What data looks like for this category
Data for this category comes from the low-income population dynamic monitoring platform, resident family economic status verification system, and cross-department shared data from public security, human resources and social security, medical insurance, housing and urban-rural development, and tax authorities. Daily handled documents include minimum living security application forms, family economic status verification reports, household survey forms, democratic review records, public notice materials, and assistance approval forms. Data updates occur monthly. Most document structures combine structured fields with free text explanations. Core content includes family member information, income and property details, and verification conclusions.
What constraints do these characteristics impose on the vector model and indexing link
Data characteristics of this category impose multiple constraints on the vector model and indexing link. Cross-department heterogeneous data sources create inconsistent field formats. Indexing must support adaptive embedding for both structured and unstructured text. The monthly update frequency requires setting up scheduled synchronization logic for incremental indexing, to avoid excessive resource use from full reconstruction. Documents include short structured fields and long free text content. Chunking strategies must balance text processing needs across different lengths. Core data links to family assistance eligibility. Index recall relevance must align with the strict standards of business review.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Adapt to the content length of long-text household survey records and verification reports in this category of documents, avoid losing context from overly small chunks or embedding failure from overly long chunks. |
| Overlap length | 100–200 characters | Connect adjacent chunked text, ensure semantic coherence for long documents, align with the logical relevance of family economic status-related text. |
defaultToken | 512 | Match the input token limit of commonly used embedding models in this category, balance text processing efficiency and semantic completeness. |
maxToken | 4096 | Adapt to the context window requirements of large models, carry spliced content of multiple verification reports and approval materials. |
| Single search limit | Top 8 entries | Cover the amount of associated data required for review in this category, avoid recalling excessive irrelevant information that interferes with business judgment. |
Reference only; verify against the target FastGPT release and deployment documentation.
Three common mistakes
- Phenomenon: The exported knowledge base dataset.csv only contains the index field and no content field. Cause: The data source field associated with the index is not correctly configured, and the text content parsed from the document is not mapped to the corresponding storage item.
- Phenomenon: Index construction fails with an error when deploying the bge-large embedding model locally. Cause: Insufficient video memory resources are reserved, or the hardware adaptation parameters for model loading are not correctly specified, causing the vector embedding process to interrupt.
- Phenomenon: Custom indexing cannot associate the corresponding MongoDB table and fields. Cause: The table name and field mapping path of the data source are not filled in according to platform requirements, or the field type does not match the indexing configuration.
How to confirm configurations are correct
- Run text parsing and index construction for a single minimum living security application form, check if the parsed content field fully contains all structured and unstructured content in the application form.
- Initiate a vector search for a family economic status verification report, verify that the number of recall results matches the configured single search limit.
- View the index construction logs, confirm that the incremental synchronization task is automatically triggered according to the monthly cycle, with no consecutive failure records.
- Test cross-data source field association search, verify that the embedding results of heterogeneous data conform to business logic.
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.