What this category of data looks like
Core data for the engineering construction project bidding category comes from industry competent departments such as development and reform, housing and urban-rural development, transportation, water resources, public resource trading centers, and bidding agencies. Daily documents include bidding announcements, qualification prequalification documents, bidding documents, bid documents, evaluation reports, and successful bid candidate public notices. Data sources cover national public resource trading platforms, local trading platforms, engineering approval systems, and bidding supervision systems, with an update frequency of once per day. Documents contain both structured fields such as section numbers, qualification requirements, and bills of quantities, as well as semi-structured text. Single-document length varies widely, ranging from announcements with hundreds of characters to complete bidding documents with tens of thousands of characters.
What constraints do these characteristics impose on the vector model and indexing link
Multi-source data updated daily requires indexes to support incremental synchronization and batch construction, to avoid excessive resource usage from full reconstruction. Large differences in document structure require adaptive text chunking logic, to prevent key information such as sections and qualification requirements from being truncated. Vector encoding of structured fields must match business semantics, otherwise retrieval results will deviate from core links such as evaluation and objection handling. At the same time, the accuracy of retrieval results directly relates to the business processing flow. Incorrect matches may cause bid suspension, project delay, and other consequences, so the recall precision of the index must strictly match business verification requirements.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxToken | 8000–12000 | Adapts to the context window requirements of long texts such as bidding documents and evaluation reports |
| Chunk Length | 800–1200 characters | Matches the information density of common documents like bidding announcements and public notices, avoids overloading single chunks |
| Overlap Length | 100–200 characters | Prevents key cross-chunk information such as section numbers and qualification requirements from being split |
| Similarity Threshold | 0.75–0.85 | Balances precise matching and recall coverage, adapts to business scenarios such as evaluation verification and objection review |
| Single Search Limit | Top 10 results | Matches the volume of candidate result checks during the evaluation phase, avoids redundant information interfering with business judgment |
defaultToken | 4096 | Adapts to the default context window of FastGPT v4.17.0, ensures rationality of multi-document splicing |
Three common mistakes
- Symptom: After updating the FastGPT version, vector retrieval for the original knowledge base returns no results, and the interface displays "No relevant content matched". Cause: The storage format of vector indexes changed after version iteration, and the old knowledge base did not perform a reindexing operation.
- Symptom: When uploading CSV-format bid documents or qualification prequalification documents, the chunking step throws a format error prompt. Cause: The updated chunking logic added verification for structured nested fields, and does not adapt to special field formats such as bills of quantities and section numbers contained in CSV files.
- Symptom: When retrieving evaluation report content, the returned results have low matching degree with the query keywords. Cause: The similarity threshold was not adjusted according to the business scenario, either the threshold was too high and valid results were filtered out, or the threshold was too low and a large amount of irrelevant content was mixed in.
How to confirm the configuration is correct
- Upload a standard-length bidding announcement document, perform keyword retrieval, and check that the chunk length of the returned results falls within the configured range.
- View the index construction logs, confirm that the incremental synchronization tasks for multi-source data are running normally, with no data synchronization failure error records.
- Adjust the similarity threshold, compare the number of retrieval results under different thresholds, confirm that the matching accuracy meets business verification requirements.
- Test composite queries containing section numbers and qualification requirements, confirm that the retrieval results accurately hit the target document fragments.
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.