What the data for this category looks like
Data for this category comes primarily from internal party building management systems in the coal chemical sector, official documents issued by party committees, party branch lecture slides, party member learning ledgers, and similar sources. Update schedules align with party building work milestones. Daily party building updates are added weekly. Quarterly thematic learning materials are updated in batches. Documents fall into two categories: structured and unstructured. Structured ledger fields include learning topic, speaker, number of attendees (unit: people), and learning duration (unit: hours). Unstructured slides are mostly PDF or Word files with titles and main text.
What constraints these characteristics impose on vector models and indexing
Mixed structured and unstructured data sources require vector indexes to support differentiated chunking configurations. This prevents loss of semantic associations caused by improper long document splitting. Fluctuating update schedules require adaptation of incremental indexing trigger logic. This reduces resource consumption from full index rebuilds. Proprietary terminology combining party building themes and coal chemical business requires vector model embeddings to cover relevant domain terms. This ensures retrieval accuracy. Wide variation in document length requires index systems to support dynamic adjustment of chunk granularity adaptation rules. This meets splitting requirements for different document types.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Aligns with paragraph lengths of coal chemical party building slides and average length of single ledger entries |
| Overlap Length | 100–150 characters | Prevents semantic breaks after chunking, and ensures contextual coherence of party building themed content |
| Similarity Threshold | 0.72–0.85 | Matches accuracy requirements for party building Q&A, and filters low-relevance non-thematic content |
| Single Search Limit | Top 8 results | Adapts to context length limits for single-round Q&A, and avoids interference from redundant information |
defaultToken | Calibrated based on actual testing | Adapts to model input length limits for local deployment and API calls, and ensures embedding consistency |
maxToken | 4096 token | Adapts to input length limits of mainstream party building Q&A models, and prevents truncation of critical semantic content |
Three common configuration errors
- Scenario: After building and running a Docker image, vector retrieval results have identical scores. Cause: The model API authentication parameters were not correctly configured in the image. All requests call the default test interface, which cannot generate differentiated vector embeddings.
- Scenario: Index construction always stops at the final batch of data. Cause: The incremental indexing trigger logic fails to correctly distinguish the boundary between full and incremental updates. This causes repeated loading of the final batch of data, and prevents submission of the full index.
- Scenario: Some party building ledger files cannot be vectorized. Cause: The structured data parsing plugin is not enabled. Only unstructured document vectorization is supported, and valid semantic information from ledger fields cannot be extracted.
How to confirm configurations are properly applied
- Upload a single party building slide and a single structured ledger entry. Chunked text length is checked to fall within the preset chunk length range.
- Submit two identical retrieval requests. Vector scores are checked to have reasonable differences, confirming model API calls function normally.
- Trigger an incremental indexing task. Index progress bar movement is verified to be normal, with no stagnation at the final batch of data.
- Enable hybrid retrieval functionality. Response times are verified to fall within the preset business threshold range.
The values provided are common starting points, and should be evaluated against the user'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.