What the data for this use case looks like
Internal party building data sources include official documents issued by superior party committees, learning activity records submitted by grassroots party branches, and internal group party building manuals. Superior policy documents update immediately upon release. Internal daily learning materials update quarterly. Documents typically include title, main text, issuing body, and release date. Fields include classification tags, document owning unit, and creation time. Supported formats are primarily PDF and Word.
What constraints these characteristics impose on vector models and indexing
Indexes must support unified access and incremental updates for multi-source scattered data. Data sources include official superior party documents, grassroots activity records, and more. Document structures vary across sources. Some files only contain plain text main content. Others have formatted layouts. Vector models must accurately extract core semantic content. Fields such as classification tags and document owning units provide pre-filtering dimensions for indexes. These dimensions narrow retrieval scope. Materials with different update schedules require batch incremental indexing logic. This avoids resource consumption from full reindexing. A high share of documents use PDF and Word formats. The text extraction process must fully retain core content. No key information can be lost.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Aligns with the main text length of party building documents, avoiding overly scattered or redundant single chunks |
| Overlap length | 100–200 characters | Ensures semantic continuity between adjacent chunks, preventing key information from being truncated at chunk boundaries |
defaultToken | 1024 | Matches the semantic extraction needs of most general party building text, and can be adjusted after calibration based on actual testing |
maxToken | 4096 | Adapts to the upper limit of vector generation for long documents, avoiding exceeding the model's supported range |
| Reranking | Enabled, take top 3 results | Filters redundant content from initial recall, improving the accuracy of party building Q&A |
| Single search limit | Top 20 results | Balances retrieval efficiency and recall coverage, matching the scale characteristics of internal materials |
Three common configuration errors
Error 1
Phenomenon: Calls to a private reranking model return a 500 status code, or results show no sorting change. Cause: FastGPT configuration does not correctly specify the private reranking model’s deployment address and authentication parameters. This causes model call failure.
Error 2
Phenomenon: Retrieval results only return a small number of entries. They fail to cover all relevant party building documents. Cause: The system does not set a reasonable single search limit and similarity threshold. Or incremental indexing is not enabled, so old data is excluded from retrieval scope.
Error 3
Phenomenon: Generated answers deviate from the party building theme. They include irrelevant content. Cause: The system does not use classification tag fields for pre-filtering. Or the vector model is not adapted to party building field semantic characteristics, leading to recall content deviation.
How to confirm the configuration is properly set
- Access the FastGPT knowledge base management page. View the chunked document preview. Confirm chunk length and overlap length match the preset configuration. Ensure no obvious semantic truncation occurs.
- Submit a party building-related test question. Check the number and ranking of retrieval results. Confirm the reranking function is active. Ensure result relevance to the question meets expectations.
- Upload a new party building document. Check the indexing task status logs. Confirm incremental indexing runs normally. Confirm the new document can be retrieved properly.
- Check vector model call logs. Confirm
defaultTokenandmaxTokenvalues stay within the model’s supported range. Ensure no call errors occur.
The values listed here are common starting points, and should be evaluated against the actual samples used in deployment.
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.