This page covers configuration, parameters, and failure handling logic for vector models and indexes, tailored to the declaration materials and file review stage in department budget preparation scenarios for state-owned enterprises, government entities, and public service sectors. It adapts to the unique data characteristics of this category.
What This Category's Data Looks Like
The core documents for this category include department budget drafts, project application forms, performance target forms, personnel expense calculation forms, budget approval documents, and similar materials. Data is primarily sourced from unified budget management systems, department staffing systems, prior year final accounts, project repositories, and budget preparation notices issued by the relevant fiscal authority. Data updates follow an annual cycle. Most documents contain fields such as project codes, expense amounts, performance indicators, and compiling units, with units including currency values, headcount, and percentages.
What Constraints These Characteristics Impose on Vector Models and Indexes
Full historical data updated annually requires indexes to support bulk loading, avoiding repeated processing of prior year budget documents. Structured budget documents with multiple fields require vector models to adapt to semantic associations between structured fields. Differences in data formats across source systems require index configurations to support unified parsing and standardization of multi-source data. The rigor of budget review requires index recall results to accurately match core budget fields, filtering irrelevant content to reduce interference with the review process.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the typical length of single project declaration content in budget documents, avoiding splitting that breaks semantic integrity |
| Overlap Length | 100–200 characters | Covers cross-chunk budget project related information, preventing key review basis from being split and lost |
maxToken | 4096 token | Matches the input limit of mainstream vector models, adapting to the vectorization needs of long paragraphs in budget documents |
| Similarity Threshold | 0.75–0.85 | Filters low-match non-budget-related content, meeting the rigor requirements of the review process |
| Single Search Limit | Top 10 results | Controls the scale of recall results, avoiding redundant information interfering with core judgments in budget review |
defaultToken | Calibrated based on actual testing | Adapts to the average token proportion of different budget documents, optimizing index loading and retrieval efficiency |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Mistakes
- Phenomenon: Calling the language model after creating an index returns empty results. Cause: The vector index and language model invocation chain are not properly associated, causing retrieval results to not be passed to the context window.
- Phenomenon: Index creation times out in local deployment versions. Cause: No external index service is configured, using the built-in vector library to process annual full budget data exceeds memory or time limits.
- Phenomenon: Recall results are abnormal after vector storage migration. Cause: No field mapping verification is performed on the migrated index data, leading to loss of semantic associations between structured budget fields.
How to Verify Proper Configuration
- Upload a single department budget draft document, perform index creation, and check that the index status returned in the FastGPT interface is completed.
- Enter query terms related to budget review, and verify that the recall results include core document types such as project application forms and performance target forms.
- Compare the number of index recall results before and after vector storage migration, confirming that data completeness meets expectations.
- When testing language model calls, ensure retrieval results are automatically included in the context window, with no empty content errors.
The values provided here are common starting points, and should be evaluated 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.