What data looks like for this category
Gas industry investment research data mainly comes from public reports of urban gas industry associations, annual and semi-annual financial reports of listed gas enterprises, urban pipeline network operation logs, upstream gas supply guideline prices and policy documents. Data update frequencies are divided into four categories: real-time (gas source prices, pipeline scheduling data), monthly (regional total gas supply statistics), quarterly (enterprise operation briefings), and annual (industry development white papers). Documents include structured statistical tables, policy and regulation texts, and technical operation and maintenance specifications. Most fields involve professional metering items such as gas supply pressure, gas transmission volume, sales price, and pipeline diameter, with corresponding units of kilopascals, 10,000 cubic meters, yuan per cubic meter, and millimeters.
Constraints on context and token management
The mixed update frequencies and professional metering characteristics of gas investment research data create multiple constraints for context and token management. First, the update frequency gap between real-time gas source data and monthly statistical data is large. Frequent recall of new data increases total token consumption. Second, there are many professional metering fields and terms. Context must retain complete unit association information to prevent the model from confusing different measurement units. Third, some individual technical documents or industry reports are lengthy. If chunking logic does not match the document structure, key parameter-related context may be split, leading to the model failing to accurately understand the full requirements of technical specifications.
Configuration recommendations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunkSize | 4000–5000 tokens | Gas investment research documents contain a large number of structured metering tables and professional terms. This range retains field association information and avoids truncating key parameters |
maxContext | 8000–12000 tokens | Gas investment research conversations need to retain multi-round professional terms and data associations. This range covers context requirements for multi-round interactions while controlling total token consumption |
recallTokenLimit | 1200–1500 tokens | Matches the density of gas professional terms, avoids token overflow caused by recalling too much fragmented data, and ensures sufficient core information |
enableTokenStatistics | Enabled | Allows real-time viewing of token usage for each question and answer, matches the global.workerPoll.countGptMes statistics logic, and facilitates troubleshooting of over-limit issues |
workflowContextClearCondition | Triggered by session end or specified keywords | Adapts to context reset requirements for different scenarios in gas investment research, avoids occupying token quota with irrelevant historical data |
recallCountLimit | Top 5–8 entries | Balances the comprehensiveness of recalled information and token consumption, avoids context overload caused by too many entries |
The parameter values provided on this page are common 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 against your own samples before finalizing.
Three common misconfigurations
- Phenomenon: In FastGPT 4.6.7, after setting the knowledge base chunk size to 5000 tokens and recall reference limit to 1500 tokens, the actual recalled single knowledge base entry still exceeds 1500 tokens, leading to excessive context token consumption. Cause:
recallTokenLimitonly restricts the total recall token quota, and does not enforce a check on the length of individual chunks. Overlong document fragments are not automatically split to fit the reference limit. - Phenomenon: After configuring context clearing rules in the workflow, the session history still retains old gas investment research interaction data and does not reset as expected. Cause: The clearing condition is not bound to the trigger node of the corresponding workflow, or the configured trigger keywords do not match the actual content in the conversation.
- Phenomenon: Unable to view the token usage details for a single question and answer, and cannot find the corresponding statistics entry. Cause: The
enableTokenStatisticsglobal switch is not turned on, and the system does not load theglobal.workerPoll.countGptMesstatistics logic, so token usage records cannot be generated.
How to verify proper configuration
- Go to the parsing configuration page of the target knowledge base, check whether the
chunkSizeconfiguration value matches the preset token range. - Initiate a conversation query focused on gas investment research topics, confirm that the returned knowledge base reference content fully retains professional metering fields and associated information, with no truncation.
- Go to the system statistics module, confirm that the token statistics function is enabled, and can view the token consumption details and sources for a single question and answer.
- Trigger the preset context clearing condition in the associated workflow, verify that the session history is correctly reset, with no residual old interaction data.
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.