What the data for this category looks like
Daily documents for this category include tooling requirement specifications, positioning benchmark schemes, fixture drawings, gauge operation manuals, acceptance reports, calibration records, and change histories. Data sources cover product digital models and GD&T, process routes, coordinate measuring machine data, on-site trial feedback, and metrology systems. Data updates follow project milestones, each product change, and scheduled calibration. Single project data includes fields such as positioning coordinates, clamping force parameters, tolerance thresholds, and calibration cycles. Units include millimeters, newtons, days, and other standard units. Individual document content lengths vary widely, from short calibration records of a few dozen words to detailed fixture design drawings spanning dozens of pages.
What constraints these characteristics impose on knowledge base retrieval and recall
The data for this category includes high-precision tolerance parameters and structured metrology data. Retrieval results must precisely match field details to avoid recalling low-match irrelevant documents. Data updates follow project and change nodes. The knowledge base must support incremental indexing and project-grouped retrieval to avoid redundant full reindexing operations. Multiple document types are present. The system must adapt to content chunking and metadata extraction for different formats. Individual document lengths vary widely. Retrieval result context length must be controlled reasonably to avoid exceeding model window limits. Equipment inspection and maintenance questions often link to benchmark schemes and change records for specific projects. Retrieval must cover implicit project-related information.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | This category’s data includes high-precision tolerance parameters. Filtering low-match irrelevant documents ensures retrieval result accuracy |
limit | Top 10 results | Relevant documents for this category are often grouped by project. Too many results will exceed the model context window and harm answer coherence |
searchMode | embedding | Core data consists of structured parameters and drawing metadata. Vector retrieval can precisely match relationships between numerical and text data |
usingReRank | Enabled | In mixed multi-type document scenarios, re-ranking improves the ranking priority of high-match results and optimizes answer accuracy |
datasetSearchUsingExtensionQuery | Enabled | Equipment inspection and maintenance questions often contain implicit benchmark or change information. Extended queries can cover more relevant project-level documents |
collectionIds | Configured by project group | This category’s data is managed by project. Grouped retrieval reduces irrelevant document recalls across projects and improves retrieval efficiency |
Three Common Mistakes
- A
404 Not Founderror is returned when calling the OpenAPI interface, with a prompt that the session interface does not exist. The cause is failure to reference the latest FastGPT v4.17.0 OpenAPI documentation. Some third-party documentation from older versions omits the correct path for the session creation interface. - Answer accuracy for the same question decreases after multi-turn dialogue, and returns to accurate after starting a new conversation. The cause is failure to enable
datasetSearchUsingExtensionQuery. Cumulative context from multi-turn interactions dilutes the original query with redundant information, and extended queries to cover related project documents are not triggered. - After switching embedding models, the imported knowledge base fails to return matching retrieval results. The cause is failure to trigger vector re-generation for the old dataset. The index generated by the old embedding model is directly retained, causing a mismatch between the new model and old index.
How to Confirm Correct Configuration
- Navigate to the FastGPT v4.17.0 knowledge base management page, view the retrieval configuration of the target dataset, and confirm that the
similaritythreshold,limitcount,usingReRankswitch, anddatasetSearchUsingExtensionQueryswitch match the settings in the configuration table. - Upload the latest tooling change history document to trigger incremental indexing, and verify that the index generation status is successful with no format errors.
- Simulate an equipment inspection scenario query, such as "Corrective action for positioning benchmark deviation of 0.02mm", and verify that the recall results include the corresponding calibration records and change history documents.
- Invoke the knowledge base retrieval OpenAPI interface, and verify that the number of returned results matches the
limitconfiguration, and the matching degree falls within the range set bysimilarity.
The values provided are common starting points and should be measured against the reader's own samples.