Citation Source and Traceability for Railway and Highway

What the Data for This Category Looks Like

Railway and highway financial report data mainly comes from publicly released statistical documents from transportation authorities, annual and quarterly reports of listed transportation enterprises, and monthly operation briefings of railway operating institutions.

The update schedule follows monthly industry statistics, quarterly corporate operation data, and annual full financial reports. Document structures include core fields such as operating mileage, passenger and freight turnover, revenue composition, and fixed asset depreciation. Units include kilometers, ten thousand people, ten thousand tons, and hundred million yuan, among others. Each financial report contains structured tables and unstructured written explanations, with total lengths ranging from dozens to hundreds of pages.

Constraints for the Citation Source and Traceability Link

Differences in statistical standards across multiple sources of railway and highway financial report data require marking the data publishing entity and statistical cycle during the traceability link, to avoid confusion of values from different statistical systems.

For monthly updated industry operation data, a high-frequency recall trigger logic must be configured to ensure that the latest published content is cited.

Each document has a long length and contains structured tables and unstructured text, so splitting must be done according to financial report chapters or data modules to avoid context breaks caused by cross-module citations.

For content mixing structured fields and unstructured explanations, structured matching and unstructured text recall rules must be configured separately, to ensure that the exact page number or table position of the original data can be located during traceability.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`chunkSize`	`800–1200 characters`	Railway and highway financial reports contain long paragraphs of business analysis and table explanations. This range ensures that a single chunk contains a complete business logic unit, while avoiding reduced recall accuracy caused by overly long chunks
`recallTopK`	`Top 6–8 results`	Core data of railway and highway financial reports is scattered across multiple chapters. Too many recalled results will introduce irrelevant content, while too few will fail to cover all key data. Adjust the range based on the information density of financial reports
`similarityThreshold`	`0.72–0.85`	Financial report data contains a large number of professional terms. A relatively high similarity threshold is needed to filter irrelevant recall results, while ensuring that core data is not accidentally filtered out
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	A single railway and highway financial report document contains a large number of tables and text content. Sufficient timeout is required to ensure complete parsing
`sourceDisplayMode`	`Show full source path`	Traceability of railway and highway financial reports requires clear labeling of publishing institutions, release times, and specific page numbers. A full path meets compliance requirements
`MAX_EMBEDDING_BATCH_SIZE`	`Set based on actual testing`	Adapt to the long text processing requirements of railway and highway financial reports, avoiding timeouts caused by overly large single batch processing data volume

The parameter values provided on this page are common recommendations for starting point configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Configuration Mistakes

Issue: A request timeout may occur when using the text-embedding-3-large model. Restarting the service after commenting out the model configuration does not resolve the issue. Cause: Railway and highway financial report documents have a long length, and the MAX_EMBEDDING_BATCH_SIZE parameter was not configured to limit the single batch processing data volume, leading to excessive model resource usage causing timeout.
Issue: The generated answer does not cite the original question-answer pairs in the knowledge base, but instead generates new expressions. Cause: The strict_reply_mode parameter was not enabled, or the similarityThreshold setting for recall results does not match business requirements, making it impossible to lock the original question-answer pairs.
Issue: The knowledge base citation variable cannot be selected in the workflow's code run node, and the first search result cannot be output. Cause: The knowledge base recall node was not added first in the workflow and bound to the corresponding knowledge base, so the code node cannot obtain the context output variable of the knowledge base.

How to Verify Successful Configuration

View the parsed text chunks in the knowledge base, confirm that each chunk's length falls within the configured chunkSize range, with no overly long or short split units.
Initiate a test query, check the source field in the returned results, confirm that complete traceability information such as data publishing entity, release time, and specific page numbers is included.
Adjust the similarityThreshold value, verify that the number and relevance of recall results meet business requirements, and ensure that core data is correctly recalled.
Run a workflow test, confirm that the code run node can obtain the output variable from the knowledge base recall node, and can normally select the corresponding search results.

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.