What data for this category looks like
Data for this category mainly comes from raw material quality certificates, heating furnace systems, press collection data, MES systems, non-destructive testing instruments, and customer forging drawings. Daily documents include forging process specifications, cutting cards, heating furnace records, forging process cards, mold life records, heat treatment entrustment slips, and ultrasonic testing reports. Data is updated per individual forging, batch of materials, or furnace heat. Document fields include forging ratio, fiber direction, furnace batch number, heating temperature range, pressure parameters, and other items. Some documents include numeric detection data and standardized process steps.
What constraints these characteristics impose on vector models and indexing
Data is updated frequently per furnace heat and batch. Incremental indexing configuration is required to avoid long blocking caused by full reconstruction. Multiple document types have distinct structural differences. Long-text process specifications and short-text testing reports need adapted chunking rules. Documents contain industrial technical terms such as forging ratio and fiber direction. Vector models must adapt to the semantic features of this field. Single data entries are small in size, but total data volume grows with production batches. Index sharding parameters must be set reasonably to balance query efficiency and storage usage. Cross-review requires associating furnace batch numbers with forging batches. Indexing must support multi-dimensional field binding to ensure retrieval results match the corresponding production batches.
Recommended configuration settings
This page applies to FastGPT v4.17.0 vector model and indexing configuration.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Adapts to the long-text structure of forging process specifications, avoiding semantic breaks in key process steps |
| Overlap length | 50–100 characters | Covers process step associations across chunks, meeting the context coherence requirements for cross-review |
defaultToken | 2000–3000 token | Adapts to the total length of single-batch documents, balancing vector generation quality and computing overhead |
maxToken | 4000–6000 token | Meets vector generation requirements for long documents, avoiding truncation of important parameters and process descriptions |
| Single search limit | Top 10 results | Matches the typical number of documents related to a single furnace heat, avoiding redundant results interfering with cross-review |
| Similarity threshold | Calibrated via actual testing | Adapts to semantic similarity judgment for industrial terms, adjusted based on actual review scenarios |
Three common mistakes
- Switching knowledge base indexes returns a 60-second timeout error, or the interface shows that index reconstruction is incomplete. The cause is that incremental indexing configuration is not enabled, and the amount of data processed per batch is not restricted during full reconstruction, leading to blocking of index construction tasks.
- The knowledge base contains only a small number of documents, but remains in a training or reconstruction state for more than 24 hours without completion. The cause is that the chunk length is set too small, resulting in the number of generated chunks far exceeding the system's processing limit, leading to task backlog.
- Incorrect request parameter configuration when adding indexes in bulk, or incorrectly creating a separate index for each document. The cause is failing to combine the batch association requirements of forging production. Unified indexing is more conducive to cross-document cross-review, and incorrect configuration will destroy batch association logic or cause index association failure.
How to confirm configuration is correctly applied
- Perform bulk indexing of single furnace heat documents, check the system task log to confirm there are no timeout errors.
- Search for technical terms such as forging ratio and fiber direction, check whether the chunk semantic coherence of returned results conforms to process logic.
- After switching indexes, wait for the system to finish loading, verify that the retrieval response time meets production rhythm requirements.
- Check the index configuration to confirm that a unified collection index is used, and dedicated business fields such as furnace batch number and forging ID have been bound.
The values provided here are common starting points, and should be evaluated against the reader's own samples.