Data Characteristics for Target Discovery
Quality documentation in target discovery primarily includes experimental reports, validation protocols, data analysis records, instrument calibration certificates, and compliance review materials. Data sources are diverse, encompassing high-throughput screening (HTS) results, structural biology data, pharmacodynamics and pharmacokinetics (PK/PD) reports, and various bioinformatics analysis outputs. Documents often exist as PDFs, Word files, Excel files, or structured text. Update frequency is closely tied to project progress, with concentrated revisions typically occurring after experiments, during phased evaluations, or before regulatory submissions. Document structures are complex, containing extensive specialized terminology, chemical formulas, gene sequences, protein structure diagrams, and statistical charts. Fields and units are highly specific, such as IC50 values (unit nM), binding constant Kd (unit M), gene expression levels (unit FPKM or TPM), and m/z ratios in mass spectrometry data. These require high precision for numerical values and strong context dependency.
Constraints on Deployment and Upgrade from Data Characteristics
The data characteristics of target discovery documents impose specific requirements on FastGPT's deployment and upgrade process. Dispersed document sources and inconsistent update frequencies necessitate flexible data synchronization mechanisms to ensure the timeliness of knowledge base content. Diverse file formats, especially PDFs containing complex charts and specialized symbols, require FastGPT's file parsing capabilities to effectively extract text information and preserve structured data where possible. Specialized terminology, chemical formulas, and gene sequences challenge the accuracy of model understanding and recall, requiring more refined text segmentation and embedding strategies. Accurate identification of high-precision numerical values and units is crucial for knowledge base query quality, preventing critical information deviations due to unit confusion or misinterpretation of values. Furthermore, deployment environment security and compliance requirements, ensuring the privacy protection of sensitive R&D data, are principles that must be strictly followed during upgrades.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Target discovery experiment reports often contain numerous images and charts, leading to large file sizes. |
Chunk size | 800–1200 characters | Balances specialized terminology context and semantic completeness of long sentences. |
Similarity threshold | Calibrate empirically 0.75–0.85 | Ensures recall relevance to professional queries, filtering out irrelevant results. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles parsing of large PDF files, preventing timeout interruptions. |
maxContext | 32k tokens or higher | Addresses complex target discovery queries, providing sufficient contextual information. |
Rerank result count | Top 5 entries | Refines recall results, improving user efficiency in obtaining key information. |
Common Mistakes
- Files remain in a processing state for an extended period after upload, eventually showing a parsing failure. This typically occurs when
PARSE_FILE_TIMEOUT_SECONDSis set too low, preventing the completion of content extraction for large or complex PDF files within the specified time. - Key numerical values or units are incorrect or missing in knowledge base query results. This may stem from the file parser failing to correctly identify specific formatted numerical values in the text, leading to skewed embedding vectors.
- User permission controls are ineffective, allowing interns to access unauthorized documents. This usually indicates incorrect configuration of FastGPT's user groups and document access permissions after deployment, or that permission updates have not taken effect promptly.
How to Verify Configuration
- Upload a large PDF experimental report containing complex charts and specialized symbols. Verify that it parses successfully and generates high-quality embedding vectors.
- For target discovery documents in the knowledge base, pose queries that include specific numerical values and units. Verify that the relevant information in the returned results is accurate.
- Log in to the system with user accounts having different permissions. Attempt to access restricted documents to confirm that the permission control strategy is functioning as expected.
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.