Reference only; verify against the target FastGPT release and deployment documentation.
What data in this category looks like
Data for this category primarily comes from local public data open platforms, integrated big data platforms, departmental business databases, and electronic credential repositories. Documents produced daily include public data resource catalogs, data open lists, data item descriptions, desensitization rules, interface documents, and update frequency descriptions.
Update frequency is monthly. Each document includes core fields: data item name, open permission scope, desensitization requirements, call interface address, and update cycle. Some documents include structured authorization boundary descriptions.
Constraints imposed by these characteristics on vector models and indexes
Monthly update requirements mean indexes must support regular incremental refreshes, to avoid full reconstruction consuming excessive virtual machine resources.
Data includes structured fields and unstructured description text. Chunk processing must balance field attributes and context integrity, to avoid losing key information such as authorization boundaries after splitting.
Data involves permission scopes and desensitization rules. Vector retrieval must link with permission verification logic, to ensure returned results meet open conditions.
Most documents are technical descriptions with high content density. Reasonably control single chunk text length to fit vector model input limits.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Most documents in this category are technical descriptions with high content density. This length preserves context integrity and fits input limits for most vector models |
| Overlap Length | 100–150 characters | Monthly updated datasets need to retain key information like update cycles and open conditions across chunks, to avoid chunk breakage |
defaultToken | 8192 | Token usage for single chunk text in this category fits this upper limit, ensuring complete vectorization of data item descriptions and interface information |
| Similarity Threshold | 0.72–0.80 | Filter low-match irrelevant data items, while retaining retrieval results that meet open conditions |
| Single Recall Count | Top 8 entries | Government service consultation scenarios require limited and precise results, to avoid information overload harming service experience |
maxToken | 16384 | When splicing retrieved result contexts, this covers the total length of multiple chunks, ensuring complete display of full data item descriptions |
Three Common Configuration Mistakes
- Phenomenon: Index construction returns a 503 error, with a prompt that no available channel exists for the
text-embedding-v3model under the default group. Cause: No dedicated embedding model for indexing is configured, and the default external model call channel is still used, leading to model call failure. - Phenomenon: Index construction progress stalls after Docker deployment, with logs showing memory usage exceeding virtual machine limits. Cause: Chunk count and concurrent processing threads are not adjusted based on the host machine's 8c16G configuration, leading to single-batch data processing volume exceeding resource capacity.
- Phenomenon: Undesensitized data item details appear in retrieval results. Cause: Context of desensitization rule fields is not linked during chunking, leading to omission of key permission verification information during vectorization, returning content that does not meet open conditions.
How to Verify Configuration Completion
- Manually upload a public data open list document, trigger index construction, and check that no abnormal error messages appear in the construction logs.
- Initiate a simulated government service consultation, retrieve content related to a specific data item, and verify that returned results include desensitized information and correct open conditions.
- View index update records, confirm that the monthly incremental update task has automatically triggered per the preset cycle.
- Check model call configuration, confirm that indexing tasks use dedicated embedding model keys, and do not reuse the default external call channel.
The values provided are common starting points, and 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.