What Data for This Category Looks Like
This category of data is primarily sourced from customer requirements, public data repositories, collection tools, and sample libraries. Four standard document types are regularly produced: collection plans, sample specifications, authorization statements, and task schedules. Data updates follow either a per-project cycle or weekly synchronization. Core fields include sample scope, field specifications, collection permissions, and schedule milestones. Common units are project numbers, character counts, and days. No unified standard format exists, so adjustments must be made based on specific project scenarios.
Constraints Imposed on Vector Models and Indexes
Decentralized data sources and inconsistent formats require vector models to support tokenization logic for multi-source text. Index construction must accommodate mixed input of short text such as authorization statements and long text such as collection plans. The per-project or weekly update frequency requires indexes to support incremental updates, avoiding repeated calculations of already processed data. The customized nature of sample scope and field specifications requires index chunking rules to support dynamic adjustment based on project fields, making general fixed chunking parameters unusable directly. Authorization text related to collection legality must be separately indexed with high weight to prevent incorrect matching of sensitive information.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic integrity for long text such as collection plans and sample specifications, avoiding semantic breaks |
| Overlap Length | 100–200 characters | Compensates for semantic gaps between adjacent chunks after splitting, adapting to contextual association for short text such as task schedules |
defaultToken | Calibrated via actual testing | Adapts to the vector encoding length of short text such as authorization statements, matching the total token count of per-project data |
maxToken | 4096 tokens | Aligns with the input limit of common open-source vector models, preventing long collection plans from exceeding the model’s capacity |
| Similarity Threshold | 0.75–0.85 | Filters low-match sample scope descriptions, reducing invalid recalls |
| Single Search Limit | Top 10 results | Adapts to per-project data volume, balancing recall efficiency and semantic coverage |
This page is compatible with FastGPT v4.17.0.
Three Common Misconfigurations
- Phenomenon: QA split collections created via the OpenAPI interface have retrieval response times exceeding preset thresholds. Cause: Incremental indexing is not configured for the weekly update frequency of collection tasks, triggering full vector calculations for each retrieval.
- Phenomenon: Previously imported knowledge base data cannot match new embedding models, with retrieval results being empty or having extremely low matching scores. Cause: Index rebuilding is not performed after replacing the vector model, and vector results generated by the old model are retained directly.
- Phenomenon: A large number of irrelevant sample specification texts appear when retrieving collection plans, with obvious semantic matching deviations. Cause: Chunk length is set too large, causing adjacent semantic units to merge and destroying precise matching of sample scope.
How to Confirm Configurations Are Correct
- Upload a single collection plan document, check the number of chunks after indexing, and confirm that the chunk length and overlap length configurations match expectations.
- Call the knowledge base retrieval interface, pass typical sample scope query terms, and verify that the returned result matching logic aligns with project field rules.
- After replacing the vector model, run an incremental index rebuilding task, and check whether the vector encoding of historical data is updated synchronously.
- Simulate weekly data updates, and check whether the incremental index generation speed meets project schedule requirements.
The values given 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 2026-10-19.