What Data Looks Like for This Category
Data for this category originates from enterprise contract management systems, compliance risk databases, legal and regulatory repositories, case management systems, and reporting platforms. The update frequency is monthly.
Commonly processed documents include compliance risk lists, contract legal review opinions, legal opinions on major matters, compliance review forms, and violation report investigation reports. Most documents are semi-structured or structured text, containing fields such as compliance level, risk level, and case number. There is no unified fixed format, and formats are adjusted based on specific business scenarios.
Constraints Imposed on Embedding Models and Indexing
Monthly batch-updated data must align with batch indexing scheduling logic to avoid overly high single indexing load. Semi-structured documents have inconsistent formats, so vector chunking must balance field integrity and semantic coherence to prevent loss of key compliance information after splitting.
Data is associated with sensitive content such as compliance risk levels and case outcomes. The indexing process must retain field metadata to support precise retrieval, while ensuring data access permission isolation. Since errors may lead to major legal risks, indexing failures must be configured with fast rollback and alert mechanisms to prevent dirty data from entering the retrieval link.
How to Set Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the semantic coherence of semi-structured documents, avoiding loss of key fields such as compliance level and risk level after splitting |
| Overlap Length | 100–150 characters | Compensates for semantic gaps at chunk boundaries, ensuring complete retrieval of cross-chunk compliance risk descriptions |
defaultToken | 8192 | Matches the context window of mainstream embedding models, meeting the conventional text length requirements of compliance documents |
maxToken | 16384 | Covers the complete semantics of long documents such as legal opinions on major matters, preventing truncation of core compliance judgment content |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance historical documents, avoiding mixing irrelevant non-compliance content in retrieval results |
| Single Search Limit | Top 10 entries | Controls the volume of retrieval returns, adapting to the work habit of legal staff quickly locating core compliance basis |
Three Common Mistakes
- Issue: After importing Excel files or multi-dimensional documents from a Feishu folder, the indexing status shows failure, and the log displays the error "Failed to parse unstructured file metadata". Cause: The semi-structured file parsing switch is not enabled, or the chunking configuration does not adapt to the nested content structure of multi-dimensional documents.
- Issue: When deploying a knowledge base index with a PG database via Docker, the index cannot be created. The host has 8 cores and 16GB of resources with no GPU, and the task times out and exits. Cause: The batch indexing concurrency number is not adapted to the low-resource environment, and the number of files per single indexing is not limited, leading to excessive memory usage triggering OOM.
- Issue: When connecting to OneAPI to call an embedding model, errors persist and indexing construction cannot be completed. Cause: The indexing model is not configured separately, the same OneAPI key as the conversation model is used, or the
defaultTokensetting of the embedding model does not match the OneAPI interface limits.
How to Confirm Configurations Are Correct
- Upload one compliance review form and one legal opinion on major matters, perform manual indexing, and check whether the chunked text retains key fields such as compliance level and case number.
- Initiate a retrieval related to compliance risks, verify that the number of returned results matches the similarity threshold setting, with no redundant low-relevance content.
- Check the indexing task logs to confirm there are no batch indexing timeout or out-of-memory errors, and the task status shows success.
- Verify the indexing permissions of sensitive documents, confirm that unauthorized users cannot retrieve the corresponding content.
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.