Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data for this category originates from national petition information systems, local petition systems, the 12345 citizen hotline, grassroots governance platforms, and historical petition archives. Daily documents include petition registration forms, acceptance notices, transfer and assignment letters, handling opinions, review and re-examination applications, resolution plans, and similar materials. Data is updated on a daily basis. Most individual records include fields such as the requesting party, matter details, handling process nodes, and policy basis. Some data uses structured form formats, while others are unstructured text records with no unified fixed layout.
What constraints do these characteristics impose on the vector model and indexing workflow
Decentralized data sources and inconsistent formats require the indexing workflow to support unified vectorization adaptation for multi-source heterogeneous data. The daily update frequency requires configuring an incremental indexing mechanism to avoid excessive computing resource usage from full reindexing. Individual records include process nodes and policy basis, so differentiated chunking rules must be set for different fields to ensure accurate splitting of request and policy-related content. Historical archive data contains non-standardized transcribed text, so vectorization processing for low-quality text must be supported, and duplicate indexing of repeated petition data must be avoided.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Petition-related documents contain structured form fields and unstructured process descriptions. This range balances semantic completeness and indexing density |
| Overlap Length | 100–150 characters | Prevents key policy basis and request details from being truncated after chunking, ensuring cross-chunk semantic relevance |
maxToken | 4096 token | Documents such as petition handling opinions and resolution plans have relatively long lengths, requiring adaptation to large model context window requirements |
| Similarity Threshold | 0.72–0.85, calibrated via actual testing | Petition request expressions vary widely, requiring a balance between recall accuracy and coverage of historically similar matters |
| Single Search Limit | Top 10 results | Petition matter handling requires association with multiple historical handling records and policy documents; too few results are insufficient to support complete judgment |
defaultToken | 2048 token | Adapts to the input length requirements of vector models in basic party building learning Q&A scenarios, ensuring vectorization effect for short texts |
Three common mistakes
- Phenomenon: When the hardware configuration is 8 cores, 64GB memory, and RTX2070, knowledge base search responses are slow, and timeout prompts are displayed on the interface. Cause: Incremental indexing is not configured for daily updated petition data, and full reindexing consumes excessive CPU and graphics card resources.
- Phenomenon: After adding a custom petition data index, newly uploaded documents are not included in retrieval results. Cause: The incremental indexing synchronization switch is not enabled, and the system still uses the old index library for retrieval.
- Phenomenon: Policy Q&A related to party building cannot accurately match petition requests. Cause: Separate chunking rules are not set for policy texts, resulting in insufficient relevance between vector representations of policy content and request content.
How to confirm configurations are properly set
- Upload one test petition registration form and one party building policy document, check the index generation progress, and confirm that incremental indexing synchronization works normally.
- Enter one petition request, retrieve matching results, and adjust the similarity threshold to a range that aligns with business judgment.
- Test the Q&A chunking function, and check whether the split text blocks fully retain core policy and request content.
- View the vector model call logs, and confirm that the configured
maxTokenanddefaultTokenvalues match the actual input lengths.
The values provided here 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.