What this category of data looks like
Service subjects for this category include government service centers, administrative approval bureaus, and industry authority approval departments. Data is sourced from the national integrated government service platform item database, administrative licensing item list, departmental business approval systems, and electronic certificate repositories. The update frequency is daily. Daily processed documents cover service guides, application forms, commitment letters, acceptance notices, correction notices, administrative licensing decision documents, and license certificates. Data fields include item codes, applicant information, processing progress, approval results, and more. There is no unified fixed unit; values adjust based on specific item types.
What constraints these characteristics impose on the vector models and indexes workflow
Data sources are scattered and updated daily. Indexes must support incremental synchronization to avoid reprocessing full historical data and reduce synchronization time. Document types include multiple categories such as application materials and approval documents. Content lengths vary significantly across different documents, so flexible chunking rules are required. Data fields change with item types. Field-level metadata must be retained to support precise matching. The rigor of legal approval processes requires index recall results to strictly match item setting bases and processing conditions, preventing irrelevant content from interfering with approval judgments.
Reference only; verify against the target FastGPT release and deployment documentation.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the content density of common documents such as service guides and approval decision documents, avoiding overloading or over-splitting single chunks |
| Overlap Length | 100–200 characters | Retains contextual connections across chunks, ensuring approval process step information is not truncated |
| Similarity Threshold | Calibrated based on actual testing | Must match the precise matching requirements of administrative licensing items, avoiding recalling materials from unrelated items |
| Single Search Limit | Top 10 entries | Controls the number of returned results, focusing on core approval basis and material requirements |
| defaultToken | 4096 | Adapts to the context length requirements of most approval-related Q&A, avoiding exceeding the model's processing limit |
| maxToken | 8192 | Supports complete indexing and Q&A for long documents, covering application materials with multi-page attachments |
Three common configuration mistakes
- No progress displayed in the index for an extended period: This occurs when the system fails to adapt to the daily update frequency of government data, fails to configure incremental index trigger rules, and performs full synchronization of large volumes of historical data leading to timeout.
- Error "no available vector model" when calling the knowledge base: This occurs when text-embedding-ada-002 is not correctly bound to the exclusive knowledge base channel for government approval, or the calling path of the corresponding model is not specified in the configuration file.
- Recall results include approval materials from non-current items: This occurs when the similarity threshold is set too low, and secondary filtering is not performed using item code metadata, leading to the recall of irrelevant data.
How to confirm the configuration is correct
- Check the knowledge base's data source configuration, confirm that the national integrated government service platform item database, electronic certificate repository, and other specified sources are associated, and that the synchronization frequency is set to daily.
- Test the indexing process for a single approval document, verify that the chunked content length and overlap settings meet expectations, with no obvious content truncation or overloading.
- Initiate a retrieval for a specific administrative licensing item, check that the recall results include the service guide and approval basis for that item, with no irrelevant cross-item content.
- Review the model call logs, confirm that the call request for text-embedding-ada-002 has been initiated normally, with no format errors or permission abnormalities.
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.