What this type of data looks like
Business process data for power generation groups primarily comes from internal business handling systems, offline service guide manuals, and ticket archive repositories. Data updates trigger synchronously with business policy adjustments and process optimizations. Incremental data is synced weekly for daily operations. Most individual documents are structured text, containing fields such as process steps, required material lists, processing time limits, and consultation channels. Common units include workdays, copies, and person-times. Some documents also include OCR recognized text from attached scanned files.
What constraints these characteristics impose on vector models and indexes
Power generation group business process data has high structural complexity and includes time-sensitive fields. This requires indexes to support filtering expired process documents by field. The incremental update sync rhythm requires indexes to support incremental construction to reduce resource usage. Multi-source OCR attachment text mixed with structured main text needs unified vectorization adaptation logic to avoid vector distribution bias across different text sources. The hierarchical structure of business process steps requires retaining contextual associations during chunking. This prevents splitting that breaks process logic and ensures step coherence in retrieval results.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the single-paragraph process description length of power generation group business documents, avoids splitting that breaks step associations |
| Overlap Length | 100–150 characters | Retains contextual connection between adjacent chunks, ensures complete logic for process steps |
| Similarity Threshold | 0.72–0.85, calibrated via actual testing | Adapts to the semantic similarity distribution of power generation group business terminology, filters low-relevance process documents |
| Single Search Limit | Top 10 results | Controls the number of returned retrieval results, avoids excessive redundant information interfering with process guidance |
maxToken | 4096 token | Adapts to the input length limit of mainstream vector models, ensures complete vectorization of business documents |
defaultToken | 8192 token | Adapts to the default input context length of vector models, covers the vectorization needs of most business documents |
| Maximum Context | 3–5 retrieval results | Matches the single consultation requirement for business handling, avoids overly long contexts exceeding model processing limits |
Three Common Misconfigurations
- After creating a new knowledge base, the index construction step gets stuck, and the interface progress bar stops without changes. The cause is that the incremental index trigger rule is not configured. Full index loading of historical business data for power generation groups uses excessive disk and memory resources.
- After replacing the
bge-m3vector model, search test results show abnormally high semantic matching, with a large number of irrelevant process documents appearing. The cause is that the similarity threshold is not adjusted for the professional terminology of power generation groups, leading to low-relevance process documents being misjudged as high-match results. - When logging into the PostgreSQL database to query, only vector data fields can be found, and original business document content cannot be retrieved. The cause is that the original text storage switch is not enabled. Only vectorized vector data is uploaded, and no original document copies are retained.
How to Verify Proper Configuration
- Upload one power generation group business handling guide document, check the FastGPT v4.17.0 index construction log, confirm that the chunk length and overlap length match the preset configuration.
- Enter a natural language query related to business handling, verify that the retrieval results include matching process steps and material requirements, adjust the similarity threshold to the range that meets business needs.
- Enter the vector model configuration page, confirm that the original text storage option is enabled, check whether both vector data and original document content exist in the database.
- Simulate incremental update of business data, verify that the index only syncs newly added process documents, and no full reconstruction is required.
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 2026-09-28.