What Data for This Category Looks Like
Document sources for oilfield engineering services include on-site construction records, equipment operation and maintenance ledgers, safety operation specifications, team training materials, and emergency response manuals. Update rhythm follows real-time updates during on-site construction phases in line with construction progress, and weekly synchronization during non-construction phases. Most documents are long text passages, containing fields such as equipment numbers, operation times, and working condition parameters. Units include engineering-specific units such as MPa, m³, and ℃. Some documents embed resources such as on-site photos of equipment and pipeline diagrams.
What Constraints These Characteristics Impose on the Knowledge Base Retrieval and Recall Link
The high proportion of long-text document structures requires retrieval to preserve the binding relationship between professional terms and working condition parameters, avoiding loss of key associated information after chunk splitting. Content with precise fields requires the retrieval link to support field-level matching, prioritizing recall of results that include specified operation parameters. Real-time updated construction records require the index to support incremental synchronization, avoiding data lag that affects operation decisions. Markdown documents with embedded images require correct parsing of image paths during retrieval to ensure associated resources load normally.
How to Set the Configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.85 | Oilfield engineering documents contain a large number of professional terms and precise parameters. A threshold that is too low introduces irrelevant non-operation documents, while a threshold that is too high may miss results matching working conditions. Calibrated based on actual measurements |
limit | Top 8–12 results | Single oilfield engineering document content is lengthy. Too many recall results exceed the context window limit, while too few fail to cover alternative solutions for multiple working conditions |
searchMode | Semantic + field hybrid retrieval | Documents contain both semantic operation processes and structured equipment parameters and operation times. Hybrid mode balances semantic association and precise matching |
usingReRank | Enabled | Semantic fragmentation may occur after long text chunking. Reranking corrects context deviation caused by chunking and improves the ranking accuracy of professional content |
datasetSearchUsingExtensionQuery | Enabled | Employees in oilfield engineering scenarios often use colloquial operation descriptions for queries. Extended queries map colloquial expressions to professional terms, improving recall accuracy |
collectionIds | Bind to the exclusive dataset ID of the construction team | Operation specifications and equipment ledgers of different teams are independent. Limiting collection IDs avoids recall of irrelevant documents across teams |
Three Common Errors
- Phenomenon: After uploading Markdown-format construction documents, embedded on-site pipeline diagrams fail to load normally, and the preview interface displays broken image icons. Cause: Local images were not uploaded to the FastGPT resource library, and direct use of local file paths results in inaccessible resources during retrieval.
- Phenomenon: Retrieval results only show recall results from a single model, and cannot switch to other text understanding models. Cause: The multi-model adaptation switch was not enabled in the knowledge base configuration, and only the default bound model is used.
- Phenomenon: Imported construction log documents are not sorted by operation time field, and the time order of retrieval results is chaotic. Cause: No sorting field was specified during index creation, and the default sorting is based on document upload time, which does not match the time dimension required by the business.
How to Confirm the Configuration is Properly Set
- Upload a test document that includes specified equipment numbers and working condition parameters, enter a query containing the equipment number during retrieval, and confirm that the recall results include the corresponding paragraphs of the target document.
- Upload a Markdown test document with embedded images, check whether the images display normally in the knowledge base preview interface, and confirm that path parsing is correct.
- Switch the text understanding model option, confirm that the retrieval link can call the newly added model, and verify that the model configuration takes effect.
- Import incrementally updated construction records, check the index update log, and confirm that only newly added documents are synchronized, and no full reconstruction is triggered.
The values provided here 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.