Data Characteristics
Monoclonal antibody (mAb) quality documentation primarily includes inspection reports for production batches, stability study data, raw and auxiliary material certificates, manufacturing process records, and deviation reports. Data sources are typically Laboratory Information Management Systems (LIMS), Manufacturing Execution Systems (MES), and Quality Management Systems (QMS). These documents have a low update frequency, usually generated per batch or project phase, with annual updates or revisions upon changes. Document structures are often PDF, Word, or scanned images, containing extensive tabular data, chromatograms (e.g., HPLC, SDS-PAGE), and plain text descriptions. Key fields include batch number, production date, expiration date, test item, test result, unit (e.g., mg/mL, AU·min, %), standard range, deviation description, and approver. Data accuracy requirements are extremely high; any decimal or unit error can lead to severe consequences.
Constraints from "Multi-turn Conversations and Prompts"
The low update frequency and high accuracy requirements of mAb quality documents mean multi-turn conversations must heavily rely on pre-indexed knowledge bases. This avoids potential delays or inaccuracies from real-time retrieval. Documents containing tables, chromatograms, and specialized terminology require prompt design that effectively guides the model to identify structured information and accurately parse non-text content. For example, when querying a specific test item value for a batch, the prompt must explicitly state the batch number and test item to guide the model to extract data from the corresponding table row. The strictness of units and fields requires prompts to specify expected units during questioning or to validate units after an answer, preventing the model from generating unitless or incorrectly unitized results. Due to the specialized nature of the documents, the model's depth of contextual understanding is crucial. Multi-turn conversations must maintain focus on a specific batch or test dimension to prevent topic drift.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000 | Ensures the model can process lengthy monoclonal antibody production batch reports, covering key information. |
Chunk size (Segment Length) | 500 characters (characters) | Balances the mixed structure of tables and text in documents, preventing semantic loss due to splitting. |
Recall count (Recall Count) | 15 entries (items) | Increases the probability of recalling relevant segments from complex quality documents, covering more potential associated information. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements, e.g., 0.78 | Ensures recalled segments are highly relevant to the query, filtering out a large amount of irrelevant specialized terminology. |
Rerank result count (Reranked Return Count) | 5 entries (items) | Selects the top few most relevant items from a large number of recalled segments, improving the accuracy of the final answer. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Provides sufficient file parsing time when processing large PDF or scanned documents. |
Common Pitfalls
- The workflow execution data in the interface call within the conversation log is empty. This occurs because file parsing timed out or the format was incompatible, preventing data from being successfully loaded into the knowledge base.
- Inputting any parameter into the code block of the "Text Content Extraction" module prompt results in the full response code block being
undefined. This happens when the syntax or variable names in the prompt's code block do not match the actual data structure. - When a user deletes a conversation, the log records also disappear. This is a result of the system design where conversations and logs are bound, typically for privacy protection and data cleanup, but it can affect subsequent problem tracing.
Confirmation of Correct Configuration
- For typical batch queries, such as "What is the purity of batch XYZ?", check if the model accurately returns the value and unit, and cross-reference with the original document.
- Test the multi-turn conversation's context retention capability. For example, first ask "What is the production date of batch ABC?", then ask "What is the expiration date of this batch?", confirming the model correctly associates batch information.
- Randomly select complex questions from documents that include tables and chromatograms, such as "Please summarize the stability study results for batch PQR." Check if the model can integrate different types of information to provide a coherent answer.
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.