Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
The data for this category mainly comes from the budget management integrated system, department staff establishment system, prior year final accounts, project repository, and budget formulation notices issued by the Ministry of Finance. The update frequency is annual. Daily processed documents include department budget drafts, project application documents, performance target forms, personnel expense calculation forms, and budget approvals. The data contains both structured table fields and unstructured natural language content. Fields include project code, applied amount, staff establishment number, performance target indicators, and other related items.
What constraints these characteristics impose on the vector models and indexes link
The annual update feature means indexes do not require frequent incremental synchronization. However, the volume of data for a single full index is large, so batch processing logic must be supported. The mixed structured and unstructured document format requires vector models to support both table field encoding and natural language text encoding. Data fields contain numeric content such as amount and staff establishment number, so vector encoding must preserve the semantic association of numerical values. The strict requirements of department budget review mean the accuracy of index recall must match review standards, to avoid missing key budget application information.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph and table block lengths of department budget documents, avoid cross-semantic chunking |
| Overlap Length | 100–200 characters | Retain cross-chunk budget project association information, prevent semantic breakage |
maxToken | 8192 token | Match the maximum input limit of text-embedding-ada-002, adapt to long budget documents |
defaultToken | Calibrated based on actual measurements | Adapt to the input length requirements of locally deployed vector models such as m3e |
| Similarity Threshold | 0.75–0.85 | Filter low-correlation historical budget data, match the rigor requirements of the review process |
| Single Search Limit | Top 10 entries | Control the amount of returned information, adapt to the batch processing scenario of budget review |
Three common mistakes
- Phenomenon: "No results available" type errors appear when calling the knowledge base. Cause: Vector models such as text-embedding-ada-002 have not been correctly added to the open API channel, or the configuration file has not taken effect synchronously.
- Phenomenon: When using a locally deployed m3e vector model, the interface continuously displays the "Indexing" status. Cause: The timeout threshold for batch indexing has not been configured, or the local model's inference speed is insufficient, leading to task blocking.
- Phenomenon: Search speed is too slow. Cause: The search speed issue is incorrectly attributed to the vector model. The context length of the language model and the redundancy of recall results are not investigated, resulting in resource occupation by invalid calculations.
How to confirm the configuration is correctly set
- Check the vector model channel configuration, confirm that the target model has been added and synchronized to the system configuration file.
- Initiate a small-batch test indexing, verify that the interface completes the indexing status transition normally, with no continuous blocking prompts.
- Initiate a test search, confirm that the correlation and quantity of returned results meet expectations, with no abnormal errors.
- After adjusting configuration items, compare search time and recall results, confirm that the parameters match business requirements.
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.