What the Data for This Category Looks Like
Data comes from official documents of central state-owned enterprises, government agencies, and public service departments. These include standardized documents such as requests, replies, notifications, and meeting minutes. Updates follow on-demand submission or scheduled batch synchronization, with no fixed cycle. Document structures include fixed format elements: document number, receiving authority, main body paragraphs, attachment list, signature and date. Some documents include metadata fields such as classification level and subject terms. The statistical unit for data is "copy". The main body length of a single document varies widely, ranging from hundreds to thousands of characters.
What Constraints Do These Characteristics Impose on Vector Models and Indexing?
Official documents have fixed format requirements that prevent key metadata and main body content from being separated during chunking. If this binding is broken, format verification loses critical supporting evidence. Some official documents contain large amounts of standard boilerplate text. Vector models must distinguish between generic expressions and personalized business content to avoid misjudgment. The wide range of single-document lengths requires indexing to balance long-text chunking and fast short-text recall. On-demand update scenarios require indexes to support incremental synchronization, avoiding resource consumption caused by full index reconstruction.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of official document main body paragraphs, avoids single chunk content exceeding the vector model's processing capacity |
| Overlap Length | 100–200 characters | Retains contextual association between adjacent chunks, prevents chunking from cutting critical business logic |
| Similarity Threshold | 0.72–0.85 | Calibrated based on actual testing, balances accurate recall and false recall rates for official document format review |
| Single Recall Limit | Top 10 entries | Adapts to the precise matching needs of official document review, avoids redundant results interfering with verification |
maxToken | 8192 | Adapts to the maximum input limit of the Alibaba multimodal-embedding-v1 model |
| Index Storage Limit | 500 GB | Adapts to the storage scale of batch official document data, avoids disk resource exhaustion |
Three Common Configuration Errors
- Documents remain in indexing for an extended period. Phenomenon: In the indexing task list of FastGPT v4.17.0, the status of the corresponding task continues to show "Building" for more than 12 hours. Cause: The chunk length setting exceeds the
maxTokenlimit of the currently bound vector model, and automatic chunk processing is not enabled. - Chunked index content cannot be retrieved normally. Phenomenon: When calling the chunked index query interface, the returned content lacks metadata fields such as the official document number and date. Cause: The metadata binding option is not enabled in the chunk configuration.
- Duplication rate of multi-index retrieval results is too high. Phenomenon: Multiple chunks of the same official document appear repeatedly in retrieval results. Cause: The chunk overlap length setting exceeds 20% of the total chunk length, leading to excessive overlapping chunk boundaries.
How to Verify Configurations Are Correct
- Upload a test official document, check the indexing construction log, confirm that the chunk length and overlap length match the configured values.
- Navigate to the vector model configuration page, verify that the bound Alibaba multimodal-embedding-v1 model can normally generate test vectors.
- Enter a query term matching the test document content, check whether the chunked content in the retrieval results includes the correct metadata fields.
- Adjust the similarity threshold, verify that the number of retrieval results changes as expected with the threshold adjustment.
The values provided here are common starting points, and should be evaluated against the user's own sample data.
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.