What this category of data looks like
This category of data primarily comes from the 12345 hotline platform, contractor business systems, official knowledge bases, return visit evaluation data, and local hotline management regulations. Daily generated documents include request registration forms, dispatch records, handling responses, return visit records, extension applications, return instructions, and standardized knowledge base entries. Fields cover request numbers, contracting entities, request content, handling nodes, evaluation results, and more. The data updates daily, and covers full-process tickets and associated business documents.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Multi-source and heterogeneous data sources require the retrieval link to support cross-platform and cross-system document associated recall. This ensures simultaneous matching of original requests from the hotline platform and business system data from contractors. The daily update frequency requires configuring an incremental synchronization mechanism. This ensures the knowledge base covers the latest ticket handling regulations and return visit evaluation results. Diverse document structures require adapting retrieval weights for different fields. For example, the request content field should take priority in matching, while the contracting entity field can serve as a recall filter. Additionally, the timeliness of ticket handling requires retrieval response to match the hotline access waiting window. This avoids delaying ticket circulation efficiency due to recall latency.
Configuration Recommendations
The following configurations follow the FastGPT v4.17.0 specification:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | 12345 ticket request content is mostly colloquial. This balances recall relevance and coverage, avoiding missing request types or introducing irrelevant results |
limit | Top 8 entries | Associated documents for hotline tickets are usually concentrated in similar handling records from the past three months. Too many recalls increase context length and reduce model response efficiency |
searchMode | Vector retrieval + keyword hybrid | Balances semantic matching for colloquial requests and precise keyword matching for standardized documents. Adapts to multi-source and heterogeneous ticket data |
usingReRank | Enabled | Improves sorting priority for highly relevant documents. Reduces errors in responsible entity judgment caused by initial recall sorting deviations |
datasetSearchUsingExtensionQuery | Enabled | Automatically optimizes request expressions. Converts colloquial public requests into retrieval keywords that better fit ticket documents. Improves recall accuracy |
collectionIds | Bind dedicated knowledge base collection for 12345 tickets | Isolates non-ticket documents. Avoids interference from irrelevant content on retrieval results. Ensures recalled content only relates to hotline handling business |
Three Common Misconfigurations
- Symptom: Retrieval results only return a small number of irrelevant documents, or fail to recall local ticket knowledge base content at all. Cause: Failed to correctly configure
collectionIdsto bind the dedicated knowledge base collection. This causes the retrieval scope to cover non-business related documents, or has incorrect filter settings. - Symptom: Retrieval response times out, leading to excessively long hotline access waiting times. Cause: Did not set the
limitparameter appropriately. Recalling too many documents causes the context length to exceed the model's processing limit. Or did not enable incremental synchronization, leading to an excessive volume of knowledge base documents. - Symptom: Some PDF format handling response documents cannot be recognized normally. Cause: Did not preprocess PDF documents, or the documents contain encrypted or scanned formats. This causes failures in the chunking and vectorization stages.
How to Verify Proper Configuration
- Access the FastGPT knowledge base management page. Check whether
collectionIdsis bound to the dedicated document collection for 12345 tickets. Confirm no non-business documents are included. - Submit a test public request text. Check whether the retrieved documents include handling responses and dispatch records for similar tickets. Verify the relevance of recalled results to the request.
- Simulate a high-frequency call scenario. Verify whether retrieval response time meets the hotline access waiting window. Confirm no timeouts occur due to too many recalled documents.
- Upload a test PDF document. Check whether chunking and vectorization can be completed normally. Confirm no abnormalities exist in the document recognition stage.
The values provided are common starting points. They should be evaluated 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.