What the data for this category looks like
Party building learning related data for communication towers and infrastructure comes from party building learning materials issued by party committees at all levels, party building activity records from operation and maintenance teams, electronic versions of party building promotional materials supporting tower sites, and archived documents from internal party building systems. Update frequency: Learning materials issued by superior authorities are updated quarterly. Activity records are archived in real time as party building activities take place. Site promotional materials are updated every six months. Documents fall into three categories: plain text learning manuscripts, activity briefings with images, and structured party building assessment ledgers. Fields include document title, issuing unit, release date, content body, and associated site number. Site numbers are 6-digit numeric codes. Release dates use the YYYY-MM-DD format.
Constraints on vector models and indexing
Activity briefings with mixed text and images require vector models to support multimodal input, enabling joint vectorization of text and image content. Structured fields with associated site numbers require separate metadata indexing during the indexing phase, to support filtering search results by site dimension. There are significant differences in update frequencies. Real-time activity records require incremental indexing logic. Batch materials updated quarterly or semi-annually can use full indexing workflows. Document length varies widely. Plain text learning manuscripts can reach thousands of characters, while explanatory text attached to briefings is relatively short. A flexible chunking strategy is needed to adapt to content units of different lengths.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the length of plain text learning manuscripts, avoiding excessive single chunk length that causes vectorization overflow |
| Overlap Length | 100–200 characters | Covers context at chunk boundaries, preventing party building knowledge points from being split and lost |
defaultToken | 1024 token | Matches the vectorization token limit of most party building texts, adapting to general vector model input requirements |
maxToken | 4096 token | Supports vectorization of longer explanatory text in briefings with images |
| Similarity Threshold | 0.75–0.85 | Filters low-correlation party building learning Q&A results, ensuring retrieval accuracy |
| Single Search Limit | Top 8 entries | Controls the number of returned results, adapting to the context length requirements of party building Q&A |
Three common misconfigurations
- Vectorization tasks return token over-limit errors, with the
413 Request Entity Too Largestatus code displayed in the interface. Cause: ThemaxTokenparameter value is not adjusted based on the actual length of party building manuscripts, exceeding the input limit of the vector model. - A large number of non-party building related operation and maintenance data are mixed into retrieval results, and the number of returned entries is far higher than the set single search limit. Cause: Metadata filtering by site number is not enabled, and structured fields are not included in the indexing metadata configuration.
- Attempting to mix text vector models and multimodal vector models to process the same batch of documents, causing multimodal vectorization process failures. Cause: The input format requirements of the vector models are not unified, and mixed use of different types of vector models is not adapted.
How to confirm configurations are properly set
- Upload a typical party building learning manuscript, check whether the chunked content meets the preset chunk length and overlap length requirements.
- Initiate a retrieval with site number filtering, verify whether the results only include party building related content for the corresponding site.
- Test the multimodal document vectorization process, confirm that there are no format errors, then check whether the generated vector data is complete.
- Adjust the similarity threshold, observe whether the correlation change of retrieval results conforms to the expected configuration logic.
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.