What Data Looks Like for This Category
Data for this category comes from the full-process electronic enterprise registration system, the National Enterprise Credit Information Publicity System, the Electronic Business License System, and the residence information database. The update frequency is daily. Daily processed documents include establishment registration applications, articles of association, shareholder resolutions, residence certificates, business licenses, food business license applications, and more. Data fields include subject name, residence address, shareholder information, license type, and others. All use standard government registration formats, with no custom units or special field formats.
Constraints Imposed on Vector Models and Indexing
Daily updated data requires index construction to support incremental synchronization. This avoids full reconstruction that consumes excessive system resources. Government documents have strong field correlations. For example, the correspondence between residence address and business license, and the binding relationship between shareholder information and establishment registration. Vector retrieval must match cross-document related information. The length of different documents varies widely, from short residence certificates to full articles of association. This requires adapting text chunking strategies to different lengths. Additionally, cross-domain data sources from multiple systems require consistent vector embeddings. Deviations in text vectors from different sources could reduce retrieval matching accuracy.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to medium-length government documents such as articles of association and establishment registration applications, avoids overly fragmented chunks that cause semantic breaks, and aligns with FastGPT v4.17.0 chunk processing logic |
| Overlap Length | 50–100 characters | Retains contextual connections between chunks, ensuring cross-chunk fields such as residence address and shareholder information can be fully matched |
maxToken | 8192 token | Adapts to the maximum input limit of mainstream embedding models, avoiding text truncation that loses key license application information |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Filters low-match irrelevant documents, ensuring retrieval results focus on valid content related to registration and licensing |
| Single Search Limit | Top 10 results | Controls the number of returned results, adapts to the conciseness requirements of service consultation responses, and avoids information overload |
defaultToken | 2048 token | Configures the default context window length, balancing the completeness of response content and inference efficiency |
Three Common Misconfigurations
- Symptom: Retrieval response latency does not decrease significantly when submitting the same service consultation query repeatedly. Cause: Retrieval result caching configuration is not enabled, causing each query to re-execute vector matching and index retrieval, adding unnecessary computational overhead.
- Symptom: Index construction progress displays abnormally after batch uploading government documents. Cause: Mistakenly using collection data addition as the only method for batch index construction, without using training orders to handle incremental or full index updates for large-scale data.
- Symptom: Retrieval results do not match license application documents containing images. Cause: Image OCR and vectorization configuration is not enabled, only text documents are embedded, and vector indexes for image content cannot be generated.
How to Verify Proper Configuration
- Execute a high-frequency service consultation query, wait for the response to complete, then submit the same query again. Compare the two response latencies to confirm whether the caching configuration is effective.
- Upload a single large-scale government document to trigger the index construction process. Check the index status and results to confirm that the functional boundaries between training orders and collection data addition are correctly configured.
- Upload a food business license application document containing images, perform a retrieval operation, and check whether text matching results associated with the images are returned. This confirms that the image vectorization configuration is correct.
- Test text chunks of different lengths, check the completeness of vector embeddings, and confirm that the chunk length and
maxTokenconfiguration are compatible.
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.