What this category's data looks like
The document data for power generation equipment manufacturing comes from sources including complete machine design specifications, process procedure documents, quality inspection reports, bidding technical documents, and operation and maintenance manuals. The data update rhythm shifts based on project phase. Update frequency is high during new model development. In mass production, updates occur quarterly or annually. Document structures vary significantly. Types range from dozens of pages of long technical documents, to single-page quality inspection forms, to structured bidding parameter tables. Common fields include equipment model, rated power, rotational speed, batch number, and quality inspection date. Dedicated supporting units include megawatts (MW) and revolutions per minute (r/min).
Constraints on Vector Models and Indexing
The high share of long texts requires retaining contextual links between professional terms and parameters during chunking. This prevents cutting cross-paragraph technical descriptions due to overly short chunks. Dedicated fields and units require preserving original field formats during preprocessing. This stops vector models from confusing the semantics of different parameters. Mixed unstructured and structured documents require the indexing link to support parsing logic for different text formats. Fluctuating update rhythms require indexing strategies to flexibly switch between incremental and full updates. This adapts to differing data update needs across development and mass production phases.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the long-sentence structure of power generation equipment technical documents, balancing contextual integrity and model processing efficiency |
| Overlap Length | 150–200 characters | Prevents chunks from cutting core associated information such as professional parameters and equipment models |
maxToken | 8192 token | Matches the token limit of mainstream open-source embedding models, preventing long text truncation and loss of key content |
| Similarity Threshold | 0.72–0.80 | Filters low-match non-professional documents, focusing on technically relevant results for the power generation equipment field |
| Single Search Limit | Top 8 results | Reduces redundant result interference, focusing on the most relevant technical parameters and document content |
defaultToken | 4096 token | Serves as the default context length, adapting to retrieval needs for routine operation and maintenance documents |
Three Common Configuration Mistakes
- Calling the indexing interface returns a 400 status code with no response body. The cause is failure to adapt to the length of power generation equipment long documents. Exceeding the model's token limit causes an abnormal request body.
- Server memory usage continues to rise until a crash occurs. The cause is failure to limit the number of files indexed in a single batch, and not enabling chunk caching. This leads to memory overflow during batch processing.
- Knowledge base upload files get stuck in 1 or 2 indexing groups. The cause is failure to unify document field formats. Some documents lack professional parameter fields, leading to indexing shard failure.
How to Verify Correct Configuration
- Upload a typical power generation equipment operation and maintenance manual. Check in FastGPT v4.17.0 to confirm the chunked text retains complete equipment parameters and dedicated units.
- Initiate a retrieval for a specific equipment model. Verify that the similarity scores of returned results fall within the preset threshold interval.
- Batch upload 10 different types of power generation equipment documents. Confirm that the indexing progress completes normally without lag.
- Verify knowledge base export and import operations. Confirm that the indexing structure of multiple document types is not lost.
The values provided here are common starting points and should be evaluated 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.