Data Profile for This Category
Ledger data for general mechanical equipment assembly comes from production processes of manufacturers of pumps, valves, compressors, reducers and similar equipment. It includes documents such as assembly process cards, BOMs, torque records, clearance adjustment records, commissioning reports, and factory inspection sheets. Data sources include PLM/ERP systems, part inspection data, assembly tool collection, test benches, MES systems, and customer technical agreements. The update frequency is once per equipment unit or per order. Ledger fields include physical parameters such as torque in N·m, clearance in mm, process numbers, inspection dates, customer information and more. The dataset contains both structured BOM hierarchical data and unstructured process description text.
Constraints on Vector Models and Indexing
The ledger data for this category updates frequently and has diverse types, so vector indexes must support incremental updates to match the high-frequency update rhythm per equipment unit or per order. The mixed multi-type document characteristic requires chunking strategies that balance the hierarchical integrity of structured BOMs and the semantic coherence of unstructured process text, to avoid losing key assembly parameters after splitting. Fields with physical parameters bound to units require vector models to retain unit information to avoid semantic confusion. The index recall logic must also support hierarchical association matching to meet the check requirements for BOM and physical part completeness.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic integrity for single documents such as assembly process cards and torque records, avoids splitting key assembly process descriptions across chunks |
| Overlap Length | 100–150 characters | Retains BOM hierarchical association information between adjacent chunks, prevents assembly process context from being truncated by chunking |
| Similarity Threshold | 0.75–0.85 | Filters low-correlation ledger records, adapts to the precise matching requirements of physical parameters such as torque and clearance |
| Single Search Limit | Top 6 entries | Covers core ledger entries for a single equipment unit, avoids redundant data interfering with production report generation |
maxToken | 4096 token | Adapts to the long-text generation requirements of assembly reports, prevents parameter descriptions from being truncated |
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. |
Common Misconfigurations
- No index data: Chunk length is set too short, splitting a single torque record or clearance adjustment record into multiple semantically incomplete chunks. Some chunks cannot be correctly encoded by the vector model, resulting in no matching results during retrieval.
- Insufficient search result relevance: No vector model configured to adapt to physical parameters such as torque N·m and clearance mm, leading to low semantic matching between recalled ledgers and queried assembly requirements.
- Final index chunk validation failure: Overlap length is not adjusted during chunking for query processing, resulting in missing context for the final chunk set, which fails index validation and triggers retrieval failure.
How to Verify Proper Configuration
- Upload the complete ledger sample for a single equipment unit, perform a retrieval test, and check whether the returned results include all core documents for that equipment unit.
- Input a query containing physical parameters, confirm that the field matching degree of the returned results meets the preset verification logic.
- Upload newly generated order ledgers, perform an incremental index operation, and verify that new data can be retrieved normally.
- Check the FastGPT v4.17.0 index logs to confirm there are no prompts for chunking failures or encoding errors.
The values listed here are common starting points and should be evaluated against the samples relevant to the intended use case.