Data Characteristics
Contract Development and Manufacturing Organizations (CDMOs) generate a large volume of highly structured and semi-structured documents during biopharmaceutical R&D. Data sources include R&D project reports, batch production records, quality control reports, analytical method validation files, equipment validation files, and preclinical study data. These documents are updated frequently, especially at critical project milestones, with new experimental data or batch records potentially generated daily. Document structures typically follow industry standards, such as ICH guidelines or GMP regulations, and contain numerous tables, chromatograms, formulas, and specialized terminology. Field types are diverse, covering compound structures, reaction conditions, purity, yield, stability data, dosage, and toxicity reports. Units include molar concentration, mass percentage, temperature (Celsius/Kelvin), time (hours/days), and pressure (Pa/psi), often accompanied by abbreviations and internal codes.
Constraints on Context and Token Management
The highly structured and specialized nature of CDMO R&D documents imposes specific requirements on context management. First, the extensive table and chromatogram data within documents require preservation of their intrinsic relationships during vectorization and retrieval to avoid fragmented information loss. Second, specialized terminology and abbreviations demand that the model possess domain knowledge for context understanding; otherwise, ambiguity or misinterpretation may occur. High update frequency necessitates efficient incremental indexing capabilities for the RAG system to ensure retrieved context is always current. The complex field and unit system, such as chemical structural formulas or coexistence of multiple units, challenges the precision of semantic understanding. This requires a longer context window to capture these details for accurate structured information extraction. Simultaneously, this can lead to a relatively high token count per query, requiring a balance between cost and effectiveness.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4000-6000 characters | Ensures completeness of complex tables and specialized terminology context |
Chunk size (Chunk Size) | 800-1200 characters | Balances semantic integrity and vector retrieval efficiency |
Recall count (Recall Count) | 5-8 items | Covers key information points, avoids missing relevant data |
Similarity threshold (Similarity Threshold) | Calibrated by actual measurement | Adjusts based on CDMO document characteristics to ensure high relevance in retrieval |
Rerank result count (Reranked Return Count) | 3 items | Refines final context, focuses on the most critical information |
Model Max Input Tokens | 8000 or higher | Accommodates queries containing extensive specialized vocabulary and structured data |
Common Pitfalls
- The token consumption displayed for model queries does not match the actual API billing. This may be due to the platform's additional token calculation for context, historical conversations, and system prompts not being fully presented on the frontend.
- Confusion between the "new context" output by the AI conversation node and the "AI response content" leads to incorrect processing logic in subsequent nodes. This typically results from failing to understand that "new context" maintains multi-turn conversation state, while "AI response content" is the model's answer for the current turn.
- The model incorrectly parses or omits chemical formulas or structural formulas in documents. This occurs when the chunking strategy fails to effectively preserve the integrity of these special formats, leading to critical information being truncated.
Verification of Configuration
- Conduct multi-turn Q&A tests on typical CDMO R&D reports. Check the model's understanding of specialized terminology, data tables, and experimental procedures, as well as the accuracy of its answers. Ensure context covers critical information.
- Monitor actual token consumption for each query through the FastGPT log system. Compare it with expectations or model API billing rules to verify consistency in token calculation logic.
- Perform spot checks on system-generated vector data. Verify that key fields, units, and structured information in documents are correctly vectorized. Validate the relevance threshold setting for retrieval through similarity searches.
Note: The values provided are common starting points and should be measured against specific 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.