Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data mainly comes from party building policy documents issued by the mine party committee, party building learning records from pre-shift meetings of each production work area, party members’ online learning ledgers, and special archives integrating safety production and party building. Update cadence follows three schedules: upper-level policy documents issued as needed, branch learning records updated weekly, and real-time submitted party member learning feedback. Document structures include plain text notices, learning materials with tables, and party lecture transcripts converted from audio. Fields include issuing unit, release time, and applicable work area. Units involved include copies, items, and person-times.
Constraints on vector models and indexing
Long audio-transcribed party lecture transcripts in the documents may cause single input length to exceed the basic chunking threshold, so long text splitting rules must be adapted. Fields include targeted labels for applicable work areas, so layered indexes must be built based on business dimensions to narrow recall scope. Real-time updated party member learning feedback data requires support for incremental index triggers to avoid resource consumption from full index rebuilding. Learning materials with tables require vector extraction logic that retains table semantics to prevent loss of structured party building knowledge points.
Configuration settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Adapt to long text and structured content of coal mine party building documents, avoid breaking single-block information |
| Overlap length | 100–200 characters | Retain semantic association between adjacent chunks, prevent splitting and missing party building knowledge points |
| Similarity threshold | 0.72–0.80 | Filter low-relevance non-party building content, focus on core party building Q&A results |
| Single search limit | Top 8 entries | Match the content scale of the coal mine party building knowledge base, control redundancy of recall results |
maxToken | 4096 token | Adapt to input limits of mainstream external vector models, avoid truncation of critical content |
defaultToken | 1024 token | Serve as the base length for default recall context, balance answer accuracy and resource usage |
Three common mistakes
- Phenomenon: After importing Excel files and multi-dimensional documents from the Feishu folder, the index result is empty. Cause: The parsing switch for the corresponding document format is not enabled, and the structured parsing rules for coal mine party building documents are not matched.
- Phenomenon: Frequent timeout errors occur after connecting an external vector model. Cause: A reasonable
maxTokenparameter is not set, exceeding the model's input limit causes truncation and retries. - Phenomenon: Full index rebuilding takes too long, and server resource usage is too high. Cause: Incremental index trigger logic is not configured, and full rebuilding is performed for real-time updated party member learning feedback data.
How to confirm the configuration is correct
- Upload a typical coal mine party building document, check whether the chunked text length meets the preset chunk length requirements.
- Submit a test question related to party building, verify whether the number of recall results matches the single search limit setting.
- Import a party building learning material with tables, check whether the index result retains the structured information in the table.
- Adjust the
defaultTokenparameter, check whether the loading length of the Q&A context meets expectations.
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.