What Data Looks Like for This Category
Declaration materials and files for power generation equipment manufacturing originate from internal enterprise R&D systems, qualification declaration platforms, and archived documents from quality inspection departments. Contents include project initiation declaration documents, design drawings, process specifications, quality inspection reports, manufacturing licenses, and more. Updates follow a batch schedule during project initiation phases. Daily maintenance only updates a small number of revised files. Document structures cover long texts (up to hundreds of thousands of characters per document) and structured fields such as equipment model, rated thermal power, and manufacturing date, with common units including MW, hours, and sets. Attachment formats include PDF, CAD exported documents, and Word process files. Files are archived by project number, and each file set contains multiple associated documents.
Constraints on Vector Models and Indexing Workflows
The characteristics of declaration materials and files for power generation equipment manufacturing impose multiple constraints on vector model and indexing workflows. A high proportion of long texts, with individual documents reaching hundreds of thousands of characters, requires chunking strategies that avoid truncating professional parameter descriptions. Mixed structured fields and unstructured text requires targeted configuration of index weights for metadata and main text. Batch updates during project phases require indexing to support incremental synchronization, reducing resource consumption from full reconstruction. The presence of multi-format attachments requires vector models to support text content converted from different formats. The strong association of files archived by project number requires indexing to support associated retrieval by project dimension, improving review matching accuracy.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to long texts and professional terms in power generation equipment manufacturing declaration materials, avoids splitting critical parameter descriptions |
| Overlap Length | 100–200 characters | Retains contextual association between adjacent chunks, prevents truncation of process steps and parameter sequences |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Meets matching accuracy requirements for similar declaration materials in file review |
defaultToken | 4096 token | Adapts to the context window length for vector embedding of long documents, covers core content of a single design proposal |
| Single Search Limit | Top 10 results | Controls the number of candidate file sets in review scenarios, avoids information overload |
maxToken | 8192 token | Matches the context window upper limit of FastGPT v4.17.0, supports integrated retrieval of multiple associated file sets |
Three Common Configuration Errors
- Phenomenon: When uploading a full-machine design PDF larger than 10MB, some chunks fail vectorization after chunking, and repeated clicking to retry recovers partial tasks. Cause: Chunk length does not adapt to table and line break structures in the document, causing chunk format exceptions that trigger vectorization verification failures.
- Phenomenon: After a FastGPT service deployed on an Ubuntu system crashes and restarts, the status of uploaded file knowledge bases stays at "Not Ready", with no automatic indexing triggered. Cause: Automatic retry configuration for incremental indexing is not enabled, and no scheduled indexing trigger rule is set, causing offline tasks to fail to recover.
- Phenomenon: After upgrading the FastGPT version, the original file knowledge bases cannot return retrieval results. Cause: Vector indexes for the original version are not regenerated, and vector model embedding dimensions between old and new versions do not match, causing failure to hit the original vector database during retrieval.
How to Verify Correct Configuration
- Upload a single full-machine design proposal PDF of around 15MB, check the chunk preview interface, confirm that core parameters are not truncated in each chunk.
- Trigger batch upload of 10 historical file sets, check the progress bar of the vector task queue, confirm there are no abnormally failed chunk counts.
- Perform an associated retrieval across file sets, enter a specified project keyword, confirm that retrieval results include associated quality inspection reports and process documents.
- Restart the FastGPT service, check the status in the knowledge base list, confirm all uploaded file sets automatically enter the index ready status.
The values provided 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.