What the data for this category looks like
The data mainly comes from official Party building meeting records, study and dissemination documents, and full texts of leaders' speeches released by Party organizations at all levels. The update rhythm follows the holding schedule of important Party building meetings. The length of individual documents varies widely, mostly in long text format. Each individual document includes fixed fields such as meeting theme, holding time, core speech content, and resolution matters. The basic data storage unit is a single meeting material, and there is no unified fixed unit of measurement.
Constraints for vector models and indexing
A high proportion of long texts exist in meeting spirit summaries. When performing vector indexing, adaptive chunking must be supported to avoid semantic fragmentation caused by splitting continuous key speech points. Updates are triggered randomly according to meeting schedules, so incremental indexing must support precise synchronization based on document update time to avoid excessive resource consumption from full index rebuilding. Documents contain multiple types of structured fields, and vectorization should prioritize the core speech content field to ensure that retrieval focuses on key information. There are exclusive professional terms in the Party building field, so the vector model must be adapted to Chinese professional corpora to avoid deviations in semantic encoding.
Configuration settings
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the long text characteristics of meeting materials, preserves the semantic integrity of single speech segments |
| Overlap Length | 100–200 characters | Avoids semantic fragmentation between adjacent chunks after splitting, ensures retrieval continuity |
maxToken | 4096 token | Adapts to the input length limit of mainstream Chinese vector models, avoids truncation of core speech content |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Filters low-relevance retrieval results, focuses on core meeting key points |
| Single Search Limit | Top 8 entries | Controls the number of returned results, avoids excessively long context exceeding model processing limits |
defaultToken | 2048 token | Configures the default context window length, adapts to semantic encoding of Party building professional terms |
Three common configuration mistakes
- Phenomenon: After upgrading from an older version to FastGPT v4.17.0, existing Party building meeting materials can no longer be retrieved. Cause: Incremental indexing or full index rebuilding was not performed. The index storage format of the new version differs from older versions, so existing index data cannot be read normally.
- Phenomenon: After uploading an Excel-format meeting minutes file, the vectorized retrieval results show content misalignment. Cause: The column matching rule for Excel files was not configured. The system reads all cell content by default, causing non-core auxiliary meeting fields to be mixed into the vectorized corpus.
- Phenomenon: Interface connection failure occurs when calling the m3e vector model. Cause: The call parameters of the vector model were not configured correctly. Only the oneapi channel was used for access, and the request format of other proxy services was not adapted.
How to verify correct configuration
- Upload a single long-text meeting material, check the chunk preview interface, confirm that the chunk length matches the preset value, and adjacent chunks have overlapping content.
- Initiate a retrieval request for the core key points of the meeting, check the similarity scores of the retrieval results, confirm that the scores fall within the preset threshold range.
- Upload multiple meeting materials on the same theme, test the search citation merging function, confirm that duplicate entries are correctly deduplicated.
- Switch the vector model to m3e, initiate a retrieval request, confirm that the interface call is successful and returns valid results.
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.