Context and Token for Water Industry Investment Research

What This Type of Data Looks Like

Data sources for water industry investment research include internal SCADA dispatching systems of water utilities, water supply operation ledger systems, public utility statistical bulletins from housing and urban-rural development departments, operation white papers from industry associations, and project bidding documents.

Data update frequencies fall into three categories: SCADA real-time monitoring data updates every second, monthly operation reports update once per month, and project documents such as feasibility study reports and bidding documents update irregularly.

Document structures include structured Excel ledgers (each sheet contains thousands of business records), multi-chapter PDF feasibility study reports, and plain text policy documents. Core field units include water supply volume in cubic meters, pipe network pressure in megapascals, water turbidity in NTU, project investment amount in ten thousand yuan, and operation duration in hours.

What Constraints Do These Characteristics Impose on Context and Token Management

The multi-type and large-scale characteristics of water industry investment research data impose multiple constraints on context and token management.

The token count of a single structured ledger after transcription can reach tens of thousands, far exceeding the token limit of general documents. Directly passing such data will trigger the model's length restriction.

The second-level update feature of real-time monitoring data requires the context to cover the latest monitoring values. If the recall range is too wide, it will quickly exhaust the model's token quota.

Differences in field units across different business scenarios require unified unit annotations in the context, which increases additional token consumption.

Professional content in long documents such as feasibility study reports requires complete context to support analysis. Direct transmission will occupy a large number of tokens, so targeted adjustments to recall and truncation rules are needed.

How to Configure the Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000–12000 token`	The token count of a single core water industry operation ledger after transcription is relatively high. It is necessary to cover at least 3 months of data and the latest monitoring data to avoid token overflow
`chunkSize`	`1500–2000 characters`	The transcribed character count of a single field group of water industry structured ledgers is approximately 1000-1800 characters. Excessively long segments will destroy data relevance, while excessively short segments will increase token consumption
`similarityTopK`	`Top 6–8 entries`	Water industry investment research needs to cover three core data types: water supply, pipe network, and water quality. Excessive recall will increase the token burden, while insufficient recall will lose key comparative information
`PARSE_FILE_MAX_SIZE`	`2000 MB`	A single water industry feasibility study report can exceed 500 MB, so support for large file upload and parsing is required
`tokenLimitPerMessage`	`10000 token`	Investment research prompts need to include multiple sets of business data and analysis instructions, so sufficient token space must be reserved to complete complete analysis
`contextWindowAdjust`	`Enabled`	The token fluctuation range of water industry data is relatively large, and automatic adjustment can adapt to the token requirements of different scenarios

The parameter values provided on this page are conventional recommendations used as a starting point for configuration. The actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to conduct tests on your own samples before finalizing the configuration.

Three Common Misconfigurations

A 422 "Messages token length must be less than or equal to X" error is returned when calling the model. The cause is that the incoming context token count exceeds the model's preset limit, and the maxContext parameter has not been adjusted for water industry long documents and structured ledgers.
The model operation statistics interface only displays the first token latency, and does not show the full context token consumption, which does not match the actual billing rules. The cause is that the fullTokenStat configuration item is not enabled, and the system only records the first token loading time, and does not count the full context token consumption.
The recalled water industry data field units are inconsistent, resulting in unit errors in the model's analysis results. The cause is that unit standardization preprocessing is not enabled in the chunkPreprocess configuration, and the units of water industry data from different sources are not unified, which increases unnecessary token consumption.

How to Verify That the Configuration Is Correct

Upload a typical water industry monthly operation ledger, check the parsed token statistics, and verify whether it is within the range set by maxContext.
Initiate an investment research test call, check whether the returned context fragments include unified unit annotations, and confirm that the preprocessing configuration takes effect.
Check the model call logs to confirm that the counted token consumption matches the upper limit set by maxContext, with no abnormal overflow.
Compare the online and offline model operation statistics data to confirm that the correct token statistics rules have been configured, and the display meets expectations.

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.