Multi-Turn Conversations and Prompts for Structured Analysis

Data Characteristics

Rehabilitation equipment R&D documents originate from various sources. These include clinical trial reports, engineering design drawings, material specifications, draft user manuals, regulatory compliance documents, and patent applications. Document update frequencies vary. Core technical documents, such as design specifications, may update annually. Clinical data or user feedback may see monthly or even weekly additions. Document structures are highly heterogeneous. PDF design drawings may embed unstructured text annotations. Clinical reports often combine structured tables with narrative text. Fields and units are diverse. Engineering design documents commonly feature geometric dimensions (e.g., millimeters, inches), mechanical parameters (e.g., Newtons, Pascals), and electrical specifications (e.g., volts, amperes). Clinical reports involve biomedical indicators (e.g., mg/L, heart rate beats/minute) and frequently use specific medical abbreviations.

Constraints Imposed by Data Characteristics on Multi-Turn Conversations and Prompts

The heterogeneous nature of rehabilitation equipment R&D documents challenges context management in multi-turn conversations. The mix of structured and unstructured information requires flexible parsing strategies. For example, the system must identify dimension parameters in design drawings and treatment plans in clinical reports. Varying update frequencies necessitate knowledge base support for incremental updates and version management to prevent conversations based on outdated information. The prevalence of specialized terminology and abbreviations in documents requires prompt engineering to incorporate domain ontologies or synonym libraries, improving semantic understanding accuracy. Rehabilitation equipment R&D often involves interdisciplinary knowledge, such as biomechanics and materials science. Multi-turn conversations must effectively aggregate information from different document sources and dynamically adjust based on the user's query focus. The precision of units and fields demands consistency in information extraction and presentation to avoid ambiguity.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8192 token`	Balances long document context with conversation turns, suitable for complex R&D questions.
`Chunk size` (Segment Length)	`800–1200 characters` (characters)	Covers common design descriptions or clinical observation paragraphs in rehabilitation equipment documents, maintaining semantic integrity.
`Recall count` (Recall Count)	`Top 10 entries` (top 10)	Ensures coverage of potentially related key information points from multi-source heterogeneous documents.
`Similarity threshold` (Similarity Threshold)	`0.75`	Filters out low-relevance document snippets, improving recall quality, especially in terminology-dense scenarios.
`Rerank result count` (Rerank Return Count)	`Top 5 entries` (top 5)	Focuses on the most relevant few pieces of information, reduces model processing load, and improves response speed.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Accommodates parsing time for large design drawings or detailed clinical reports, preventing timeouts.

Common Pitfalls

Conversation records fail to delete or update. This may occur if the backend service does not correctly synchronize the database or cache state when processing delete requests.
File upload results in parsing failure, with UI prompts like "unsupported file format" or "parsing timeout." This typically happens when the file size exceeds the UPLOAD_FILE_MAX_SIZE limit, or file content complexity causes PARSE_FILE_TIMEOUT_SECONDS to be set too short.
The "suggest follow-up questions" feature is enabled, but no suggestions appear at the end of a conversation. This may be due to insufficient current conversation context to generate meaningful follow-up questions, or a Similarity threshold (Similarity Threshold) set too high, preventing matching enough relevant information points.

Verification Steps

Upload typical rehabilitation equipment R&D documents (e.g., PDFs with mixed text and images, structured clinical data CSVs). Engage in multi-turn conversations to verify the system accurately extracts key parameters and specialized terminology, and generates expected responses.
Simulate user queries on the same topic at different times. Observe if the system correctly identifies document version differences and provides consistent or updated information, confirming the effectiveness of the knowledge base's incremental update mechanism.
Examine conversation logs. In complex question scenarios, confirm that Recall count (Recall Count) and Rerank result count (Rerank Return Count) effectively cover all relevant document snippets required for the query, avoiding information omissions.

The values provided are common starting points. Measure them against your 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.