Multi-turn Conversations and Prompts for Recombinant Protein

Data Characteristics

Recombinant protein quality documents primarily include laboratory batch production records, quality inspection reports, stability study data, and method validation reports. These documents are typically stored in PDF, Word, or Excel formats. Update frequency depends on production batches and research progress, usually monthly or quarterly. Document structures are highly standardized. For example, quality inspection reports contain fixed fields such as batch number, production date, test item, test method, result, unit, and acceptable range. Units may include mg/mL, AU/min, ng/μL. Field names often include abbreviations like HPLC, SDS-PAGE, ELISA. Documents may also contain charts (e.g., chromatograms, electropherograms) and tabular data.

Constraints on Multi-turn Conversations and Prompts

The standardized structure and frequent updates of recombinant protein quality documents require multi-turn conversation systems to accurately identify the latest data for specific batches and test items. Specialized abbreviations and units in documents necessitate prompt engineering for context supplementation or mapping to avoid semantic ambiguity. The presence of charts and tabular data implies that the conversation system needs optical character recognition (OCR) capabilities or pre-processing to extract key data into structured text. High-frequency data source updates challenge the real-time synchronization and indexing efficiency of the knowledge base. Prompt design must consider how to guide users to query the latest information and handle queries for historical data versions.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8192` tokens	Recombinant protein quality inspection reports and experimental records often contain extensive details. A longer context window maintains coherence in multi-turn conversations and prevents loss of critical information.
`Chunk size` (Segment Length)	`400` characters	Documents contain much structured internal data. Shorter segment lengths improve retrieval accuracy and ensure completeness for individual test items or results.
`Recall count` (Recall Count)	`8` items	Quality document queries often involve multiple related test indicators. Increasing the recall count covers more comprehensive information and reduces omissions.
`Similarity threshold` (Similarity Threshold)	`0.78`	Test indicator names for recombinant proteins often have high similarity. Appropriately raising the threshold more precisely matches user intent and reduces recall of irrelevant content.
`Rerank result count` (Reranked Return Count)	`4` items	After similarity filtering, reranking the recalled results further focuses on the most relevant items, improving the accuracy of the final answer.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	A single batch document may include multiple reports and charts. The file size limit must support uploading large PDFs or Word documents with embedded objects.

Common Pitfalls

The conversation results show outdated or incorrect batch data because the knowledge base failed to synchronize the latest batch documents or update its index.
When a user asks about a specific test item, the system returns data with mismatched units or missing fields. This occurs because the prompt failed to effectively guide the model to identify and extract numerical values with units from the document.
After a user uploads a file, the conversation interface fails to parse the file content, reporting unsupported file type or parsing timeout. This happens because the application configuration lacks OCR support for specific document formats (e.g., scanned PDFs) or PARSE_FILE_TIMEOUT_SECONDS is set too short.

How to Verify Configuration

Upload quality inspection reports and stability study documents for multiple batches. Test queries for both the latest batch and historical batches, checking if the returned batch numbers and dates are correct.
Conduct multi-turn conversation tests for common recombinant protein test items (e.g., purity, activity, endotoxin). Verify if the returned values, units, and acceptable ranges match the original documents.
Simulate a user uploading a PDF document containing charts and complex tables. Verify if the conversation system successfully parses the file and extracts key data. Observe for OCR related log outputs.
Construct queries containing specialized abbreviations (e.g., HPLC, SDS-PAGE) to check if the system correctly understands and associates them with corresponding test methods or results.

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.