This document is written based on FastGPT v4.17.0.
What data for this category looks like
Data for this category primarily comes from cloud billing systems, tag data, order systems, and resource monitoring tools. Documents processed daily include billing details, cost allocation tables, resource utilization reports, and optimization suggestion sheets. Data update frequency is typically daily or monthly. Individual documents contain fields such as resource ID, cost amount, timestamp, tag key-value pairs, and allocation ratio. Units include yuan, hours, GB, and others. The data structure is mostly structured tables, with some semi-structured text for associated resource logs.
What constraints these characteristics impose on vector models and indexing
Data sources include structured and semi-structured content from multiple systems, requiring vector indexes to support chunking and vectorization of mixed-format data, and adapt to the table structure of billing details and semi-structured text of resource logs. Update frequencies are divided into daily and monthly, requiring configured index refresh mechanisms for different cadences. Incremental indexing is used for daily data to reduce compute overhead, while full indexing is used for monthly data to ensure data completeness. Fields include multi-dimensional attributes such as resource ID and cost amount, so vector models must support multi-field associated retrieval, ensuring retrieval results can link to complete business information. For business scenarios such as abnormal cost troubleshooting and cost allocation explanation, recall accuracy must match the precision requirements of business verification, avoiding invalid recalls that add extra billing verification workload.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-page content length of billing details and cost allocation tables, avoids semantic loss caused by chunk breakage |
| Overlap Length | 100–200 characters | Covers cross-chunk cost association logic, prevents allocation rules from being truncated by chunking |
| Similarity Threshold | 0.75–0.85 | Matches the accuracy requirements of abnormal cost troubleshooting, filters low-relevance historical billing data |
| Single Search Limit | Top 20 entries | Adapts to the business scenario where account managers quickly locate costs for a single resource type, controls the volume of returned results |
maxToken | 4096 token | Adapts to the long-text context requirements of cost allocation explanations, ensures complete transmission of associated report content |
defaultToken | Calibrated via actual testing | Adapts to interface call limits of different embedding models, avoids token limit exceeded errors |
Three common mistakes
- Interface displays the prompt "This token does not have permission to use the model", and the error contains fields related to text-embedding-3-large or bce-embedding. Cause: Direct access channels for the corresponding embedding model are not configured, and one API forwarding is still used with incorrectly configured channel permissions.
- Knowledge base search takes more than 30 seconds, with hardware configuration of 8 cores, 64GB memory, and RTX2070 graphics card. Cause: Chunk length and index shard count are not adjusted for locally deployed embedding models, leading to compute load during vector retrieval exceeding hardware adaptation limits.
- Billing data fields are missing from retrieval results. Cause: The association between resource ID and cost amount is not preserved during chunking, leading to the vector index being unable to link to complete structured field information.
How to confirm configurations are properly set
- Initiate retrieval with business keywords such as "abnormal cost troubleshooting" and "cost allocation", verify that the document types of returned results match target billing-related documents.
- Check embedding model call logs to confirm no token limit exceeded or channel permission denied error messages appear.
- Adjust chunk length and overlap length parameters, compare the semantic integrity of retrieval results to confirm cross-chunk association logic is not truncated.
- Simulate daily data update process to confirm index refresh tasks can complete normally without data loss.
The configuration 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 2026-10-19.