What this category of data looks like
The policy and regulatory basis data for state-owned capital investment companies mainly comes from normative documents issued by the state-owned assets supervision authority, the group’s own investment and operation management systems, subsidiary companies’ compliance implementation rules, and annual supervision and assessment requirements. Data updates follow no fixed cycle, and are released synchronously with regulatory policy revisions, group strategy adjustments, or subsidiary system updates. Each individual document includes fields such as document number, issuing entity, effective date, clause hierarchy, applicable scenario, etc. Clauses use articles, paragraphs, and sub-paragraphs as the smallest units, and the overall document structure is rigorous and clearly hierarchical.
What constraints these characteristics impose on vector models and indexing
Decentralized data sources and non-fixed update cycles require indexing systems to support multi-source incremental synchronization, avoiding performance losses from full reconstruction. Documents include clear effective date and document number fields, requiring indexing to bind metadata for precise filtering by time and document number, ensuring recalled policy documents align with current applicable scope. The clear clause hierarchy of individual documents requires retaining hierarchical identifiers during chunking, preventing retrieval results from failing to locate specific policy clauses. The rigor of policy documents requires the indexing recall logic to filter low-match irrelevant content, avoiding misleading compliance judgments. defaultToken can be used to adjust default context filling parameters, adapting to policy documents of different lengths.
Configuration settings
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the typical length of single clauses in state-owned capital policy documents, avoiding semantic fragmentation |
| Overlap Length | 100–150 characters | Retains context for policy clauses across chunks, ensuring semantic coherence during retrieval |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Adapts to the rigorous semantics of policy documents, filtering low-match irrelevant content |
| Single Search Limit | Top 10 results | Controls the number of recalled policy documents, avoiding context overload that impacts compliance judgments |
maxToken | 8000 token | Matches the context window limit of FastGPT v4.17.0, ensuring complete loading of policy clause content |
| Query Optimization | Enabled | Optimizes the wording of policy-related queries, improving retrieval accuracy |
Three common misconfigurations
- Vector recall results include expired policy documents. No effective date filtering rule is configured, and indexing does not bind release time metadata, resulting in recalled expired content.
- Permission errors are returned when calling the vector model, while curl testing works normally. The access whitelist for the corresponding service is not added in the FastGPT vector database configuration, only the basic API key is configured, causing the service to block the request.
- Direct management of vector database add/delete operations via MongoDB is not feasible. Vector database add/delete operations must follow FastGPT’s indexing synchronization rules; direct MongoDB operations will cause inconsistencies between vector data and source policy documents.
How to confirm proper configuration
- Upload a single state-owned capital policy document, check if the chunked documents retain the preset metadata fields.
- Initiate a policy-related query, verify that the filtering rules of the recall results match the configured requirements.
- Trigger an incremental update task, check that the update scope of the vector database matches the update scope of the source data.
- Call the vector model interface, confirm that the returned results match the results of direct interface testing.
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.