What data looks like for this category
Data sources for crop farming-focused finance, insurance and wealth management marketing content include public documents from agricultural technology extension stations, product specifications from agricultural input manufacturers, marketing materials from financial institutions targeting crop farmers, and interpretations of regional agricultural bureau subsidy policies for crop farming. Update cadences vary across content types: policy content updates alongside subsidy adjustments, agricultural input parameter documents update with new product launches, and marketing materials update with promotion cycles. Document structures primarily consist of structured tables and long-form explanatory text, with fields including variety name, planting scale, premium rate, subsidy amount and other relevant details. Units include mu, yuan per mu, percentage and other professional parameters.
What constraints these characteristics impose on the knowledge base retrieval and recall workflow
The high proportion of structured tables requires the retrieval system to support vector generation for structured data. Without this capability, professional parameters such as premiums and subsidies within tables cannot be effectively retrieved. Fields with dedicated units require the retrieval process to handle unit consistency, to avoid retrieving irrelevant content due to unit mismatches. Large differences in update frequencies require a phased incremental update strategy, to avoid excessive time spent on full updates. The large volume of long-form policy explanations requires reasonable setting of chunking parameters, to ensure retrieved context information is complete and not redundant. The high proportion of scenario-specific keywords requires the retrieval model to adapt to agricultural finance professional terminology, to improve matching accuracy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxChunkSize | 800–1200 characters | Crop farming financial marketing content has a large amount of long-form explanatory text. This chunk length preserves complete subsidy policy interpretations or premium plans, avoiding semantic fragmentation |
ENABLE_TABLE_VECTOR | Enabled | Crop farming financial marketing content contains a large number of structured tables, such as premium calculation sheets and subsidy application lists. Enabling this option generates vector indexes for table content |
RECALL_TOP_N | Top 6–8 results | Single crop farming financial marketing content covers narrow scenarios. Too many recalled results dilute relevance, while too few miss professional needs tailored to crop farmers |
SIMILARITY_THRESHOLD | 0.72–0.78 | Agricultural finance has many professional terms. A threshold that is too low retrieves irrelevant content, while a threshold that is too high misses professional scenarios with slightly lower matching scores |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Parsing large crop farming subsidy policy manuals takes a long time. This duration prevents timeout failures for routine document parsing |
UPLOAD_API_BATCH_SIZE | 10–20 items per batch | Crop farming financial marketing content mostly consists of bulk agricultural input financial plans or insurance product descriptions. Batch uploading reduces the number of interface calls |
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 targeted analysis, and it is recommended to test on your own samples before finalizing.
Three common configuration mistakes
- Phenomenon: A 413 status code is returned when calling the knowledge base import interface, and upload of crop farming financial marketing content cannot be completed. Cause: The
UPLOAD_FILE_MAX_SIZEparameter is not configured, or the size of a single uploaded marketing document exceeds the default limit. - Phenomenon: Imported premium calculation tables cannot be retrieved, and search results do not include parameters such as rates and insured amounts in the tables. Cause: The
ENABLE_TABLE_VECTORconfiguration is not enabled, and vector indexes for table content are not generated. - Phenomenon: After pulling crop farming subsidy data from Feishu multidimensional tables via a custom API, the search results are empty. Cause: Multidimensional table fields are not mapped to the text vector format supported by FastGPT, and correct data source synchronization trigger rules are not configured.
How to confirm configurations are set correctly
- Upload a crop farming financial marketing document containing structured premium tables, check the knowledge base parsing log to confirm that table content is fully recognized and vector indexes are generated.
- Input targeted agricultural finance scenario keywords, verify the number of returned search results, and adjust the similarity threshold to the range suitable for professional terminology.
- Call the knowledge base import interface to upload a single marketing document, confirm that the interface returns a successful status, and check that no abnormal restrictions are triggered during the upload process.
- Export the text content after knowledge base training, confirm that the data format matches the uploaded marketing materials, and verify that the storage and export processes are working properly.
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.