Reference only; verify against the target FastGPT release and deployment documentation.
What This Category of Data Looks Like
Data mainly comes from official Party building learning materials released by party committees at all levels, internal party lesson slides, policy interpretation documents, and branch study records. Update frequency: new current study content is added monthly, and policy documents are updated quarterly. Each individual document includes fields for title, body paragraphs, issuing unit, and issuing date, stored as separate articles. Some long documents are split and archived by chapter.
Constraints on Vector Models and Indexing
Party building learning documents contain a large number of formal policy terms and structured content, with a high proportion of long documents. This creates clear requirements for chunking granularity and semantic integrity. Regularly updated documents require indexes to support incremental refresh, to avoid resource consumption caused by full reconstruction. Metadata such as issuing unit and issuing date attached to documents must be bound to vector content, to support filtering recall results by metadata dimensions. Some cross-department shared learning materials have duplicate content, so light deduplication must be completed before indexing to avoid redundant vectors occupying storage space.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxToken | 4000–6000 token | Adapts to the long context requirements of Party building documents, and complies with input limits of most open-source embedding models |
defaultToken | Count by application dimension | Corresponds to the requirement of splitting token consumption by application dimension, enabling refined management |
| Chunk length | 800–1200 characters | Adapts to the formal paragraph length of Party building documents, avoiding semantic fragmentation |
| Overlap length | 100–150 characters | Retains semantic connection between adjacent chunks, preventing key information from being truncated |
| Single recall upper limit | Top 10 results | Covers core relevant content, avoiding excessive redundant results |
| Reranking threshold | 0.75, calibrated by actual testing | Filters low-correlation recall results, improving answer accuracy |
Three Common Misconfigurations
- Symptom: Vector database deployment fails, with container startup failure and logs indicating abnormal PostgreSQL configuration. Cause: Incorrect use of a Milvus Compose file that includes full components, without stripping unnecessary PostgreSQL dependencies, resulting in resource usage exceeding server carrying capacity.
- Symptom: Poor semantic coherence of vector recall results, with key policy terms being fragmented. Cause: Chunk length setting exceeds the conventional paragraph range of Party building documents, leading to incomplete semantic chunks.
- Symptom: Token statistics are not split by application dimension, with consumption data from multiple business scenarios mixed. Cause: Incorrect configuration of token statistics dimension parameters, causing the statistics logic to not meet preset requirements.
How to Verify Successful Configuration
- View vector index chunking logs to confirm that the length of individual vectors matches the preset chunk length range.
- Run test queries to verify whether recall results include preset metadata fields such as issuing unit and issuing date.
- Trigger a token statistics task to confirm that the statistics dimension matches the preset application dimension.
- Check vector database storage space usage to confirm that the index size meets preset configuration requirements.
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.