What the data for this use case looks like
Solid waste treatment research report data mainly comes from public monitoring ledgers from ecological environment authorities, industry survey summaries, daily production logs of operating enterprises, and experimental research reports from research institutions. The update rhythm falls into three categories: real-time production and operation data is updated daily, monthly compliance monitoring data is updated monthly, and industry analysis reports are updated quarterly or semi-annually. Document structure includes fields such as project name, treatment process type, daily treatment capacity, pollutant emission concentration, investment cost, and compliance level. Units involve professional measurement standards such as tons per day, mg/m³, and ten thousand yuan.
Constraints on vector models and indexing
Data sources are scattered and formats are mixed, with both structured operation parameters and unstructured analysis text. Hybrid indexing that supports both structured field encoding and unstructured text vectorization is required. Update frequencies vary greatly: real-time data requires incremental index refresh, while periodic reports require regular full index reconstruction. The index refresh strategy must adapt to multi-frequency update requirements. Fields carry professional measurement units, so vector models must adapt to parameter text with units to avoid semantic deviation caused by unit confusion. The length of individual documents varies significantly, from short hundreds-of-word operation logs to long industry reports with tens of thousands of words. A flexible segmentation strategy is needed to ensure semantic integrity.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
CHUNK_SIZE | 800–1200 characters | Balances segmentation integrity and vector recall accuracy, as solid waste treatment research reports include both short parameter text and long analysis text |
PARSE_CHUNK_OVERLAP | 50–100 characters | Prevents loss of contextual association between process parameters and preceding/following descriptions after segmentation, ensuring semantic coherence |
RERANK_TOP_N | Top 3–5 results | Core parameters of solid waste treatment research reports are concentrated; excessive recall will introduce irrelevant process descriptions, so this range filters redundant results |
EMBEDDING_BATCH_SIZE | 16–32 entries | Solid waste treatment data has many fields, and the vectorization calculation load per entry is moderate; this batch size avoids memory overflow |
INDEX_REFRESH_INTERVAL | 1 hour for real-time data, 7 days for periodic reports | Adapts to update frequencies of different source data, balancing index timeliness and computing resource consumption |
SIMILARITY_THRESHOLD | Calibrated based on actual testing | Solid waste treatment parameters are highly professional; the threshold must be adjusted according to actual retrieval scenarios to filter irrelevant results |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Each scenario requires targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Issue: When uploading attached images of solid waste treatment research reports in a local deployment environment, no image text content appears in index results. Cause: The image OCR parsing plugin for FastGPT is not enabled, and OCR dependency packages are not configured for local deployment.
- Issue: In FastGPT version 4.9.0, calling the Embedding model via OneAPI returns a connection timeout error, while calling the Chat model works normally. Cause: The port for OneAPI is not open to the Embedding model's request path, or the configured Embedding model address does not correctly point to the corresponding forwarding node of OneAPI.
- Issue: After enabling the Rerank model and checking the knowledge base index configuration item, the online recall test results do not apply the reranking logic. Cause: The number of recalled entries is greater than the number of Rerank returned entries, or the Rerank model is not bound to the current knowledge base's retrieval process.
How to Verify Proper Configuration
- Upload a solid waste treatment research report that includes both structured parameters and unstructured analysis, check the index generation logs, and confirm there are no prompts for parsing failures or vectorization exceptions.
- Enter search terms related to solid waste treatment processes and compliance requirements, check the sorting of online recall results, and confirm that the reranking logic has been applied.
- Check the model configuration page, confirm that the calling address of the Embedding model matches the forwarding node of OneAPI, and there are no connection error records.
- View the knowledge base index settings panel, confirm that the segmentation parameters are compatible with the current research report's document structure, and there are no abnormal segmentation records.
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.