This page is based on FastGPT v4.17.0.
What the data for this category looks like
The core entities of this category are electroplating facilities, electronic connector and hardware surface treatment workshops. Daily documents include bath solution analysis records, process parameter cards, fixture usage logs, film thickness and adhesion reports, hazardous waste management ledgers, and wastewater monitoring records. Data sources cover customer surface treatment specifications, bath solution test results, rectifier operation data, online water quality monitoring data, and hazardous waste registration ledgers. Update frequencies include per bath cycle, per shift, and fixed monitoring cycles. Fields include bath solution composition parameters, process current and temperature, film thickness values, discharge concentration, and more. The data includes both structured parameters and unstructured record text.
What constraints these characteristics impose on vector models and indexes
High update frequency and fine granularity of category data require indexes to support incremental updates and high-frequency synchronization. Diverse document types include structured process parameter cards and unstructured monitoring records, requiring chunking rules adapted to different text lengths. Fields cover process parameters and environmental protection data, which have high requirements for vector matching accuracy. Embedding logic must be optimized for exclusive business fields. Multi-source data requires a unified index structure to prevent text from different sources from shifting in the vector space, ensuring the accuracy of summarized reports.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average text length of single documents such as bath solution analysis records and film thickness reports, avoiding semantic fragmentation caused by overly short chunks |
| Overlap Length | 100–150 characters | Retains contextual association between adjacent chunks, ensuring semantic coherence between process parameters and subsequent monitoring data |
defaultToken | 8192 | Adapts to the maximum text length of a single hazardous waste ledger or batch monitoring record, meeting basic embedding requirements |
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. |
| Similarity Threshold | 0.75–0.85 | Calibrated based on actual testing, filters retrieval results unrelated to electroplating processes, retaining highly matched process and environmental protection data |
| Single Search Limit | Top 20 entries | Limits the number of returned results, adapting to the batch summarization needs of production ledgers and avoiding redundant data interfering with report generation |
Three Common Mistakes
- Phenomenon: Semantic similarity scores of retrieval results remain consistently high, and unrelated non-process text cannot be filtered. Cause: The vector model is not optimized for exclusive fields of the electroplating category such as bath solution concentration and film thickness parameters. Using only a general embedding model leads to deviations in semantic matching.
- Phenomenon: Only one vector model is displayed on the interface, but multiple sets of incompatible vectors are generated for uploaded ledger data. Cause: Chunking rules and index mappings are not configured. Different types of documents such as bath solution records and hazardous waste ledgers are not split into separate indexes, leading to multiple sets of vectors corresponding to one set of data that cannot be managed uniformly.
- Phenomenon: Retrieval results are empty or have extremely low matching accuracy when different vector models are used locally and on the server. Cause: Vector dimensions and embedding logic are not unified. Vector spaces generated by different models are incompatible, leading to invalid matching in cross-environment retrieval.
How to confirm correct configuration
- Upload a single bath solution analysis record, verify that the chunked text length falls within the configured 800–1200 character range.
- Enter a query containing process parameters and environmental protection data, check that the retrieval results include corresponding types of ledger documents and that similarity scores meet the set threshold.
- View the index update log, confirm that production data updated per bath cycle can be automatically synchronized via incremental indexing, with no omissions or delays.
- Switch between different vector models for testing, verify that the matching logic of retrieval results remains consistent with no obvious deviations.
The values provided here are common starting points and should be measured against the reader's own samples.