Reference only; verify against the target FastGPT release and deployment documentation.
What the Data for This Category Looks Like
The data for this category mainly comes from enterprise procurement platforms, public resource trading platforms, supplier databases, contract management systems, and audit issue repositories associated with entities such as central procurement centers and tendering offices of state-owned enterprises and public sector entities, and is updated daily. Commonly encountered documents include procurement plans, requirement specifications, tender documents, bid evaluation records, contracts, acceptance documents, and supplier complaint materials. Each document contains dedicated fields such as procurement method, budget basis, scoring rules, and supplier qualification requirements. The data structure exhibits layered characteristics aligned with the full procurement workflow nodes.
Constraints Imposed on Vector Models and Indexing by These Characteristics
Daily updated data requires the indexing process to support incremental synchronization, to avoid performance loss caused by full reindexing. Mixed storage of multiple document types requires chunking strategies adapted to the structural features of different documents. For example, long-text tender documents need proper splitting, while short-text bid evaluation records must retain complete semantics. Dedicated fields such as procurement method, budget basis, and scoring rules have high semantic weight, so priority must be given to ensuring the accuracy of vector extraction for these key fields during indexing. Aggregated data across multiple data sources requires unified field mapping rules to prevent semantic drift in indexing.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the semantic integrity of long texts such as tender documents and contracts, avoid semantic breaks across chunks |
| Overlap Length | 100–150 characters | Retain contextual continuity between chunks, prevent key compliance information from being split across different chunks |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Balance recall accuracy and retrieval efficiency for tendering and procurement documents, meet the strict requirements of compliance reviews |
| Single Search Limit | Top 8 entries | Cover typical recall volumes for multiple document types such as bid evaluation records and supplier complaints, avoid overload from redundant data |
maxToken | 4000–6000 token | Adapt to the total length of documents across the full procurement workflow, ensure the large language model can access complete compliance basis |
defaultToken | 2000 token | Serve as the default context length, adapt to conventional retrieval needs for daily procurement inquiries |
Three Common Configuration Mistakes
- Phenomenon: Relevant compliance documents are retrieved, but the large language model returns that no matching content is found. Cause: The
maxTokenparameter is not configured correctly, and recalled document fragments exceed the context limit and are truncated, preventing the large language model from reading complete content. - Phenomenon: Index update takes too long, and synchronization cannot be completed within the daily update cycle. Cause: Incremental indexing rules are not configured, and full index reconstruction is performed daily, which does not align with the data update characteristics of this category.
- Phenomenon: Retrieval results include non-procurement documents or irrelevant supplier materials. Cause: The similarity threshold is set unreasonably, and parameters are not adjusted based on the precise needs of tendering and procurement compliance reviews, leading to recall of non-target content with weak semantic association.
How to Confirm Proper Configuration
- View the index synchronization log, confirm that daily incremental tasks execute normally, with no abnormal records of full reconstruction, and verify whether the update rhythm matches business requirements.
- Enter dedicated query terms such as procurement method and scoring rules, check whether retrieval results include target document types, and confirm that the configuration logic aligns with compliance review business scenarios.
- Test queries of different lengths, check whether the context content returned by the large language model covers complete compliance document fragments, and confirm that the context parameter settings adapt to current business needs.
- Verify the configuration of chunk length and overlap length, confirm that they match the average length of common documents in this category, and avoid abnormal semantic splitting.
The values listed here are common starting points, and should be assessed against the samples associated with the intended use case.
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.