What the data for this category looks like
This category’s data covers full daily procurement process documents from entities including state-owned enterprise procurement centers, tender offices, business requirement departments, audit and discipline inspection departments. Documents include procurement plans, requirement specifications, tender documents, bid evaluation records, contracts, acceptance slips, and supplier complaint materials. Data sources cover enterprise procurement platforms, public resource trading platforms, supplier databases, contract management systems, and audit issue databases. Update frequency is daily. Documents contain core fields such as procurement method, budget basis, scoring rules, and supplier qualification requirements. They have both structured form and semi-structured text characteristics.
What constraints these characteristics impose on vector models and indexing
The daily updated data volume for this category fluctuates with procurement business frequency. An incremental indexing mechanism must be adapted to avoid excessive resource usage from full indexing. Multiple types of procurement documents have significant structural differences. Unified vectorization processing for content in different formats must be supported. Core fields such as procurement method and budget basis are key information for compliance audits. Vector retrieval must accurately match such content to avoid retrieving irrelevant documents. High compliance requirements from audit and discipline inspection teams mean the index recall accuracy must meet strict business verification standards. Original documents must be retained for subsequent traceability.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the typical length of semi-structured text such as procurement documents and bid evaluation records for this category, prevents key compliance clauses from being truncated |
| Overlap Length | 100–150 characters | Retains core field information across chunks, such as procurement method and budget basis, prevents loss of context during retrieval |
maxToken | 4096 token | Adapts to long text content such as state-owned enterprise procurement contracts and tender documents, complies with input limits of general embedding models |
| Similarity Threshold | 0.72–0.85 | Accurately matches compliance-related clause content, reduces the probability of false recalls for non-relevant documents |
| Single Search Limit | Top 10 entries | Covers multi-dimensional historical records and reference documents required for compliance audits |
defaultToken | Calibrated via actual testing | Adapts to the token output requirements of the selected embedding model, ensures consistency in vector storage and retrieval |
Three common mistakes
- When creating a knowledge base, the indexing step gets stuck with no clear error message. The cause is failure to configure incremental indexing trigger rules, and using full indexing to process daily updated procurement data. This leads to excessive system resource usage that prevents index construction from completing.
- After adding a new embedding model, indexing can be completed but search tests return errors. The cause is failure to verify that the vector output dimension of the new model matches the preset dimension of the FastGPT vector database, and failure to correctly specify model calling parameters in the configuration.
- After vectorization, only vector data is stored in the database, and original document content is missing. The cause is failure to enable the original document storage switch, or incorrect configuration of parameters that only retain vector data. This fails to meet compliance traceability requirements.
How to confirm configurations are set correctly
Reference only; verify against the target FastGPT release and deployment documentation.
- Upload a single procurement plan document, verify that the vectorization task can complete normally, and that vector data for the corresponding document can be retrieved from the vector database.
- Simulate the daily updated data synchronization process, verify that the incremental indexing task can trigger automatically with no error logs.
- Search for compliance audit-related keywords such as "procurement method" and "bid evaluation method", verify that the recall results include core document types for this category.
- View the storage content of the vector database, confirm that both original documents and vector data exist, and can be used for subsequent traceability verification.
The values provided here are common starting points, and should be evaluated 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.