What the Data for This Category Looks Like
The typical entities in this category are coating, resin, additive, agrochemical intermediate, and specialty chemical enterprises. Daily production documents include batch production instructions, feeding review sheets, reaction process records, cleaning records, inspection reports, deviation and change orders. Data sources include formula and process libraries, raw material batch information, reactor DCS data, weighing systems, LIMS systems, and warehouse systems. Update frequency is per production batch or each key process step. Data fields include batch number, feeding amount, reaction temperature, reaction duration, inspection results, and more. Units include kilograms, degrees Celsius, hours, and others. Due to the production model of switching between multiple varieties and batches, a single batch is associated with a large number of documents.
What Constraints These Characteristics Impose on the Knowledge Base Retrieval and Recall Link
The need to associate multiple batches and multiple documents requires retrieval to accurately match key fields such as batch number and raw material batch, to avoid interference from irrelevant data. Frequently updated production data requires the knowledge base to support real-time synchronization or scheduled refresh, to ensure retrieval results align with current production status. Business requirements for formula confidentiality and cleaning confirmation require isolation of sensitive formula documents and production ledger documents, so only authorized retrieval can access corresponding data. The feature of a single batch being associated with a large number of documents requires recall results to be sorted by process step, to facilitate sequential data integration during ledger summary.
How to Configure the Setup
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches strongly correlated fields such as feeding and reaction records, filters low-correlation historical batch data |
limit | Top 8 entries | Covers documents across multiple stages of a single batch, while controlling total context length to avoid retrieval timeouts |
searchMode | fullTextRecall | The query examples emphasize identifiers or specialist terms; test this mode with representative data, not as a product default. |
usingReRank | Enabled | Re-ranks recall results by reaction step sequence, aligns with reading and summary logic for production ledgers |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands query terms such as batch number and raw material name, supplements associated cleaning and inspection records |
collectionIds | Bind dedicated production ledger collections | Isolates confidential formula documents, only retrieves authorized production data |
Three Common Misconfigurations
- Retrieval returns unauthorized formula documents. Cause: Failed to bind dedicated production ledger collectionIds, so the retrieval scope covers confidential formula collections.
- Uploaded DOC format documents cannot be properly indexed by the knowledge base. Cause: Failed to preprocess DOC files via a format conversion tool. FastGPT does not support direct parsing of this format natively.
- Images within previously uploaded documents in the knowledge base fail to display during subsequent viewing. Cause: The source file was modified or deleted after upload, and the association with the knowledge base was not retained.
How to Verify Successful Configuration
- Navigate to the knowledge base management page in FastGPT v4.17.0, confirm that the bound collectionIds only include document sets related to production ledgers.
- Initiate a retrieval targeting a single batch number, check that the returned
similarityscores fall within the preset 0.75–0.85 range. - Upload a preprocessed DOC format test document, confirm that the document content can be retrieved normally after indexing is complete.
- Trigger a retrieval covering multiple stage documents, confirm that the number of returned results meets the configured
limitrequirement, and no timeout errors occur.
The values provided here are common starting points, and should be validated against the reader's own samples.