What Data in This Category Looks Like
Internal office data for power generation equipment manufacturing mainly comes from equipment operation and maintenance manuals, spare parts inventory ledgers, employee on-the-job training documents, on-site work order records, and safety operation procedures. Update rhythms vary significantly: equipment operation and maintenance manuals are revised quarterly, work order records are generated in real time alongside on-site operations, and spare parts ledgers are updated monthly. Most documents are a mix of structured and semi-structured data, including fields such as equipment model, operating duration (unit: hours), fault code, and responsible team. The length of individual documents varies widely, ranging from safety prompts with hundreds of characters to complete operation and maintenance manuals with tens of thousands of characters.
What Constraints Do These Characteristics Impose on the Vector Models and Indexes Link
The multi-source heterogeneous data characteristics of power generation equipment manufacturing impose multiple constraints on the vector models and indexes link. First, the mixed structure of long and short documents requires a chunking strategy that balances the semantic integrity of short texts and the rationality of long document splitting. Second, the coexistence of real-time updated work orders and low-frequency updated manuals requires flexible switching between incremental indexing and full indexing to avoid repeated indexing of static data. Third, semi-structured data containing standardized fields such as equipment operating duration and fault level requires associating metadata fields during indexing to improve precise filtering capabilities during retrieval. Fourth, the wide span of individual document lengths requires the indexing link to support dynamic adjustment of chunking thresholds to adapt to input texts of different lengths.
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 | Adapts to the average semantic unit length of power generation equipment operation and maintenance manuals and work orders, avoiding disjointed information within a single block |
| Overlap Length | 100–150 characters | Compensates for semantic gaps after long document chunking, ensuring continuity for cross-block retrieval |
| Similarity Threshold | 0.72–0.85 | Matches the semantic similarity distribution of terminology in the power generation equipment field, filtering low-relevance retrieval results |
| Maximum Search Results per Query | Top 20 entries | Adapts to the browsing habit of retrieval results in internal office scenarios, avoiding excessive redundant information |
| Embedding Model | Ali-emb3 | Adapts to semantic understanding of professional terminology in the power generation equipment manufacturing field, aligning with the embedding model support scope of the open-source version of FastGPT |
defaultToken | 4096 | Adapts to the total token capacity of most power generation equipment documents, ensuring complete context |
Three Common Mistakes
- Phenomenon: When retrieving multiple indexes associated with the same document chunk, duplicate and disordered returned results occur. Cause: No indexing association rules are configured for metadata fields such as equipment model specific to power generation equipment, resulting in retrieval results from multiple indexes not being deduplicated by business dimension.
- Phenomenon: Indexing time for large operation and maintenance manuals exceeds expectations. Cause: Chunking parameters are not adjusted based on document length, and incremental indexing mode is not enabled, resulting in full repeated indexing of static documents.
- Phenomenon: Fault code records with empty fields appear in retrieval results. Cause: No validation rules for structured fields are associated during indexing, resulting in semi-structured documents missing core business fields not being filtered out.
How to Confirm the Configuration Is Correct
- Upload a typical power generation equipment operation and maintenance manual, check whether the chunk length in the chunk preview interface matches the preset chunk range.
- Perform a retrieval containing equipment model keywords, verify whether the results automatically associate metadata fields of the corresponding equipment.
- Submit an updated spare parts ledger, check whether the indexing task execution status distinguishes between incremental and full update types.
- Check the embedding model configuration item, confirm that the Ali-emb3 model is currently in use.
- Verify the value of the
defaultTokenparameter, ensure it matches the token limit of the embedding model.
The values listed above are common starting points, and should be validated against the reader’s own sample data.
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.