What Data Looks Like for This Category
Relevant data for metal material processing comes from real-time production workshop collection reports, incoming material quality inspection reports, process specification documents, purchase contracts, and finished product outbound documents. Update frequencies cover real-time, daily, monthly, and annual cycles. Examples include real-time data such as furnace temperature and rolling pressure, daily updated batch quality inspection records, and annually updated qualification certification documents. Most documents use a mixed structure of structured tables and long text paragraphs. Fields include furnace number, batch number, hardness value, tensile strength (unit MPa), processing process parameters, and more. Some documents include cross-page compliance statements.
Constraints Imposed by These Characteristics on Vector Models and Indexing
Real-time and high-frequency updated production data requires indexes to support incremental synchronization, avoiding time and resource costs from full reindexing. Structured fields and mixed document structures require retaining field context associations during vector chunking, to avoid splitting that breaks the binding between process parameters and corresponding processes. Single document lengths vary widely: short quality inspection records of a few lines, up to dozens of pages of compliance documents. Dynamic chunking strategies are needed to prevent short text vector redundancy or long texts exceeding the maxToken limit. Fields include quantified values with units. Unit formats must be unified during preprocessing before vectorization, to avoid semantic deviation that reduces retrieval accuracy.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the mixed structure of metal material processing documents, balances single-block text semantic completeness and vectorization efficiency |
| Overlap Length | 100–150 characters | Retains cross-chunk process parameter and furnace number association information, avoids context breaks |
maxToken | 8192 token | Adapts to vectorization needs for long documents such as annual process specifications, aligns with general embedding model input limits |
defaultToken | 256 token | Used for vectorization of short texts such as single quality inspection records, avoids resource waste |
| Similarity Threshold | 0.75–0.85 | Filters matching process parameters and compliance clauses, meets the strict requirements of metal material processing |
| Single Recall Limit | Top 10 entries | Controls the number of candidate results returned by retrieval, adapts to batch quality inspection data retrieval scenarios |
Three Common Mistakes
- Phenomenon: After uploading a CSV file with 100,000 entries, the total number of data entries after vectorization is only over 90,000, with thousands missing. Cause: Some rows contain unescaped commas or line breaks, causing CSV parsing failure and failure to load into the knowledge base.
- Phenomenon: Collection creation shows success, but the index status on the page always shows unestablished, with no error prompt. Cause: The automatic index switch is not enabled, or the incremental synchronization trigger condition does not match the data update frequency, causing the index task to not be scheduled.
- Phenomenon: Vector index results work normally locally, but when packaged as a Docker image and run, the vector scores of all texts are completely consistent. Cause: The embedding model cache directory is not correctly mounted inside the Docker container, or the model path is not correctly passed via environment variables, triggering default pending verification vector generation.
How to Confirm Proper Configuration
- Upload a single short quality inspection record containing furnace number and hardness value, check the vectorization task log to confirm that the chunk length and
maxTokensettings do not trigger truncation warnings. - Import batch structured CSV data, verify that the number of imported data entries matches the source file, confirm no parsing omissions.
- Start the incremental index task, check the scheduling log to confirm that the index task triggers at the preset frequency, with no blocked or failed records.
- Compare vector generation results locally and inside the Docker container, confirm that scores have reasonable differences, with no abnormal situation of full consistency.
The values provided are common starting points and should be measured 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.