Data Characteristics
Recombinant protein regulations and SOP documents typically exist as PDFs, Word files, or internal knowledge base pages. Data sources primarily include standard operating procedures, quality management system documents, batch production records, and test method standards issued by R&D, production, and quality control departments. These documents update infrequently, mainly during regulatory revisions, new product development, or process optimization. Document structures are highly standardized, often including section numbers, version control information, effective dates, revision histories, scope, definitions, responsibilities, operating procedures, and record requirements. Operating procedures often specify precise experimental conditions, reagent ratios, and equipment parameters. Units like g/L, mol/L, °C, rpm, and hours are clearly defined. For example, media formulations specify milligram amounts of each component per liter, and chromatography purification steps specify flow rates in ml/min and elution gradients in M.
Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts
The standardized structure and low update frequency of recombinant protein regulation documents make knowledge base construction and maintenance relatively stable. However, they demand high accuracy in retrieval and context understanding. Precise numerical values, units, and operating procedures are critical information. Multi-turn conversations must accurately identify and track these details. For example, if a user asks "What is the flow rate for a specific purification step?" and then follows up with "If the elution buffer concentration changes, does the flow rate need adjustment?", the system must link to the specific purification SOP and understand the potential relationship between "flow rate" and "elution buffer concentration." Documents often contain numerous abbreviations and specialized terms such as HPLC, SDS-PAGE, and ELISA. Prompt design must account for the recognition and disambiguation of these terms. Additionally, documents may include many tables and figures. The extraction and representation of this non-textual information directly impact the ability to reference and explain such information in subsequent multi-turn conversations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
maxContext | 6 turns | Balances multi-turn conversation coherence with computational resource consumption, covering common SOP query scenarios. |
Chunk size (Segment Length) | 800–1000 characters | Ensures individual segments contain complete operating procedures or clauses, preventing semantic fragmentation. |
Recall count (Recall Count) | 8–12 items | Increases recall coverage, addressing the strong internal correlation characteristic of regulatory documents. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures precision of recalled content, filtering out irrelevant regulatory clauses. |
Rerank result count (Reranked Return Count) | 4 items | Prioritizes the most relevant core information, reducing user reading burden. |
Prompt Template (Prompt Template) | Includes "Based on the provided recombinant protein regulation SOP, answer in detail..." | Clearly defines the model's answer scope, emphasizing the regulation SOP as the sole knowledge source. |
Three Common Mistakes
- Numerical or unit errors in conversations, such as misreading
mg/Lasg/L. This occurs when numbers and units are not effectively associated during vectorization. - The model provides general answers instead of specific operating procedures after a user query. This happens due to an improper knowledge base segmentation strategy, where single operating steps are split across different segments.
- When integrating external system chats on the frontend,
customUidfails to correctly link to historical conversation records, leading to retrieval of all users' conversation records. This occurs when thecustomUidparameter is not correctly passed and used for filtering in the API call to retrieve historical records.
How to Confirm Correct Configuration
- Simulate multi-turn conversations for critical operating steps within core recombinant protein production SOPs. Verify if the model correctly references and explains specific parameters and steps across different turns.
- Use queries containing specialized terms and abbreviations. Check if the model accurately identifies these terms and retrieves corresponding definitions or explanations from the knowledge base, verifying term recognition capability.
- Ask the model "if..., then..." hypothetical questions. Observe if the model can infer reasonable associations or impacts based on regulatory clauses, checking logical reasoning capability.
- Check system logs to ensure
customUidand other user identifiers are correctly recorded and used for historical conversation retrieval during API calls, verifying multi-user session isolation.
Note: 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.