What this category of data looks like
This category of data comes from various business documents and system data of forging plants, aerospace and automotive forging suppliers, and forging workshops. Daily documents include forging process specifications, cutting lists, heating furnace records, forging process cards, mold life records, heat treatment work orders, and ultrasonic testing reports. Data sources cover raw material quality certificates, heating furnace systems, press collection data, MES systems, non-destructive testing instruments, and customer forging drawings. Data updates follow the rhythm of each piece, each batch, or each furnace charge. Each single data entry is associated with specific forging batch and furnace charge information, with fields including forging model, process parameters, testing results, mold wear status, and other relevant details.
What constraints do these characteristics impose on the vector model and indexing link?
The update rhythm, format types, and associated attributes of this category of data impose multiple constraints on the vector model and indexing link. High-frequency batch and furnace charge-level updates require indexes to support incremental update logic, avoiding resource consumption caused by full index reconstruction. Diverse document types, including structured parameter records and unstructured testing reports, require indexes to adapt to mixed-format text splitting rules. Each single data entry is bound to specific forging batch and furnace charge attributes, requiring indexes to retain batch-associated metadata. This ensures retrieval results can be traced back to the corresponding production link. Professional technical terms and wear descriptions in the field require vector models to adapt to the semantic features of the metal forming field, improving retrieval matching accuracy.
Configuration Settings
This configuration recommendation is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | This category of documents includes long-text process specifications and testing reports. This range preserves the semantic integrity of professional terms and avoids splitting that disrupts context association |
| Overlap length | 100–150 characters | Compensates for context breaks caused by chunking, ensuring cross-chunk process parameters and batch information can be fully associated |
| Similarity threshold | 0.72–0.80 | This category of data contains a large number of professional parameters. A threshold that is too low introduces irrelevant testing records, while a threshold that is too high fails to recall similar fault cases. This range should be calibrated based on actual testing |
| Single search limit | Top 10 entries | Equipment inspection and maintenance Q&A requires quickly locating similar faults. Too many results increase context processing burden, and 10 entries cover typical fault scenarios |
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. |
defaultToken | Calibrate based on actual testing | Matches the input length limit of the vector model, avoiding retrieval failure caused by token overflow |
Three Common Errors
- Phenomenon: After the knowledge base completes indexing hundreds of thousands of data entries, search tests return far fewer results than expected, or return empty results. Cause: Metadata indexing is not configured for the batch and furnace charge associated fields, resulting in failure to associate context of specific production links during retrieval, leading to semantic matching failure.
- Phenomenon: When processing PDF-format ultrasonic testing reports, fields appear empty or professional parameters are not fully extracted. Cause: Exclusive text splitting rules are not configured for structured documents, resulting in professional parameters being incorrectly split or lost.
- Phenomenon: Timeout errors occur during incremental index updates. Cause: Full index logic is used to handle batch-level updates, and high-frequency update requests cause excessive resource occupation, triggering timeout restrictions.
How to Confirm the Configuration Is Correct
- Randomly select 10 forging equipment inspection records from different batches, perform a retrieval test, and check whether the returned results include the associated data of the corresponding batch, confirming that the metadata indexing configuration takes effect.
- Import a single long-text forging process specification, check whether the split text retains complete process parameters and terms, confirming that the chunk length and overlap length configurations are reasonable.
- Simulate high-frequency incremental update requests, observe the system operating status, confirm that no timeout or resource overload issues occur, and verify that the update logic configuration is correct.
- Enter a professional fault description, such as "Heating furnace temperature abnormality causes forging streamline non-compliance", check whether the returned results match historical records of such faults, confirming that the vector model adapts to domain semantics.
The values provided here are common starting points and should be measured against the reader's own samples.