What the data for this category looks like
Typical entities in this category include development and reform departments, housing and urban-rural development, transportation and water conservancy industry authorities, public resource trading centers, and bidding agencies. Daily processed documents include bidding announcements, qualification prequalification documents, bidding documents, bid documents, bid evaluation reports, public announcements of successful bid candidates, and bid result announcements. Data sources include the national public resource trading platform, local trading platforms, engineering approval systems, and bidding supervision systems. Update frequency is daily. Document structures often include fields such as bid section division, qualification requirements, bid evaluation expert records, and bill of quantities. Some documents involve multi-bid section related content.
Constraints for Vector Models and Indexing
Daily updated batch documents require index construction to support concurrent processing, and adapt to multi-file batch import workflows. Different types of documents have significant structural differences. Bidding announcements focus on process information, while bid evaluation reports focus on expert and result details. Targeted chunking rules must be configured to avoid cross-document semantic confusion. Documents include structured fields such as bid sections, qualification requirements, and bill of quantities. Vector indexes must retain field association relationships to prevent semantic splitting from damaging business logic. Bid result documents have strong timeliness. Index updates must match the daily update rhythm to avoid data lag affecting Q&A accuracy.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the typical paragraph length of bidding documents, avoids single-chunk semantic breakage |
| Overlap Length | 100–150 characters | Retains semantic association between adjacent chunks, prevents key information from being split |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Filters low-relevance recall results, matches semantic matching standards for professional documents |
| Single Recall Limit | Top 8 entries | Adapts to the context carrying capacity of single-round Q&A, avoids information overload |
maxToken | 4096 | Adapts to the maximum input limit of mainstream embedding models, ensures chunk compliance |
defaultToken | 2048 | Configures the token limit for default chunks, compatible with most small and medium-sized bidding documents |
Three Common Misconfigurations
- Symptom: When deploying a PG database via Docker, knowledge base index creation fails, and no progress feedback is displayed after importing files. Cause: Database connection parameters are not configured correctly, or host machine memory resources are not allocated reasonably, causing timeout failure during index writing.
- Symptom: Persistent errors occur when connecting to the OneAPI embedding model, and index construction cannot be completed. Cause: A dedicated embedding model for indexing is not configured separately, and the default calling dialogue model does not support embedding tasks, so corresponding vector data cannot be generated.
- Symptom: A 503 error is returned when calling the embedding model, prompting that no available channels exist under the default group. Cause: A dedicated calling group is not configured separately for the indexing model, and the default group is not bound to available channels for the corresponding embedding model, causing calling requests to be blocked.
How to Verify Correct Configuration
- Import a single typical bidding document, check if the chunked text meets the configured chunk length requirements, and there are no abnormal truncation prompts.
- Initiate Q&A about bidding policies related to party building, verify the relevance of recall results, and ensure no low-relevance content is included.
- Check the index construction logs, confirm that all daily updated documents have completed vector writing successfully, with no failed records.
- Adjust the
defaultTokenormaxTokenparameters, import documents of different sizes, and verify that the chunking logic complies with the preset limits.
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.