What Data for This Category Looks Like
Typical data sources for this category include customer surface treatment specifications, bath chemistry test reports, rectifier operation data, online water quality monitoring records, and hazardous waste management ledgers. Daily generated documents include bath composition parameter cards, fixture usage records, coating thickness and adhesion test reports, bath analysis logs, and wastewater discharge ledgers. Data update frequency follows production cycles. This includes fixture and bath parameters updated per bath run, process records generated per shift, and hazardous waste and water quality data submitted per monitoring requirements. Most document fields include specific numerical values and units, such as bath concentration (g/L), operating temperature (℃), coating thickness (μm), and discharge limits (mg/L). Some documents include both text descriptions and structured parameters.
Constraints on Vector Models and Indexes
Multi-source, high-frequency updated data requires indexes to support incremental updates to avoid time-consuming full reconstruction. Structured numerical and text mixed document structures require vector models to adapt to both parameter semantics and numerical logic, to avoid deviations caused by single semantic matching. Document sources have inconsistent field naming. For example, some records label "film thickness" as "coating thickness". Field standardization must be completed before indexing, otherwise recall accuracy will decrease. Compliance requirements for hazardous waste and water quality monitoring data require indexes to have high recall rates to cover all compliance-related documents, while avoiding irrelevant data from being included in the context. As production scales expand, total document volume and update frequency continue to grow. Index sharding and recall upper limits must be properly configured to prevent search timeouts or excessive resource usage.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Documents in this category are mostly single process records or test reports. 800–1200 characters fully retain the parameter and description logic of a single data entry |
| Overlap Length | 200–300 characters | Content such as bath parameters and process cards has continuous associated logic. Overlap length prevents cross-block information breakage |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
| Single Search Limit | Top 10 results | Queries in this category are mostly precise parameter matches. Too many results increase context redundancy and reduce response speed |
| Similarity Threshold | 0.75–0.85 | Matching process parameters and compliance records requires high confidence, to avoid mismatching non-target documents |
defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
Three Common Configuration Mistakes
- Phenomenon: 60-second timeout errors appear during searches, or the number of returned results is far lower than expected. Cause: The single search limit and
maxTokenare not adjusted for the high-frequency updated multi-source data of this category, leading to excessive time consumption for vector recall and context splicing. - Phenomenon: Knowledge base index reconstruction takes more than 24 hours to complete, and the interface continues to display "Index Reconstruction in Progress". Cause: Incremental index mode is not configured, and full reconstruction is used for high-frequency updated data per bath run, leading to accumulation of reconstruction tasks.
- Phenomenon: Batch add index requests return parameter errors. Cause: Index mapping is not configured according to the document field structure of this category, and index markers for core parameters such as bath concentration and coating thickness are missing, leading to verification failure.
How to Verify Proper Configuration
- Enter the collection management page of FastGPT v4.17.0, view the index reconstruction log, confirm that the single batch reconstruction time matches the production cycle, and there are no continuously accumulated unfinished tasks.
- Enter test query terms related to process parameters, check whether the similarity scores of returned results fall within the 0.75–0.85 range, and there are no obvious mismatched contents.
- Submit a test request for batch index addition, confirm that there are no parameter verification failure prompts in the returned results, and the index status is updated synchronously.
- Configure the priority of two test knowledge bases, initiate cross-library queries, confirm that matching results from the specified knowledge base are returned first, and verify that the priority configuration takes effect.
The values listed above are common starting points, and should be evaluated against the reader's own samples.