What the Data for This Category Looks Like
Tender announcement data comes from government procurement public service platforms, public resource trading centers of various provinces and cities, and internal tender and procurement platforms of financial institutions. Updates follow the release schedule of corresponding tender projects, with higher frequency on workdays. Projects in some industries are released in concentrated batches.
Documents include fixed fields such as project number, bidder information, project budget, tender scope, qualification requirements, submission deadline, and some announcements include download links for tender documents. Budget fields use ten thousand yuan as the unit. Time fields use Gregorian year-month-day format. Section numbers use a combination of letters and numbers.
Constraints Imposed on Vector Models and Indexing by These Characteristics
Tender announcement formats differ across sources. Field order and title hierarchy vary between some platforms, requiring adaptation to diverse structures during automatic chunking. Uncertain update rhythms require indexes to support incremental updates and batch rebuilding, preventing resource waste from full index rebuilds.
Core fields such as qualification requirements and budget have higher importance than other content, so weight differentiation is required during embedding and indexing. Attachment links included in some announcements require separate processing and associated embedding with text content. Timeliness rules mandate automatic removal of expired tender announcements from the index, to avoid invalid recall.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the length of core clauses in tender announcements such as qualification requirements and tender scope, avoiding splitting critical information across chunks |
chunk_overlap | 100–150 characters | Preserves contextual connections between chunks, preventing cross-chunk logic from being truncated |
max_parsing_depth | 3 | Matches the multi-level title structure of tender announcements, accurately identifying paragraph boundaries |
embedding_model | Selected based on business scenario | Use a general text embedding model when only processing text content; use a multimodal embedding model when associating attachment content |
recall_count | 8–12 entries | Covers multi-dimensional matching needs for tender announcements, avoiding missing valid information due to insufficient recall |
similarity_threshold | 0.75–0.85 | Filters irrelevant announcements with low matching degrees, ensuring relevance of recall results |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require targeted analysis, and testing on locally collected samples is recommended before finalizing settings.
Three Common Mistakes
- Symptom: Test fails when using a multimodal embedding model, returning an error message containing
Invalid. Cause: Access permission parameters for the model are not configured correctly, or the selected embedding model does not match the vector index dimension of the current platform. - Symptom: Request timeout or empty results occur during batch index rebuilding. Cause:
index_batch_sizeis not set reasonably, and loading too much tender announcement data at one time exceeds the interface processing limit. - Symptom: Chunked documents cannot be correctly associated with core fields of tender announcements. Cause:
max_parsing_depthis not set to a value adapted to multi-level titles, leading to incorrect paragraph recognition and chunked content deviating from core information.
How to Confirm Correct Configuration
- A standard tender announcement is uploaded, and the chunking preview interface is checked to confirm that core content such as qualification requirements and tender scope is not split across chunks.
- Search terms including project number and budget range are entered, and the matching degree of recall results is verified against preset standards.
- A full index rebuilding operation is performed, and the task execution log is checked for abnormal errors.
- After migrating data from the old vector database, samples are randomly selected to verify that the dimension of embedded vectors matches the new configuration.
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.