What the Data for This Category Looks Like
Data for this category comes from tender announcement attachments, transcribed text of qualification scans uploaded by bidders, and public qualification databases from regulatory authorities. Updates follow a batch import schedule when individual tender projects launch. Incremental updates trigger when qualification certificate validity periods change or business scopes are adjusted. Each entry corresponds to one independent qualification certificate. Document structure includes qualification name, qualification number, issuing authority, validity period, covered business scope, and scanned document text content. Field formats: qualification number uses string format, validity period uses date format, and covered business scope uses free-form text paragraphs.
How These Characteristics Create Constraints for Vector Models and Indexing
Text length varies significantly across individual qualification files. Short certificates may be hundreds of characters, while some project performance proofs span thousands of characters. Vector models must adapt to varying input lengths, and avoid truncating critical information such as qualification numbers and validity periods. The batch and incremental update schedule requires indexes to support low-latency incremental writes and updates. This avoids the resource consumption of full index reconstruction. Structured metadata and unstructured text coexist. This requires support for hybrid retrieval: match semantic meaning of business descriptions, and validate compliance of metadata like qualification numbers and validity periods. Qualification compliance matching must link to requirements of the corresponding tender project. Indexes must organize associated data by project dimension, to avoid invalid recall across projects.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | m3e-base or text-embedding-ada-002 | Adapts to short to medium-length qualification text, supports Chinese semantic matching, and fits the language scenario of domestic qualification documents |
chunk_size | 800–1200 characters | Qualification files contain long paragraphs of business scope descriptions. This chunk length balances semantic integrity and recall accuracy |
chunk_overlap | 100–150 characters | Prevents semantic fragmentation in long chunks, and ensures critical information such as qualification validity periods and numbers is retained across chunks |
recall_top_k | Top 10–15 results | Qualification requirements for a single tender project usually cover multiple items. This recall volume covers matching needs while reducing computational overhead |
index_update_mode | Incremental update | Adapts to the batch and incremental update schedule of qualification data, and reduces resource usage from full index reconstruction |
metadata_filter_enabled | Enabled | Supports filtering recall results by metadata fields such as qualification number and validity period, to ensure accurate compliance verification |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: A locally deployed embedding model returns a 404 error when called. Cause: The model access address was not filled correctly in FastGPT model configuration, or port mapping has deviations.
- Phenomenon: A call to the Baidu embedding-v1 model returns a 404 error. Cause: The model's API call path was not configured correctly, or key permissions have not been activated.
- Phenomenon: The m3e model cannot be added to index configuration after deployment without a GPU environment. Cause: CPU inference mode was not enabled, or the memory threshold set during model loading was too low.
How to Confirm Configuration Is Complete
- Upload a single qualification file, check the vector generation logs, confirm that no errors occur during model calls, and that the generated vector dimensions match the configured model parameters.
- Initiate a qualification matching retrieval, check the number of recalled result chunks, confirm that the match with the set
chunk_sizemeets expectations. - Trigger an incremental update operation, check the index backend update logs, confirm that only newly added or modified qualification data is synchronized.
- Configure a metadata filtering rule, retrieve documents with a specified qualification number, confirm that only matching results are recalled.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.