What the Data for This Category Looks Like
Daily documents for this category include mold design task sheets, DFM reports, part drawings, machining process cards, trial mold reports, mold modification notices, mold history records, and similar materials. Data sources include customer product models, mold flow or forming simulation results, CAD/CAM files, machining and measurement data, and trial mold sample parameters. Data updates follow project progress. New documents and data entries are generated after each trial mold or mold modification. Documents contain professional fields and their corresponding units, such as number of mold cavities, material grades, machining tolerances, and mold flow analysis parameters.
What Constraints These Characteristics Impose on the Vector Models and Indexing Link
Data for this category includes structured metadata, semi-structured text, and CAD file content. Update frequency fluctuates with project iterations. The data also includes a large volume of manufacturing-specific technical terms. These traits require the indexing system to support mixed association of multi-source data, to avoid recall bias from single-type data. Frequent project updates need to adapt to incremental indexing logic, to reduce resource consumption from full indexing. Semantic matching for technical terms requires vector models adapted to manufacturing domain corpus, to ensure accuracy of recall results. Additionally, associated documents used for bidding must maintain cross-file contextual coherence, to avoid truncation of professional content during chunking.
Configuration Settings
Configuration settings are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to paragraph lengths of mold design documents, balances completeness of technical terms and vector recall accuracy |
| Overlap Length | 150–200 characters | Prevents technical terms from being truncated during chunking, maintains cross-block contextual coherence |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Matches semantic similarity of mold-specific technical terms, filters irrelevant bid documents |
defaultToken | 4096 | Adapts to long-form content of DFM reports and machining process cards, ensures full loading of professional parameters |
| Single Search Limit | Top 8 entries | Balances recall accuracy and context window usage, covers associated data for multi-part machining |
maxToken | 16384 | Supports complete bid documents and associated mold data, meets information requirements for quotation preparation |
Three Common Misconfigurations
- Phenomenon: Search results after indexing only return metadata for CAD files, with no text content from machining process cards. Cause: Mixed indexing rules for multi-source data were not configured. Only fields from structured files were scraped, and semi-structured text content was not associated.
- Phenomenon: An error indicating
maxTokenlimit exceeded is returned during invocation. Cause: ThemaxTokenvalue was not adjusted based on the actual length of mold documents, and a default overly small parameter was used. - Phenomenon: Key fields are empty after parsing trial mold reports in PDF format. Cause: FastGPT's enhanced PDF parsing function was not enabled, so key content such as trial mold times and defect descriptions cannot be extracted from the trial mold reports.
How to Confirm the Configuration Is Correct
- Upload a single mold design task sheet and DFM report, perform indexing, then check the indexing details. Confirm that both text content and metadata from CAD files are included.
- Enter a query containing mold-specific technical terms, such as "injection mold demolding defect handling", verify whether the semantic matching degree of the returned results meets the preset threshold.
- Upload an updated mold modification notice, perform incremental indexing, confirm that new data can be recalled normally without affecting existing indexed content.
- Test long-text queries, confirm that the system can load complete context content, with no errors related to
defaultTokenormaxToken.
The values provided are common starting points and should be measured against the reader's own samples.