What Data for This Category Looks Like
The data used for declaration materials and file review in telecommunications engineering construction mainly comes from official documents including project establishment declaration forms, site survey records, material submission forms, and completion files. Data receives staged updates as the project progresses, imported in batches across the full process from project initiation to acceptance. Document structures include structured bills of quantities, long text paragraphs with professional parameters. Fields cover project number, project owner, construction contractor, material specifications, acceptance conclusion, and other relevant items. Units include standard engineering units such as item, meter, square meter, set, and similar units.
What Constraints These Characteristics Impose on Vector Models and Indexing
The coexistence of structured bills of quantities and long text in document structures requires that indexing chunking balances the semantic integrity of table content and the contextual relevance of long text. Staged batch-updated data requires adapting to incremental indexing configuration logic. The presence of multiple types of engineering units and professional terms requires retaining field metadata in indexing to avoid matching confusion. Different source document formats such as PDF, Word, Excel require configuring compatible parsing rules to ensure chunking accuracy.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text length of long construction plans and acceptance reports in telecommunications engineering documents, avoiding splitting that disrupts the semantic integrity of professional terms |
| Overlap Length | 100–150 characters | Retains contextual relevance between adjacent chunks, preventing breakage of cross-chunk engineering terms |
maxToken | 8192 | Adapts to the token limit of most general embedding models, compatible with the token count of chunks after long document parsing |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Distinguishes similar bill of quantities entries in declaration materials, avoiding mismatches |
| Single Search Limit | Top 10 entries | Covers multiple associated materials that need to be linked during file review, ensuring retrieval completeness |
defaultToken | 4096 | Configures the system's default context token quota, adapting to multi-document associated queries for telecommunications engineering review |
Three Common Misconfigurations
- Phenomenon: In a Docker deployment environment, indexing tasks remain in a running state with no progress for an extended period, and the chat function can normally call the configured large model. Cause: Correct document parsing rules are not configured for multi-format telecommunications engineering documents, such as PDFs exported from CAD or Word documents with nested tables, leading to chunking blockages.
- Phenomenon: The number of vector retrieval return results does not match the configured single search limit, and some similar files are not recalled. Cause: Field metadata of documents is not retained, leading to confusion of quantity entries with different units during vector matching.
- Phenomenon: Indexing tasks report an error indicating token limit exceeded, and chunking and storage cannot be completed. Cause: A reasonable
maxTokenparameter is not set, causing the token count of chunks generated from long texts to exceed the model's limit.
How to Confirm Proper Configuration
- Upload a typical telecommunications engineering construction plan document, check the chunking preview interface, confirm that long text is not overly split and the integrity of professional terms is retained.
- Initiate a file similarity retrieval, verify that the number of returned results matches the configured single search limit, and the results include associated bill of quantities entries.
- Check system logs, confirm that indexing tasks do not display error messages for token limit exceeded or parsing failure, and that tasks complete chunking and storage within 300 seconds.
- Adjust the similarity threshold and perform retrieval again, observe changes in the matching accuracy of returned results, confirm that the threshold configuration meets business review requirements.
The values given 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.