Multi-turn Dialogue and Prompt Engineering for Air Pollution

What Data for This Category Looks Like

Air pollution control-related data mainly comes from environmental protection department supervision platforms, enterprise emission monitoring terminals, project completion acceptance documents, and operation and maintenance logs. Data update cycles vary by type: real-time monitoring data is pushed hourly, and operation and maintenance records are archived and updated monthly. Document structures mostly include fields such as monitoring point numbers, pollutant concentrations, governance equipment operating parameters, and rectification completion deadlines. The unit for pollutant concentrations is mg/m³, and equipment operating duration is counted in hours. Some documents include project approval numbers and acceptance conclusions, which can be used to support customized customer acquisition marketing content for air pollution control enterprises in the financial sector.

Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering

For air pollution control customer acquisition marketing scenarios in the financial sector, the real-time nature of air pollution control data requires multi-turn dialogue to prioritize calling the latest monitoring data, to avoid using expired information that reduces solution accuracy. The presence of professional fields and units requires prompts to explicitly specify data field names and their corresponding units, to prevent the model from confusing pollutant types and statistical standards. Time-series documents include rectification nodes and approval processes. Multi-turn dialogue must fully retain context such as point numbers and rectification requirements from historical interactions, to ensure subsequent generated marketing content matches the actual situation of clients. Data from different monitoring points must be bound to their exclusive numbers. Prompts must explicitly associate data with corresponding points, to avoid misuse of cross-point data.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000-12000 characters`	Air pollution control documents contain long paragraphs of monitoring data and rectification plans. This range can fully retain key context and avoid information truncation
`segment length`	`1500 characters`	Balances document parsing completeness and model processing efficiency, avoiding attention distraction caused by long paragraphs
`similarity threshold`	`0.72-0.78`	Accurately matches clients' monitoring points and emission issues, filters low-relevance historical data and solutions
`retrieval count`	`Top 4-6 entries`	Avoids information overload while covering clients' core consultation needs
`Text Content Extraction` plugin's `chat history retention count`	`6 entries`	Matches the plugin logic of FastGPT v4.8.10, retains sufficient interaction context to support multi-turn dialogue
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Air pollution control documents often contain large amounts of monitoring data and long text, reserving sufficient time for document parsing

The parameter values provided on this page are general recommendations used to establish 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 settings.

Three Common Misconfigurations

Embedding the Text Content Extraction plugin in a workflow and configuring chat history retention count does not enable multi-turn dialogue. The cause is that the workflow node is not configured with global context transfer logic. Chat records retained only within the plugin cannot be passed across nodes.
Prompt format errors occur when calling multimodal models to process air pollution control-related monitoring report images. The cause is failing to upload images in the format required by the model, or failing to correctly recognize professional data contained in the images.
The online conversation interface continuously displays "Searching knowledge base" with no response. The cause is that retrieval count is configured too high, or similarity threshold is set too low, causing the model to repeatedly retrieve a large number of irrelevant documents and exceed processing time limits.

How to Confirm Configuration Is Complete

Initiate a multi-turn consultation including monitoring point numbers and pollutant concentrations, verify whether the model can associate point information from historical conversations to generate corresponding content.
Upload air pollution control-related documents, check whether the parsed segment length matches the configured segment length.
View workflow logs, confirm that the chat history retention count configuration of the Text Content Extraction plugin has taken effect, and that context has not been truncated.
Test multimodal image uploads, confirm that the model can normally recognize air pollution control-related data in the image without format errors.

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.