What Data for This Category Looks Like
Data sources for this category include customer drawings and special characteristic lists, ERP/MES systems, measuring tools, equipment process parameters, and supplier quality certificates. Daily documents cover control plans, process flowcharts, work instructions, first article and patrol inspection records, SPC reports, PPAP materials, and 8D reports. Data is updated per batch, per shift, or collected continuously. Individual records include fields such as part number, process number, detection value, timestamp, and batch number. Some documents include tolerance requirements and unit information.
What Constraints These Characteristics Impose on Vector Models and Indexes
Data sources for this category are scattered and have diverse formats, including structured reports, unstructured work documents, and real-time collected equipment parameters. This requires vector models and indexes to support unified vectorization and storage of multi-source data. The high-frequency update feature requires indexes to support incremental synchronization to avoid performance losses caused by full reconstruction. Single batch data includes precision tolerances, detection values and other fields with units. It is necessary to ensure that semantic associations of key attributes are retained during vectorization. At the same time, constrained by vehicle customer quality systems, retrieval results must strictly match specific process and batch ranges. Indexes must support precise filtering by batch and part number.
How to Set the Configuration
Reference configurations for FastGPT v4.17.0 are listed below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the semantic integrity of single segments in documents such as work instructions and SPC reports, and avoid mixing cross-process information |
| Overlap Length | 100–150 characters | Retain process association information between adjacent chunks, and prevent context breaks in detection processes |
| Similarity Threshold | 0.75–0.85 | Filter low-match irrelevant records, and meet the high matching accuracy requirements of precision manufacturing scenarios |
| Single Search Limit | Top 20 results | Balance retrieval efficiency and result coverage, and meet the need for multi-record reference during batch audits |
defaultToken | Calibrated based on actual measurements | Adapt to token consumption of documents from different sources, and avoid exceeding model limits |
maxToken | 4096 token | Match the context window limits of mainstream open-source embedding models, and ensure complete vectorization of long documents |
Three Common Mistakes
- Phenomenon: The exported dataset.csv from the knowledge base only contains the index field and no content field. Cause: The text extraction rule after chunking is not configured correctly, only index metadata is synchronized without capturing the original document content.
- Phenomenon: The retrieval results return a large number of irrelevant cross-batch records. Cause: No filtering conditions by batch and part number are set, and the structured field association capability of the index is not used, leading to the risk of batch non-conformity during audits.
- Phenomenon: The embedding task reports a token limit exceeded error, with status code 413 returned. Cause: No
maxTokenlimit is set, and vectorization of long documents exceeds the model context window.
How to Confirm the Configuration Is Correct
- First article records and SPC reports for a single batch are randomly selected, retrieval verification is performed, and returned results are confirmed to include document content matching the process and batch.
- The field integrity of index storage is checked, and structured metadata such as part number and batch number is confirmed to have been synchronized.
- The incremental update process is tested, new patrol inspection records are submitted, and automatic synchronization updates of the index are verified.
- The similarity threshold and single search limit are adjusted to match the retrieval accuracy and efficiency requirements of the business scenario.
The values provided here are common starting points and should be measured against the reader's own samples.