What the data for this category looks like
The core data for this category is produced by architects, technical leads, and the platform R&D team. Daily documents include technical solutions, architecture diagrams, capacity assessment reports, and technical review minutes. Data sources are primarily historical traffic data, service topologies, monitoring metrics, and business growth plans. The update frequency is usually once per month, or synchronized when major business requirements are adjusted. Most document structures include fields such as constraint conditions, technical selection basis, and cross-service boundary definitions. Some documents include quantitative metrics such as peak QPS and expansion thresholds. The overall content is focused on professional technical fields with high terminology density.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Data for this category often includes structured constraints, quantitative metrics, and cross-service association definitions. Long texts account for a large proportion, and some content contains professional technical terms. The data update frequency is relatively stable but has sudden adjustment scenarios. This requires the retrieval and recall link to adapt to long text segmentation and professional term matching. At the same time, fields such as service topologies and capacity thresholds in documents must accurately match retrieval intentions to avoid recalling irrelevant non-architecture content. In addition, high-frequency updates triggered by major requirements require that recall results can quickly synchronize the latest business growth plans and constraint conditions, ensuring the timeliness and accuracy of retrieved content.
How to set the configurations
The following configurations are formulated based on FastGPT v4.17.0:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Adapts to the matching accuracy of professional terms in architecture documents, avoids recalling irrelevant low-correlation content, and balances precision and recall range |
limit | Top 10 entries | A single retrieval of architecture documents needs to cover multi-service associated content. 10 entries balances recall completeness and context length, avoiding output overload |
searchMode | hybrid | Combines keyword and semantic retrieval, adapts to documents with both structured and semantic features such as architecture diagrams and service topologies, and improves the recall effect of cross-service associated content |
usingReRank | Enabled | Performs secondary sorting on recall results, corrects deviations in professional term matching, and improves the recall accuracy of review minutes and capacity assessment documents |
datasetSearchUsingExtensionQuery | Enabled | Automatically optimizes retrieval intentions related to architecture, supplements industry keywords such as "service boundary" and "expansion threshold", and improves recall precision |
collectionIds | Knowledge base collections divided by business domain | Isolates architecture documents of different business lines, avoids recalling irrelevant cross-domain content, and adapts to multi-business scenarios of architecture design |
Three common mistakes
- When ada 002 is not selected as the embedding model, an undefined model error occurs when calling knowledge base retrieval. The reason is that the default retrieval threshold is adapted to the vector output format of ada 002, and matching parameters are not adjusted synchronously after changing the model.
- When automatically segmenting multi-column Excel-format architecture review minutes, the segmentation results are messy and cannot divide business units by row. The reason is that the default automatic segmentation logic does not adapt to structured table data and does not recognize row-level business units.
- Redundant knowledge base reference information is attached to dialogue outputs and cannot be turned off as needed. The reason is that the reference display switch is not turned off in the retrieval configuration, and source additional output is retained by default.
How to confirm the configuration is complete
- Retrieve keywords related to architecture solutions, check the matching scores of returned results, and confirm that they are within the preset
similarityrange. - Check the number of results returned by a single retrieval, and confirm that it does not exceed the configured
limitvalue. - Upload review minutes in structured table format, and verify whether automatic segmentation divides content by business units.
- Initiate a dialogue test, and confirm that the additional output of knowledge base references can be adjusted as needed.
The values provided 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 2026-10-19.