploaded documents. Vector features are still generated based on the old model and do not match the representation space of the new model. - The sorting of knowledge base search return results does not match expectations, and documents with high similarity are not ranked first. Cause: The
rerank_top_kparameter is not configured, or the number of recall entries is set too high, causing the reranking module to not cover core matching results.
How to Verify Correct Configuration
- Upload a single agrochemical financial report PDF, check the text length after automatic chunking to confirm it matches the configured
chunk_sizeparameter. - Call the knowledge base's embedding model verification interface to confirm the currently used embedding model matches the preset configuration.
- Submit an incremental update task, check that only newly added or modified documents are reindexed in the logs, and no full scan is triggered.
- Initiate a simulated retrieval request, check that the return result sorting logic complies with the preset recall and reranking 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.