What This Category of Data Looks Like
Data sources for air pollution control investment research include real-time observation data from environmental monitoring stations, monthly reporting forms from air pollutant emitting enterprises, meteorological observation datasets from meteorological departments, atmospheric model research papers from industry research institutions, and national and local air pollution control policy documents. Data update cycles vary widely. Real-time monitoring data updates every minute. Enterprise reporting forms update weekly or monthly. Policy documents and academic papers have no fixed update schedule. Document structures include structured monitoring reports, semi-structured emission reduction reports, unstructured academic texts, and policy documents. Structured reports contain fields such as site number, pollutant concentration, and monitoring duration. Field units include professional meteorological and environmental parameters such as μg/m³, tons/year, and m/s.
Constraints on Context and Token Workflows
The multi-source, heterogeneous nature of air pollution control investment research data requires context retrieval to balance two needs: the short, batch-oriented fields of structured monitoring data, and the long-document format of unstructured text. Real-time monitoring data updates frequently, which causes frequent context window refreshes. Limit the number of items retrieved per round to avoid exceeding token limits. Multi-document analysis scenarios for investment research, such as regional air pollution control plan evaluation, generate large amounts of cross-document token accumulation. Reserve sufficient context quota to preserve information integrity. Structured reports have low token density, while academic texts have high token density. Adapt to differences in token consumption across document types.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers the total token count of at least 3 core academic papers, 1 week of regional monitoring datasets, and 1 special policy document. Fits multi-source information retrieval for investment research scenarios |
chunkSize | 1000–1500 characters | Adapts to the mixed structure of air pollution control documents. Avoids truncating key clauses in long-text policies, and fits the field-dense nature of structured monitoring reports |
chunkOverlap | 100–200 characters | Ensures contextual coherence across chunks. Prevents loss of critical information such as monitoring site numbers and pollutant parameters after splitting |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports uploading large documents such as regional atmospheric model datasets and annual enterprise emission reduction reports |
similarityTopK | Top 6–8 items | Balances retrieval accuracy and token consumption. Avoids consuming context space with too many low-relevance monitoring data |
maxTokens | 2000–4000 | Fits the long-response requirements of investment research scenarios, such as complete analysis output of regional air pollution control plans |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Evaluate each scenario individually, and test against local samples before finalizing settings.
Three Common Configuration Pitfalls
- Symptom: When uploading aerial images of air pollution sources with resolution higher than 4096×4096, the interface returns an "insufficient token quota" error. Low-resolution images can be uploaded normally. Cause: The image parsing process converts pixel information into embedding tokens. The token consumption of high-resolution images exceeds the reserved context quota.
- Symptom: After setting
maxTokensto 3200, when the retrieved regional air monitoring dataset exceeds the set number of items, the single-round response token limit automatically drops to 200. Cause: The sum of total context tokens and response tokens exceeds the model's maximum supported total tokens. The system automatically compresses the response quota to avoid overflow. - Symptom: In the WeChat Official Account backend deployed on the cloud space, no configuration fields related to
tokencan be found. Cause: The lightweight configuration template for cloud space hides advanced token parameters by default. Enable the corresponding entry via a custom configuration file.
How to Confirm Successful Configuration
- Upload a single large industry document, check the system-generated chunk logs to confirm that chunk splitting matches the configured chunk parameters.
- Initiate a query that includes multiple data source types, check the retrieved context list to confirm that the number of items matches the configured retrieval limit.
- Test a response request that includes complex professional parameters, check whether the actual output content length matches the configured response limit.
- Upload a high-resolution aerial image of a monitoring site, check for token-related parsing errors to confirm that the token consumption for image processing 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.