Deployment and Upgrade for Compliance Script Private Domain

Data Characteristics

Compliance script data primarily originates from internal compliance departments, product documentation, marketing materials, and historical compliance audit records. This data is typically unstructured text, such as legal clauses in PDF format, product descriptions in Word documents, internal compliance training manuals, or business process descriptions. Data update frequency correlates with regulatory policy changes and new product release cycles, ranging from monthly to several times a year. Document structures often include titles, chapters, clause numbers, and body text; some documents contain tables and diagrams. Key fields include Regulation Name, Scope of Application, Violation Risk Level, Resolution Suggestions, Product Features, and Promotion Prohibitions. Data volume can reach millions of characters, involving various specialized terms and abbreviations.

Constraints Imposed by Data Characteristics on "Deployment and Upgrade"

Compliance script data, being primarily unstructured text, requires specific text parsing and chunking strategies. These strategies must maintain semantic integrity while avoiding excessively long text blocks that could hinder retrieval efficiency. The uncertain update frequency necessitates flexible data import and index rebuilding mechanisms, supporting incremental or full updates to quickly respond to regulatory changes. Specialized terms and abbreviations within documents require configuring specific glossaries or domain dictionaries to enhance semantic understanding and matching accuracy. The large data volume demands significant storage and computational resources, especially during vectorization and retrieval. The complexity of fields requires careful metadata tag design during knowledge base construction for precise filtering and multi-dimensional retrieval.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk Length`	`500–800 characters`	Balances semantic integrity and retrieval efficiency, avoiding overly long or short segments.
`Overlap Length`	`50–100 characters`	Ensures contextual continuity and minimizes semantic loss due to chunking.
`Similarity Threshold`	`0.75–0.85`	Filters irrelevant results, ensuring high relevance of retrieved compliance scripts.
`Recall Count`	`8–12 entries`	Covers more potentially relevant compliance clauses, improving the comprehensiveness of responses.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Handles parsing of large PDF documents, preventing timeout interruptions.
`maxContext`	`4096 tokens`	Accommodates longer compliance clauses and consultation contexts.

Common Pitfalls

In multi-variable update nodes, AI responses fail to integrate all variable information uniformly. This occurs due to unclear variable passing logic in workflow design or insufficient model context window capacity.
After deployment to an intranet environment, file upload functionality is abnormal, with logs showing connection timeouts or file parsing failures. This may be due to an undersized UPLOAD_FILE_MAX_SIZE parameter or incorrect Nginx reverse proxy configuration for large file uploads.
Channel models fail to connect to locally deployed Ollama models, reporting Connection refused or Host unreachable. This happens when Docker containers are isolated from the host network, and port mapping or network bridging mode is not correctly configured.

Verification Steps

Upload a compliance policy PDF document with multiple chapters and complex tables. Check if knowledge base chunking is reasonable and verify that PARSE_FILE_TIMEOUT_SECONDS handles it correctly.
Ask specific compliance questions. Observe if the AI response references relevant regulatory clauses and check the accuracy of retrieval results based on Recall Count and Similarity Threshold.
Simulate multiple concurrent consultations. Monitor system resource utilization to confirm that concurrent requests are processed without significant delays or errors under the maxContext limit.

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.