What Data Looks Like for This Category
Core data for this category comes from order design systems, sheet material and wood batch ledgers, CNC cutting and drilling equipment logs, ERP/MES systems, and moisture content testing equipment. Daily generated documents include custom order breakdown drawings, cutting lists, edge banding and drilling programs, coating process cards, hardware material bills, trial assembly and packaging records. Data updates follow the rhythm of per order, daily, or per material batch. A single order corresponds to multiple sets of panel data, with fields including size parameters, batch numbers, process parameters, test results. Some documents contain vector graphics and numeric fields.
Constraints Imposed on Vector Models and Indexes
The custom order-driven nature, multi-type associated documents, and high-frequency update characteristics of this category impose multiple constraints on the vector models and indexes stage. Associated documents such as custom order breakdown drawings and cutting lists must be grouped and indexed by order dimension to avoid cross-order data interfering with matching results. Fields with units such as sheet material batches and moisture content must be standardized before vector generation, to prevent matching deviations caused by unit differences. High-frequency updated equipment logs and test data require indexes to support incremental synchronization, without full reconstruction each time. The characteristic that a single order corresponds to multiple sets of panel data requires setting reasonable chunking rules, to avoid breaking associated process information after document splitting.
Configuration Settings
This configuration is based on FastGPT v4.17.0, with specific parameters listed below:
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the information density per chunk of documents such as order breakdown drawings and process cards, avoid breaking associated process parameters after splitting |
| Overlap Length | 50–80 characters | Retain process parameters and size information of adjacent chunks, prevent loss of critical associations during cross-chunk matching |
| Similarity Threshold | 0.72–0.85 | Match fields with high accuracy requirements such as sheet material batches and size parameters, filter irrelevant data with low matching degrees |
| Single Search Limit | Top 10 entries | Limit the number of panel data returned per query, adapt to the retrieval scenario of multiple panels for custom orders |
| defaultToken | 4096 | Adapt to the text length of process cards and cutting lists, meet the token requirements for basic vector encoding |
| 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. |
Three Common Mistakes
- Phenomenon: After uploading a file, calling the retrieval interface returns an empty result, and it is impossible to confirm whether the index has been built. Cause: Mistaking successful file upload for completed index generation, failing to configure an index build status query interface or callback mechanism, leading to inability to determine whether vector data has been indexed.
- Phenomenon: The retrieval results contain a large number of irrelevant sheet material batch data, with insufficient matching accuracy. Cause: Failing to standardize units for sheet material moisture content and size parameters, including unit difference dimensions during vector encoding, leading to matching deviations.
- Phenomenon: The token statistics result does not match the actual document length, and it is impossible to perform split statistics by application dimension. Cause: Failing to configure application-level token statistics rules, not including vector graphics fields in order breakdown drawings in the counting scope, leading to inaccurate statistical data.
How to Verify Proper Configuration
- Upload the custom order breakdown drawing and cutting list for a single order, call the retrieval interface to query the size parameters of the corresponding panel, confirm that the returned results contain the target data.
- Configure a callback interface for index build completion, upload batch documents and check whether the callback information matches the actual number of index entries.
- View the token usage logs of the vector model, confirm that the token consumption of single-order documents does not exceed the preset maxToken limit.
- Retrieve sheet material data from different batches, check the unit consistency of matching results, confirm that the standardized configuration has taken effect.
The values provided are common starting points and should be measured against the reader's own samples.