What the data for this category looks like
Daily documents for this category include control plans, process flow diagrams, work instructions, first-piece and patrol inspection records, SPC reports, PPAP materials, and 8D reports. Data sources include customer drawings, special characteristic lists, ERP/MES systems, measurement and inspection tool data, equipment process parameters, and supplier quality certificates. Data updates occur per production batch, per shift patrol inspection, and with continuous collection of equipment parameters. Document structures cover tables, paragraphs, structured forms, and other formats. Fields include part number, process number, tolerance value, inspection frequency, equipment number, and more, with industry-specific units such as millimeters, newton meters, and hours.
Constraints on Vector Models and Indexes
Diverse document types and significant structural differences—from standardized table-based control plans to long-paragraph 8D reports—require vector models and indexes to support flexible chunking strategies. Dispersed data sources and frequent updates require support for incremental index updates to accommodate real-time data collection per batch and per shift. Fields include industry-specific units, so indexes must retain metadata to ensure precise matching of recall results. The large volume of documents generated by high-volume production requires index sharding and recall efficiency to support the high-concurrency demands of batch reviews.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of documents in this category, covering most fields and paragraph content |
| Overlap Length | 100–150 characters | Retains cross-chunk process association information, preventing key associated content from being truncated |
maxToken | 8192 token | Adapts to longer PPAP materials, ensuring complete content is covered during vector generation |
| Similarity Threshold | 0.75–0.85 | Meets the strict recall accuracy requirements for automotive component documents, filtering low-relevance results |
| Single Search Limit | Top 10 results | Adapts to the rapid positioning needs of batch reviews, avoiding excessive redundant results that interfere with judgment |
defaultToken | 4096 | Matches the average token consumption of daily technical documents, optimizing vector generation efficiency |
Three Common Mistakes
- Switching vector models results in no knowledge base progress and inability to revert. The vector task status shows as stalled in the interface, and the console returns a 404 error. This occurs when the model channel’s API key and access path are not configured correctly, leading to failed model calls.
- Some documents lack vector results during batch re-embedding, with empty corresponding fields in the embedding task log. This happens when special characters and non-standard units in documents are not handled properly, interrupting the vector generation process.
- Recall results fail to target process documents, with search results showing low matching accuracy. This is caused by excessively large chunk length, which truncates key process information such as tolerances and inspection requirements.
How to Confirm Proper Configuration
- Access the knowledge base settings page of FastGPT v4.17.0, and verify that the vector model configuration’s API key and model name match the applied resources.
- Upload a typical control plan document, initiate a manual embedding task, and confirm that the task progress updates normally with no error logs.
- Input a query containing a part number and process requirements, and check that the number and matching degree of recall results align with the preset threshold.
- Run a batch re-embedding operation, review the task log for abnormal errors, and confirm that all documents have generated valid vectors.
The values provided are common starting points and should be evaluated against the reader’s own samples.