Knowledge Base Retrieval and Recall for IT Service Research

What the data for this category looks like

Data sources for IT service research knowledge bases include IT service vendor official service manuals, operational SLA documents, technical parameter whitepapers, industry compliance requirement documents, and real-time archived operational alert logs. Update cadence varies by document type: vendor standard documents are updated quarterly or semi-annually, compliance documents are updated annually, and operational logs are archived in real time. Document structures include thousand-page long PDFs, structured parameter tables, operational reports with topology diagrams and data charts. Fields covered include service levels, response times, fault repair cycles, and covered technology stack types. Units include hours, minutes, and similar units.

What constraints these characteristics impose on knowledge base retrieval and recall

Thousand-page long documents require segment configuration to balance semantic completeness and context window usage. Avoid segments that are too short, which causes semantic breaks, or too long, which exceeds the model’s context window. Structured parameter tables and chart-containing documents require retrieval to support both vector semantic matching and structured field retrieval. Without this support, accurate matching of service parameters and key data in charts is not possible. Real-time archived operational logs require the knowledge base to support incremental update mechanisms. Full reindexing consumes excessive computing resources. OCR parsing results of embedded images must be included in the retrieval vector database. Otherwise, operational data carried by images cannot be retrieved and matched.

How to set the configurations

Configuration Item	Recommended Value	Rationale
`chunk_size`	800–1200 characters	IT service documents are mostly long texts. Setting segment length within this range balances semantic completeness and context window usage
`retrieve_top_k`	Top 8–12 results	IT service research requires covering multi-dimensional service parameters and cases. Too few results will miss key matching outcomes
`rerank_top_n`	Top 3–5 results	Filters redundant recall results and focuses on highly matched service documents and operational data
`parse_image_enable`	`true`	IT service documents often contain topology diagrams and SLA charts. Enabling this extracts OCR text for retrieval
`UPLOAD_FILE_MAX_SIZE`	2000 MB	Adapts to the upload requirements of thousand-page PDF documents and avoids file size excess errors
`incremental_update_interval`	3600 seconds	Matches the real-time archiving rhythm of operational logs and ensures knowledge base timeliness

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

Three common mistakes

Embedded images in uploaded IT service documents cannot be matched during retrieval. The interface displays image placeholders or no content. This occurs when the parse_image_enable configuration is not enabled, or when image OCR parsing fails to generate retrieval-ready text.
The number of retrieval results is far lower than the preset value, or a 413 Request Entity Too Large error appears. This occurs when the UPLOAD_FILE_MAX_SIZE parameter is not adjusted to adapt to thousand-page PDF documents, or when segment configuration is unreasonable and causes long text truncation.
Calling the RAG knowledge base in a workflow returns empty results, and the log shows the embedding_not_found field. This occurs when the context transfer configuration between the RAG retrieval node and the MCP tool call node is not set correctly, causing retrieval results to not be passed to downstream nodes.

How to confirm the configuration is correct

Upload a single thousand-page IT service document. Check that no errors appear after the upload progress bar completes. Verify that the UPLOAD_FILE_MAX_SIZE configuration adapts to the document size.
Retrieve embedded image description text in the document. Confirm that the returned results include the OCR text corresponding to the image. Verify that the parse_image_enable configuration is active.
Trigger an incremental update task. Check that the knowledge base index log displays newly added operational log entries. Verify that the incremental_update_interval configuration is correct.
Run a test workflow that includes RAG retrieval and MCP calls. Check that the context received by downstream nodes includes retrieved document fragments. Verify that the inter-node context transfer configuration is correct.

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.