What Data for This Category Looks Like
Data for this category typically originates from procurement systems, supplier quotation documents, budget spreadsheets, and contract systems. Daily generated documents include purchase requisitions, comparison records, formal contracts, and acceptance reports. Each data entry corresponds to one procurement process node. Updates trigger when a purchase order is created. Monthly bulk synchronization covers budget adjustments or supplier onboarding documents. Document fields include application number, budget amount, procurement category, acceptance pass rate, and more. Some documents include structured quotation details and clause text. No unified fixed format exists, and fields vary by procurement category.
Constraints on Vector Models and Indexing
The characteristics of this category’s data create multiple constraints for vector model and indexing workflows. Multi-source heterogeneous data sources require indexes to support multi-format parsing and retain associations between fields and documents. The per-order update priority means incremental indexing logic should be configured first, to avoid resource usage from full reindexing. Document fields vary by procurement category. Chunk processing must retain field metadata as context, to prevent confusion between procurement parameters of different categories during vector matching. Text includes professional content such as IT equipment specifications and contract clauses. Vector models must adapt to semantic encoding of professional terms, to reduce matching bias.
Configuration Recommendations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average paragraph length of texts such as purchase requisitions and contracts, avoids semantic fragmentation within chunks |
| Overlap Length | 100–200 characters | Retains cross-chunk context association, prevents chunking from cutting critical clauses or parameters |
defaultToken | 2000 token | Matches the context window requirements of single queries, accommodates core information from multiple procurement documents |
maxToken | 6000 token | Supports full embedding of batch-recalled documents, adapts to context length for multi-document Q&A |
| Similarity Threshold | 0.72–0.85 | Filters low-relevance comparison records or contract clauses, avoids irrelevant recall |
| Single Search Limit | Top 8 entries | Adapts to the number of associated documents for a single procurement process, controls redundancy of returned results |
Three Common Configuration Mistakes
- Scenario: After upgrading from FastGPT v4.9.0 to v4.9.3, previously queryable procurement documents can no longer be recalled, and search results are empty. Cause: The default incremental indexing logic changed after the upgrade, and re-vectorization of historical documents was not triggered. This leads to incompatibility with older index formats.
- Scenario: After uploading an Excel-format supplier quotation spreadsheet, vectorization completes but search results have mixed-up fields. Cause: No column mapping rules were configured for Excel tables, and the association between key fields such as procurement category and quotation amount was not retained during chunking.
- Scenario: When connecting to the m3e vector model, using the aiproxy proxy fails to generate vectors normally. Cause: The proxy path and request headers for the vector model were not configured correctly, and the aiproxy forwarding rules do not adapt to the m3e interface format.
How to Verify Correct Configuration
- Manually upload a single purchase requisition, run a targeted search, confirm that core clauses of the corresponding document can be recalled.
- Trigger an incremental indexing task, check the indexing logs for FastGPT v4.17.0, confirm that only newly added purchase orders are processed, and there is no abnormal time consumption from full reindexing.
- Adjust the similarity threshold, test the number of recalled results across different thresholds, confirm that the expected filtering effect is achieved.
- Verify multi-source data import: upload a PDF contract and an Excel quotation sheet separately, confirm that both types of documents can generate vector indexes normally.
The values provided above are common starting points, and should be evaluated against local sample data.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.