What the data for this category looks like
The main entities in this category are enterprises focused on coatings, resins, additives, agrochemical intermediates, and specialty chemicals. Daily generated documents include batch production instructions, feeding verification slips, reaction process records, cleaning records, inspection reports, deviation and change orders. Data sources cover formula and process libraries, raw material batch information, reactor DCS systems, weighing systems, LIMS systems, and warehouse systems. Updates are triggered per production batch or each critical step. Data structure changes dynamically with production steps, including fields such as batch number, raw material batch number, reaction temperature, feeding amount, and timestamp. Due to the characteristics of multiple product varieties and batch-based production switching, document fields vary across different batches. Sensitive information such as formula confidentiality and cleaning confirmation is also involved.
What Constraints Do These Characteristics Impose on Vector Models and Indexing
The data characteristics of this category impose multiple constraints on the vector model and indexing workflow. First, multi-source heterogeneous data sources require indexes to support unified vector mapping across system data, avoiding vector deviations caused by differences in data formats. Second, frequent updates based on batches and critical steps require indexes to have fast incremental update capabilities, avoiding performance losses caused by full reconstruction. Third, dynamically changing document structures and sensitive fields require that key business identifiers be retained during vector chunking, while sensitive content is processed for compliance. Finally, document differences caused by multiple product switches require index recall rules to adapt to the document characteristics of different batches, avoiding mismatching of cross-category data.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of single documents for this category, balancing vector representation accuracy and indexing efficiency |
| Overlap Length | 100–200 characters | Retains key business-related information across chunks, avoiding loss of critical clues such as feeding sequence and reaction stages due to chunk breakage |
defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
| Similarity Threshold | 0.75–0.85 | Filters low-correlation historical batch data, adapting to recall accuracy requirements after switching between multiple product varieties |
| Single Search Limit | Top 5 entries | Focuses on the most relevant production records, avoiding redundant data interfering with ledger summary logic |
maxToken | 8192 | Limits the total token count of single-round recall results, complying with the context processing limit of FastGPT v4.17.0 |
Three Common Errors
- After uploading production ledgers using the chunk mode to call the pushdata API, the index remains marked as incomplete for a long time. This occurs because chunk parameters are not adjusted to match the batch-based document granularity of this category, leading to the number of chunks exceeding the system's default processing limit and triggering indexing queuing.
- The console returns an error stating "This token does not have permission to use the model", making it impossible to generate vectors. This occurs because a valid key for the corresponding embedding model is not configured in FastGPT, or an unauthorized model such as text-embedding-3-large is selected, resulting in a failed call.
- Vector recall results include production records from unrelated batches, leading to errors in ledger summary. This occurs because the similarity threshold is not adjusted to match the characteristics of multiple product switches for this category, resulting in a threshold set too low and mismatching irrelevant data from other batches.
How to Confirm the Configuration Is Correct
- The FastGPT backend is accessed, and the vector model configuration item is verified to select the target model (such as bce-embedding-v1) with a correctly configured key.
- After a single batch document is uploaded, the chunk preview is viewed to confirm the chunk length and overlap length match the preset parameters, with no content truncation.
- A ledger summary test is performed, and the batch number and raw material information in the recall results are checked to match the uploaded original documents, with no irrelevant data included.
- System logs are reviewed to confirm there are no abnormal records such as indexing queuing or model permission errors.
The values provided are common starting points and should be measured against samples appropriate for the specific deployment.