What Data for This Category Looks Like
Data for this category comes from various daily documents of electroplating plants and surface treatment workshops. These include bath solution analysis records, process parameter cards, fixture records, film thickness and adhesion reports, hazardous waste ledgers, and wastewater monitoring records. Data sources cover customer surface treatment specifications, bath solution tests, rectifier data, online water quality monitoring, and hazardous waste management ledgers. Update frequency follows per bath cycle, per shift, or as required by monitoring protocols. Document fields include tank number, analysis time, nickel ion concentration (g/L), pH value, temperature (℃), current density (A/dm²), fixture type, hazardous waste generation amount (kg), and other structured content.
Constraints Imposed on Knowledge Base Retrieval and Recall
Multi-source heterogeneous data characteristics of this category create multiple constraints for retrieval and recall:
- Multiple document types contain numerical parameters, enumerated values, and compliance text. Matching logic must be adapted to different fields to avoid conflicts between semantic matching and precise numerical matching.
- Frequently updated real-time data requires the retrieval link to support incremental indexing and low-latency recall. This prevents calls to outdated bath solution or process data.
- Retrieval results for compliance documents must be strictly linked to the corresponding production batch. This avoids compliance risks from cross-batch matching.
- Data from multiple sources is scattered across different document collections. Precise limitation of the retrieval scope is required to prevent irrelevant content from interfering with results.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.85 | This category contains many numerical parameters. This threshold filters low-match irrelevant results, retains precisely matched bath solution and process data, and is compatible with FastGPT v4.17.0 and later semantic matching logic. |
limit | Top 8 entries | Single-batch production ledger data volume is moderate. 8 results cover the three core document types of bath solution, process, and compliance, avoiding context overflow that affects report generation. |
searchMode | mixedRecall | Balances semantic matching for process requirement descriptions and keyword matching for numerical parameters such as nickel ion concentration, adapting to structural differences across multiple document types. |
usingReRank | Enabled | Semantic similarity of multi-source data is prone to deviation. Reranking corrects the ranking of initial recall results, prioritizing core parameters of the current production batch. |
datasetSearchUsingExtensionQuery | Enabled | Production issues often include vague descriptions such as "low bath solution pH". Extended queries associate with document records corresponding to numerical thresholds, improving recall accuracy. |
collectionIds | Bind three collections: bath solution analysis, process parameter, and hazardous waste ledger | Only retrieve core documents of this category, avoiding interference from irrelevant knowledge base content on recall results, and adapting to the exclusive retrieval scope of production ledgers. |
Three Common Misconfigurations
- Symptom: Retrieval returns no results, but testing the retrieval in the knowledge base management page returns normal results. Cause: The corresponding production batch's
collectionIdsare not correctly bound during retrieval, leading to calls to the wrong knowledge base collection. - Symptom: A query unrelated to production ledger content still returns knowledge base document snippets. Cause: The
similaritythreshold is set too low, causing low-match irrelevant results to be recalled. - Symptom: Formulas in the knowledge base do not display correctly. Cause: Formula recognition configuration during document parsing is not enabled, or LaTeX formula rendering configuration is not enabled for the model.
How to Verify Proper Configuration
- Navigate to the FastGPT knowledge base management page, check the bound collection for retrieval configuration, and confirm that
collectionIdsis set to the target production ledger-related collection. - Submit a test query that includes specific numerical parameters such as "What is the nickel ion concentration for tank number T03", and verify that the retrieval results include the corresponding bath solution analysis record.
- Adjust the
similaritythreshold to 0.75, submit an unrelated query such as "How to grow houseplants", and confirm that no irrelevant production ledger content is returned. - Upload a test document that contains LaTeX formulas, submit a corresponding query, and confirm that the model can correctly render and output the formula content.
The values provided here are common starting points and should be evaluated against the reader's own samples.