Knowledge Base Retrieval and Recall for Refining Intelligent

What the Data for This Category Looks Like

Data for refining intelligent due diligence reports primarily comes from operational logs of internal enterprise production management systems, material balance reports from quality inspection departments, supply chain procurement ledgers, and public refining process standard documents. Production operation data is updated daily, ledger data is updated monthly, and standard documents are updated quarterly. Most documents are structured multi-row tables containing fields such as equipment numbers, process parameters, quality inspection indicators, and timestamps. Field units include cubic meters, kilowatt-hours, tons, degrees Celsius, and others. Some parameters require association with multiple sets of upstream and downstream data to form a complete logical chain.

Constraints for the Knowledge Base Retrieval and Recall Workflow

The structured multi-row tables common in refining due diligence reports require retrieval workflows to support table structure parsing and cell-level recall, covering retrieval needs for structured tables. Data sources with varying update frequencies require retrieval workflows to distinguish trigger logic for full and incremental indexes, preventing delays in synchronizing real-time data. Fields with specific units require recall to associate unit parameters for matching, preventing accidental recall of similar parameters with different units. Associations between multiple sets of upstream and downstream data require recall to retain contextual connections between parameters, preventing loss of logical integrity when individual fields are split.

Configuration Settings

Configuration Item	Recommended Setting	Rationale
`PARSE_TABLE_ENABLE`	Enabled	Refining due diligence reports contain many structured multi-row tables, requiring cell-level recall support
`UPLOAD_FILE_MAX_SIZE`	`1000 MB`	Refining due diligence reports often include multi-page, multi-row-column table data, requiring support for large file uploads
`chunk_size`	`800–1200 characters`	Retain contextual connections between table rows and columns, preventing splitting from breaking logical chains between parameters
`recall_top_k`	`Top 8 results`	Refining parameters often have upstream and downstream connections, requiring enough associated parameters to be returned for context stitching
`similarity_threshold`	`0.75–0.85`	Refining process and quality inspection parameters have high precision requirements, requiring filtering of low-match irrelevant data
`INCREMENTAL_INDEX_ENABLE`	Enabled	Refining production data is updated daily, incremental indexes avoid the time cost of full index reconstruction

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. Specific issues require targeted analysis. Testing on samples is recommended before finalizing configuration settings.

Three Common Configuration Errors

Symptom: After uploading a multi-row table in XLSX format, only scattered text is extracted in the knowledge base, with no table structure. Cause: The PARSE_TABLE_ENABLE configuration is not enabled, and file content is split as plain text only.
Symptom: A timeout error occurs during knowledge base search in version 4.8.20, returning status code 504 Gateway Timeout. Cause: The PARSE_FILE_TIMEOUT_SECONDS parameter is not configured, or its value is set too small to complete multi-row table parsing and index construction.
Symptom: The number of knowledge base retrieval results remains zero, with no matching content. Cause: Incremental indexing is not enabled, full indexing is not completed, and corresponding data source files are not correctly uploaded to the knowledge base.

How to Verify Successful Configuration

Upload a test refining due diligence table file, check if parsed text retains row and column connections of cells, with no scattered plain text fragments.
Run a retrieval test, input a query containing specific parameters and units, verify returned results include matching fields and unit information.
Check the knowledge base index progress panel, confirm full indexing or incremental indexing has completed, with no failed file parsing records.
Adjust the similarity_threshold parameter, observe changes in retrieval result matching accuracy, confirm the parameter takes effect.

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.