What this category of data looks like
Data for this category comes from enterprise contract management systems, compliance risk databases, law and regulation repositories, case management systems, and reporting platforms. Daily generated 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 or semi-structured compliance-related text, containing fields such as risk level, review opinions, and case number. There is no unified fixed unit; values vary depending on the document type.
What constraints these characteristics impose on the vector models and indexing stage
The monthly update frequency requires indexes to support incremental synchronization, avoiding full rebuilds that take too long and harm the timeliness of compliance reviews. Heterogeneous data from multiple sources needs unified text cleaning rules to adapt to format differences across documents and ensure consistency in vector generation. Compliance documents contain specialized legal terminology, which has high requirements for the semantic understanding accuracy of vector models. Matching semantic features tailored for the legal domain is necessary. Individual documents have long content, so a reasonable chunking strategy is needed to avoid semantic fragmentation and prevent key compliance clauses from being split. Errors have severe consequences, so the recall accuracy of indexes must meet the rigor of compliance reviews, and key risk information must not be omitted.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Fits the semantic integrity of single segments in compliance documents, avoiding losing locally critical information in vectors due to excessive length |
| Overlap Length | 100–150 characters | Retains semantic connection between chunks, preventing compliance clauses containing complete legal basis from being split across two chunks |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Balances the precision and recall rate of compliance searches, avoiding missing potential risks or introducing irrelevant content |
defaultToken | 4096 | Adapts to the context carrying requirements of long-text compliance documents, ensuring long content such as review opinions can be fully passed to the model |
maxToken | 8192 | Meets the total length limit after splicing multiple compliance documents, supporting retrieval of batch risk-related content |
| Single Search Limit | Top 10 results | Controls the amount of information in search results, focusing on highly matched compliance clauses and historical cases |
Three common mistakes
- Symptom: After restarting the server, the compliance knowledge base status shows not ready, there is no automatic index progress prompt in the interface, and retrieval cannot be initiated normally. Cause: Incremental index trigger rules are not configured, and the full index task after multiple file uploads is not persistently stored. The task queue is lost after restart, requiring manual re-triggering of indexing.
Reference only; verify against the target FastGPT release and deployment documentation.
- Symptom: After batch importing compliance documents, some legal opinions on major matters are not retrieved, and key risk descriptions are missing from recall results. Cause: Chunk length is set unreasonably, splitting opinions containing complete legal basis into multiple semantically disjoint fragments, leading to failed vector matching.
How to confirm the configuration is correct
- Upload a single compliance review form, check if the knowledge base status shows ready after triggering indexing.
- Enter specialized legal terminology from the document, verify that the search results contain the corresponding content.
- Simulate a monthly incremental update by uploading a new compliance risk list, check if an index task is automatically triggered.
- View system logs to confirm there are no error messages about vector generation failures or index write timeouts.
The values provided here 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.