What data looks like for this category
Construction engineering investment research data sources include industry quota standard documents, design institute construction drawings, project bidding documents, cost analysis reports, and monthly progress ledgers. Update rhythms vary by document type. Bidding documents update in real time with project progress. Industry quota standards update once per year. Construction drawings and cost reports update in stages as projects advance. Document structures include structured tables such as bill of quantities and material price lists, and unstructured long text such as feasibility study reports and construction plans. Core fields include project number, engineering category, material specification, work quantity, and project milestones. Corresponding units include engineering-specific units such as cubic meters, tons, and days.
What constraints these characteristics impose on context and token management
Long text and structured features of construction engineering investment research documents create multiple constraints for context and token management. A single feasibility study report or full set of construction drawings can reach dozens or hundreds of pages. The total token count after parsing easily exceeds the model's context window limit. Structured fields are numerous and closely linked. If too many irrelevant fields or segments are retrieved, additional token resources will be occupied, leading to truncation of valid information. Differences in update frequencies across document types require context retrieval to distinguish between temporarily updated bidding documents and statically updated quota standards. Frequent loading of frequently updated documents should be avoided to prevent token overflow. In continuous questioning scenarios, project-specific parameters such as concrete unit prices for a specific section must be retained. If context is truncated too early, subsequent responses cannot associate prior information.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Construction engineering documents often contain professional tables and long paragraphs. This range balances the integrity of single-segment information and token usage, and is compatible with the parsing logic of FastGPT 4.9.7 |
similarityTopK | Top 3–5 entries | Construction engineering fields are closely linked. Too many retrieved entries will exceed the context token limit, while too few will miss critical cost or progress information |
similarityThreshold | 0.75–0.85 | Filters low-relevance engineering document content, avoids irrelevant terminology occupying tokens, and matches the precision requirements for professional terminology |
UPLOAD_FILE_MAX_SIZE | 50–100 MB | Single construction engineering documents such as full sets of construction drawing PDFs have large file sizes. This range avoids upload timeouts and token overflow |
contextWindowRatio | 0.6–0.7 | Reserves tokens required for the model to generate responses, preventing context from filling the entire window and causing response failures |
rerankTopN | Top 2–3 entries | Retains core relevant content after reranking retrieval results, reducing redundant token consumption |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Each scenario requires targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: Token overflow errors occur after uploading documents that exceed the model's supported context, or parsed document content is truncated. Cause: The
chunkSizeandUPLOAD_FILE_MAX_SIZEconfigurations are not adjusted, and single-segment or single-file token usage exceeds model limits. - Phenomenon: Starting from the second question in a continuous questioning session, responses become irrelevant and fail to associate with project-specific parameters from prior steps. Cause: Context memory configuration is not enabled, or
contextWindowRatiois set too low, leading to early truncation of prior context. - Phenomenon: Workflow API calls return results without context information, and the
contextfield in the API response is empty. Cause: The session ID for context association is not bound in the workflow node, or context transfer parameters are not configured.
How to confirm the configuration is correctly set
- Upload a single 100-page construction engineering feasibility study report, check the number of parsed segments and the character count per segment to confirm they fall within the
chunkSizeconfiguration range. - Launch three consecutive rounds of questioning about the same construction project, verify that each round of responses associates with prior project parameters, with no irrelevant answers.
- Call the workflow API, include the session ID parameter in the request body, check if the returned results contain prior conversation context information.
- View the token consumption statistics in the knowledge base backend, confirm that token usage per conversation round does not exceed the preset proportion of the model's window.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.