What the Data for This Category Looks Like
Data sources include official guides for government and enterprise business handling, window process specifications, system operation manuals, and supporting policy documents. Updates are triggered on demand when policies adjust, institutional functions change, or processes are optimized. No fixed update cycle exists. Each document typically contains structured fields such as handling subject, required materials, handling steps, time limit requirements, and consultation channels. Some supporting materials exist as attachments. Field units use standardized government affairs expressions such as working days, copies, and steps.
What Constraints Do These Characteristics Impose on the "Vector Models and Indexes" Link?
Government and enterprise business documents often mix structured fields and unstructured process descriptions. Core business fields must be distinguished from auxiliary explanatory content. This prevents irrelevant fields from interfering with vector matching. Data updates have no fixed cycle. Indexes must support incremental synchronization to avoid resource consumption from full reconstruction. Individual documents may have lengthy content, plus attachment-type supplementary materials. When chunking, business logical connections must be preserved. Cross-step process descriptions cannot be arbitrarily truncated. Government data has strict matching accuracy requirements. The proportion of irrelevant recall results must be limited to ensure accurate business guidance.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-step process length of government and enterprise business documents, avoiding cross-logical truncation |
| Overlap Length | 100–150 characters | Ensures contextual continuity between adjacent chunks, preventing step breaks |
| Similarity Threshold | 0.75–0.85 | Government business expressions are precise, filtering low-match irrelevant results |
| Single Search Limit | Top 3–5 results | Focuses on core business steps, avoiding excessive results disrupting guidance logic |
defaultToken | 4096 token | Adapts to the relatively long content length of government documents, accommodating complete process context |
maxToken | 3000–4000 token | Matches the complete reply requirements of business guidance, preventing context overflow |
Three Common Configuration Errors
- Phenomenon: When calling the vectorization service, only requests for single text chunks are received, and batch processing cannot be triggered. Cause: The post-chunk data submission logic is not correctly configured. The interface is called by default for individual chunks, and the batch packaging mechanism is not enabled.
- Phenomenon: Empty values are returned when retrieving chunk index content via the API. Cause: The index storage path configuration is not updated synchronously, so the API cannot read the generated chunk data.
- Phenomenon: After adding the Alibaba multimodal-embedding-v1 vector model, vector generation fails. Cause: Multimodal adaptation parameters are not correctly specified in the model configuration, leading to incompatibility between the model and text input.
How to Verify Correct Configuration
Vectorization service request logs in the FastGPT backend can be reviewed to confirm each request includes the number of text chunks matching the configured batch requirements. Typical government and enterprise business handling questions can be input manually, and the number of retrieved results can be verified to match the set single search limit. A standard government and enterprise business document can be imported, and chunked content can be checked to retain complete process steps without logical breaks. Connectivity of the newly added vector model can be verified on the model management page, and normal vector result generation can be confirmed.
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.