What the data for this category looks like
Data for this category comes from the unified budget management system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. Daily processed documents include performance target forms, self-assessment forms, performance evaluation reports, supporting material lists, and rectification reports. The data update frequency is annual. Most documents are a mix of structured tables and semi-structured text. Fields include project number, fund amount, performance indicators, completion status, and others. There are no fixed unified units, so specific units must be matched according to the corresponding project scenario.
What constraints these characteristics impose on the vector model and indexing link
The annual update rhythm means indexes do not need frequent incremental synchronization. Regular full index construction meets business requirements. The mixed structured and semi-structured document structure requires a chunking strategy that balances table integrity and text coherence. It avoids splitting cross-table performance indicator associations. Multi-source heterogeneous data format differences require a unified preprocessing workflow before vector generation. This prevents semantic deviation. Fields have no fixed units, so original field names and semantic associations must be retained. This ensures accurate vector matching. The binding relationship between supporting materials and main documents must retain association logic in the index. This facilitates linked retrieval calls.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph and table unit length of performance-related documents, avoid splitting critical performance indicator associations |
| Overlap Length | 100–150 characters | Retain contextual associations for cross-chunk policy clauses and performance targets |
defaultToken | 2000 token | Match the total semantic length of single policy documents and project data |
| Similarity Threshold | 0.75–0.85 | Filter low-match policy clauses, ensure relevance between retrieval results and performance evaluation basis |
| Single Search Limit | Top 8 entries | Cover policy basis and project supporting materials required for multi-dimensional performance evaluation |
maxToken | 4000 token | Limit the total context length of retrieval results, avoid exceeding the model input limit of FastGPT v4.17.0 |
Three Common Configuration Mistakes
- A 503 status code indicates no available model channel. The cause is that the vector model channel is not configured separately, and the global OneAPI configuration is still relied on, leading to model call failure.
- The container fails to start normally, with an API key invalid prompt. The cause is that the CHATAPIKEY environment variable is not correctly configured in docker-compose.yml, and the container is not rebuilt for the configuration to take effect.
- The knowledge base retrieval test page cannot adjust index parameters. The cause is that the index configuration entry is mistakenly located to the retrieval test page. Parameter adjustments must be completed in the knowledge base content management module.
How to Confirm Successful Configuration
- Navigate to the FastGPT knowledge base management page, check the vector model configuration items, confirm the embedding model channel is set separately, and no global OneAPI configuration is used.
- Perform a full index build, review the build logs, confirm there are no format errors or text truncation prompts.
- Submit a test query related to policy basis, verify the number of returned results matches the single search limit configuration, and the similarity meets the threshold requirements.
- Review the index chunk length configuration, randomly sample chunked text, confirm critical performance indicator and policy clause associations are not split.
The values provided 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.