Multiturn Conversation and Prompts for Structured Analysis

Data Characteristics

Supplier audit R&D documents in the biopharmaceutical sector include quality management system files, production process flows, inspection reports, and change control records submitted by suppliers. These documents are typically in PDF, Word, or Excel formats. Individual files can be several MB or even tens of MB in size, with high content density. Updates usually occur during annual reviews or when significant changes happen. Document structures often follow industry standards (e.g., GMP, ISO 13485), including standardized sections and appendices. Fields and units are highly specialized, such as active ingredient content (mg/tablet), impurity limits (% w/w), batch numbers, expiration dates, and instrument calibration parameters (e.g., temperature °C, pressure kPa).

Constraints Imposed by These Characteristics on Multiturn Conversation and Prompts

Large, dense, and highly specialized documents demand high parsing performance and accuracy. This directly impacts the response speed and content quality of multiturn conversations. The document update frequency determines the knowledge base refresh strategy, ensuring conversations use the latest audit information. Standardized document structures help improve structured parsing efficiency through predefined templates or chunking strategies. The presence of specialized fields and units requires prompt design to accurately identify and associate this information, preventing incorrect answers due to semantic misinterpretation. The conversation system must handle user follow-up questions about specific batches, parameters, or sections. This requires robust context management capabilities and precise retrieval mechanisms.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`50 MB`	Accommodates large audit report files submitted by suppliers, balancing upload efficiency and storage costs.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Large files require more parsing time. This prevents parsing failures due to timeouts, especially for complex PDF documents.
`maxContext`	`800–1200 characters`	Retains sufficient conversation history to support multiple user follow-up questions on audit details and maintain conversational coherence.
`Chunk size`	`400 characters`	Balances semantic completeness with retrieval efficiency. Avoids irrelevant information from overly long chunks and context loss from overly short chunks.
`Recall count`	`Top 5 entries`	Ensures enough relevant contextual information is retrieved from the knowledge base during multiturn conversations.
`Similarity threshold`	`0.75`	Improves the accuracy of retrieval results, filtering out information with low relevance to supplier audit content.

Common Pitfalls

Symptom: After a user query, the system remains unresponsive for an extended period or returns a parsing failure error. Reason: The file is too large or complex, and PARSE_FILE_TIMEOUT_SECONDS is set too low, causing document parsing to time out.
Symptom: In multiturn conversations, the system fails to correctly associate batch numbers or specific parameters mentioned in previous turns. Reason: maxContext is set too short, preventing the system from effectively retaining and utilizing prior conversation context.
Symptom: Conversation responses include general biopharmaceutical knowledge unrelated to the audit topic, or misinterpret specialized terminology. Reason: Similarity threshold is set too low, or prompts do not sufficiently guide the model to focus on the supplier audit domain, leading to the retrieval of irrelevant knowledge snippets.

Verification of Configuration

Upload and parse multiple typical supplier audit documents (e.g., PDFs with images and tables). Check if parsing is successful and within an acceptable time frame.
Conduct multiturn conversation tests. Verify if the system accurately understands and answers follow-up questions about specific batches, production dates, and key quality parameters. Evaluate conversational coherence.
Formulate questions containing specialized terminology and industry standards. Check if system responses are accurate, professional, and free of hallucinations or irrelevant information. Manually assess response quality.
Monitor system logs. Observe if timeout errors related to PARSE_FILE_TIMEOUT_SECONDS significantly decrease and how maxContext utilizes conversation history.

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.