What the data for this category looks like
This category of data primarily comes from project approval archives, construction operation and maintenance logs, bidding and tendering public announcement documents, service consulting work orders, and official response texts. The update rhythm aligns with project progress milestones. Daily consulting work orders are updated on a calendar day basis. Each individual document includes fields such as project unique identifier, item classification, original consultation text, official response, handling time limit, responsible unit, and update time. The time limit unit is working days, and the identifier is an unformatted string.
What constraints these characteristics impose on the vector models and indexing link
Data such as project approval archives and construction logs contains large amounts of long-form text. Long-text splitting rules must be adapted to avoid semantic fragmentation. Structured fields including project number and handling time limit must retain unit information. This prevents the loss of business semantics during vector encoding. The mixed update rhythm based on project milestones and calendar days requires the index to support incremental synchronization by update time. This avoids resource consumption from full reindexing. Heterogeneous document formats from multiple sources require unified preprocessing logic. This ensures consistency in vector generation. The maxToken configuration must be used to set the input length limit of the embedding model. This prevents semantic loss caused by long-text truncation.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic integrity for long texts such as construction logs and service responses, avoids overly fragmented single chunks |
| Overlap Length | 100–150 characters | Compensates for semantic gaps after long-text chunking, ensures contextual coherence across chunks |
maxToken | 8192 token | Adapts to the maximum length limit of official response texts, meets the input requirements of common embedding models |
| Similarity Threshold | Calibrated based on actual testing | Adjust according to the semantic matching degree of consultation questions in the business scenario, avoid false matches or missed matches |
| Single Search Limit | Top 10 results | Balances recall rate and response speed of search results, meets the precise query needs of service consulting |
defaultToken | 2048 token | Configures the default context window size, adapts to the response length requirements of common service consulting |
Three common misconfigurations
- Symptom: After importing a text dataset, the dataset status remains stuck at "Indexing" with no progress updates. Cause: The chunking rules for infrastructure construction long texts are not adapted, and the chunk length is set too large, causing the processing time for individual data to exceed expected limits.
- Symptom: The similarity value returned by a search exceeds the normal range, showing 10000+. Cause: The normalization logic for similarity calculation is not configured correctly, or the input parameters of the embedding model do not match the document length requirements, leading to abnormal results.
- Symptom: Duplicate recall or response timeout occurs during multi-replica deployment. Cause: Distributed synchronization rules for the index are not configured, causing inconsistent index data across replicas, or the single search limit is set too high, increasing node load.
How to verify proper configuration
- Manually import a single typical construction log text, check whether the split results in the chunk preview interface meet semantic integrity requirements.
- Initiate a simulated service consultation, verify that the similarity values of search results fall within a reasonable range, with no abnormally high or low values.
- View the index update log, confirm that the incremental synchronization task only processes datasets with update times later than the last synchronization, and no full reindexing is triggered.
- After deploying multiple replica instances, initiate multiple search requests, confirm that the response time is stable and there are no duplicate recall results.
The values provided here 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.