What this category of data looks like
Multiple sources provide the data for this category: the national integrated government service platform item database, administrative licensing item list, departmental business approval systems, and electronic certificate database. Updates occur daily.
Daily documents include work guidelines, application forms, commitment letters, acceptance notices, correction notification letters, administrative licensing decision letters, and license certificates related to party building learning. Each document includes standardized fields. For example, work guidelines have item codes and handling conditions. Application forms include applicant information and application items. Electronic certificates include certificate numbers and validity periods.
The documents combine structured and semi-structured content, exclusive to government approval scenarios. They cover all process documents for party building learning licensing.
Constraints on Vector Models and Indexes
This category of data comes from multiple sources and updates daily. Indexes must support incremental construction and incremental update. This avoids the time cost of full reconstruction, and meets the real-time data needs of party building learning Q&A.
Documents include standardized fields and unstructured main text. When splitting chunks, balance field integrity and semantic coherence. This prevents loss of key identifiers such as item codes and handling conditions after splitting. It also ensures that approval requirements related to party building learning are accurately retrieved.
Government approval documents have legal attributes. Index retrieval must strictly match the item boundaries of party building licensing. This prevents semantic confusion across items. It also adapts to the text structure of different document types to ensure Q&A accuracy.
Reference only; verify against the target FastGPT release and deployment documentation.
How to Set the Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the semantic unit length of documents such as party building learning work guidelines and decision letters, and avoids breaking key approval logic after splitting |
| Overlap Length | 100–150 characters | Retains semantic connection between adjacent chunks, and prevents key information such as handling conditions and basis across chunks from being split apart |
| defaultToken | 8192 | Matches the total token requirements of most government documents, and covers complete party building licensing item lists and approval process content |
| maxToken | 16384 | Supports the construction of long-text indexes containing multiple electronic certificates and complete party building learning work processes |
| Similarity Threshold | 0.75–0.85 calibrated via actual testing | Matches the strict matching requirements of administrative licensing items, and filters low-correlation documents across party building licensing items |
| Single Search Limit | Top 10 entries | Controls the number of retrieval results, and avoids excessive non-core approval documents interfering with the accuracy of party building learning Q&A |
Three Common Misconfigurations
- Phenomenon: When a system preset compliant embedding model is not selected, an error triggers when creating a party building learning knowledge base index. The error message includes "undefined model must match "^(text". Cause: The system does not correctly match its supported embedding model naming rules. An unregistered model name is selected.
- Phenomenon: After replacing the embedding model, the previously imported party building learning knowledge base cannot reuse the original index. The index must be regenerated. Cause: The original index builds on the vector space of the old model. The new model’s vector space is incompatible with the old model. Direct mapping is not possible.
- Phenomenon: The search result ranking for party building learning Q&A does not meet expectations. Results do not display in priority order of similarity to approval items. Cause: The similarity threshold and search limit are not correctly configured. The reordering logic based on party building licensing item matching is not enabled. The ranking logic does not take effect as required by the business.
How to Verify Proper Configuration
- Import a single party building learning work guideline document. Check the semantic integrity of the index after chunk splitting. Confirm that the chunk length and overlap length configurations adapt to the document structure.
- After replacing the embedding model, trigger index reconstruction for the existing party building learning knowledge base. Verify that no errors occur and the generation process runs normally.
- Initiate a Q&A about approval items related to party building learning. Check that the number and ranking of retrieval results meet business requirements. Confirm the adaptability of the relevant configurations.
- View the index update log. Confirm that the daily incremental update task runs as scheduled. Match the daily update frequency of the data.
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 2026-09-28.