What the data for this category looks like
The ledger data for infrastructure construction mainly comes from project documents such as construction logs, material delivery acceptance slips, progress approval forms, and completion acceptance reports. Data updates follow project progress rhythms, added in batches weekly or by construction node during a single project cycle. Most individual documents are structured tables, containing fixed fields such as project number, construction section, material specification, incoming quantity (unit: ton, cubic meter, or square meter), inspection personnel, inspection time, etc. Some attachments include unstructured on-site photo description text.
What constraints do these characteristics impose on vector models and indexes
The structured fields of infrastructure construction ledgers are numerous and have clear units, which requires vector indexes to retain field semantic associations during encoding, avoiding matching deviations caused by unit confusion. Data updated in batches according to project nodes requires indexes to support incremental updates, reducing the computational overhead of full reconstruction. The length of individual documents varies greatly, ranging from tens of lines of tables to multi-page on-site descriptions, so indexes need to adapt to text blocks of different lengths, while avoiding long text blocks exceeding the processing limits of vector models. Most ledger data is associated with a unique project identifier, so indexes need to support grouped recall by project ID to improve accurate matching. FastGPT v4.17.0 has optimized related configuration logic for such scenarios.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the mixed length of structured tables and short text descriptions in ledgers, avoiding semantic fragmentation caused by too many associated fields in a single chunk |
| Overlap Length | 100–150 characters | Retains field association information between adjacent chunks, preventing loss of key information such as material models and project numbers across chunks |
| Similarity Threshold | 0.75–0.85 | Matches the structured semantic features of ledger data, filtering out low-relevance non-project matching results |
| Single Search Limit | Top 10 results | Controls the number of results recalled in a single round, adapting to the rapid integration of multi-dimensional information during ledger summary |
| Maximum Context | 6000 characters | Accommodates associated information of multiple ledgers under a single project, meeting the context filling requirements during report generation |
defaultToken | 4096 | Matches the input limit of general vector models, avoiding truncation errors triggered by long text blocks |
maxToken | 8192 | Adapts to the vector encoding requirements of long-document ledgers, covering the text length of most project files |
Three common mistakes
- Phenomenon: A 60-second timeout error occurs when calling the API. Cause: Incremental indexing is not configured for the ledger batch update scenario, and full index reconstruction occupies too many computing resources, resulting in request timeout.
- Phenomenon: The knowledge base status remains "training" or "rebuilding" for a long time, exceeding one day. Cause: The index reconstruction frequency is not adjusted according to project nodes, and full reconstruction covers multiple frequently updated ledger files, leading to task backlog.
- Phenomenon: The batch add index request returns an incorrect format. Cause: Request parameters are not passed according to the structured field format of the ledger, and the mapping configuration of required fields such as project number and material specification is missing.
How to confirm the configuration is correct
- Upload a single typical ledger file, check the chunk preview interface, and confirm that the chunk length and overlap length match the preset configuration.
- Launch a ledger summary query for a specific project, verify that the number of recalled results and project identifiers align with the preset single search limit.
- Test incremental update of a single ledger file, confirm that the index reconstruction time meets expectations, and there is no timeout risk triggered by full reconstruction.
- Check the vector model log output, confirm that the
defaultTokenandmaxTokenparameters do not trigger text truncation errors.
The values listed 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.