What data looks like for this category
Data for this category comes from state-owned enterprise procurement centers, tendering offices, business demand departments, audit and discipline inspection departments, as well as tendering agencies’ enterprise procurement platforms, public resource trading platforms, supplier databases, contract management systems, and audit problem repositories. Daily documents include procurement plans, demand specifications, tender documents, bid evaluation records, contracts, acceptance receipts, and supplier complaint materials. Fields cover procurement methods, budget basis, scoring rules, supplier qualifications, performance clauses, and more. Data updates run daily. Individual document sizes range from hundreds to thousands of characters. Some structured data includes fixed enumeration values and process text.
Constraints imposed on vector models and indexes
Full daily updated data requires the indexing process to support incremental updates. This avoids excessive resource usage from full reindexing. Multi-source, heterogeneous document types include structured fields and unstructured process text. These require tailored chunking and indexing strategies. The strategies must ensure precise matching of structured compliance fields and semantic recall of unstructured text. Sensitive procurement-related text covers compliance content such as procurement methods and scoring rules. The indexing process must configure permission isolation rules to prevent sensitive information leaks. The wide range of individual document sizes, from short entries to thousands-of-character bid evaluation records, requires balancing reasonable chunking for all text lengths.
How to set configurations
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 wide range of document sizes for this category, from short entries to thousands of characters, balancing semantic integrity and indexing efficiency |
| Overlap Length | 50–100 characters | Avoids semantic breaks across chunks, ensuring contextual coherence for long documents such as bid evaluation records and contract text |
| Similarity Threshold | 0.75–0.85 | Meets the strict requirements of compliance review for this category, reducing false recalls of irrelevant procurement documents |
| Single Search Limit | Top 10 results | Adapts to the multi-dimensional verification needs of state-owned enterprise procurement compliance review, covering common procurement scenario combinations |
defaultToken | 1024–2048 token | Meets the vector encoding requirements for long documents in this category, avoiding truncation of critical compliance clauses |
maxToken | 4096 token | Matches the general context limit of the FastGPT platform, ensuring complete display of retrieval results |
Three common errors
- Phenomenon: The exported knowledge base dataset.csv only contains the index field, with no content field. Cause: The content extraction switch was not enabled during chunk configuration, or only metadata was captured during chunking without associating the core text of the original document.
- Phenomenon: The number of vector retrieval return results does not match the configured single search limit. Cause: The indexing process did not correctly associate the source data of procurement documents, or the similarity threshold was set too high, filtering out a large number of valid recall results.
- Phenomenon: The specified index model fails to load after Docker deployment. Cause: The model mounting directory was not mounted in the docker-compose configuration, or the OneAPI API key configuration was not correctly bound to the FastGPT vector model service.
How to confirm configurations are correct
- Manually upload a typical procurement plan document. Check if the chunked content after vector index generation fully covers key fields such as procurement methods and budget basis.
- Input simulated compliance review questions. Verify that the number of retrieval results matches the configured single search limit, and that the results include document types corresponding to this category.
- View the indexing logs of the FastGPT platform. Confirm that the daily incremental update task runs normally with no errors or truncation prompts.
- Export the knowledge base dataset.csv. Check that both index and content fields exist, and that the content field contains the core text of the document.
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.