What the Data for This Use Case Looks Like
Typical users of this use case include budget offices of finance departments, financial departments of budget units, and financial divisions of competent authorities. Daily processed documents include departmental budget drafts, project application documents, performance target forms, personnel expense calculation forms, and budget approval documents. Data sources mainly include the unified budget management system, departmental staff establishment system, prior-year final account data, project repository, and the Ministry of Finance’s budget preparation notices. The update frequency is once per year. Most documents contain structured fields and detailed data, covering content such as staff establishment numbers, project funding amounts, and performance indicators. Document formats vary, with some files using nested table and text structures.
Constraints Imposed on Vector Models and Indexes
The annual data update has a stable scale but large single-batch volume. This requires indexes to support incremental updates and batch import workflows, and adapt to the stability of batch data uploads. Document formats vary significantly across multiple sources, including text, nested tables and other structures. Vector models must adapt to feature extraction for different content types. Documents contain a large number of structured numerical fields such as staff establishment numbers and project funding amounts. Vector models must accurately capture numerical correlations and business logic. The party building learning Q&A scenario requires associating policy clauses with budget preparation requirements. Indexes must enable cross-document content association and retrieval.
Reference only; verify against the target FastGPT release and deployment documentation.
How to Set the Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the mixed content length of tables and text in budget documents, avoiding business logic breaks within chunks |
| Overlap Length | 100–200 characters | Retain cross-chunk context associations, ensuring complete coverage of continuous descriptions of budget projects |
defaultToken | Calibrated based on actual tests | Adapt to the mixed text length of party building policies and budget documents, avoiding exceeding the model’s input limit for single requests |
maxToken | 4096 token | Match the maximum input length of mainstream embedding models, compatible with processing requirements for batch budget documents |
| Similarity Threshold | 0.72–0.85 | Filter low-relevance retrieval results, ensuring Q&A content focuses on the association points between budget and party building |
| Single Search Limit | Top 8 entries | Control the scale of retrieval results, avoiding overly scattered response content |
Three Common Misconfigurations
- Phenomenon: After using chunk mode to call the pushdata API to upload budget documents, the interface remains in the "Indexing" state with no progress. Cause: The single batch of uploaded documents is too large, exceeding the concurrent processing threshold of the indexing service, and no retry mechanism for chunked uploads is configured.
- Phenomenon: An error "This token does not have permission to use the model" is returned when calling the embedding model. Cause: The access credential for the embedding model is not configured correctly, or the credential is not bound to the usage permission for the corresponding model.
- Phenomenon: A large number of irrelevant non-budget party building content are included in the retrieval results. Cause: The similarity threshold is set too low, and low-relevance retrieval entries are not filtered, leading to irrelevant content being mixed into the Q&A results.
How to Confirm Configurations Are Set Correctly
- Upload a single typical budget document, check whether the vector generation progress is completed within a reasonable time, and confirm that the chunk length and overlap length configurations match the document structure.
- Test calling the embedding interface for party building learning Q&A, verify that vector results can be returned normally, and confirm that the
defaultTokenandmaxTokenconfigurations do not exceed the model’s limits. - Input test questions that associate party building policies with budget preparation requirements, check the number and relevance of retrieval results, and adjust the similarity threshold to a range that meets business needs.
- Perform a batch upload of the annual budget document package, check whether the indexing status is completed normally, and confirm that the batch import configuration adapts to the data scale.
The values provided are common starting points and should be measured against the user'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.