What this category of data looks like
Data is sourced from government administrative inquiry tickets for new energy development, official guidelines for project filing and grid connection approval, and on-site question and answer records. Updates follow new energy project batch schedules, with incremental updates based on new inquiry frequency within a single project cycle. Each data entry includes inquiry subject, project type (wind power, photovoltaic, energy storage, etc.), original inquiry question, official response content, processing progress, and project number fields. All fields are text or short enumeration values, with no complex nested hierarchical structures.
Constraints for vector models and indexes
Data for new energy development administrative inquiries is scattered across tickets of different project types. Vector models must adapt to semantic features of multiple scenarios including wind power, photovoltaic, and energy storage to avoid excessive deviation between recall results and inquiry scenarios. The incremental update rhythm requires indexes to support incremental synchronization mechanisms. Full index reconstruction is not needed each time, saving processing time. Each data entry includes short inquiry questions and long response content. Vector chunking must balance semantic completeness of short texts and contextual coherence of long texts. The rigor of government administrative inquiry data requires vector recall matching accuracy to align with standardized official response expressions.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Response content for new energy inquiries is mostly standardized government administrative expressions. This length preserves complete semantics of a single response and avoids semantic fragmentation |
| Overlap Length | 100–150 characters | Context must be retained at the junction of short inquiry questions and long responses to avoid loss of key associated information after chunking |
| Similarity Threshold | 0.72–0.80 | Government administrative inquiries have a high degree of standardized expression. This interval filters irrelevant recall results while retaining valid matching content |
| Single Search Limit | Top 8 entries | Scenarios for new energy project inquiries are relatively concentrated. Too many recall results increase the burden of contextual processing |
maxToken | 8192 | Adapts to the maximum input limit of mainstream embedding models, avoiding truncation of valid inquiry and response content |
defaultToken | 1024 | Covers the token quota required for vector extraction of a single conventional inquiry content, adapting to content lengths in most scenarios |
Three common configuration errors
- Phenomenon: When connecting a local embedding model, the interface continuously displays the "Indexing" status without updates. Cause: The API access path and port of the local model are not configured correctly, or the model service is not started, resulting in vector generation timeout.
- Phenomenon: After configuring the embedding model and using the knowledge base, an error "No matching content found" is returned. Cause: The embedding model is not bound to the vector index configuration of the corresponding knowledge base, or the semantic matching degree between the query text and existing index data is too low.
- Phenomenon: Search response speed is too slow, and the performance bottleneck is mistakenly attributed to the vector model link. Cause: The time consumption ratio between vector recall and large model response generation is not distinguished. In actuality, the time consumption ratio of large model response generation is higher, and the vector index processing link is not the performance bottleneck.
How to verify configuration effectiveness
Under FastGPT v4.17.0, configuration effectiveness can be verified through the following actions:
- Manually upload a standard new energy inquiry ticket data, and check whether the index generation progress proceeds normally.
- Input an inquiry question that matches the semantics of the uploaded data, and check whether the number and matching degree of recall results conform to preset rules.
- View system operation logs to confirm that there are no abnormal errors or timeout records in embedding model API calls.
- Adjust the chunk length parameter, compare the integrity of index content under different configurations, and confirm adaptation to business data characteristics.
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.