What data looks like for this category
Ledger data for metal material processing comes primarily from process parameters collected in real time by production equipment, offline quality inspection reports, and warehouse inbound and outbound documents. The system triggers data updates per production batch, and synchronizes updates once a single batch of processing finishes. A single ledger document includes structured fields and unstructured attachments. Structured fields include furnace number, batch number, tensile strength (MPa), hardness (HRC), and warehouse entry date. Unstructured attachments include metallographic micrographs and process parameter record sheets.
What constraints these characteristics impose on vector models and indexes
The mixed format of structured fields and unstructured attachments requires vector models to support both text and image embeddings, preventing lost field associations during content splitting. The batch-based update rhythm requires indexes to support incremental synchronization, eliminating resource consumption from full index reconstruction. Numeric fields with mixed units need to retain field identifiers during chunking, to ensure correct matching of corresponding process parameters during retrieval. Multi-type content in a single document requires the index chunking strategy to preserve associations between text and attachments, preventing information fragmentation.
How to set configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of structured fields and unstructured text in a single ledger, avoiding lost associations from overly fragmented chunks |
| Overlap Length | 50–100 characters | Retains field associations between adjacent chunks, preventing cross-chunk process parameter information from breaking |
maxToken | 8192 | Adapts to the input upper limit of mainstream multimodal vector models, covering the full chunked content of a single batch ledger |
| Similarity Threshold | 0.75–0.85 | Differentiates process parameter similarity for metal materials, avoiding mismatched furnace number data from different batches |
| Single Search Limit | Top 10 entries | Meets batch processing needs for ledger summary, balancing retrieval efficiency and recall completeness |
Three common misconfigurations
- Symptom: The interface displays an error prompt "multimodal vector model incompatible with current index configuration". Cause: Only single-type embedding models (text or image) are configured, so only single-type vector input is supported. This cannot handle metallographic photo attachments in ledgers.
- Symptom: The number of retrieved results is far lower than expected, with only single-digit entries returned. Cause: The similarity threshold is set too high, filtering out batch data that meets business requirements.
- Symptom: Retrieved results include material data from non-target categories. Cause: Vector models and index configurations from other industries are used, with no filtering rules set for dedicated fields such as furnace number and batch number.
How to confirm correct configuration
This verification process uses the functional logic of FastGPT v4.17.0.
- Upload a single standard ledger document that includes metallographic photos and process parameters. Check that the vector embedding task completes normally with no error prompts.
- Simulate retrieval using dedicated field keywords such as "tensile strength" and "furnace number". Verify that returned results include corresponding field identifiers.
- Add a test batch of ledger data. Confirm that the index automatically synchronizes and updates without requiring manual full reconstruction.
- Compare retrieval recall ranges across different chunk lengths. Confirm that the current configuration's recall results meet business requirements.
The settings listed above are common starting points, and should be evaluated 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.