Knowledge Base Retrieval and Recall for Infrastructure

What this category of data looks like

Infrastructure construction investment research data is mainly sourced from bidding documents, construction logs, project budget documents, industry quota standards, and on-site monitoring reports. Update rhythm varies by project phase: bidding documents update with project approval, construction logs sync daily, and quota standards update per industry release cycles. Documents combine structured lists and unstructured reports, containing fields such as section number, project quantity, material unit price, schedule milestones, and units including cubic meters, square meters, man-hours, ten thousand yuan, and other engineering-specific measurement standards.

What constraints these characteristics impose on knowledge base retrieval and recall

Dispersed sources and uneven update cycles of infrastructure engineering data lead to inconsistent timeliness of knowledge base content. The retrieval link needs to distinguish recall weights between static industry quotas and dynamic project data to avoid outdated content interfering with results. Documents contain a large number of engineering-specific fields and measurement units, so general tokenization models cannot accurately recognize specialized terminology, requiring adjustment of tokenization rules to adapt to engineering context. The length of single documents varies widely, with some construction reports reaching tens of thousands of characters. Text exceeding the context window requires pre-segmentation, and segmentation must retain associated fields such as section and schedule milestones to avoid losing context links during retrieval. Document structures differ significantly across projects, so standardized recall rules cannot fit all sections, requiring support for filtering recall results by project dimension.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`chunk_size`	`800–1200 characters`	Infrastructure engineering documents contain long sentences of quantity descriptions and specialized terminology. 800–1200 characters preserves the integrity of single-segment engineering logic and avoids splitting that disrupts professional context.
`recall_top_k`	`Top 10–15 results`	Infrastructure construction investment research requires balancing comprehensiveness and accuracy. Too many recall results increase subsequent processing burden, while too few may miss key section or material data.
`similarity_threshold`	`0.72–0.85`	Semantic similarity for engineering specialized terminology needs to be higher than general scenarios. A threshold that is too low will introduce irrelevant non-engineering documents, while a threshold that is too high will fail to recall reference data for similar sections.
`parse_file_timeout_seconds`	`300 seconds`	Parsing large engineering budget documents or monitoring reports takes significant time. 300 seconds covers the parsing needs of most long documents.
`enable_rag_chunk_filter`	`Filter by project ID`	Infrastructure engineering documents are mostly managed by project dimension. Filtering by project ID can quickly locate target project investment research data and avoid interference from cross-project data.
`max_context_length`	`8000 characters`	Infrastructure engineering retrieval results need to splice multiple relevant data segments. 8000 characters covers the core analysis needs of a single investment research report.

The parameter values provided on this page are all conventional recommendations used to determine a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.

Three Common Misconfigurations

Scenario: When a DOCX document containing images is uploaded, the interface returns a 400 Bad Request error, prompting that the file format is incorrect. Cause: The enable_image_parse configuration item is not enabled, preventing the parsing module from processing embedded image content in the document.
Scenario: When the file list interface is called, the returned file_list field is empty. Cause: The project_id query parameter is not specified, or the parameter format is incorrect, causing the system to fail to match the document list of the corresponding project.
Scenario: After the question and answer splitting mode is selected, no image-related question and answer pairs can be generated. Cause: The image_qa_split configuration item is not enabled, and the pre-processing flow for image parsing is not configured.

How to Verify Successful Configuration

When a DOCX document containing engineering specialized terminology is uploaded, verify that the parsed text retains core fields such as project quantity and section number to confirm the tokenization and parsing configurations are active.
Initiate a retrieval request, and check if the number of returned results meets the configured recall number requirements to verify that the recall number configuration is correct.
Call the file list interface, pass the correct project_id parameter, and confirm that the returned file_list contains the expected engineering documents to verify that the filtering configuration is active.
Upload a DOCX document containing images, and confirm that the parsed text includes OCR recognition content of images to verify that the image parsing configuration is enabled.

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.