What the data for this category looks like
Urban water service business data comes primarily from official business manuals, archived processes from offline service windows, and service guidance documents synchronized via pipeline operation and maintenance. Update cycles are triggered by policy adjustments or business process changes, with no fixed schedule. Each document corresponds to a single business type. Its structure includes fields such as business name, application conditions, required materials list, handling location, handling time limit, and consultation channels. Common field units include working days, copies, and specific addresses.
Constraints on vector models and indexes from these data characteristics
Each document focuses on a single water service business, with scattered fields and structured information. This requires balancing business integrity and text granularity during chunking, to avoid losing process order after splitting. No fixed update cycle requires indexes to support incremental synchronization, reducing resource consumption from full reconstruction. Structured fields such as addresses and consultation phone numbers are mixed with unstructured handling step text. Targeted vector extraction rules must be configured to avoid over-compressing structured information. Business process steps have sequential logic, so indexes must retain order features to avoid disrupting the user-identifiable handling workflow.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average content length of single water service business documents, avoids cross-business splitting, and ensures semantic integrity of vector extraction |
| Text Overlap Length | 50–100 characters | Retains the front-back association of handling steps, avoids breaking the logical chain of the process after chunking |
maxToken | 4096 token | Adapts to the input limits of mainstream vector models, covering the content length of water service business document chunks |
defaultToken | Follow FastGPT v4.17.0 default value | Adapts to the vector model interface rules of the current version, avoiding exceeding input limits |
| Similarity Threshold | 0.75–0.85 | Filters low-match non-target business content, ensuring retrieval result relevance to user queries |
| Single Search Limit | Top 3–5 results | Water service handling options are relatively concentrated, too many results will interfere with user decision-making |
Three Common Misconfigurations
- Phenomenon: Index creation shows as successful, but the page cannot load retrieval results. Cause: Chunk length is set too large, causing some document chunks to exceed the input limit of the vector model, resulting in loss of partial chunk data during index generation.
- Phenomenon: All vector scores are identical after Docker deployment. Cause: Text overlap length is not configured correctly, leading to over-compression of semantic features across all chunks, making it impossible to generate differentiated vectors.
- Phenomenon: Retrieval tasks stop at the last index group. Cause: The incremental index update trigger condition is not set correctly, resulting in the index of the last batch of documents failing to submit, causing task blocking.
How to Confirm Proper Configuration
- Upload a single water service business document, check the chunk preview interface, confirm that chunks retain complete handling steps with no cross-business content splitting.
- Initiate a simulated query, review the similarity score distribution of retrieval results, confirm that reasonable differences exist, with no scenario where all scores are identical.
- Trigger an incremental update, check the index update log, confirm that index generation for the last batch of documents is successful with no blocking errors.
- Test the time difference between mixed retrieval and pure vector retrieval, confirm that the configured rearrangement function operates correctly.
The values provided are common starting points and should be evaluated 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.