This document is based on FastGPT v4.17.0. All configuration items below align with the interface logic of this version.
What This Category of Data Looks Like
Data for this category comes from cell banks and seed lots, process analysis systems, clean zone monitoring records, MES, LIMS, and equipment integrity test results. Daily documents include cell bank or seed lot records, culture and purification records, aseptic filling records, environmental monitoring data, media fill simulation reports, sterility test results, and batch release documents. Data update cycles are post-each batch production, triggered by real-time monitoring, and updated per fixed monitoring plans. Document fields include batch numbers, operating parameters, environmental temperature and humidity, test results, equipment operating status, and more. Some records require association with aseptic boundary and personnel/material path information.
Constraints Imposed by These Characteristics on Vector Models and Indexes
Data for this category is scattered and has highly varied update cycles. It must support incremental indexing and real-time synchronization of multi-source heterogeneous data. Documents contain both structured parameter records and semi-structured test reports, requiring vector extraction rules adapted to different fields to avoid irrelevant content interfering with matching accuracy. Each batch of production corresponds to an independent dataset, requiring index isolation by batch dimension to ensure only associated data for the target batch is called during audits. Aseptic process records have extremely high requirements for data consistency; indexes must ensure the timeliness of query results to avoid quality judgment deviations caused by delayed data.
How to Configure Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the mixed length of structured parameters and semi-structured reports in this category of documents, avoiding splitting critical logic across chunks |
| Overlap Length | 100–150 characters | Retains critical parameter associations across chunks, ensuring information such as batch numbers and aseptic boundary descriptions is not truncated by chunking |
| Similarity Threshold | 0.75–0.85 | Aligns with the strict data consistency requirements of this category, filters low-correlation historical records, and avoids mismatches |
| Single Search Limit | Top 10 results | Controls the volume of data returned during audits, ensuring auditors can quickly locate key records while covering associated data from multiple sources |
maxToken | 4000 token | Adapts to vector indexing of long documents such as batch release materials, preventing indexing failures caused by token limit exceedance |
defaultToken | 2000 token | Used for indexing conventional process records, balancing content completeness and retrieval efficiency |
Three Common Configuration Errors
- Symptom: A token limit exceeded error occurs during index generation, with logs showing content exceeding the upper limit. Cause: The
maxTokenconfiguration was not adjusted for long documents, and the default value was used, preventing long records from completing indexing. - Symptom: Search results include irrelevant data across batches, or records for the target batch cannot be found. Cause: Indexes were not isolated by batch dimension, resulting in mixed datasets from different batches and confused matching logic.
- Symptom: Abnormal search results or call errors occur when attempting to introduce relational association retrieval or connect to non-general vector models. Cause: The heterogeneous data structure and model call rules of the category were not adapted, and flat entity indexing or general model logic was used, failing to meet customized requirements.
How to Verify Proper Configuration
- Upload typical documents for a single batch, check that index generation status is normal with no error prompts.
- Enter search terms related to batch numbers or aseptic boundaries, verify that returned results only include associated records for the target batch with no irrelevant cross-batch data.
- Adjust the value range of the similarity threshold, confirm that the number of search results changes as expected with the threshold.
- Test multi-source data synchronization, confirm that real-time updated clean zone monitoring records can be retrieved in the index in a timely manner.
The values listed here are common starting points, and should be evaluated against samples relevant to the specific use case.