Data Characteristics
Bispecific antibody quality documents include R&D reports, manufacturing batch records, quality standards, stability study data, and method validation reports. Data sources are internal laboratory information management systems (LIMS), electronic batch record systems (EBRS), and document management systems (DMS). These documents have a high update frequency, especially during preclinical and clinical trial phases, with continuous data generation and revision. Document structures are complex, often containing numerous charts, chemical structures, sequence information, and biological data. Fields are highly specific, such as antibody sequences (heavy chain, light chain variable regions), affinity constants (Kd values, in nM), yield (mg/L), purity (%), aggregate content (%), cell line information, and media components. Units are diverse and specialized.
Constraints from "Multi-turn Conversation and Prompts"
The complexity of bispecific antibody quality documents imposes specific requirements on multi-turn conversation and prompt design. Sequences, chemical structures, and numerous charts in documents mean that plain text RAG struggles to capture key information accurately. This requires enhanced multimodal processing capabilities. High update frequency demands an efficient incremental update mechanism for the knowledge base to ensure conversations are based on the latest data. The precision of specialized fields and units means prompts must guide the model to focus on specific values and units, avoiding vague responses. For example, when querying affinity, the Kd value range and nM unit must be clear. Multi-turn conversations need to support in-depth inquiries about specific batches, targets, or quality attributes. This requires the system to effectively manage conversation context and dynamically adjust knowledge retrieval strategies based on user intent.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 500–800 characters | Balances long text information completeness with retrieval efficiency, preventing individual chunks from diluting the topic. |
Chunk Overlap Length (Overlap Length) | 50 characters | Ensures context continuity and reduces the risk of critical information loss due to chunk truncation. |
Recall count (Recall Count) | top 8–12 items | Covers multi-dimensional quality control information and improves the accuracy of complex queries. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Filters irrelevant content while retaining highly relevant document segments for specialized terms. |
Rerank result count (Rerank Count) | top 5 items | Further refines results to best match the multi-turn conversation context, improving answer precision. |
maxContext | 4096 tokens | Accommodates the need for detailed background information and follow-up questions in specialized conversations. |
Common Pitfalls
- Symptom: The model replies "No relevant documents found" or provides incomplete information, even if the knowledge base contains the content. Reason: Inadequate document chunking strategy, where key information is split across different chunks, leading to incomplete context during retrieval.
- Symptom: During a conversation, the model cannot adjust the knowledge base retrieval scope based on previous follow-up questions. Reason: Prompts do not effectively guide the model to parse and utilize user intent in multi-turn conversations, preventing dynamic updates to knowledge base query parameters.
- Symptom: The system prompts "File parsing timeout" or displays garbled content. Reason: Uploaded PDF, image, or table files contain complex biological structure diagrams or sequence diagrams, and the parser cannot correctly identify or extract text content.
Validation Steps
- Ask questions about specific quality attributes (e.g., purity, aggregate content) for different batches of bispecific antibodies. Check if the model accurately cites numerical values and units from documents and provides relevant batch numbers when prompted for follow-up.
- Upload R&D reports containing complex charts and sequence information. Test if the model can identify and interpret key data points in charts and specific regions in sequences.
- Engage in multi-turn conversations, starting with a broad question and gradually narrowing down to a specific experimental method or validation parameter. Observe if the model maintains context and adjusts information retrieval strategies with each follow-up question.
The values given 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.