What Data Looks Like for This Category
Daily documents for this category include basic government service item catalogs, implementation lists, service guides, standard material catalogs, flowcharts, and frequently asked questions. Data sources are the national basic government service item catalog, administrative licensing item list, power and responsibility list, and local government service item databases, with a typical monthly update frequency. Core documents contain fields such as item code, item name, acceptance conditions, application materials, and processing procedures. Some documents include cross-level and cross-departmental associated item information. The most time-consuming daily tasks are comparing superior catalogs with local implementation lists, maintaining acceptance conditions and application materials, and verifying the completeness of service guide fields.
What Constraints These Characteristics Impose on Embedding Models and Indexes
The monthly update rhythm requires indexes to support incremental synchronization to avoid time costs from full reconstruction. The mixed structure of multiple document types requires setting metadata filtering rules by document type to ensure recall results only come from target documents. Unique identifier fields such as item codes must be used as metadata indexes for precise matching of official standard item entries. Long-text process descriptions and material lists require a reasonable chunking strategy to avoid semantic fragmentation that causes vector recall bias. Text for cross-level associated items requires the model to support long-context association to ensure recalled associated items align with official logic.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-paragraph text length of government service guides to avoid semantic fragmentation after splitting |
| Overlap Length | 100–200 characters | Covers coherent content such as cross-chunk process descriptions and material lists |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Distinguishes official standards from unofficial interpretations to avoid recalling irrelevant items |
defaultToken | 8192 | Matches the context length adapted to the embedding dimension of bge-large-zh-1.5 to ensure complete vector generation |
| Single Recall Limit | Top 10 entries | Controls the number of returned item entries to avoid excessive interface response data volume |
| Maximum Context | 6000 characters | Adapts to the post-recall splicing length of long-text government documents, complying with model input limits |
Three Common Misconfigurations
- Leaving a single government service guide file in the index long-term without completing vector ingestion. Cause: The file contains extremely long unsegmented paragraphs, or the chunk length setting exceeds the maximum token limit supported by the model.
- 60-second timeout error when switching to the government service item database index. Cause: Index shards are too large, or historical updated data is not filtered during incremental synchronization, leading to excessive single synchronization data volume.
- Failure to call the bge-large-zh-1.5 vector service, or large deviation between recall results and official standards. Cause: Embedding dimension matching was not confirmed, or the correct model interface address was not configured, leading to unexpected vector generation.
How to Verify Successful Configuration
- Upload a standard government service guide document, check if the chunked text is coherent with no obvious semantic fragmentation.
- Initiate an item matching query, verify that the similarity scores of recall results fall within the preset threshold range.
- Test the incremental synchronization function, upload an updated government catalog file, confirm that the index only updates newly added or modified entries.
- View vector generation logs, confirm that the
maxTokenconfiguration does not exceed the maximum length supported by the model, with no truncation errors.
The values listed above 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.