What the data for this category looks like
Survey and design business data primarily comes from original project survey records, geological survey reports, transcribed text of architectural structure design drawings, construction organization designs, change negotiation records, industry specification clauses, and engineering calculation documents. Data is updated in real time as projects progress, with a high update frequency during a single project cycle. Data is archived after project completion. Most documents are long texts containing a large number of professional terms, numerical parameters, and coordinate information. Fields include project number, drawing version number, survey point coordinates, load parameters, and more. Common units include millimeters, megapascals, square meters, and cubic meters.
Constraints on Vector Models and Indexing from These Characteristics
The high share of long texts requires chunking strategies that balance semantic integrity and token limits. This avoids cutting off professional terms or continuous engineering logic. The real-time update feature requires indexes to support incremental updates. This accommodates frequent modifications during project progress. The presence of professional numerical and coordinate information requires indexes to support hybrid matching of text vectors and structured data. This ensures associated retrieval across different types of documents. In addition, a large number of engineering-specific terms require domain-fine-tuned embedding models. This improves the accuracy of semantic recall.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic integrity for long survey and design texts, avoiding cutting off professional terms or continuous calculation logic |
| Overlap Length | 100–200 characters | Compensates for semantic breaks at chunk boundaries, ensuring professional associations across chunks are retained |
| Similarity Threshold | 0.75–0.85 | Matches the professional semantic similarity range of engineering documents, filtering low-relevance general matching results |
| Single Search Limit | Top 10 results | Balances recall accuracy and context length, avoiding exceeding the model's processing limits |
maxToken | 8000 | Adapts to the processing requirements of long contexts in engineering documents, matching the token limits of mainstream engineering domain embedding models |
defaultToken | Calibrated based on actual measurements | Adapts to the actual token limit of the deployed embedding model, avoiding text truncation after chunking |
Three Common Mistakes
- Issue: A large number of irrelevant general engineering documents appear in search results. Cause: The similarity threshold is not adjusted for the professional semantics of survey and design, and default threshold parameters from general domains are used.
- Issue: Index tasks time out and fail, with logs showing token limit exceeded. Cause: A reasonable
maxTokenconfiguration is not set, and unchunked long documents are submitted directly to the vector model for processing. - Issue: Cross-document associated retrieval cannot be implemented, such as linking drawings and corresponding calculation documents. Cause: Only flat entity indexing is used, and no relational indexing is configured to adapt to cross-document professional association logic.
How to Confirm the Configuration Is Correct
- Use the knowledge base module of FastGPT v4.17.0, upload a typical survey report and transcribed text of design drawings, perform professional term retrieval, and check whether the professional matching degree of the returned results meets expectations.
- Check the running logs of index tasks to confirm that there are no token limit exceeded or timeout errors, and verify that the
maxTokenconfiguration matches the actual document length. - Test cross-document associated retrieval to verify that drawing text and corresponding design descriptions can be recalled simultaneously.
- Adjust the similarity threshold, observe the change in the number of search results, and confirm that the threshold setting meets business screening 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.