What this category of data looks like
Party building learning Q&A data in the new energy development field mainly comes from internal party building work ledgers, special party building learning documents for new energy issued by superior party committees, records of theme party day activities, and minutes of three regular meetings and one party class session. The data update rhythm is as follows: new current learning materials are added monthly, and annual party building summary documents are updated quarterly. Each single document includes fields such as title, body, issuing unit, and release date. The length of the body varies widely, ranging from a few hundred words of learning points to several thousand words of special party lecture materials. There is no unified fixed format, but most documents have clear title hierarchies and paragraph divisions.
What constraints do these characteristics impose on the vector models and indexes link
The clear title hierarchies and paragraph divisions in party building documents require vector indexes to balance the semantic association between titles and bodies, avoiding title semantic loss caused by indiscriminate chunking of only the body. Special new energy documents often involve technical terms and policy requirements, which have higher requirements for semantic accuracy. The chunking logic must therefore avoid destroying the integrity of professional terms. The wide range of document lengths requires the chunking logic to adapt to texts of different lengths, while matching the token limit of the embedding model to avoid single chunks exceeding the model's input upper limit. Regularly updated data sources require the index update strategy to support incremental updates, reducing resource consumption from full reconstruction. The existence of structured ledger data requires the chunking logic to adapt to non-pure text formats such as tables and lists, avoiding chunk granularity that is too large or too small.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the title hierarchy and body length of party building documents, balancing semantic integrity and model token limits |
| Overlap Length | 100–150 characters | Retains cross-chunk contextual association, avoids semantic truncation of key paragraphs |
maxToken | 8192 | Adapts to the maximum input token count of Qwen/Qwen3-Embedding-8B, matches the post-chunk text length |
| Similarity Threshold | 0.72–0.80 | Filters low-relevance recall results, meets the accuracy requirements of party building Q&A |
| Single Search Limit | Top 6 entries | Balances recall coverage and context window capacity |
| Reranking | Enabled, take top 3 entries | Improves the relevance of final returned results, adapts to the rigor requirements of party building learning |
Three common configuration errors
- Phenomenon: After configuring Qwen/Qwen3-Embedding-8B, adding a model with the same name again causes the original configuration to be overwritten. Cause: A separate model configuration group is not created for the new energy development party building Q&A scenario, and embedding model configurations from different businesses are mixed.
- Phenomenon: Public network version knowledge base returns results containing unformatted markdown first- and second-level titles. Cause: Format markers in original files are not cleaned when importing documents, and the chunking logic does not retain the semantic weight of titles.
- Phenomenon: After importing party building learning ledgers in Excel format, chunk granularity is too large, leading to insufficient retrieval accuracy. Cause: Chunking parameters are not adjusted for structured table data, and the default chunking logic does not adapt to multi-row and multi-column data with headers.
How to confirm the configuration is complete
- In the FastGPT v4.17.0 interface, upload a single typical party lecture report document, check whether the chunk length and overlap length in the chunk preview interface match the set values.
- Submit a test question targeting the current new energy party building learning content, check whether the number of recall results matches the single search limit setting.
- Check the embedding model configuration items, confirm that the
maxTokenvalue of Qwen/Qwen3-Embedding-8B matches the official parameters of the model. - Test adding an embedding model with the same name repeatedly, check whether the interface pops up a configuration conflict prompt, confirm that the original configuration will not be automatically overwritten.
The values listed 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.