Knowledge Base Retrieval and Recall for Automated Equipment

What the data for this category looks like

Automated equipment research report data primarily comes from publicly available reports from securities research institutions, public materials from industry associations, official technical documents from equipment manufacturers, public announcements of listed companies on the Shanghai, Shenzhen and Hong Kong Stock Exchanges, and public materials from third-party research institutions.

Updates are synchronized monthly using weekly industry dynamic reports and quarterly capacity analysis reports. New equipment parameters are added to the knowledge base immediately upon release.

Most documents combine structured parameter tables and technical description text. Core fields include equipment model, rated load, operating accuracy, certification number, and production capacity scale. Units mostly follow industrial standard units such as kN, μm, mm/s, etc.

What constraints do these characteristics impose on the knowledge base retrieval and recall link?

Automated equipment research reports have a high proportion of structured parameters. Field units are standardized, but minor differences may exist across sources. The retrieval link must support both semantic text matching and exact field matching. This prevents missing precise parameters when relying solely on semantic recall.

Multi-source update rhythms create demands for incremental and full updates. Configure differentiated scheduling rules to ensure new product parameters are added to the knowledge base promptly and historical data is synchronized regularly.

The length of individual document chunks varies significantly. Some parameter tables may still exceed context window limits after splitting. Add extra long text splitting logic to handle this.

Units of the same type of parameter from different sources may be inconsistent. Complete unit normalization during the preprocessing stage to avoid unit confusion in recall results.

How to set the configuration

Configuration Item	Recommended Value	Rationale
`maxContext`	800–1200 characters	Structured parameter blocks in automated equipment research reports are mostly 500-1000 characters per segment. Splitting them fits the single-segment input limits of most models.
`Recall count`	Top 8–10 entries	Automated equipment research reports have a large number of related documents. Too many recalled entries will exceed the context window. Too few will fail to cover all relevant parameters.
`Similarity threshold`	0.75–0.85	Semantic matching for structured parameters has high precision requirements. A threshold that is too low will introduce irrelevant equipment parameters. A threshold that is too high may miss core technical descriptions with slightly lower matching scores.
`PARSE_FILE_TIMEOUT_SECONDS`	120 seconds	Parsing a single long research report takes a long time. This avoids parsing failures caused by timeouts.
`Incremental Update Scheduling Cycle`	Every 12 hours	New equipment is released frequently. Latest parameter data needs to be synchronized in a timely manner.
`Field Exact Match Toggle`	Enabled	Automated equipment research reports have a large number of fields such as model and certification number that require exact matching. Enabling this improves recall accuracy.

The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Each situation requires separate analysis. It is recommended to test on your own samples before finalizing settings.

Three common errors

Symptom: Cannot convert undefined or null to object error pops up during workflow runtime. Cause: The exact field matching switch is not enabled. Empty structured field parameters are passed to the retrieval logic, causing a type conversion error.
Symptom: Knowledge base chunks exceeding the set maxContext length of 1500 characters are still referenced after configuring the value to 1500 characters. Cause: Long text is not segmented during preprocessing. Unsplit full parameter tables from research reports are uploaded directly, causing chunk lengths to exceed the configured limit before being included in the context.
Symptom: Knowledge base creation hangs on the open-source 4.8.11 version. Ollama logs show try reducing the size of the batch. Cause: The total size of uploaded research report files exceeds the default batch limit. The UPLOAD_BATCH_SIZE parameter was not adjusted.

How to confirm the configuration is active

Upload a typical automated equipment research report. Check if the parsed segment length matches the maxContext configuration. Adjust the segmentation rules to a reasonable range.
Run a retrieval test. Enter equipment model and parameter keywords. Verify that the number and similarity of recall results match the expected configuration.
Check the knowledge base update logs. Confirm that incremental update tasks trigger automatically per the set schedule, and new product data is synchronized to the knowledge base in a timely manner.
Call the API interface. Verify that the returned results include correct external image links and structured field data. Confirm that the parameter configuration 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.