What data for this category looks like
Agrochemical marketing content data mainly comes from product registration certificate documents supporting agricultural insurance institutions, field test reports, product manuals, dealer training materials, compliance policy documents, and customized promotional copy. Data update rhythm follows new product registrations, compliance policy changes, and quarterly marketing plan adjustments, with no fixed cycle. Document structure includes structured fields and long text paragraphs. Structured fields include product name, active ingredient, concentration, registration certificate number, applicable crops. Units are mostly g/L, %, or per mu dosage. The long text section includes usage methods, precautions, and test details. Some documents include dosage tables.
What constraints these characteristics impose on knowledge base retrieval and recall
The specialized field attributes of agrochemical products require retrieval to strictly match precise parameters such as active ingredients and registration certificate numbers, to avoid compliance errors. The mixed presence of structured dosage tables and long-text test reports requires the retrieval system to support retained chunked semantics and structured field extraction. The non-fixed update rhythm requires support for incremental index synchronization, to avoid full repeated index construction. As supporting content for agricultural insurance customer acquisition, it needs to support filtering recall results by applicable crops and insurance scenarios, to ensure content matches the actual needs of growers. Text with many specialized terms must avoid semantic fragmentation caused by excessive chunking, which reduces retrieval accuracy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Agrochemical documents include long-text usage instructions and structured dosage tables. This range preserves complete semantic blocks and prevents professional data from being split and fragmented |
recall_top_k | Top 10 results | Agrochemical marketing content needs to balance compliance accuracy and scenario coverage. Too many recall results will increase the burden of context processing |
similarity_threshold | 0.75–0.85 | Precise matching of specialized fields such as active ingredients and registration certificate numbers is required. This threshold filters irrelevant content with low similarity |
PARSE_STRUCTURED_TABLE | Enabled | Agrochemical documents include structured data such as dosage tables and ingredient lists. Enabling this allows field extraction for precise retrieval |
incremental_sync_interval | Every 24 hours | Agrochemical data updates have no fixed cycle. Daily incremental synchronization balances timeliness and system resource consumption |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: In FastGPT 4.8.20, the page crashes when loading a knowledge base containing a large number of agrochemical test reports. Cause: The
chunk_sizeparameter was not adjusted. Excessively large text chunks cause memory usage to exceed the default threshold of this version. - Phenomenon: Authentication failure or empty results are returned when calling the knowledge base retrieval function via an external API. Cause: API access permissions were not configured, or request parameters were not passed correctly.
- Phenomenon: Newly uploaded agrochemical documents cannot be retrieved after creating a knowledge base data index. Cause: Incremental sync trigger rules were not configured. Using only full synchronization causes new data to not be indexed in a timely manner.
How to confirm the configuration is correct
- Upload an agrochemical product manual containing a structured dosage table, check whether the chunking results retain complete dosage information, and verify the actual effect of the
chunk_sizeconfiguration. - Initiate a query containing an active ingredient name, check whether the similarity scores of the recall results fall within the preset range, and verify the effect of the
similarity_thresholdconfiguration. - Upload a new agrochemical marketing material, wait for synchronization to complete, then initiate a corresponding query, check whether the new content is successfully recalled, and verify the effect of the incremental sync configuration.
- Initiate a composite query containing historical context, check whether the recall results are associated with the crop type from the historical query, and verify the effect of the context association configuration.
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.