What the Data for This Category Looks Like
Declaration materials and file review data for non-ferrous metal smelting mainly comes from production ledgers, quality inspection reports, capacity filing documents, and approval process records. It covers production parameters and approval documents for sub-categories such as electrolytic copper and aluminum ingots. Updates follow a fixed schedule aligned with monthly declaration batches. Each document includes structured fields such as furnace number, metal grade, and energy consumption values, plus unstructured approval notes. Common document formats include docx approval forms, pdf quality inspection reports, and excel production ledgers. Some long documents span thousands of characters, containing continuous production process records.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
Mixed structured and unstructured data requires the retrieval system to support both field-level precise matching and full-text semantic retrieval. This avoids missing key parameter-related information. Longer individual documents require chunking processes to retain contextual connections between approval workflows and production records. This prevents critical approval basis from being cut off. Fixed-batch update cycles require limiting the recall dataset scope to the latest declaration batch during retrieval. This avoids recalling expired data. Densely packed professional terminology text needs matching corresponding industry semantic vectors. This prevents semantic misjudgments from general models.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Semantic similarity threshold for matching non-ferrous metal smelting professional terms, filters non-professional matching results such as generic administrative templates |
limit | Top 8–12 results | Covers the typical number of associated documents for a single batch of declaration materials, avoids excessive results exceeding the context window |
searchMode | Full-text + field retrieval | Supports matching both professional text within documents and structured fields such as furnace number and grade, adapts to mixed data characteristics |
usingReRank | Enabled | Re-ranks recall results by professional semantics, corrects semantic misjudgments of smelting terms by general vector models |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands professional synonyms of declaration questions (for example, "electrolytic copper" expands to "cathode copper") to match documents with different expressions |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Misconfigurations
- Phenomenon: A large number of irrelevant generic administrative template documents appear in retrieval results. Cause: The
similarityparameter is not set, or the threshold is set too low, failing to filter non-professional semantic matching results. - Phenomenon: Single retrieval response times out. Cause: The
limitparameter is not properly configured, andusingReRankis not enabled, causing the system to process too many recall results. - Phenomenon: Key approval paragraphs are truncated during retrieval of long documents such as monthly production ledgers. Cause: Chunking settings do not adapt to the professional paragraph structure of long documents, failing to retain contextual connections of approval workflows.
How to Confirm Proper Configuration
- Enter a professional question that includes furnace number and metal grade, check whether the recall results include the declaration material documents of the corresponding batch.
- View the retrieval log to confirm that the
similaritythreshold takes effect, and the similarity scores of recall results fall within the set range. - Test retrieval of a single long document, confirm that key approval process paragraphs are not truncated, and contextual connections are complete.
- Switch the dataset of different declaration batches, check whether the recall results are limited to the current batch range.
The values given 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.