What This Category’s Data Looks Like
Survey and design sector policy and regulatory basis data is primarily sourced from normative documents, qualification management regulations, project review guidelines, technical standards, and similar materials released by the Ministry of Housing and Urban-Rural Development and housing and urban-rural development authorities at provincial, municipal, and county levels. Data updates occur irregularly alongside new policy releases and existing policy revisions. Most documents follow a chapter-based structure, including fields such as document number, issuing authority, effective date, clause title, and main text content. Clauses are hierarchically organized using "article, paragraph, sub-paragraph" as units. Document numbers mostly adhere to standard official document formats, and individual clause content is generally lengthy.
Constraints Imposed on Vector Models and Indexing
The lengthy individual clause content of survey and design policy documents requires vector chunking to preserve clause integrity and avoid disrupting the logical coherence of policy content. Documents include exclusive metadata such as document numbers and effective dates, which requires the index to support metadata filtering for accurate result screening by issuing entity and effective time. The irregular update rhythm requires the index to support incremental updates to avoid resource consumption caused by full reconstruction. The survey and design sector uses many industry-specific technical terms, which requires the embedding model to adapt to semantic understanding of engineering texts, and the vector database to set a reasonable similarity threshold to filter low-match results.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average length of individual clauses in survey and design policy documents, avoiding chunking that breaks complete clause logic |
| Overlap Length | 100–200 characters | Retains contextual connections across chunks, ensuring that the front and back logic of policy clauses is not interrupted by chunking |
| Similarity Threshold | 0.75–0.85 | Filters low-match irrelevant results, adapting to the semantic matching accuracy of professional policy texts |
| Single Search Limit | Top 10 results | Controls the number of retrieval results, avoiding excessive redundant content that increases the context processing burden |
maxToken | 8192 | Matches the maximum input length of the Qwen3-Embedding-8B model, preventing key policy content from being truncated |
defaultToken | 2048 | Balances the storage overhead of individual vectors and retrieval accuracy, adapting to the embedding requirements of survey and design policy texts |
Three Common Misconfigurations
- Issue: Dependency conflict errors occur when deploying the vector database, and the service cannot start normally. Cause: The generic vector database compose file was incorrectly bound to the PostgreSQL metadata storage configuration, and storage rules were not adjusted for the metadata fields specific to the survey and design scenario.
- Issue: Connection timeout or model incompatibility errors occur when connecting to the Qwen3-Embedding-8B model deployed via VLLM. Cause: Correct model interface address and request timeout parameters were not configured, and adaptation for embedding requirements of engineering-specific professional texts was not implemented.
- Issue: A large number of irrelevant policy files appear in retrieval results, making it impossible to accurately match target clauses. Cause: A reasonable similarity threshold was not set, or the metadata filtering function was not enabled, and results were not filtered using fields such as document number and effective date.
How to Verify Proper Configuration
- Upload a local survey and design policy document, perform chunking and vectorization operations, and check whether the number of chunks and single-chunk length after chunking match the configured chunk length and overlap length.
- Initiate a query targeting survey and design policies, and check whether the number of returned retrieval results matches the single search limit configuration, and whether the similarity scores fall within the set threshold range.
- Check the vector database metadata fields to confirm that fields unique to survey and design policies such as document number, issuing entity, and effective date have been correctly indexed.
- View the FastGPT v4.17.0 system monitoring panel to confirm that the storage proportion of embedded vectors meets expectations, and no abnormal disk usage growth has occurred.
The values given 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.