What the Data for This Category Looks Like
Typical data for this category comes from fiscal bureau performance offices, budget units, third-party evaluation institutions, and competent departments. Commonly involved documents include performance target forms, self-evaluation forms, performance evaluation reports, supporting material lists, rectification reports, and more. Data sources include the unified budget management system, project declaration materials, fund payment data, business system ledgers, and satisfaction survey results. The update frequency is annual. Data fields include project ID, fund amount, performance indicators, self-evaluation scores, supporting material association items, and more. Units include ten thousand yuan, person-times, completion rate, and others. The size of documents associated with a single project ranges from one page to dozens of pages, and the overall structure varies slightly by project type.
Constraints Imposed on Vector Models and Indexing
The annual update frequency means high-frequency incremental indexing is not required. A single indexing process must handle multiple batches of project documents, and adapt to different document chunking rules. Documents from multiple sources and types require vector models to support semantic association between structured fields and unstructured text. This ensures matching accuracy between performance indicators and supporting materials. The size difference of single-project associated documents requires chunking strategies to balance the integrity of short text and semantic coherence of long text. Verifying the correspondence between fund expenditure and performance targets is a core link. The accuracy of index retrieval directly affects the quality of subsequent performance scoring and report generation. The proportion of false retrievals and missed retrievals must be strictly controlled.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the typical paragraph length of performance reports and self-evaluation forms, avoiding splitting key associated information across semantic chunks |
| Overlap Length | 100–150 characters | Preserves the association logic between cross-chunk performance indicators and supporting materials, preventing key corresponding relationships from breaking |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Distinguishes semantic matching degrees between performance targets and supporting materials, reducing the proportion of false retrievals from irrelevant data |
| Single Search Limit | Top 10 results | Covers the number of main supporting materials for a single project, avoiding redundant data entering the context for report generation |
defaultToken | 4096 | Adapts to the typical context length of performance evaluation reports, and complies with the model input limits of FastGPT v4.17.0 |
maxToken | 8192 | Supports integrating associated content from multiple supporting materials, meeting the information requirements for complete performance report generation |
Three Common Misconfigurations
- A call rejected error occurs when using ollama to connect a vector model, but the curl test works normally. The cause is that the ollama API request header does not correctly carry the authentication parameters required by FastGPT, or the model loading status is not synchronized to the API gateway.
- Add or delete operations do not take effect when synchronizing a vector database directly from MongoDB. The cause is that two-way synchronization triggers between the vector database and the source system are not configured, causing the vector index to not update with the source data.
- The number of index retrieval results does not match expectations, and a large number of irrelevant fiscal fund transaction data appears. The cause is that the chunk length is set too small, splitting associated performance indicators and fund transactions into different chunks, leading to deviations in semantic matching.
How to Verify Correct Configuration
- Import a single performance self-evaluation form and its corresponding supporting materials, perform a vector indexing process, and check in the chunk preview interface of FastGPT v4.17.0 whether the chunked text retains the association logic between performance indicators and supporting materials.
- Initiate a performance target matching search, verify that the similarity scores of the retrieval results fall within the preset threshold range, and confirm that there is no obvious false retrieval of irrelevant data.
- Trigger an incremental update task for the vector database, verify that add or delete operations of source data are synchronized to the index database, and check whether the number of indexes matches the number of source data.
- View the FastGPT system logs, confirm that the return status code of the vector model call is 200, and there are no timeout or authentication-related error messages.
The values provided 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 2026-09-28.