What data looks like for this category
This use case typically serves manufacturing enterprise PMC, planning, shop scheduling, and material control departments. Daily processed documents include sales orders, master production schedules, material requirements plans, weekly and daily production schedules, work orders, material shortage lists, and delivery change notices. Data is sourced from ERP orders and inventory, BOMs and process routes, equipment capacity, staff shifts, work in progress, and supplier deliveries. Updates occur daily, weekly, or immediately when orders or exceptions arise. Document fields commonly include IDs, timestamps, quantities, equipment identifiers, process parameters, and more. Units include pieces, hours, shifts, and others.
Constraints imposed on vector models and indexing
Data sources are scattered and field types vary, so differentiated chunking rules must be adapted for different document types. Update frequencies are inconsistent, so flexible switching between incremental and full indexing must be supported. Documents include multi-dimensional business-related parameters, so vector models must support encoding logic that mixes long text and structured fields. Single-pass data processing volumes are large, so chunk token limits must be enforced to avoid exceeding model processing capabilities. Production scheduling-related data has high business relevance, so indexes must retain semantic associations across documents to avoid fragmented search results that harm business decision-making. Real-time update requirements also require index task latency to be kept within business-acceptable ranges.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the single-segment business logic length of production scheduling documents, avoids splitting complete semantics of processes or orders |
| Overlap Length | 100–150 characters | Retains process association information across chunks, prevents semantic breaks between adjacent chunks |
| Similarity Threshold | 0.72–0.78 | Filters low-relevance scheduling exception and material data, retains core business matching results |
| Single Search Limit | Top 8 results | Production data has high relevance, a small number of high-match results covers business needs, avoids redundant information interference |
maxToken | 4096 token | Matches the input limit of the built-in embedding model for FastGPT v4.17.0, avoids exceeding processing bounds |
defaultToken | 200 token | Retains semantic encoding length for basic business fields, balances retrieval accuracy and processing efficiency |
Three common mistakes
- Document indexing takes an unusually long time. Symptoms include index progress stalling for more than 30 minutes in the interface, with logs returning chunk processing timeouts. The cause is failing to adjust chunk length based on the long text characteristics of production scheduling documents, leading to single-chunk tokens exceeding model limits and repeated retries triggering timeouts.
- Embedding model matching deviation. Symptoms include returning irrelevant equipment shift records when searching work order information, or failing to accurately match core content of delivery change notices. The cause is incorrect configuration of
defaultTokenlength, leading to deviations in semantic encoding of structured business fields, or failing to adapt embedding model parameters to the business scenario. - Retrieval accuracy does not improve after enabling enhanced indexing. Symptoms include no obvious optimization in search results for scheduling change notices after enabling enhanced indexing. The cause is failing to limit the association scope of enhanced indexing to documents within the same order or process, leading to redundant associations across unrelated businesses that interfere with search results.
How to confirm configurations are properly set
- Upload a typical weekly and daily production schedule document, check the chunk preview interface, confirm that chunk length falls within the set range and that process and order information is not split into broken segments.
- Initiate a work order query, check the similarity scores of search results, confirm that scores fall within the set threshold range.
- Simulate a document update for an emergency order insertion, check the completion duration of index tasks, confirm that no long stalls occur.
- After enabling enhanced indexing, query the same scheduling-related question again, compare the relevance of search results, confirm that results are more focused on the target business scenario.
The values provided are common starting points and should be measured against the reader's own samples.