Data Characteristics in This Category
Lead optimization data primarily originates from high-throughput screening, in-vitro activity testing, ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) predictions and experiments, and early toxicology research reports. This data exists as a mix of structured (e.g., compound libraries, screening result databases) and unstructured formats (e.g., experimental records, research reports, spectral files). Structured data updates frequently, possibly daily or weekly, and includes numerical or enumerated fields like compound ID, IC50 values, solubility, and LogP. Unstructured documents, such as experimental protocols and analysis reports, are typically generated after experiments, update less frequently, but contain rich contextual information covering experimental conditions, method descriptions, and result interpretations. Document lengths range from a few pages to dozens, potentially including chemical structure images, tables, and biological activity data. Fields and units can vary across experiments; for example, concentration units might be nanomolar (nM) or micromolar (µM), and time units might be hours or days.
Constraints Imposed by These Characteristics on "Model Access and Configuration"
The highly heterogeneous nature of lead optimization data dictates that model access must support diverse data sources and file formats. High-frequency updates of structured data require knowledge base synchronization mechanisms to operate in real-time or near real-time, ensuring the model accesses the latest information. The complexity of unstructured documents, especially reports containing chemical structures and numerous tables, challenges document parsing capabilities. Accurate text extraction, table structure recognition, and even processing of embedded image information are necessary. The variety of fields and units means the model needs semantic understanding to identify and convert values across different unit systems, preventing misunderstandings due to inconsistent units. Additionally, early research reports may contain many specialized terms and abbreviations, requiring the model to correctly interpret their context during knowledge recall and generation. These constraints collectively demand fine-tuned configuration in FastGPT for data preprocessing, knowledge chunking, vectorization, and retrieval strategies.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates experimental reports containing numerous spectra and tables. |
maxContext | 3000 Tokens | Ensures capacity for complex experimental background, methods, and result descriptions. |
Chunk size (Chunk Length) | 800–1200 characters | Balances contextual completeness and retrieval efficiency, suitable for long report paragraphs. |
Recall count (Recall Count) | Top 8 entries | Increases the probability of recalling relevant information from diverse sources, covering multi-dimensional data. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Requires adjustment based on specific data quality and query types to avoid irrelevant recalls. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles parsing time for large PDFs or complex format documents. |
Three Common Mistakes
- Symptom: Model responses show confusion in compound activity data units, for example, mistaking nM for µM. Reason: The knowledge base failed to effectively identify and standardize unit information from different sources during data preprocessing.
- Symptom: The model cannot accurately answer questions about specific experimental conditions, even if the information exists in the report. Reason: During unstructured document parsing, key fields embedded in tables or images were not correctly extracted and vectorized.
- Symptom: When calling the model in a workflow, specific parameters (e.g.,
Session ID) are not correctly passed, leading to context loss. Reason: In the workflow configuration, the parameter mapping for the model call module did not correctly point to or match the upstream data flow.
How to Confirm Proper Configuration
- Perform a series of queries involving different units (e.g., concentration, time) and check if the model correctly identifies and processes the units in its responses.
- Upload reports containing complex tables and figures, then ask questions about key information within the report and verify the accuracy of the model's answers.
- Simulate a high-frequency update of structured data streams and observe if the model can immediately access and utilize the latest data after the knowledge base update.
- Execute tests involving multi-turn conversations to confirm if the model maintains contextual consistency throughout the session.
Note: The values provided are common starting points. Measure performance against your own samples to determine optimal settings.
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.