This document uses FastGPT v4.17.0 for configuration.
What this category of data looks like
Grain reserve policy and regulatory basis documents mainly come from official released documents of the National Food and Strategic Reserves Administration and local grain management departments, including management regulations, reserve rotation measures, implementation rules, and more. Document update timing follows policy revisions or new implementation requirements, with no fixed schedule. Each single document includes fields such as release document number, issuing unit, effective date, clause text, and scope of application. Most clause content consists of coherent management requirements, covering reserve quantity, rotation cycle, supervision standards, and other topics, involving professional measurement terms such as ton, kilogram, and mu.
What constraints do these characteristics impose on the vector model and index link
Grain reserve policy documents have fixed fields and professional terms, requiring vector indexes to associate metadata such as release document number and effective date to facilitate subsequent filtering of recall results by policy attributes. Documents have no fixed update cycle, and policy changes have wide impact, so incremental index trigger logic must be configured to only sync updated policy document content. Clause content is coherent and includes professional measurement terms, so chunk length must adapt to clause integrity to avoid splitting that breaks semantic connections of management requirements. Cross-regional local reserve policies also need to establish partitioned indexes by region to improve recall accuracy.
How to set the configuration
| Configuration Item | Suggested Value | Basis for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the coherent length of grain reserve policy clauses, avoiding splitting that breaks the semantic integrity of management requirements |
| Overlap Length | 100–150 characters | Connects adjacent chunked policy content, preventing cross-clause management requirements from being split |
| Similarity Threshold | 0.75–0.85 | Filters irrelevant policy clauses, ensuring matching accuracy between recall results and queries |
| Single Search Limit | Top 8 entries | Controls the context length passed to large models, adapting to input limits of general large models |
maxToken | 4000 | Matches the maximum context token limit of mainstream large models, ensuring complete recalled policy content is passed |
defaultToken | 1500 | Sets the maximum token count per vector, adapting to input length requirements of embedding models |
Three Common Misconfigurations
- When deploying the PG database via Docker, the knowledge base index fails to build consistently. Cause: The host machine is configured with 8 cores and 16GB RAM without GPU, and the batch processing batch size for vector indexes was not reduced, causing memory usage to exceed system limits and the process to be terminated.
- A 503 error is returned when calling the embedding model, with the prompt that no available channels exist in the current group. Cause: No dedicated channel was configured separately for the embedding model, and the aggregated call link of OneAPI is still used, causing the target model to not be assigned to the default group.
- The matching degree between recalled policy clauses and queries is low. Cause: The similarity threshold is set too high, causing some eligible policy clauses to not be recalled.
How to Confirm the Configuration Is Correct
- Enter the FastGPT knowledge base management page, check that the index status is completed and there are no error logs.
- Input a test question related to grain reserve policies, check that the number of recalled results matches the preset single search limit.
- Check the embedding model call logs, confirm that no 503 errors occur and the model call channel is correct.
- View the vector table in the PG database, confirm that the number of chunked documents matches the number of original files, with no abnormal null values.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.