What the data for this category looks like
The core data for this category comes from contract systems, copyright registration databases, content libraries, and complaint inboxes. Daily processed documents include authorization contracts, copyright ledgers, removal notices, and infringement complaint materials. Each document is a single business document, with fixed fields including authorization scope, validity period, authorized party information, infringement fact description, and more. Updates are triggered by submitting a single document or monthly ledger summaries, with no fixed batch update schedule.
Constraints on vector models and indexing
This category's data characteristics impose three constraints on the vector model and indexing workflow:
- Document types are diverse and field structures vary widely. Chunking strategies must adapt to the core fields of different documents such as authorization contracts and copyright ledgers, to avoid including unrelated cross-field content in the same chunk.
- Updates are tied to single business documents. Incremental indexing must be bound to document submission events, rather than using scheduled full updates, to reduce system resource consumption.
- Core texts such as authorization scope and infringement facts require precise matching. The number of recall results must be controlled to fit the single-batch verification workload of legal staff, to avoid redundant information interfering with judgment.
Configuration settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the standard text length of authorization contracts and copyright ledgers, avoiding unrelated cross-field content in a single chunk |
| Overlap Length | 50–80 characters | Covers key cross-chunk information such as authorization scope and validity period, preventing truncation of core business clauses |
| Similarity Threshold | 0.75–0.85 | Enables precise matching of authorization scope text, avoiding misjudgment of non-authorized content or omission of valid matches |
| Single Search Limit | Top 10 results | Controls the number of recall results, fitting the single-batch verification workload of legal staff |
maxToken | 4096 token | Adapts to the long-text processing needs of authorization contracts, avoiding truncation of complete authorization clause content |
defaultToken | Calibrated via actual testing | Adapts to the video memory limits of locally deployed vector models, balancing recall accuracy and system performance |
Three common misconfigurations
- Symptom: After locally deploying FastGPT, calling the vector model returns an error indicating invalid token or connection timeout. Cause: The
defaultTokenconfiguration of the vector model is not set to match the token returned by oneAPI, or the vector model access address is not correctly bound in the backend. - Symptom: After importing an authorization contract into the knowledge base, the number of matching results for the authorization scope is insufficient. Cause: The chunk length is set too large, truncating text across authorization scopes, causing key matching information to fail to be recalled by the vector index.
- Symptom: Incremental indexing fails to update copyright ledger expiration reminder information on time. Cause: Index update triggers are set to scheduled full scans, not tied to single ledger submission events, leading to updates lagging behind business processes.
How to verify proper configuration
- Upload a single standard authorization contract, check that the chunked text fully includes core fields such as authorization scope and validity period, with no obvious truncation.
- Enter a query related to authorization scope, verify that the number of recall results matches the single search limit setting, and that similarity scores fall within the preset range.
- Submit a new copyright ledger, check that incremental indexing is automatically triggered, and that the ledger's expiration reminder information can be recalled normally.
- View the vector model monitoring panel for FastGPT v4.17.0, confirm that the
maxTokencall value does not exceed the model's maximum limit, with no overflow errors.
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 2026-10-19.