Reference only; verify against the target FastGPT release and deployment documentation.
What This Type of Data Looks Like
Data sources for survey and design work include survey field observation records, structural design calculation documents, industry engineering construction specifications, and project change negotiation documents. The update rhythm adjusts with the progress of individual survey and design projects or revisions of industry standards. The length of individual documents varies significantly, ranging from a few hundred words of point descriptions to tens of thousands of words of complete construction drawing design explanations. Document fields include project number, survey coordinates, material performance parameters, and compliance clause number. Units mostly involve engineering professional units such as meters, megapascals, and square meters.
What Constraints Do These Characteristics Impose on Vector Models and Indexes
The wide length span, dense technical terminology, and exclusive engineering units of survey and design documents require vector models to adapt to domain-specific semantic features, avoiding semantic deviation from general-purpose models. The multi-field document structure requires indexes to support precise filtering by fields such as project code and specification document number, to narrow the recall scope. The non-fixed update rhythm requires indexes to support incremental updates without full reconstruction, reducing resource usage. The high proportion of long paragraphs in professional documents requires adjusting the chunking logic to retain complete professional semantic units, avoiding semantic fragmentation.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the professional paragraph length of survey and design documents, retains complete semantics of parameter descriptions or specification clauses |
| Overlap Length | 100–150 characters | Prevents semantic breaks caused by truncation of professional paragraphs, connects context between adjacent chunks |
| Similarity Threshold | 0.72–0.85 | Filters low-match non-professional related results, adapts to the semantic similarity characteristics of engineering terms |
| Single Search Limit | Top 8 results | Balances recall accuracy and response speed, covers common consultation scenario requirements |
defaultToken | 4096 | Adapts to the context carrying requirements of long documents, matches the average length of engineering documents |
maxToken | 8192 | Reserves sufficient token space for integrating recalled chunks and user questions |
Three Common Misconfigurations
- Phenomenon: The knowledge base import generates a dataset.csv that only contains the index field, with no content field. Cause: Main body content extraction configuration was not enabled, and only document metadata was captured.
- Phenomenon: Connection timeout errors occur after integrating a vector model in a Docker deployment environment. Cause: Model access permission for the intranet environment was not configured, or port mapping parameters in docker-compose.yml were not correctly mapped.
- Phenomenon: After importing text using default chunking parameters, professional design paragraphs are truncated into multiple meaningless chunks. Cause: Default chunk length does not adapt to the long professional paragraphs of survey and design documents, leading to semantic fragmentation.
How to Verify Proper Configuration
- Upload a typical survey report document, check whether the system-generated chunks retain complete professional parameters and clause content.
- Submit a consultation question containing engineering technical terms, verify that the similarity scores of the recall results fall within the set threshold range.
- Test the vector model loading and query process in the intranet environment, confirm that no connection timeout errors occur.
- Export the dataset.csv file of the knowledge base, verify that the content field contains the main text of the document.
The values listed here are common starting points and should be validated 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.