What Data for This Category Looks Like
Data for this category primarily comes from the national integrated government service platform item database, administrative licensing item lists, departmental business approval systems, and electronic certificate repositories. The update frequency is daily. Daily documents include service guides, application forms, commitment letters, acceptance notices, correction notification letters, administrative licensing decision letters, license certificates, and similar materials. The structure is bounded by statutory licensing items, with each document linked to clear item codes, handling conditions, and legal basis fields. Most data formats are a mix of structured forms and standardized official documents.
What Constraints These Characteristics Impose on the Knowledge Base Retrieval and Recall Link
The daily data update requirement means the retrieval pipeline must synchronize with the latest item database and approval system data to avoid returning expired licensing item content. Documents include structured forms and standardized official documents, so the system must support both precise keyword matching for form fields and semantic recall of official document content. The structure bounded by statutory licensing items requires that recall results be strictly tied to the legal basis and handling conditions of the corresponding item, and no irrelevant content from other items may be returned. Government scenarios have high requirements for data accuracy, so the recall link must filter document fragments unrelated to the current consultation item to ensure matching precision.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Meets the strict accuracy requirements for government documents, filters low-relevance fragments, and avoids returning irrelevant licensing item content |
limit | Top 6 results | Controls the number of single-recall results, adapts to the length limits of single replies for government consultations, and prevents overly long context |
searchMode | hybrid | Combines keyword and semantic retrieval, adapts to the mixed document structure of structured forms and official documents, and covers different query scenarios |
usingReRank | Enabled | Performs secondary sorting on recall results, prioritizes content strongly related to handling conditions and legal basis, and improves reply accuracy |
datasetSearchUsingExtensionQuery | Disabled | Administrative licensing items have clear boundaries, no query extension is needed, and redundant information from unrelated items is avoided |
collectionIds | Configured by licensing item groups | Strictly binds knowledge base collections corresponding to items, ensuring recall results only relate to the current consultation licensing item |
Three Common Mistakes
- Some documents are stored as original text without generating independent question-answer pairs. Phenomenon: Some uploaded service guides or decision letters are not generated as independent question-answer pairs, and are only stored in the knowledge base as original text fragments. Cause: The document format does not meet the automatic splitting rules, such as pure image formats or unstructured long official documents, and the question-answer pair generation logic is not triggered.
- The "insufficient_quota Current group upstream load is saturated" error occurs. Phenomenon: The specified error message is returned after a query is submitted, and a reply result cannot be obtained normally. Cause: The configured model quota is insufficient or concurrent requests exceed the node load limit, and no request queue or retry mechanism is set up.
- Interactions with Ollama-mounted models produce replies that do not match the knowledge base. Phenomenon: The reply content generated by the model does not reference the recalled knowledge base fragments, which does not meet the requirements of actual government matters. Cause: The context binding between the retrieval pipeline and the model is not configured correctly, and the model is not limited to only using the recalled knowledge base content to generate replies, resulting in generated content that deviates from the data source.
How to Confirm the Configuration Is Correct
- A test query targeting a single licensing item is submitted, and the recall results are verified to only include documents corresponding to that item, with no content from other licensing items.
- Retrieval logs are checked to confirm that the number of recall results meets the requirements of the configured
limitparameter, and the similarity value falls within the setsimilarityrange. - High-concurrency query requests are simulated to check whether the upstream load saturated error occurs, and confirm that a reasonable request current limiting strategy has been configured.
- Structured form keyword queries and unstructured official document semantic queries are tested at the same time, to verify that when
searchModeis set to hybrid, both types of content can be matched.
The values given 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.