Context and Token for Thermal Investment Research Knowledge

What the data for this category looks like

Thermal investment research data primarily comes from pipeline monitoring systems of thermal operation enterprises, energy consumption settlement reports, and government thermal supply supervision ledgers. Update cycles cover real-time, daily, and monthly dimensions. Real-time data includes indicators such as supply and return water temperature, pressure, and instantaneous flow at pipeline nodes. Daily and monthly data includes regional total heat supply, unit energy consumption, user payment records, and other metrics. Most individual documents are structured tables or time-series CSV files. Fields include timestamp, equipment number, parameter value, corresponding unit of measurement, and some text descriptions associated with upstream and downstream heating demand.

Constraints on context and token usage

The multi-dimensional time-series characteristics of thermal investment research data cause higher token usage per recall result than conventional text knowledge bases. The high-frequency updates of real-time monitoring data require the context to include the latest parameter snapshots. Otherwise, the timeliness of investment research conclusions will be compromised. The large number of structured fields and scattered units require complete parameter identifiers to be retained during context recall. Field simplification cannot reduce token consumption. Individual regional monthly summary documents have relatively high length. Without reasonable segmentation, the token quota for a single round of conversation will be quickly exhausted. Cross-node correlation analysis requires recalling multiple sets of time-series data. This further increases the token requirements for the context.

Configuration settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000–12000 token`	Thermal investment research requires associating multiple sets of time-series parameters and regional summary data. This range can cover 3 to 5 complete structured document contents
`Chunk size`	`1000–1500 characters`	Thermal data has many structured fields. Excessively long segments lead to too high token usage per block. Excessively short segments destroy the integrity of parameter associations
`Rerank result count`	`Top 8–12 entries`	Thermal investment research needs to cover monitoring data from multiple nodes. Too few entries lose key parameters. Too many entries exceed the context token quota
`searchMaxTokens`	`4000 token`	Balance the amount of recalled data and remaining context quota, avoid triggering model token overflow errors
`Similarity threshold`	`0.65–0.75`	Thermal data has strong parameter correlation. This interval filters irrelevant node data while retaining necessary associated parameters
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Monthly regional thermal summary files are usually large in size. This value covers most bulk upload requirements

The parameter values provided on this page are common recommendations used to determine the starting point for 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 the settings.

Three common mistakes

Phenomenon: Setting Rerank result count too high triggers a rerank error error. Cause: The input token of the reranking model exceeds its own quota, making parameter reranking impossible.
Phenomenon: Single-round conversation replies trigger a LLM model response empty error, or the returned results are truncated. Cause: The context token quota is not limited. Long thermal data content exhausts the total token threshold of the model and FastGPT.
Phenomenon: The number of reference entries returned by knowledge base search does not match the configured Rerank result count. Cause: The searchMaxTokens configuration is not set. The total token of multiple recalled thermal documents exceeds the limit, and the system automatically truncates some results.

How to confirm the configuration is correct

Upload a typical thermal monthly summary document, check the number and length of parsed segments, confirm they fall within the Chunk size configuration range.
Initiate a conversation with a query covering multi-node thermal data, check the number of reference entries in the returned results, confirm they match the Rerank result count configuration.
Check the conversation logs, confirm no rerank error or LLM model response empty errors appear, verify that the context token quota settings are reasonable.
Adjust query keywords to cover thermal parameters from different regions, confirm that the similarity of recalled results falls within the configured threshold range.

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.