Workflow Orchestration for Cleanroom Management

Data Characteristics

Cleanroom management data primarily originates from internal quality management system documents, SOPs, training manuals, and regulatory requirements. These documents are typically in PDF, Word, or scanned image formats. Their update frequency is relatively low, usually occurring during regulatory revisions, process changes, or annual audits. Document structures commonly include chapters, sections, and appendices. Content covers personnel conduct, material ingress/egress management, environmental monitoring standards, and equipment cleaning and maintenance. Fields often involve specific operational steps, responsible persons, record form numbers, and parameters like temperature, humidity, and differential pressure, with clear units such as ppm, °C, and Pa.

Constraints Imposed by These Characteristics on Workflow Orchestration

The low update frequency and structured content of cleanroom management documents enable a parallel approach of preprocessing and structured extraction during knowledge base construction. The extensive process descriptions and parameter requirements in these documents necessitate a focus on multi-step Q&A and conditional logic in workflow orchestration. For example, when a user asks about a specific operational step, the system must identify the relevant SOP, then extract the detailed description of that step, required tools, or subsequent actions. The precision required for parameters means that when quoting specific values in responses, accurate retrieval from the document is essential, avoiding incomplete values due to context truncation. Queries for fields like responsible persons and record form numbers require the workflow to support entity recognition and associated queries.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`500-800 characters`	Ensures each segment contains sufficient operational step information while preventing excessive length that could lead to semantic drift.
`Recall count` (Recall Count)	`Top 5`	Considering the specialized and interconnected nature of cleanroom management documents, increasing the recall count helps cover more comprehensive information.
`Similarity threshold` (Similarity Threshold)	`0.75-0.85`	Guarantees the precision of recalled content, avoiding interference from irrelevant or low-relevance segments, especially when dealing with specific parameters and steps.
`Rerank result count` (Rerank Return Count)	`Top 3`	After reranking, focus on the most relevant items to improve the accuracy and conciseness of the final answer.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Allows ample time for parsing large SOPs or quality manuals, preventing timeouts.
`maxContext`	`8000 tokens`	Enables the AI model to process longer contexts, aiding in the understanding of complex processes and multi-step Q&A.

Common Pitfalls

AI model responses contain incomplete operational steps or missing parameter values. This often results from the knowledge base segmentation granularity being too large or too small, causing critical information to be truncated or scattered across different segments.
The code execution node in the workflow fails to correctly parse tabular data within documents, preventing the subsequent AI model from quoting specific parameters. This may be due to improper configuration of the text extraction node, which fails to recognize table structures or fields.
In scenarios requiring dynamic knowledge base selection, the knowledge base selection in Global Variable (Global Variables) does not automatically switch based on user queries. This indicates a flaw in the dynamic assignment logic of Global Variable in the workflow orchestration, failing to correctly associate user intent with specific knowledge bases.

Verification Steps

Select multiple typical cleanroom management questions, covering different SOPs and parameter queries, to verify the accuracy and completeness of the AI model's responses.
For documents containing tabular data, test whether the text extraction node in the workflow can accurately identify and output table content, and cross-check the output fields and values.
Simulate user queries on various cleanroom management topics and observe whether the workflow can correctly invoke the corresponding knowledge base and provide relevant responses based on the question content.
Examine the workflow execution logs to confirm that all nodes operate normally, without timeouts or error messages, especially for nodes involving file parsing and code execution.

The values provided are common starting points and should be measured against the reader's own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.