What the data for this category looks like
Feed due diligence data primarily comes from raw material purchase vouchers, production formula archives, finished product quality inspection reports, inventory ledgers of feed production enterprises, and sampling records from third-party testing institutions. Data update frequency changes with business nodes: Raw material purchase data updates with each batch of purchased inventory, production formulas only update when formula adjustments are made, finished product quality inspection reports are tied to each batch of finished products and update synchronously with outbound shipments, and inventory ledgers update daily. A single due diligence report document usually includes four modules: raw material traceability table, production process record, batch quality inspection sheet, and inventory ledger. Fields include raw material name, batch number, inspection date, crude protein content, moisture content, and inventory quantity. Content fields use grams per kilogram as the unit, and quantity fields use tons as the unit.
What constraints these characteristics impose on model access and configuration
The multi-source nature and differentiated update rhythms of feed due diligence data require that model access supports batch parsing and incremental synchronization of multiple data sources, to avoid repeatedly loading expired purchase or inventory data. A single report contains multiple structured modules, so model configuration must adapt to structured field extraction and cross-module association verification, such as the matching logic between raw material content and inventory quantity. The differences in update frequencies across modules require configuring incremental update trigger rules, only initiating model calls for changed modules to reduce invalid calculations. Field units are uniformly grams per kilogram and tons, so model access must preset unit verification rules to avoid extraction errors caused by unit mismatches. Additionally, feed raw material categories are diverse, so the model must support custom field mapping rules to adapt to exclusive inspection items for different raw materials.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Feed due diligence reports usually contain multi-module structured data, and many fields need to be processed during parsing. Extend the timeout period to avoid parsing interruptions. |
chunkSize | 800–1200 characters | Most fields in feed due diligence reports combine short text and numerical values. This segment length preserves field association information and avoids breaking data logic during splitting. |
similarityThreshold | 0.75–0.85 | Low-similarity redundant document fragments must be filtered, while weakly associated matching results for feed raw materials and quality inspection data must be retained. |
rerankTopN | Top 6 results | Feed due diligence data has many modules. Returning an appropriate number of re-ranked results balances recall completeness and calculation efficiency. |
UPLOAD_FILE_MAX_SIZE | 500 MB | A single feed due diligence report may contain multiple batches of quality inspection sheets and inventory ledgers. Allow large file uploads to avoid requiring split uploads. |
AIPROXY_CHANNEL_CONFIG | Calibrated via actual testing | Feed due diligence requires calling professional domain models. Use this configuration to add custom model channels and adapt to inference needs for specific scenarios. |
The parameter values provided on this page are all 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 settings.
Three common mistakes
- When calling an API-published task classification workflow, the returned result is empty or the classification logic does not match expectations. Cause: The
modelfield corresponding to the task type is not explicitly specified. An AI chat model is mistakenly used for task classification, causing the model's execution logic to not match the task requirements. - In FastGPT 4.9.7, the newly added Deepseek AI model channel cannot be selected in the application's AI model settings list. Cause: The custom model channel is not configured via
AIPROXY_CHANNEL_CONFIG. Only the local interface key is configured, and it is not synchronized to the platform's global settings. - When parsing a single feed due diligence report, the system returns a
500 Internal Server Error, and logs show field parsing failure. Cause: A reasonable range for thesimilarityThresholdparameter is not configured, causing some low-similarity feed raw material fields to be incorrectly filtered, preventing structured extraction from being completed.
How to confirm the configuration is complete
- Upload a standard feed due diligence report, check that the parsed structured fields fully match the preset raw material, quality inspection, and inventory modules. Adjust relevant configuration items until there are no missing or redundant fields.
- Send a task classification request in the API debugging tool, specify the
modelfield as the task classification model, verify that the returned classification results match the preset tags, and confirm that the model task matches. - Enter the AIPROXY channel configuration page, verify that the added model key and interface address can connect normally, with no authentication failure prompts.
- Upload a feed due diligence report that exceeds the preset threshold, check that the upload and parsing processes are not interrupted, and confirm that the upload limit configuration meets business requirements.
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.