What Data for This Category Looks Like
Typical data for this category comes from full project workflows at injection molding, stamping, and die casting mold enterprises, as well as mold centers of manufacturing enterprises. This includes documents such as mold design task sheets, DFM reports, part drawings, machining process cards, trial mold reports, mold modification notices, and mold history records.
Data sources include customer product models, mold flow or forming simulation files, CAD/CAM output files, machining and measurement data, and records related to trial mold samples. Data is updated on a per-project schedule, with new documents generated or existing records updated after each trial mold or mold modification.
Individual documents typically include fields such as part dimensions, material grades, machining tolerances, and trial mold defect descriptions. Common engineering units such as millimeters, megapascals, and hours are used widely.
What Constraints Do These Characteristics Impose on Vector Models and Indexing?
The diverse document types and large number of engineering-specific fields require the indexing step to adapt to non-general content structures such as CAD-formatted text and simulation result data.
Data volume updated per project accumulates gradually as the project progresses, and the number of documents indexed in a single operation fluctuates widely. This requires the indexing workflow to adapt to dynamic data volumes.
Units and professional expressions of engineering fields require the vector model to accurately recognize technical terms to avoid semantic confusion. Frequent updates from trial molds and modifications require indexing to support incremental synchronization, avoiding the time costs of full reindexing.
Individual document lengths vary widely, ranging from short mold modification notices to complete DFM reports. This requires chunking and indexing rules to have flexibility.
Configuration Settings
These configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-block text length of mold drawings and process cards, avoiding splitting professional terms |
| Overlap Length | 50–100 characters | Retains associations of professional expressions across chunks, avoiding truncation of complete descriptions for mold modification notices and trial mold defects |
| Similarity Threshold | 0.75–0.85 | Distinguishes approximate expressions from precise matches in engineering scenarios, avoiding incorrect recall of irrelevant mold project data |
| Single Search Limit | Top 10 entries | Matches the number of associated documents per project, avoiding loading excessive historical records and affecting response speed |
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 | 1024 | Adapts to the basic embedding length of general engineering text, balancing semantic accuracy and computational overhead |
Three Common Misconfigurations
- Search response timeouts, with the number of results returned per search far lower than expected. The cause is failure to adjust the single search limit for the dynamic data volume of mold projects, and failure to enable incremental indexing. Full retrieval of hundreds of thousands of mold-related data entries leads to excessive time consumption.
- Vector storage structures cannot recognize CAD-formatted part primitive descriptions. The cause is failure to configure chunking rules adapted to engineering-specific text, leading to incorrect splitting of professional fields and loss of metadata associations.
- Recalled results include irrelevant mold data across projects. The cause is a similarity threshold set too low, treating approximate technical term matches as valid recalls and confusing process documents of different projects.
How to Confirm Proper Configuration
- Import a single complete trial mold report, and check that chunked text retains complete associations between defect descriptions and mold modification requirements, with no obvious term truncation.
- Initiate a search for a specific project's mold modification notice, and confirm that recalled results only include documents related to that project, with no cross-project data included.
- Test multiple searches for the same technical question, record response times, and adjust relevant configurations until they meet the time consumption requirements of the business scenario.
- Verify the incremental indexing function: After adding a new mold modification notice, search results can include the new document in a timely manner without requiring full index rebuilding.
The values provided are common starting points and should be measured against the reader's own samples.