What the Data for This Category Looks Like
This category of data primarily originates from enterprise contract management systems, compliance risk databases, laws and regulations repositories, case management systems, and reporting platforms. Daily documents include compliance risk lists, contract legal review opinions, legal opinions on major matters, compliance review forms, and violation report investigation results. The data update frequency is monthly. Most documents are structured forms or semi-structured text, containing fields such as risk level, approval status, and associated matters. There are no unified fixed units, and field extraction rules must be adapted based on document type.
How These Data Characteristics Impact Vector Models and Indexing
Monthly batch data updates require indexes to support incremental synchronization, avoiding additional time costs from full reindexing. Semi-structured documents have large field variations, so key metadata must be retained during chunking to prevent loss of information that affects matching accuracy, such as compliance level and approval status. Data from multiple sources has inconsistent formats, so format adaptation must be completed before indexing; otherwise, vector calculation deviations will occur. The large scale of documents processed in a single run requires controlling the upper limit of single-batch indexing to prevent task timeouts and interruptions.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the field length of semi-structured compliance documents, avoid cutting critical approval nodes or risk descriptions |
| Overlap Length | 100–200 characters | Retain compliance-related information across chunks, prevent context breaks for policy bases |
maxToken | 4096 token | Match the context limits of mainstream large models, adapt to the long-text review requirements of compliance documents |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Distinguish matching accuracy for compliance risks, avoid misjudging low-relevance policy provisions |
| Single Search Limit | Top 10 entries | Control the scale of retrieval results, adapt to the needs of legal staff to quickly locate core compliance bases |
defaultToken | 1024 token | Set the context window for basic vector generation, balance calculation efficiency and text integrity |
Three Common Configuration Mistakes
- Phenomenon: After importing a CSV file into the dataset, the total number of vector entries is less than the number of source data entries. Cause: No null value filtering rule is configured, and fields missing compliance risk level or approval node in the source data are automatically skipped.
- Phenomenon: The vectorization task remains in the indexing state for a long time with no progress updates. Cause: No reasonable
maxTokenparameter is set, and long-text compliance documents exceed the model's supported limits, leading to vector generation timeouts. - Phenomenon: Vector calculation scores are abnormally high, and multiple results have identical scores. Cause: The similarity threshold is set too low, and no reranking step is enabled, leading to low-relevance policy provisions being misjudged as high-matching items.
How to Verify Correct Configuration
- View the indexing task logs, confirm that each source document generates a corresponding vector entry with no batch losses.
- Manually enter a compliance-related question, check that the number and ranking of retrieval results meet expectations, with no abnormal scores.
- Adjust the similarity threshold, observe changes in the matching accuracy of retrieval results, and confirm that the threshold setting adapts to business needs.
- Verify the incremental indexing function: After adding a new compliance document for monthly updates, the indexing task can complete normally and synchronize to the retrieval library.
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.