What This Category’s Data Looks Like
Power grid enterprise business process data primarily comes from marketing business application systems, the 95598 work order archive database, and service guide documents published at offline service halls. The data is updated regularly alongside changes to business rules, adjustments to electricity price policies, and the launch of new service items.
The data has two structural types. The first is structured tables with clear headers, including fields such as business type, application conditions, required materials, processing time limits, and contact numbers. The second is unstructured PDF service guides, which contain complete step-by-step process descriptions and frequently asked questions.
The text length of individual business data varies widely. Some entries are as short as tens of characters for brief application conditions, while others span thousands of characters for full process explanations.
Constraints Imposed on Vector Models and Indexes by These Characteristics
The mixed structured and unstructured nature of power grid business data requires vector indexes to support both table field matching and long-text semantic matching. The high update frequency of the data demands support for incremental index refreshes to maintain the timeliness of process rules.
Some business data has clear hierarchical relationships, such as sub-processes under a main business flow, or specific requirements under a material list. Chunking operations must preserve hierarchical context, and avoid splitting cross-hierarchy business units. Additionally, the wide variation in single-chunk text length requires support for generating valid vectors for different text lengths, to prevent generation failures caused by token limit exceedance.
Configuration Settings
Configuration references for FastGPT v4.17.0 are listed below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Aligns with natural unit length of power grid business processes, avoids splitting complete processing links or material lists |
| Overlap Length | 100–150 characters | Preserves context of adjacent business links, prevents process steps from being truncated during chunking |
defaultToken | 1024 token | Matches input token limit of most open-source embedding models, fits common chunk lengths of power grid business data |
maxToken | 4096 token | Ensures sufficient context splicing length after retrieval, covers complete business process description |
| Similarity Threshold | 0.75–0.85 | Distinguishes matching accuracy for similar business types, avoids mismatching different business application items |
| Single Search Limit | Top 3 results | Focuses on the 3 most relevant business processes, prevents result redundancy from reducing the accuracy of process guidance |
Three Common Misconfigurations
- Phenomenon: Retrieved business processes have broken steps, and complete processing links cannot be obtained. Cause: Chunk length is set beyond the natural length of the business unit, splitting complete processing steps or material lists.
- Phenomenon: After locally deploying ollama, vector generation fails, and the interface displays a token limit exceeded error. Cause:
maxTokenis not configured to match the input limit of the embedding model, causing long text chunks to fail to generate valid vectors. - Phenomenon: After importing Excel business data, matching results are completely unrelated to the query requirements. Cause: The similarity threshold is set too low, incorrectly matching unrelated business types, or context from table headers is not preserved, leading to failed field matching.
How to Verify That Configurations Are Set Correctly
- Import a single piece of Excel business data, and check the FastGPT chunk preview interface. Confirm that each chunk contains complete business links and required materials, with no cross-business unit splitting.
- Initiate a test query, and check that the returned text has no truncation prompts. Confirm that the
maxTokensetting fits context splicing requirements. - Adjust the similarity threshold, and verify that the number of matching results changes as expected across different thresholds. Confirm that the threshold setting is reasonable.
- Upload updated business documents, wait for the index to refresh, then initiate a query. Confirm that new process rules can be retrieved correctly.
The values provided are common starting points and should be measured against the user'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.