Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Party building learning data in the basic chemical industry comes from internal party building records, monthly party lesson slides, industry party building bulletins, and branch study records from state-owned chemical enterprises. Data updates occur once per month, synced upon completion of that month’s party building activities. Documents are mostly long text, with structured fields including uniform chapter numbers, publish dates, and affiliated branches. The main body consists of coherent expressions thousands of characters long. Some documents combine specialized chemical terms and party building policy content, with no fixed format template.
What constraints do these characteristics impose on the vector model and index link
The long-length nature of basic chemical party building documents requires vector indexing to chunk along chapter boundaries. This prevents arbitrary hard cuts that split complete party building statements into independent chunks. The monthly fixed update rhythm allows using a scheduled full synchronization strategy instead of real-time incremental updates. This reduces system resource consumption. The structured fields in documents require specifying party building-specific fields as the retrieval scope during indexing. This avoids mixing irrelevant content such as chemical production data. The cross-domain term combination in documents requires vector models to have accurate encoding capabilities. This ensures the precision of retrieval matching.
How to set the configuration
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the per-chapter length of basic chemical party building documents, avoids overly scattered or redundant single chunk content |
| Overlap Length | 100–150 characters | Retains contextual association between adjacent chunks, prevents key party building statements from being cut |
maxToken | 4096 token | Matches the input limit of mainstream open-source vector models, adapts to the splicing needs of long document chunks |
| Similarity Threshold | 0.72–0.78 | Filters low-relevance general expressions, accurately matches the thematic content of basic chemical party building |
| Single Search Limit | Top 6 entries | Covers the number of common party building questions in this scenario, avoids missing key retrieval results |
defaultToken | 2048 token | Sets the default context length, balances the completeness of retrieval results and model call costs |
Three Common Misconfigurations
- Retrieval results are irrelevant to the query and contain large amounts of non-party building content: The interface returns results including chemical production data and other irrelevant information. The cause is that only party building-specific fields were not restricted, and the entire table data was mistakenly included in the index scope.
- Application configuration cannot be imported normally after export: Retrieval logic fails after import, and the associated knowledge base is lost. The cause is that the export option for knowledge base association items was not checked, causing the configuration to be disconnected from the data source.
- Vector model returns token limit exceeded error: Logs show
maxTokenexceeds the limit. The cause is that the chunk length is set too large, not adapted to the long-length characteristics of basic chemical party building documents.
How to Confirm the Configuration Is Correct
- Upload a standard basic chemical party building document, check if the chunked content retains chapter logic, with no forced cuts of key party building statements.
- Submit a party building-related query, verify that the similarity scores of returned results fall within the set threshold interval, with no obvious low-relevance content.
- Perform a full index synchronization, check if the synchronization time meets expectations, with no timeout errors.
- Export the application configuration and re-import it, confirm that the retrieval logic and knowledge base association relationship have not changed.
The values provided are common starting points and should be measured 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.