What This Category of Data Looks Like
Data sources for forging and die forging scenarios include raw material quality certificates, heating furnace system collected data, press operation data, MES system records, non-destructive testing instrument reports, and customer forging part drawings. Daily generated documents include forging process specifications, cutting cards, heating furnace records, forging process cards, mold life records, heat treatment work orders, and ultrasonic testing reports. Data updates occur per forging part, per batch of materials, or per furnace load. Documents include professional fields such as forging ratio, fiber direction, heating temperature, and furnace batch number. Units include Celsius, tons, millimeters, and other industrial standard units.
What Constraints Do These Characteristics Impose on the Vector Models and Indexes Link
Data that updates frequently requires indexes to support incremental synchronization, avoiding resource consumption from full reconstruction. Scenarios with mixed multi-type documents need chunking rules adapted for structured quality certificates and unstructured process specifications, preventing professional terms such as forging ratio and fiber direction from being split incorrectly. The strong correlation between forging part quality and retrieval results means vector matching must strictly filter low-confidence results. It also needs to adapt to the semantic expression of industrial-specific terms, avoiding semantic confusion between descriptions of different process links.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the standard paragraph length of forging process specifications, avoiding splitting continuous heating zones and deformation steps |
| Overlap Length | 50–80 characters | Preserve process step context across chunks, ensuring complete process logic can be associated during retrieval |
| maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
| Similarity Threshold | 0.75–0.85 | Filter non-relevant documents with low matching degrees, avoiding recall of incorrect process parameters, and meeting the strict requirements of forging part quality control |
| Single Search Limit | Top 6 entries | Cover the full process requirements of blanking, heating, forging, and testing for a single batch of forgings |
Three Common Misconfigurations
- Phenomenon: Calling the knowledge base interface to generate an index triggers a model change exception, or existing embedding-ada-002 data cannot match new embedding-3 retrieval results. Cause: The embedding model was not configured uniformly, and the batch reindexing operation for the existing knowledge base was not performed.
- Phenomenon: Retrieval delay for the QA split file collection created via OpenAPI exceeds 10 seconds, and returned results take too long. Cause: The chunk length and single search limit were not adjusted, resulting in redundant index shards or an excessive amount of recalled data.
- Phenomenon: Retrieval results include descriptions that confuse forging ratio and casting shrinkage, or irrelevant casting process documents are mixed in. Cause: A reasonable similarity threshold was not set for industrial professional terms, and the document type range for retrieval was not limited.
How to Confirm the Configuration Is Correct
- View the index generation log to confirm that the chunk length and overlap length match the preset configuration, and there are no abnormally split professional term paragraphs.
- Initiate a test retrieval, input "Heating zone and deformation steps for a certain type of aeronautical forging part", and verify that the returned document types are all forging process specifications and process cards.
- Test the reindexing operation for existing data, confirming that vector updates can be completed without re-importing original files.
- View the similarity scores of retrieval results, confirming that they all fall within the preset threshold interval.
The values provided are common starting points and should be measured against the reader's own samples.