What This Category of Data Looks Like
Data for this category comes primarily from tender announcements, tender documents, enterprise qualification databases, and supplier quotations. Daily processed documents include tender documents, qualification certificates, business deviation tables, and technical deviation tables. Most content uses fixed-format clause response structures. Some documents must strictly follow the format rules set by the tenderer. Data updates follow individual tender project cycles. Each completed project triggers a full round of data updates and archiving. Data fields focus on tender item requirements, qualification items, and quotation details. No universal standard units exist. Adaptation must align with specific tender requirements.
Constraints Imposed by These Characteristics on Vector Models and Indexing
The single-project update nature of tender data requires indexes to support fast rebuilding and switching. Long waits must be avoided to prevent delays to tender progress. Fixed document structures mean chunking must match clause paragraphs. This prevents semantic confusion across clauses and ensures accuracy of response content. Multi-source qualification and quotation data requires indexes to precisely match fields specified in tender requirements. Index recall accuracy must support field-level semantic matching. Content without universal units requires indexes to retain original field context during embedding. This prevents loss of unit or format information. Additionally, tender projects have strong time sensitivity. Index loading speed must support fast invocation for urgent tender requests.
Configuration Recommendations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the length of single clause paragraphs in tender documents. Prevents semantic fragmentation and ensures each chunk corresponds to complete business content |
| Overlap Length | 100–150 characters | Retains contextual association before and after clauses. Prevents semantic breaks between tender requirements and response content across paragraphs |
defaultToken | 8192 | Adapts to the total context length of most tender documents. Meets embedding requirements for complete clause responses. Optimized based on default parameters of FastGPT v4.17.0 |
| Similarity Threshold | 0.75–0.85 | Filters low-match, non-tender-related content. Ensures recall results meet precise matching requirements for tender scenarios |
| Single Search Limit | Top 8 entries | Controls the number of returned results. Prevents tender specialists from processing excessive redundant information. Focuses on matching content for core tender items |
maxToken | 16384 | Adapts to batch indexing of long documents. Meets combined processing requirements for multiple qualification files. Supports full data import for single projects |
Three Common Configuration Mistakes
- Symptom: The index rebuilding status display persists for more than 1 day, and fast switching to the new index is not possible. Cause: Incremental indexing is not configured for single-project updates. Full rebuilding is executed instead, leading to repeated processing of large volumes of duplicate data.
- Symptom: The batch add index interface returns a parameter missing error. Cause: The configuration logic for independent index blocks corresponding to single documents is not clearly defined. Unified index parameters are used incorrectly, leading to the interface failing to recognize embedding requests for single documents.
- Symptom: A large number of non-tender-related general documents appear in search results. Precise matching to tender clauses cannot be achieved. Cause: The similarity threshold is set too low. A large volume of irrelevant semantic content is recalled, which does not meet the precise matching requirements of tender scenarios.
How to Verify Proper Configuration
- Upload a single technical deviation table document. Check if the chunked content fully retains the context of a single deviation clause, with no obvious semantic breaks.
- Initiate a search for a specific tender item. Verify that the number of returned results matches the preset single search limit, with no unexpected redundant content.
- Execute an incremental index update for a single project. Confirm that the speed of index switching meets the time requirements for urgent tenders.
- Adjust the similarity threshold. Verify that recall results only include qualification and quotation content related to tenders, with no irrelevant documents included.
The values listed here are common starting points. They 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 2026-10-19.