What This Category of Data Looks Like
This category of data primarily comes from the 12345 citizen hotline platform, contractor business systems, internal knowledge bases, public review feedback data, and local hotline management specifications. Daily processed documents include request registration forms, dispatch records, handling responses, review records, extension applications, return explanations, and knowledge base entries. Data updates occur daily. Common fields include request number, contracting entity, request content, handling progress, and review results. Overall data follows the full workflow of public requests. It has no fixed legal format, but must align with the business transfer logic of each stage.
Constraints for Vector Models and Indexing
This category of data is multi-source and scattered. It includes structured business records and unstructured text content, with full daily updates. This requires the indexing link to support multi-format data access and incremental update logic. Request documents move through the full ticket workflow. Fields must link to core identifiers such as request number and contracting unit. Indexing must support grouped retrieval by business identifiers to avoid information confusion across tickets. Errors in ticket handling directly affect public reviews and overall workflow efficiency. The recall scope of vector retrieval must cover all key business documents. It must not miss records from abnormal stages such as extensions and returns. This lets internal employees quickly retrieve complete ticket background information. Use defaultToken to control the token limit per text chunk. This prevents model encoding failures caused by overly long single chunks.
How to Configure Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Aligns with the length of single business descriptions in ticket documents. It avoids breaking semantic integrity of requests and responses, and matches the chunking logic of FastGPT v4.17.0 |
| Overlap Length | 100–200 characters | Retains overlapping content between adjacent chunks. It prevents key business information from being truncated by chunking, and ensures retrieval continuity |
maxToken | 8192 | Adapts to the input limit of multimodal embedding models. It meets the requirement for complete semantic transmission of long documents |
defaultToken | 2048 | Serves as the maximum token limit for default chunks. It balances retrieval accuracy and computational efficiency |
| Similarity Threshold | 0.72–0.78, calibrated via actual testing | Filters low-relevance ticket records. It prevents internal employees from being distracted by irrelevant information |
| Maximum Single Retrieval Count | Top 10 entries | Controls the number of documents returned per retrieval. It adapts to the quick review needs of internal office work |
Three Common Misconfigurations
- Issue: Indexing processes take too long. A single run exceeds 30 minutes. Cause: The deployment uses full indexing mode instead of configuring incremental indexing for the daily update cadence of ticket data. This leads each indexing run to traverse all historical ticket documents.
- Issue: Missing configuration item errors occur when adding a new indexing data source in a docker-compose deployment environment. Cause: The local knowledge base directory is not mounted, or the vector service port is not exposed in the docker-compose.yml file. The platform cannot read external data source files.
- Issue: Semantic matching accuracy of vector retrieval is insufficient. It cannot accurately distinguish ticket content of different request types. Cause: A suitable embedding model is not selected based on the text-dominant characteristics of ticket data. A multimodal embedding model is mistakenly used to process pure text ticket data, reducing semantic encoding efficiency.
How to Verify Proper Configuration
- View the vector index update logs. Confirm that the daily incremental update task automatically triggers according to the preset cycle.
- Randomly select a historical ticket number. Perform associated document retrieval. Confirm that all business records related to that ticket are included in the retrieval scope.
- Test adding a small ticket data source in the deployment environment. Confirm that no configuration errors occur after the indexing task starts.
- Adjust the test parameters of the similarity threshold. Confirm that the number of returned retrieval results changes correspondingly with the threshold adjustment.
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.