What Data for This Category Looks Like
Policy and regulatory basis data for urban investment platforms comes primarily from regulatory documents, investment and financing management systems, and internal control compliance manuals issued by local state-owned assets supervision and administration commissions, people's governments at the corresponding level, and industry competent departments. Data updates are triggered by policy releases, with no fixed cycle. Updates are typically completed within 1 to 3 working days after official policy issuance. Each single document typically includes fields such as document number, issuing entity, release date, scope of application, and main text clauses. The core content is structured clauses. Some supporting documents include attached tables. Field units are mostly clause numbers, document pages, and similar metrics.
Constraints Imposed on Vector Models and Indexing by These Data Characteristics
Urban investment platform policy data characteristics impose multiple constraints on the vector model and indexing workflow. First, policy documents center on structured clauses. Chunking must retain complete single clauses, and avoid splitting content across key regulatory clauses to prevent vector semantic breaks. Second, data metadata includes fields such as document number, issuing entity, and release date. Metadata must be bound to main text before vectorization, to ensure precise filtering by regulatory hierarchy and release time during retrieval. Third, updates have no fixed cycle, and single document volume varies widely. The index incremental build module must support on-demand single-file or small-batch incremental indexing, to avoid resource consumption from full reconstruction. Fourth, some supporting documents include structured tables. The vectorization process must support complete extraction and encoding of table text.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average length of single clauses in urban investment policies, avoids splitting key regulatory clauses |
| Overlap Length | 100–150 characters | Preserves semantic connection between adjacent clauses, prevents semantic gaps after chunking |
| maxToken | 4096 | Adapts to the maximum input limit of mainstream vector models, avoids long text truncation |
| defaultToken | 1024 | Sets the default token quota for vectorization requests, balances encoding accuracy and resource consumption |
| Single Search Limit | Top 10 results | Covers the common recall range for urban investment policy retrieval, avoids redundant results |
| Reranking Threshold | 0.75 | Filters low-relevance policy clauses, improves retrieval result accuracy |
Three Common Misconfigurations
- Issue: The vectorization interface only accepts single text slices, and cannot use batch array submissions to improve processing efficiency. Cause: The default vectorization configuration of FastGPT v4.17.0 does not enable batch processing mode. Manual parameter adjustment is required to enable it.
- Issue: Retrieval results only return basic document information, not the specific chunked text fragments. Cause: The storage switch for chunked content is not enabled in the index configuration. The index only records vectors and document IDs, and does not retain the original chunked text.
- Issue: After adding the Alibaba multimodal-embedding-v1 vector model, some long policy documents have semantic loss in vectorization results. Cause: The maxToken parameter is not configured to match the model's maximum input limit, resulting in long text truncation and loss of key regulatory clause content.
How to Verify Correct Configuration
- Upload a single typical policy document, check the chunking preview interface, confirm that complete regulatory clauses are not split during chunking.
- Initiate a policy keyword search, verify that returned results include chunked original text fragments and associated metadata such as release date and document number.
- Submit multiple text slices to the vectorization interface, confirm that the interface accepts batch inputs and returns a corresponding number of vectorization results.
- Import policy attachments containing structured tables, verify that the vectorization process can fully extract text content from tables.
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.