What this category of data looks like
Data for this category comes from enterprise internal control systems, ERP, financial shared service systems, audit management systems, and procurement contract project systems. Daily documents include internal control self-evaluation reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closure certificates. Data is updated annually. Most documents are a mix of structured and semi-structured data, containing fields such as rectification responsible parties, issue descriptions, supporting material numbers, and closure times. There are no fixed, uniform standards for the length of individual documents.
What constraints these characteristics impose on vector models and indexing
Low-frequency data updated annually means high-frequency incremental synchronization is not required for index construction. Full refresh mode can be used to reduce system resource usage. Multi-source, heterogeneous document structures require chunking strategies to support text and attachments in different formats. This avoids vector representation deviations caused by mixed fields. The strong correlation between audit issues and rectification ledgers requires indexes to support cross-document associated recall. This ensures that during party building learning Q&A, issues, rectification requirements and supporting materials can be matched together. The large volume of sample and voucher data processed in a single run requires limiting the total token count of chunks per batch. This prevents exceeding the model's capacity limits.
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 | Adapt to the single-paragraph text length of documents such as audit working papers and rectification ledgers. Avoid overly fine chunking that causes semantic fragmentation, or overly large chunking that exceeds the maxToken limit |
| Overlap length | 100–150 characters | Retain cross-chunk context connections, ensuring that associated information between rectification requirements and supporting materials is not truncated during chunking |
| Similarity threshold | Calibrated based on actual testing | Adapt to the matching accuracy between question phrasing and audit documents during party building learning Q&A, and avoid incorrectly recalling irrelevant rectification records |
| Single search limit | Top 8 entries | Cover the associated audit issues, rectification measures and supporting materials needed for a single round of Q&A, balancing recall efficiency and result relevance |
defaultToken | 4096 | Adapt to the average length of audit documents for Chinese state-owned enterprises and public sector entities, ensuring that associated rectification ledgers and closure certificates can be fully loaded during a single round of Q&A |
| Reranking | Enabled | Optimize the sorting logic for audit issues and rectification measures during party building learning Q&A, improving the recall priority of core information |
Three common mistakes
- An interface error with a 400 status code occurs after configuring the vector model. The cause is that adaptive preprocessing is not performed for structured fields and attachment text in audit documents. Directly passing uncleaned raw data triggers model format verification failure.
- The number of search results is insufficient after index construction. The cause is that the chunk length is set too large, causing some audit working papers to not be fully chunked and stored in the database. This results in the loss of semantic fragments containing rectification requirements.
- The vector model type is selected incorrectly. The cause is that the requirement that party building learning Q&A only processes text-based audit documents is not clarified. A general multimodal vector model is incorrectly invoked, leading to matching accuracy that does not meet business requirements.
How to confirm the configuration is complete
- Import a single typical audit working paper, and check whether the chunked text fragments fully retain the core semantics of rectification requirements and issue descriptions.
- Initiate an audit question query related to party building learning, and verify whether the recall results include associated rectification ledgers and closure certificates.
- View the index construction logs, and confirm that there are no records of chunking failures or vector generation errors.
- Adjust the chunk length parameter, and verify whether the relevance of the recall results meets business expectations.
The values provided 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.