Knowledge Base Retrieval and Recall for Environmental

What the Data for This Category Looks Like

Environmental monitoring financing daily report data comes from real-time monitoring station data of local ecological environment departments, green financing filing ledgers of financial institutions, and financing vouchers for environmental governance projects submitted by enterprises. Data is updated daily. Each daily report includes a structured set of fields. Core fields are:

Monitoring station code
Monitoring project name
Monitoring concentration value
Unit (such as mg/L, μg/m³)
Financing subject name
Financing amount
Financing arrival date
Supervision verification mark

Documents are primarily structured tables. Some attachments include original monitoring screenshots for corresponding monitoring points. The overall format is unified, and field boundaries are clearly defined.

Constraints Imposed on Knowledge Base Retrieval and Recall

Daily updated data sources require the knowledge base to use scheduled incremental sync tasks. This avoids resource consumption and data lag caused by full re-imports. Structured fields with units require retrieval to match both field names and their corresponding unit parameters. Without this, invalid recall results will occur where monitoring concentration values do not match their units. The requirement for documents to include original monitoring point screenshots necessitates enabling mixed image-text retrieval capabilities. This ensures key monitoring information in images can be recalled. Numeric fields such as financing amount and monitoring concentration value support range retrieval. This requires a vector database that supports numeric embeddings. Relying only on text keyword matching cannot meet the retrieval needs of this scenario.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Structured files for environmental monitoring financing daily reports typically contain single-day data for multiple stations. Most single files are under 100 MB, so a reasonable upper limit is reserved
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Field parsing for long tables requires reading the full dataset. The timeout period must cover the complete parsing process
`Segment Length`	`800–1200 characters`	Structured documents include field names, numerical values, and descriptive text. The segment length must balance context completeness and vector retrieval accuracy
`Recall Count`	`Top 6 entries`	Retrieval demands for environmental monitoring financing daily reports focus on relevant data for the current day. A small recall count covers core query scenarios
`Similarity Threshold`	`0.78–0.85`	Structured data has strong field correlation. A higher threshold must be maintained to avoid accidental recall of unrelated data
`ENABLE_IMAGE_RETRIEVAL`	`Enabled`	Documents include original screenshots of monitoring points. Retrieval and recall of image content must be supported

The parameter values provided on this page are standard recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Mistakes

Issue: When configuring EMBEDDING_MODEL, text-embedding-ada-002 is not selected. The interface displays the error undefined model must match "^(text-embedding-.*)$". Cause: FastGPT's embedding model validation rule requires the model name to match the specified prefix. Failing to fill in the model name according to the rule triggers a validation failure.
Issue: After uploading an .xlsx format environmental monitoring financing daily report, the knowledge base cannot recognize the structured fields in the table. Only plain text content is returned. Cause: The structured table parsing plugin for FastGPT is not enabled. The default parsing mode only extracts plain text, and cannot recognize the field and numerical value association relationship of the table.
Issue: The number of returned retrieval results does not match the configured Recall Count. The actual number of recalled entries is less than the configured value. Cause: Both Similarity Threshold and Recall Count are not configured at the same time. The system filters results by threshold first, resulting in insufficient qualifying results to meet the configured recall count.

How to Confirm the Configuration Is Complete

Upload a single .xlsx file of an environmental monitoring financing daily report. Check the parsed document structure in the knowledge base to confirm that the fields match the original table.
Initiate a query that includes a monitoring project name and financing amount. Check the similarity matching degree of the returned results, and adjust the Similarity Threshold to a range that meets business requirements.
After enabling the ENABLE_IMAGE_RETRIEVAL configuration, upload a document that includes monitoring screenshots. Initiate a query for the monitoring values in the screenshots, and confirm that the corresponding results can be recalled.
After configuring the scheduled sync task, check the knowledge base update log to confirm that the daily incremental sync task runs at the preset time.

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.