What the data for this category looks like
Data mainly comes from internal process specifications, equipment maintenance logs, shift handover records, employee safety manuals, and production scheduling documents. Process specifications and safety guidelines are updated quarterly. Equipment maintenance logs and scheduling records are generated in real time per shift. Employee manuals are revised every six months. Document formats include full long-text operation procedures, structured fielded logs (with fields such as equipment ID, temperature, pressure, output, with units ℃, MPa, tons), and short Q&A documents.
What constraints these characteristics impose on the vector model and indexing link
Long-text process specifications and operation documents account for a large share. Single documents can reach tens of thousands of characters, so vector chunking needs to balance cutting accuracy and information integrity. Structured maintenance logs have multiple fixed fields, so the indexing link must adapt to the field association logic of structured data. Real-time shift logs require indexing to support incremental updates, to avoid excessive resource usage from full reindexing. Significant length differences exist across documents, so support is needed for text inputs ranging from hundreds to tens of thousands of characters, while avoiding exceeding the vector model's maxToken processing limit.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the long-text specifications of steel smelting, avoids single chunk information overload or loss |
| Overlap Length | 100–200 characters | Retains contextual association between adjacent chunks, avoids breaks in cross-chunk information |
maxToken | 8192 | Adapts to the input limit of mainstream vector models, supports vectorization processing of long texts |
defaultToken | 4096 | Used as the default context window, balances information integrity of retrieval recall and computing overhead |
| Single Search Limit | Top 10 results | Matches the information density of internal office scenarios, avoids excessive redundant results |
| Reranking | Enabled | Performs secondary sorting on recalled results, improves matching accuracy in long-text scenarios |
Three common mistakes
- The knowledge base upload status remains in indexing for a long time. The cause is that the incremental indexing trigger logic is not configured, and full indexing takes too long when processing long-text process specifications.
- Setting the chunk length to 3000 characters leads to missing text blocks. The cause is failure to adapt to the structure of long steel smelting documents; cutting breaks complete operation step paragraphs, leading to some content not being correctly identified.
- Rate limit errors occur during the vectorization stage. The cause is failure to adjust the vectorization thread count and API call frequency, with concurrent requests exceeding the carrying limit of the vector model service.
How to confirm the configuration is properly set
- Upload a typical steel smelting maintenance log document, check if the chunked text fully retains core fields such as equipment ID and operation time.
- Submit a retrieval request for process specifications, verify that the number of recalled results matches the expected value based on the chunking settings.
- Check the vector model call logs, confirm that the token count of a single request does not exceed the
maxTokensetting limit. - Test incremental upload of shift logs, confirm that the indexing status completes updates within a reasonable time frame.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.