What This Category of Data Looks Like
Data sources include government portal websites, official document systems, government information disclosure directories, policy document repositories, and press conference materials. Daily processed documents include policy documents, policy interpretation drafts, public disclosure directories, information release approval forms, press releases, and website column update records. Data is updated daily. Document fields include public disclosure attribute, confidentiality level, assigned publication column, main content, and others. Document formats and structures vary between types, and some documents include approval process records.
Constraints on Vector Models and Indexes
Daily updated data volume must align with incremental index generation logic to avoid resource waste from full reindexing. Significant differences in document length require adjusting chunking strategies to preserve semantic integrity for each document type. Some documents contain confidential or sensitive fields, which must be filtered before indexing to prevent sensitive information from entering the vector database. Documents assigned to different columns must be indexed by category to enable targeted retrieval of public content by column in subsequent steps.
How to Configure
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Aligns with paragraph lengths of policy documents and interpretation drafts to avoid semantic fragmentation |
| Overlap Length | 100–150 characters | Preserves cross-chunk context and prevents disruption to policy clause integrity during chunking |
defaultToken | 8192 | Matches the maximum input token limit of text-embedding-3-large |
maxToken | 4096 | Controls the per-chunk token cap for index storage to reduce vector database load |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance public directory entries to avoid redundant recall results |
| Single Search Limit | Top 10 entries | Aligns with the hierarchical structure of government public disclosure documents to control returned result volume |
Three Common Mistakes
- Dataset automatically generates multiple index groups. Phenomenon: The number of index groups for the original dataset increases over time, with multiple sets of duplicate index entries. Cause: No deduplication rules for incremental indexes are configured, and the system repeatedly generates indexes at the daily update frequency without filtering existing document hashes.
- Index creation process stalls. Phenomenon: The interface shows a timeout when calling text-embedding-3-large, and recovery fails after restarting the service. Cause: No reasonable timeout parameters are set, or no retry mechanism for model calls is configured, causing a single request to occupy resources for an extended period.
- Abnormal association between indexes and language models. Phenomenon: After creating an index model, the associated language model configuration item disappears. Cause: The index model and language model are mistakenly bound as mutually exclusive items, or context call permissions for the language model are not retained in the system configuration.
How to Confirm Proper Configuration
- View the dataset update log in the index management interface to confirm that only one incremental index is generated per day with no duplicate entries.
- Trigger a manual index creation and check the model call log to confirm that the request parameters for text-embedding-3-large comply with the configured token limits.
- Check the system configuration items to confirm that the association between the language model and index model has not been cleared, and that calls to generate responses work normally.
- Retrieve a public directory entry to confirm that the returned similarity scores fall within the preset threshold range, with no abnormally low-relevance results.
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.