What This Category of Data Looks Like
This category of data comes from formula systems, raw material batch logs, internal mixers, Mooney rheometers, vulcanizers, and laboratory testing equipment. Daily documents include formula sheets, weighing records, mixing curves, rubber quick test reports, vulcanization process cards, mold usage records, and finished product performance reports. Fields include batch numbers, rubber type proportions (unit: parts), mixing temperatures (unit: ℃), vulcanization times (unit: minutes) and other process parameters and test results. Data updates occur per batch of rubber, per batch of raw materials, or per production mold cycle. Updates are frequent, with small per-record data volume but a large total volume.
What Constraints These Characteristics Impose on the "Vector Models and Indexes" Link
Frequently updated production data requires indexes to support incremental update logic. This avoids fully rebuilding indexes every time a report is generated, reducing computing overhead. Mixed fields of multiple types of process parameters and test results require vector models to adapt to mixed numeric and text inputs. This ensures that semantic associations between different fields are correctly encoded. Associated metadata such as batch numbers and mold numbers must be stored in the index along with documents. This enables rapid filtered recall by production batch in subsequent steps. Small per-record data volume but large total volume requires reasonable chunking rules. This prevents index files from becoming too large and slowing down retrieval. Time-series data such as mixing curves and vulcanization parameters requires indexes to support time-series associated recall. This improves parameter matching accuracy during report generation.
How to Set the Configuration
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average length of a single production document. Avoids overly fragmented chunks that cause semantic breaks, or overly long chunks that lead to vector encoding deviations |
| Overlap Length | 50–100 characters | Retains associations between process parameters across chunks. Prevents mixing curves and vulcanization time-series data from being truncated by chunking |
| Similarity Threshold | 0.75–0.85 | Filters low-match historical data. Ensures that recalled log data is relevant to current production needs |
maxToken | 4096 token | Adapts to the maximum input length limit of the vector model. Avoids overflow during encoding |
defaultToken | 1024 token | Serves as the token limit for default encoding of a single document. Balances encoding accuracy and computing speed |
| Single Recall Limit | Top 20 entries | Controls the volume of data returned per retrieval. Avoids overly long context during report generation |
Three Common Mistakes
- A timeout error occurs when calling the knowledge base interface to generate an index. The cause is that incremental indexing is not configured for frequently updated small-volume data. Full index reconstruction is triggered every time, leading to excessive time consumption.
- After importing mixing curve documents, associated information such as torque and temperature fields is missing from search results. The cause is that the chunk length is set too large. Multiple sets of time-series data are merged into one chunk, preventing vector encoding from accurately matching single-segment process parameters.
- After replacing the vector model, previously imported documents cannot match the new vector space, resulting in empty search results. The cause is that the index for imported knowledge base documents is not regenerated. Vector encoding rules differ between old and new models.
How to Confirm the Configuration Is Correct
- Upload a single mixing curve document, check the index generation log, and confirm that the number of chunks matches expectations, with no excessive truncation or redundant chunks.
- Submit a retrieval request with vulcanization process keywords, and check that the similarity scores of returned results fall within the preset threshold range.
- Perform an incremental update operation, check the index update progress, and confirm that only newly added data is processed, with no full reconstruction triggered.
- After replacing the vector model, run a batch reindexing task, and confirm that the vector encoding of existing documents has been updated.
The values given are common starting points and should be measured against the reader's own samples.