What the data for this category looks like
This category’s data originates primarily from concentrate and auxiliary material analysis, DCS systems, cell voltage and current monitoring, laboratories, and energy and environmental monitoring equipment. Daily documents include batching lists, smelting records, electrolytic cell status records, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Data is updated in real time per batch of raw materials, per furnace, or per cell. Document fields include raw material composition parameters, equipment operation parameters, test results, batch identifiers, and timestamps. Some records include unit identifiers.
What constraints these characteristics impose on the vector model and index link
The data for this category has high update frequency and tight batch association. Indexes must support incremental synchronization to avoid processing delays. Documents contain specialized industrial parameters and multiple types of business forms. Vector models must adapt to specialized terminology and varying single-record lengths to ensure accurate semantic understanding. Fields vary widely across different documents. Indexes must support classified storage by document type to facilitate precise matching during subsequent retrieval. The data chain for a single batch is long. Indexes for associated records must retain batch association identifiers to improve consistency across document retrieval.
How to configure the setup
This configuration is compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of documents in this category, avoiding overly scattered or overly long single chunks |
| Overlap Length | 100–150 characters | Retains contextual association across chunks, matching the continuity characteristics of batch records |
| Similarity Threshold | Calibrated based on actual testing | Adapts to semantic similarity judgment for specialized terminology, avoiding mismatches or missed matches |
| Single Search Limit | Top 20 results | Covers retrieval needs for multi-document associations, balancing retrieval efficiency and recall range |
maxToken | 4096 token | Adapts to vector encoding requirements for long forms, avoiding content truncation that loses critical parameters |
| Reranking | Enabled | Corrects semantic sorting bias for specialized terminology, improving the accuracy of retrieval results |
Three common mistakes
- Phenomenon: An error stating the model does not match appears when creating a knowledge base without selecting
text-embedding-ada-002. Cause: The system does not preconfigure call adaptation parameters for other embedding models, and only has call rules for this model configured by default. - Phenomenon: Knowledge base search results do not sort according to semantic similarity, and do not display results in matching priority order. Cause: Reranking configuration is not enabled, or the reranking logic does not adapt to the semantic characteristics of industrial specialized terminology, leading to sorting deviations.
- Phenomenon: Retrieval results include auxiliary data records that are not associated with the current audit task. Cause: Auxiliary data is not configured as an independent index category, resulting in auxiliary data and business documents being vectorized together and included in retrieval.
How to confirm the configuration is correctly applied
- Review vector model call logs to confirm the configured embedding model has loaded, with no undefined model errors.
- Run a chunking test on a typical single document to confirm chunk length and overlap length match the preset configuration, with no content truncation or excessive splitting.
- Initiate a simulated audit retrieval to check if result sorting conforms to business logic, with no obvious irrelevant records included.
- Upload a new batch record to confirm index synchronization completes, and the record can be retrieved normally.
The values provided are common starting points and should be measured against the reader's own samples.