What data in this category looks like
This category of data comes from enterprise contract management systems, compliance risk databases, legal and regulatory repositories, case management systems, and reporting platforms, with a monthly update frequency. Commonly processed documents include compliance risk lists, contract legal review opinions, legal opinions on major matters, compliance review forms, and violation reporting investigation reports. Most documents contain fields such as risk level, involved parties, processing progress, and legal basis. The content span of individual documents is large, covering everything from short clause details to complete incident reports.
Constraints imposed on vector models and indexing by these characteristics
The monthly updated data scale is relatively stable, but the single batch import volume is large, so it is necessary to adapt to the format and field differences of multi-source heterogeneous documents. The document content spans a wide range, from short legal clauses to complete incident reports, which requires flexibility in chunking strategies. Compliance documents have extremely high accuracy requirements. The indexing link must ensure that key risk points and legal basis are not missed, while avoiding semantic fragmentation caused by improper chunking. Field differences in multi-source data require indexing configurations to be compatible with different metadata formats, ensuring field matching efficiency during subsequent ledger summary.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | This category of documents covers short clauses and complete reports. This range balances semantic integrity and chunk granularity to avoid chunk loss |
| Chunk Overlap Length | 100–150 characters | Compliance documents contain cross-chunk references to regulations and risk descriptions. The overlap length ensures semantic coherence |
| Single Import Limit | 500 MB | Adapts to the scale of monthly batch imports, avoiding single task timeouts |
| Maximum Context | 4000 token | Adapts to the default parameters of FastGPT v4.17.0, adjusted with defaultToken and maxToken to meet the long-text recall requirements of compliance reports |
| Recall Count | Top 6 entries | Compliance risk investigation requires covering multi-dimensional associated information. A small number of precise recalls improves the efficiency of subsequent report generation |
| Vector Model | Prioritize open-source models that support local deployment, such as m3e | Meets the privacy requirements of compliance data, no need to rely on external proxies, and the call path can be configured via aiproxy |
| Embedding Thread Count | 2–4 | Reduces concurrency to avoid exceeding embedding rate limits, calibrated based on actual testing |
Three common misconfigurations
- Symptom: The knowledge base remains in the indexing state for a long time after upload. Cause: The total document size of a single import exceeds the configured single import limit, or the chunking strategy does not adapt to the long-text structure of compliance documents, leading to indexing task backlog.
- Symptom: Chunk loss occurs when the chunk length is set to 3000 characters. Cause: Compliance documents contain a large number of cross-paragraph regulatory references and associated descriptions. A 3000-character chunk length exceeds the semantic boundary, causing the system to fail to correctly identify chunk boundaries and resulting in partial content not being indexed.
- Symptom: Rate limit errors are triggered during the vectorization process. Cause: The embedding thread count is configured too high, and concurrent requests exceed the call limits of the vector model. Adjusting the thread count reduces concurrency pressure and resolves this issue.
How to verify the configuration is correct
- View indexing task logs to confirm that the total document size of a single batch import does not exceed the configured single import limit, and the number of chunks after chunking meets expectations.
- Randomly select 10 compliance documents of different types, verify that the chunked text blocks have no obvious semantic fragmentation or content loss, and confirm that the chunking strategy adapts to the document structure.
- Simulate a vectorization task, adjust the embedding thread count and observe the call rate, confirm that no rate limit errors are triggered, and verify the rationality of the thread count configuration.
- Check the indexed metadata fields, confirm that the metadata of documents from different data sources has been correctly identified, ensuring field matching for subsequent ledger summary.
The values given 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.