What the Data for This Category Looks Like
Basic chemical industry internal office data mainly comes from process specifications, safety operation manuals, equipment operation and maintenance logs, raw material quality inspection reports, and emergency response guidelines. Data updates are triggered by process iterations, equipment overhauls, or adjustments to raw material standards. Daily operation and maintenance records are synchronized weekly. Most documents are a mix of structured and semi-structured formats, containing fields such as equipment numbers, operating parameters, and safety thresholds. Units often involve industrial standard units like megapascals, degrees Celsius, and tons. The length of individual documents varies widely, ranging from hundreds to tens of thousands of characters.
What Constraints Do These Characteristics Impose on Knowledge Base Retrieval and Recall?
The document characteristics of the basic chemical industry impose multiple constraints on retrieval and recall. A high proportion of long documents requires adaptation to long-text chunking rules to avoid splitting and losing key parameters. Documents contain clear industrial parameters and units, so retrieval must support numerical range matching and unit normalization; otherwise, operation guidelines for corresponding working conditions cannot be accurately recalled. Data updates have no fixed cycle, so an incremental synchronization mechanism must be supported to ensure recalled content aligns with the latest process and operation and maintenance requirements. Field distribution in semi-structured documents is uneven, so entries containing target equipment numbers or parameter thresholds must be prioritized for recall.
How to Configure the Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Documents in the basic chemical industry mostly use professional terminology, so a high similarity threshold is needed to filter irrelevant content and avoid mistakenly recalling procedures for non-target working conditions. |
limit | Top 8–12 entries | Single chemical industry documents are relatively long; too many recall results will exceed the context window. 8-12 entries can cover core working condition parameters and operation steps. |
searchMode | semantic + keyword | Balances semantic matching for professional terminology and precise keyword matching for equipment numbers and parameter values, adapting to the characteristics of semi-structured documents. |
usingReRank | Enabled | After long documents are chunked, semantic dispersion is likely to occur; re-ranking can prioritize recall of chunked content containing core parameters. |
datasetSearchUsingExtensionQuery | Enabled | Basic chemical industry terminology is mostly industry-specific; extended queries can supplement synonyms and unit variants to improve recall coverage. |
collectionIds | Bind corresponding process and operation and maintenance dataset IDs | Isolates knowledge base content from different workshops and equipment to avoid interference from cross-scenario recall. |
Three Common Misconfigurations
- An
invalid configuration parameter name "hnsw.max_scan_tuples"error is returned during search. Cause: A retrieval parameter not defined by FastGPT official is misused. Check whether the configuration item is in the specified list. - A non-directory creation API is mistakenly used to set up knowledge base directories, resulting in failure to mount datasets normally afterward. Cause: The API interfaces for dataset management and retrieval configuration are confused. Use the dedicated knowledge base directory creation interface.
- After upgrading the version, knowledge base content can be viewed on the page, but the model prompts that the knowledge base is empty during conversations. Cause: The database migration step adapted for the version was not executed, or the dataset vector index was not rebuilt after migration.
How to Confirm the Configuration Is Properly Set Up
- Perform a test retrieval, enter the target equipment number and parameter value, and check whether the recalled results contain the core content of the corresponding document.
- Check whether all configuration items are enabled or set to values that meet business requirements, and confirm that there are no undefined parameters.
- Trigger an incremental synchronization, wait for the index construction to complete, then initiate cross-scenario retrieval to confirm that content from irrelevant datasets is not recalled.
- View the retrieval log to confirm that parameters such as similarity and re-ranking have taken effect, and there are no abnormal errors.
The values provided here are common starting points and should be measured against the samples relevant to the specific deployment.
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.