What the data for this category looks like
The data for this category mainly comes from project document libraries, configuration lists, meeting minutes, and test reports. Daily circulating documents include completed construction documents, configuration manuals, training records, and acceptance certificates. The data update rhythm is triggered at the end of each project phase or when a single project is completed. Documents usually contain fields such as project association number, version number, generation time, and signature identification. The size of a single document ranges from hundreds of characters to tens of megabytes. There is no fixed unified format. Some files contain mixed content of structured tables and unstructured text.
What constraints these characteristics impose on the vector models and indexes link
The data sources for this category are scattered and mixed in format, including structured tables and unstructured text. This requires the indexing link to support parsing and adaptation of multiple types of files. The data update rhythm is not real-time, only triggered at project phases or upon completion. Incremental indexing can be used to save computing resources. Documents contain metadata such as project association numbers and version numbers. Corresponding fields must be retained during indexing to support precise retrieval by project dimension. The size of single files varies greatly. The upper limit of single file chunking must be restricted to avoid memory overflow. Non-text identification fields such as signatures must be processed separately to avoid interfering with vector matching accuracy.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the mixed format text length of single documents in this category, avoids overly fragmented or overly long chunks that affect vector accuracy |
| Overlap Length | 100–200 characters | Retains contextual association between adjacent chunks, prevents project association information from breaking across chunks |
maxToken | 4096 token. This is only a configuration example for models supporting this length. The actual value should be determined based on the selected vector model documentation and actual interface testing | This is the maximum input token count for the vector model. Do not treat the example value as the input upper limit for all models |
| Similarity Threshold | 0.75–0.85 | Filters low-match irrelevant documents, meets the precise retrieval requirements of project document handover and archiving |
| Single Search Limit | Top 10 entries | Controls the number of returned results, avoids redundant information interfering with the handover and archiving verification process |
defaultToken | Calibrated based on actual testing | Adapts to token calculation rules for different deployment environments, avoids token overflow errors during indexing |
Three Common Mistakes
- Files imported into the dataset remain in the indexing state for a long time. Cause: The chunk length is not adjusted for large-volume mixed documents of this category, resulting in too many chunks per file and a backlog of indexing tasks.
- Vector calculation scores are abnormally high, and multiple results have identical scores. Cause: A reasonable similarity threshold is not set, or document metadata is not filtered separately, leading to disrupted matching logic.
- The number of entries in a CSV file with 100,000 entries after import does not match expectations. First check parsing success, failure, and pending retry records. Then check null value filtering, deduplication, and input length.
defaultTokenis the default segment token count, andmaxTokenis the maximum input token count of the used vector model. Do not determine the root cause solely based on the change in quantity.
How to Confirm Proper Configuration
- A single typical completed construction document is uploaded. Chunk preview results are reviewed to confirm the chunk length falls within the preset range, and adjacent chunks contain overlapping content.
- Preset project association keywords are retrieved. The similarity distribution of returned results is reviewed, and the similarity threshold is adjusted to a range that meets business verification requirements.
- A 100,000-level CSV test file is imported. Parsing and import status are verified against source record identifiers. The number of segments for each source data entry is recorded. The number of vector segments cannot be directly treated as the number of CSV rows.
- Indexing logs of FastGPT v4.17.0 are reviewed to confirm there are no token overflow or timeout errors, and that matching task execution duration meets expected standards.
The values provided here are common starting points. They should be evaluated using samples tailored to the specific deployment context.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.