What Data Looks Like for This Category
Coal-power integration document and material data primarily originates from operational documents across the full coal mining and power production workflow. This includes production scheduling reports, safety operation procedures, equipment maintenance records, project feasibility study reports, and contract documents. Data update cycles cover real-time daily maintenance logs, daily fixed production reports, and monthly and quarterly compliance reports. Document structures include fixed business fields such as mine number, unit number, power generation, and coal consumption. Some long text documents include approval process nodes and timestamps.
Constraints Imposed on Vector Models and Indexes
Coal and power business documents have significant structural differences. Short text approval forms and long text feasibility study reports coexist, requiring vector chunking strategies to adapt to varying text unit lengths. Real-time updated maintenance logs and regularly submitted production data coexist, requiring indexes to support a mechanism combining incremental updates and scheduled full refreshes. Documents include structured fields such as mine number and unit number, alongside numerical units such as power generation and coal consumption. Vector models must retain semantic associations of structured fields during encoding, and avoid incorrect truncation of numerical units.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the mixed structure of long-text feasibility study reports and short-text approval forms in coal-power integration documents, avoiding overly long or short single chunks that negatively impact vector encoding results. |
| Overlap Length | 50–100 characters | Retains contextual associations across chunks, prevents key logic of long documents from being truncated by chunking, and adapts to continuous data paragraphs in production reports. |
| Similarity Threshold | 0.75–0.85 | Matches the density of professional terminology in coal-power business documents, filters low-relevance search results, and ensures content accuracy for document drafting. |
| Single Search Limit | Top 8 entries | Balances search efficiency and content coverage, adapts to search requirements for concentrated professional terminology in coal-power business documents. |
maxToken | 8000 | Adapts to the vector encoding limit for long-text feasibility study reports, preventing content loss from exceeding model processing boundaries. |
defaultToken | 2000 | Serves as the default token limit for chunks, adapting to the length requirements of most daily maintenance documents. |
Three Common Mistakes
- An interface prompt indicates a failed connection to the external vector database instance, preventing index construction. This occurs when the access address and permission key for the external vector database are not correctly filled in the FastGPT knowledge base configuration.
- The number of vector search results does not match the set single search limit, and search content deviates from business themes. This happens when the similarity threshold setting does not match the density of professional terminology in coal-power business documents, leading to misclassification of a large number of low-relevance results.
- No valid vector data is generated after enabling the index model, and empty results are returned during retrieval. This is caused by failure to trigger the knowledge base index refresh task, or uploaded documents containing no valid text content that can be encoded.
How to Verify Proper Configuration
- Navigate to the FastGPT knowledge base management page, check the vector database binding status, and confirm it shows a connected status.
- Upload a coal-power integration maintenance log document, trigger a manual index refresh, wait for the task to complete, and review the vector generation progress.
- Input a search term related to coal-power business, perform a vector search, and confirm returned results include matching business document fragments.
- Check the configuration values of
maxTokenanddefaultToken, and confirm they align with the processing limits of the currently used vector model.
The values provided here are common starting points, and should be evaluated against applicable samples.
Reference only; verify against the target FastGPT release and deployment documentation.
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.