Model Access and Configuration for Infrastructure

What the data for this category looks like

Infrastructure engineering investment research data mainly comes from official bidding announcements, project budget documents, construction log ledgers, industry cost quotas and project compliance standard documents. Data update frequency adjusts dynamically with project progress. Bidding data updates in real time alongside project approval and opening. Cost quotas follow industry release cycles. Document structures typically include fields such as bid package division, bill of quantities, material unit prices, schedule milestones and compliance clauses. Units include cubic meters, tons, ten thousand yuan, workdays and other engineering-specific measurement identifiers.

What constraints these characteristics impose on model access and configuration

The scattered nature and specialized field properties of infrastructure engineering investment research data require the model access link to support multi-format long text parsing. Dynamically updated data sources require configuring scheduled synchronization trigger rules to prevent knowledge base content from lagging. Engineering-specific measurement fields and units require the model to accurately match context associated with units during recall and reranking, to avoid invalid recall. The multi-bid package, multi-milestone document structure requires configuring segment parsing to retain bid package association information, preventing context fragmentation from impacting investment research judgments.

How to set the configurations

Configuration Item	Recommended Setting	Rationale
`maxContext`	`8000–12000 characters`	Meets the context recall requirements for single long documents in infrastructure engineering, avoids truncating critical engineering data
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates the processing cycle for large project bills of quantities, drawing and similar documents
`rerank_top_n`	`Top 8–12 results`	Covers multi-bid package, multi-dimensional engineering investment research information, avoids missing critical data
`SIMILARITY_THRESHOLD`	`0.75–0.85`	Filters low-relevance non-engineering recall results, focuses on professional investment research data
`TOOL_CALL_ENABLED`	Enabled	Supports calling specialized tools such as engineering quota queries and quantity calculations to supplement investment research basis
`LOCAL_MCP_SERVICE_URL`	`http://127.0.0.1:8765`	Connects to locally deployed infrastructure engineering-specific MCP services to enable customized tool calls

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, 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

Phenomenon: After enabling the tool call function, the model output only contains tool call results, with no intermediate thought process. Cause: The TOOL_VERBOSE parameter was not configured to enable thought process output.
Phenomenon: When selecting a question category on the knowledge base configuration page, the target model cannot be selected from the drop-down menu. Cause: The corresponding model identifier was not added to the MODEL_SELECTOR_WHITELIST configuration item, or the model service is not properly connected.
Phenomenon: After deploying the rerank model, the reranking step is not triggered during retrieval. Cause: The RERANK_MODEL_PATH parameter was not correctly filled in the system configuration, or the reranking function switch was not enabled.

How to confirm the configuration is complete

Upload a single long document, check that the parsed text is not truncated, and verify that the PARSE_FILE_TIMEOUT_SECONDS configuration matches the document processing duration.
Submit a retrieval request containing engineering-specific units, check that recall results focus on professional engineering data, and verify that the SIMILARITY_THRESHOLD and rerank_top_n configurations meet scene requirements.
Trigger a tool call request, check that the model output includes both the thought process and tool call results, and verify that the TOOL_CALL_ENABLED and TOOL_VERBOSE parameters are correctly configured.
Access the configured local MCP service address, check that the corresponding service can be properly connected during tool calls, and verify that the LOCAL_MCP_SERVICE_URL configuration is correct.

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.