Reference only; verify against the target FastGPT release and deployment documentation.
What This Category of Data Looks Like
This category of data mainly comes from daily work documents of judicial administrative departments, departmental legal affairs offices, and policy and legal positions in government offices. These include draft regulatory documents, drafting explanations, legality review opinions, summaries of solicited comments, and filing reports.
Data sources include official law and regulation repositories, upper-level law databases, departmental policy document repositories, and administrative regulatory document filing systems. Updates have no fixed cycle, with new data generated as policy drafting and filing processes progress.
Most documents are a mix of structured and semi-structured data, containing fields such as document title, issuing authority, article content, and review opinions. The core content is policy articles and legal basis. Reviews must focus on drafting authority, procedural compliance, and the legality of individual articles.
What Constraints Do These Characteristics Impose on Vector Models and Indexes
Data sources are scattered across multiple official systems. Indexes must support cross-database associated retrieval, so they need to be compatible with multi-source data formats.
Document updates have no fixed cycle. An incremental index synchronization mechanism must be configured. This avoids resource consumption caused by full index rebuilding.
Documents mix long articles and short text such as review opinions. A chunking strategy that adapts to different lengths is required.
Core review needs focus on upper-level law comparison and authority compliance. Indexes must prioritize semantic association between policy articles and upper-level law basis. They should prioritize semantic matching related to compliance.
Review errors can lead to document filing corrections or policy release failures. Index recall accuracy must meet strict compliance requirements. Key legal basis must not be omitted.
How to Configure the Settings
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the natural paragraph length of regulatory document articles, avoid semantic fragmentation |
| Overlap Length | 100–200 characters | Retain semantic association between adjacent chunks, prevent cross-paragraph legal basis from being split |
maxToken | 8000 token | Match the input limit of mainstream vector models, carry complete policy articles and upper-level law comparison content |
| Similarity Threshold | Calibrated based on actual testing | Adjust based on the compliance review accuracy requirements of this category, avoid missing key basis or introducing irrelevant content |
| Single Search Limit | Top 10 results | Balance retrieval efficiency and recall coverage, cover common upper-level law and policy basis types |
defaultToken | 1500 token | Configure the truncation length for a single search result, ensure reviewers can quickly view core basis content |
Three Common Mistakes
- The interface displays a vector model addition failure, with the error prompt "Unsupported model type". The cause is incorrect configuration of local vector model port mapping, or failure to load compatible model weights in the system. This relates to model deployment adaptation issues in environments without a GPU.
- Exported knowledge base data cannot be split by business category, and only full knowledge base content can be exported. The cause is that permission-based index grouping configuration is not enabled. The system only supports data backup and export by knowledge base dimension by default.
- The number of returned search results does not match the set single search limit. Empty results or results exceeding the limit may occur. The cause is that classification tags of multi-source data were not correctly associated during index construction, leading to failure to match expected policy basis entries during retrieval.
How to Confirm the Configuration Is Correct
A single draft regulatory document is uploaded, and chunked text fragments are checked to confirm they retain complete article semantics without obvious fragmentation. A search targeting upper-level law basis is initiated, and the number of returned results is verified to match the set single search limit. External API calls for vector models and language models are tested, and interface responses are confirmed to have no timeouts or format errors. Full knowledge base data is exported, and it is checked whether it can be split and exported according to preset business categories.
The values provided here are common starting points. They 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.