Reference only; verify against the target FastGPT release and deployment documentation.
What the relevant data looks like
The data related to expressway operation document and material drafting comes from daily toll ledgers, road patrol records, emergency response plans, toll policy documents, service area management specifications, and other documents. The data update rhythm falls into two categories: daily operation documents such as toll ledgers and patrol records are updated daily, while policy and plan documents are updated irregularly or revised annually. Document structures include two types: long text reports and short process forms. Some documents use mixed text and image formats. Fields include section number, toll rate, patrol frequency, emergency contact person, and others. Units include kilometers, yuan per trip, hours, and others.
Constraints imposed by these characteristics on the knowledge base retrieval and recall workflow
The mix of long text reports and forms requires the retrieval system to adapt to chunking needs for documents of different lengths, and avoid content fragmentation or redundancy. The presence of dedicated operation fields requires retrieval to balance precise field matching and keyword recall, ensuring that document drafting can accurately reference compliant content for specific sections and rates. The proportion of mixed text and image documents requires retrieval to associate images with their corresponding annotated text, and avoid losing key operation guidance information. Irregularly updated documents require the knowledge base to support incremental updates and version management, ensuring that retrieval results always use the latest policy and plan content.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Expressway operation documents contain a large number of precise fields such as section number and toll rate. This range can filter irrelevant results while retaining compliant matching content |
limit | Top 8 results | A single document drafting task needs to reference multiple types of documents including policies, plans, and ledgers. 8 results can cover the core reference scope without exceeding the context limit |
searchMode | Precise matching + keyword recall | Operation documents have both standardized fields and unstructured disposal descriptions. The hybrid mode balances precision and recall rate |
usingReRank | Enabled | Long document chunks will generate a large number of similar fragments. Reranking can filter redundant results and improve the reference quality for document drafting |
datasetSearchUsingExtensionQuery | Enabled | Operation terms such as "oversized transport passage" and "clearing and rescue" have synonymous expressions. Extended queries can cover more relevant documents |
Three Common Configuration Errors
- A 422 status code may be returned during retrieval. This occurs when
datasetSearchUsingExtensionQueryis not configured correctly, leading to abnormal format of extended query parameters. - Content fragmentation or irrelevant fragments may appear after chunking long documents such as annual operation reports. This occurs when only #/## and other title separators are used, and chapter numbers common in expressway documents such as "Chapter 3 Toll Management" are not used as the separation basis.
- Image annotation text may be lost or cannot be retrieved after uploading a docx document. This occurs when the in-document text-image association extraction configuration is not enabled, and only plain text content is extracted while ignoring the annotation information corresponding to the images.
How to Verify Proper Configuration
- Upload a test document containing dedicated operation fields such as section number and toll rate, initiate a query corresponding to document drafting, and check whether the retrieval results include relevant fragments of the document.
- Initiate a query containing operation terms, check whether the retrieval results cover the synonymous expressions of the terms, and confirm that the extended query configuration is effective.
- Check the number of retrieval results, and confirm that the quantity meets the preset single search limit.
- Upload a test document containing mixed text and images, check whether the retrieval results include the annotated text corresponding to the images, and confirm that the text-image association extraction configuration is effective.
The values provided here 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.