Data Characteristics for this Category
Lead optimization quality documents in biopharmaceuticals primarily focus on experimental data such as compound synthesis, purification, structural confirmation, in vitro activity, in vivo pharmacokinetics (PK), and pharmacodynamics (PD). Data sources are diverse, including laboratory notebooks, analytical reports (e.g., HPLC, NMR, MS spectra), biological activity assay reports, and animal study reports. These documents typically exist in PDF, Word, or Excel formats; some data may be embedded directly in LIMS (Laboratory Information Management System) or ELN (Electronic Lab Notebook) systems. Document update frequency is relatively high, especially during project advancement, with new experimental data and analysis reports potentially generated weekly or even daily. Document structures commonly include fields like project number, compound number, experimental methods, experimental conditions, results data, and analytical conclusions. Units involved include nM, µM, mg/kg, and h, requiring high precision.
Constraints Imposed by These Features on "Reference and Traceability"
The diversity of data sources in lead optimization quality documents requires the knowledge base to effectively integrate information from different formats and origins, and to precisely tag the source for each information segment. High update frequency means the knowledge base content needs frequent synchronization or incremental updates to ensure timely references. Documents contain large amounts of structured and semi-structured data, as well as specialized terminology and units, which place high demands on the chunking strategy and entity recognition capabilities of a RAG (Retrieval-Augmented Generation) system. Precise numerical values and units, such as IC50 values or plasma exposure, must maintain accuracy and contextual consistency when cited, to avoid loss or misinterpretation of critical data due to improper segmentation. The traceability mechanism needs to track back to specific experimental reports, compound numbers, and even original experimental records, to meet compliance and audit requirements.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for this Value |
|---|---|---|
Chunk size | 500-800 characters | Balances contextual completeness and retrieval efficiency, adapting to paragraph lengths in experimental reports. |
Chunk Overlap Length | 80-120 characters | Ensures contextual continuity when referencing across segments, preventing critical information from being truncated. |
Recall count | Top 8-12 entries | Increases recall coverage, considering document complexity and cross-referencing needs. |
Similarity threshold | 0.75-0.85 | Filters irrelevant content, improves retrieval precision, and adapts to the similarity of specialized terminology. |
Rerank result count | Top 5 entries | Reduces model processing load while maintaining accuracy, focusing on core information. |
Document Parsing Strategy | Table Recognition Combined with Text Extraction | Addresses parsing needs for both experimental data tables and descriptive text. |
Three Common Mistakes
- The AI response fails to cite specific experimental data or reports. This manifests as a lack of numerical support or overly general statements in the response. The cause is insufficient consideration of table structures or critical numerical context during document chunking.
- The final response displayed to the user does not show citation sources, making it impossible to trace information origin. This is due to
show_referenceorstream_citationparameters not being correctly enabled in the API call or frontend configuration. - After a knowledge base update, the model still cites old data. This manifests as responses that do not align with the latest experimental results. The cause is incorrect configuration of the knowledge base's incremental synchronization mechanism or delays in index rebuilding.
How to Confirm Correct Configuration
- Randomly select lead optimization documents for several compounds. Use FastGPT to query key pharmacokinetic parameters. Check if the response includes specific numerical values and accurately traces back to the original report page number.
- In the frontend interface or API response, verify that each answer is accompanied by a complete list of citation sources and that these can be clicked to jump to the corresponding document segment.
- Upload a document containing the latest experimental data. After the knowledge base synchronization completes, query related content to confirm that the model cites the updated information.
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.