What data belongs to this category
Core data for this category originates from national and local government procurement websites, the fiscal integration system, purchaser budget systems, and supplier credit records. Data updates occur daily. Daily processed documents include procurement requirements, procurement plans, bidding documents, tender documents, bid evaluation reports, winning bid announcements, procurement contracts, and question responses. Service stakeholders include purchaser units, the government procurement supervision division of finance departments, procurement agencies, and evaluation experts. The dataset includes structured supplier credit records and unstructured business documents of various types.
What constraints these characteristics impose on the vector models and indexes link
Daily updated data requires incremental index configuration to avoid resource consumption from full reindexing. Differences in document formats from multiple sources require the preprocessing link to adapt to export specifications of different systems, filtering out irrelevant content such as headers and footers. The length span of different documents varies widely, from short question responses to lengthy bidding documents, requiring flexible chunking rules to adapt. Mixed structured supplier credit records and unstructured business documents require a unified vectorization processing logic to ensure retrieval consistency across data types. High-frequency business links require a balance between index recall accuracy and response speed to avoid impacting business processing efficiency.
How to set configurations
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic integrity for long texts such as bidding documents and procurement contracts, avoids semantic breaks across chapters |
| Overlap Length | 100–150 characters | Preserves contextual continuity between chunks, prevents loss of associated logic when long texts are split into chunks |
| Similarity Threshold | 0.75–0.85 | Meets the rigor requirements of government procurement documents, filters low-relevance non-procurement data |
| Single Search Limit | Top 8 entries | Covers the result volume for high-frequency retrievals such as bid evaluation reports and winning bid announcements, balances recall comprehensiveness and response speed |
defaultToken | 4096 | Adapts to the text length of most government procurement documents, meets basic contextual call requirements |
maxToken | 8192 | Handles long-context query scenarios including tender documents and question responses, calibrated via actual testing |
Three common mistakes
- Issue: After importing a procurement requirement document, the index automatically grabs irrelevant attachment content at the end of the document. Cause: No chunk start/end rules specified, no filtering of non-business auxiliary content.
- Issue: No model options such as Zhipu Embedding-3 and CharGLM-4 appear in the interface dropdown menu. Cause: Corresponding third-party embedding model access configuration was not added in the system settings, or the FastGPT v4.17.0 model cache was not updated.
- Issue: A connection timeout error is returned when calling the vector model. Cause: Network policies were not correctly configured to open model call ports, or the
maxTokenparameter does not match the length limit of the model interface.
How to confirm the configuration is complete
- Upload a standard bidding document, check the chunk preview interface, confirm that the chunk length matches the preset value and that there is no irrelevant header or footer content.
- Initiate a query targeting a bid evaluation report, verify that the similarity score of the returned results falls within the preset threshold range.
- Call the embedding model interface, confirm that the
defaultTokenandmaxTokenparameters take effect, with no parameter errors. - Import a winning bid announcement data set updated on the current day, confirm that the index completes incremental synchronization automatically, and no full reindexing is triggered.
The values provided here 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.