What this type of data looks like
Solid waste management investment research data mainly comes from public monitoring reports of sanitation operation departments, operation logs of solid waste treatment projects, standard specifications released by industry associations, environmental impact assessment (EIA) approval documents, and technical parameter documents from equipment manufacturers. Data update frequencies vary by scenario: monthly (such as regional solid waste disposal volume monitoring), quarterly (such as project operation weekly reports), and annual (such as industry development reports). Document structures fall into three categories:
- Structured monitoring reports, containing fields such as location number, disposal batch, pollutant type, and treatment volume
- Long-form technical documents, including process flow diagrams, equipment models, energy consumption and emission parameters
- Compliance documents, including emission standard document numbers, approval process descriptions, etc.
Common units for fields include tons per day, mg/m³, cubic meters, and other units used in engineering and environmental monitoring.
What constraints do these characteristics impose on context and token configuration?
The multi-type and multi-batch characteristics of solid waste management investment research data impose three types of constraints on context and token configuration:
- Structured monitoring reports have small per-batch data volume but high total recall count. Splicing these reports can easily exceed the model's token limit, which affects the completeness of cross-location and cross-project investment research comparisons.
- Long documents such as EIA reports require retaining contextual associations between process parameters and emission standards during splitting. Splitting too short will prevent the model from associating key constraint conditions, while splitting too long will result in excessive token usage per chunk.
- Frequently updated project data increases the token calculation pressure for incremental knowledge base recall. If the retention duration of historical context is not limited, it will lead to token pool overload and reduce subsequent call efficiency.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Solid waste management investment research requires splicing multiple sets of monitoring data and process parameters. This range can cover valid information from 3 to 5 core documents and avoid token overflow |
chunkSize | 800–1200 characters | Emission standards and equipment parameters in solid waste treatment require complete contextual association. The split size adapts to the single-chunk token carrying capacity of the model and avoids logical disconnection |
similarityTopK | Top 6–8 results | Single-location monitoring data has multiple batches. Excessive recall will increase context token consumption, while insufficient recall cannot meet the needs of cross-location investment research comparisons |
rerankTopN | Top 3–5 results | Prioritize retaining recall results of compliance standards and core process parameters, filter non-critical data to reduce token usage |
tokenLimitPerCall | 75% of the model's context window | Reserve sufficient tokens for model response generation, avoid knowledge base recall occupying all tokens and causing call failures |
contextCleanupThreshold | Retain context from the last 30 days | Solid waste treatment project data is updated frequently. Cleaning expired context reduces invalid token usage |
The parameter values provided on this page are conventional recommendations used as 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.
Three common configuration errors
- Phenomenon: After setting
similarityTopKto 10, only 5 context entries are returned during calls. Cause: The reranking function is not enabled. Reranking filters low-similarity recall results, causing the actual returned count to be lower than the configured value. - Phenomenon: The token consumption of knowledge base references viewed in FastGPT is inconsistent with the token consumption returned by the model API. Cause: FastGPT's knowledge base recall token calculation includes document splitting and vector encoding tokens, while the model API's token calculation includes context splicing, prompt words and response tokens. The two have different statistical scopes.
- Phenomenon: The context output field of the AI chat node is empty. Cause: The knowledge base recall function is not enabled, or recall results are not correctly spliced into the context window, resulting in no valid content in the output field.
How to verify the configuration is correct
- View the knowledge base recall log and confirm that the actual returned document count matches the configurations of
similarityTopKandrerankTopN. - Test a call for a single long document to confirm that the number of split chunks matches the
chunkSizeconfiguration. - View the token consumption data returned by the model API and confirm that its proportion conforms to the
tokenLimitPerCallconfiguration ratio. - Regularly check the retention duration of the context window and confirm that expired data has been automatically cleaned up.
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.