Knowledge Base Retrieval and Recall for Snack Food

What Data Looks Like for This Category

Data for this category comes primarily from four sources: the China Food Industry Association public monitoring database, official supply chain documents disclosed by brand owners, national food safety sampling public information, and third-party logistics shipping records. Three update cycles apply:

Compliance testing data updates monthly
Supply chain procurement data updates with each procurement cycle
Brand dynamics update quarterly

Single documents typically include three types of content:

Multi-batch testing details for a single product category
Omnichannel shipping lists for a single brand
Quotation entries from multiple suppliers for a single raw material

Documents include these fields: SKU number, raw material name, procurement unit price, production batch, test result value, and shipping volume. Corresponding units are: none, none, yuan/kg, none, mg/kg, and carton.

Constraints for Knowledge Base Retrieval and Recall

Scattered data sources across industry associations, brand owners, sampling institutions, and logistics channels require retrieval to support joint recall of multi-source datasets. Pre-configured weight allocation for different data sources is necessary. Significant variation in data update cycles requires setting incremental synchronization time thresholds. This prevents expired procurement quotations or outdated sampling results from being recalled. Documents include detailed fields such as SKU and production batch. Retrieval must support both precise field matching and full-text retrieval scenarios. Test results and shipping volumes have clear units. Unit normalization retrieval rules must be configured to avoid mismatching similar data with different units.

Recommended Configuration Settings

Configuration Item	Recommended Value	Rationale
`Top N Retrieval Count`	`8–12 results`	Snack food due diligence reports require coverage of supply chain, sampling, channel and other multi-dimensional data. 8–12 results balance recall coverage and retrieval efficiency
`Similarity Threshold`	`0.72–0.85`	Snack food data contains a large number of detailed fields and units. A threshold that is too low will introduce irrelevant matches, while a threshold that is too high will miss valid data
`Segment Length`	`800–1200 characters`	Single documents include multi-batch testing data and shipping details. Segments that are too long will lose field associations, while segments that are too short will destroy data integrity
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Large supply chain documents include multi-SKU data. Parsing takes significant time. 300 seconds covers parsing requirements for most documents
`Incremental Sync Interval`	`Configured by data type: compliance data 7 days, supply chain data 1 day`	Update cycles vary significantly across data sources. Configuring by data type avoids repeated synchronization or expired data
`Field Matching Toggle`	`Enabled`	Documents contain detailed fields such as SKU and production batch. Enabling this allows precise matching retrieval

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Misconfigurations

Phenomenon: Snack food documents imported into the knowledge base automatically remove custom directory structures. Cause: The system default automatic directory cleanup configuration is not disabled. The default rule removes hierarchical headings and separators within documents.
Phenomenon: Question-answer pair extraction tasks remain stuck in training for long periods, with no related call records in invocation logs. Cause: The imported documents contain a large number of detailed SKU and batch data. The field splitting logic during parsing triggers a blockage, and no sufficient timeout threshold is configured.
Phenomenon: GPT-4o-mini call error logs appear during workflow runs in FastGPT v4.9.0, but no call node for this model is configured in the workflow. Cause: The total length of context retrieved by the knowledge base exceeds the configured model context window. The system automatically triggers an implicit call for context organization, leading to the error.

How to Verify Configuration Is Successful

Run a parsing test for a single snack food supply chain document. Check if the segmented data retains the association between SKU and batch. Adjust segment_length until the data association requirements are met.
Initiate a retrieval for a specific raw material procurement unit price. Verify that the similarity of the recalled results meets expectations. Adjust similarity_threshold until the matching results align with business requirements.
Export all document metadata from the knowledge base. Check if the incremental synchronization timestamps match the update times of the data sources. Confirm that the incremental_sync_interval configuration is effective.
Run a complete due diligence report generation workflow. Check if unconfigured model call errors appear in the logs. Adjust the context window configuration until the issue is resolved.

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.