What this type of data looks like
Infrastructure engineering investment research data primarily originates from official design drawings, bidding announcements, weekly or monthly engineering progress reports, material cost quota documents, and industry policy specifications. Data update frequency is adjusted based on project progression. Bidding stages see concentrated updates of announcements and quota documents, while construction stages see weekly or monthly updates of progress data. Document formats include dozens-of-page feasibility study PDFs, structured cost tables, and parsed structured text from drawings. Fields include project volume, material unit prices, construction cycles, and more. Units are mostly engineering-specific measurement units such as cubic meters, tons, and ten thousand yuan.
What constraints do these characteristics impose on deployment and upgrade?
Infrastructure engineering investment research data includes long-text feasibility study reports, structured cost tables, and parsed professional drawing text. Individual documents have large file sizes and complex fields, which impose clear constraints on knowledge base parsing timeout settings and chunking rules. Data update frequency fluctuates with project phases; construction stages require high-frequency synchronization of progress data, so the upgrade process must support incremental update configurations. Engineering-specific measurement units and fields must match preset entity extraction rules. Industry term dictionaries must be imported in advance during deployment to avoid entity recognition errors. Additionally, the need for format compatibility across multi-source heterogeneous data requires configuring linkage adaptation rules for multiple parser types during deployment.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large individual infrastructure engineering feasibility study reports have long parsing durations, so extending the timeout avoids parsing interruptions |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Meets the upload requirements for large files such as single engineering drawings and complete feasibility study reports |
maxContext | 8000–12000 characters | Infrastructure engineering documents are long and dense with professional terms, so expanding the context window preserves complete semantic connections |
RECALL_TOP_N | Top 10–15 results | Investment research scenarios require coverage of multi-dimensional engineering data, so increasing the number of recalled results ensures information completeness |
reranker_top_n | Top 5–8 results | Filters redundant recalled results to focus on core engineering parameters and cost information |
PARSE_CHUNK_OVERLAP | 200 characters | Retains overlapping content when chunking long documents, preventing professional terms from being split across different paragraphs |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material formats, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- A "failed to pull redis image" error occurs when running
docker-compose up -d. The cause is that the Alibaba Cloud image acceleration address is not configured, and the default official image pull process is restricted. - Model calls return abnormal results after deploying bge-reranker. The cause is that model loading parameters and port mapping rules are not correctly configured in the
environmentfield ofdocker-compose.yml. - Slow workflow response occurs when running investment research workflows on an 8-core 32GB machine. The cause is that context window and recall count parameters are not adjusted for infrastructure engineering document sizes, leading to per-request data processing volume exceeding hardware capacity limits.
How to confirm proper configuration
- Upload a 100MB or larger engineering feasibility study report PDF, and confirm the parsing task completes within the preset timeout period without errors.
- Run the
docker pscommand, and confirm that the redis, bge-reranker, and FastGPT service containers are all running. - Create a test query containing engineering professional terms, and verify that the number of recalled results and reordered returned results match the preset configurations.
- Simulate uploading new engineering progress data, and check that the knowledge base only synchronizes new content without performing a full knowledge base rebuild.
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.