Deployment and Upgrade for Lead Compound Screening in

Data Characteristics for This Category

Lead compound screening data originates from high-throughput screening experimental reports, compound library information, biological activity test results, and patent literature. This data has a relatively low update frequency, typically updating with experimental batches or compound library expansions, with cycles ranging from weeks to months. Document structures often include PDF or Word formats for experimental reports, containing compound structural images, IC50/EC50 values, binding constants, and cytotoxicity data. Compound library information is usually in CSV or Excel tables, with fields like compound ID, molecular formula, molecular weight, and supplier details. Biological activity data may exist as database records, including target names, mechanisms of action, and dose-response curves. Units commonly used are µM or nM for concentration, and % inhibition or relative fluorescence units for activity values.

Constraints Imposed by These Characteristics on "Deployment and Upgrade"

The diversity of lead compound screening data sources requires FastGPT to be configured with flexible file parsing capabilities during deployment to accommodate various formats like PDF, Word, and CSV. The longer update cycle means that during upgrades, data synchronization and incremental update strategies need optimization to avoid reprocessing large amounts of historical data. Non-textual information within documents, such as compound structural images and molecular formulas, poses challenges for semantic understanding and knowledge extraction, potentially requiring OCR technology or specific molecular descriptor processing. For numerical data like activity values, ensuring correct units and value ranges is critical during knowledge base construction for subsequent numerical comparison and screening logic. The specificity of fields, such as IC50 values and target names, demands precise definition in model fine-tuning or prompt engineering to improve question-answering accuracy.

Configuration Settings

Configuration Item	Recommended Value	Rationale for Recommendation
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Experimental reports and compound library files are often large; this ensures complete data uploads.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing PDF/Word documents with many structural images or complex tables can be time-consuming.
`Chunk size` (Segment Length)	`800–1200 characters`	Preserves contextual information like compound activity data and experimental conditions, preventing key data truncation.
`Recall count` (Recall Count)	`Top 5 entries` (Top 5)	Lead compound screening typically requires precise identification of a few highly relevant results, reducing noise.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures recalled compounds or experimental reports are highly relevant to the query, filtering out vague matches.
`Rerank result count` (Rerank Return Count)	`3 entries` (3 items)	Secondary sorting among a small number of highly relevant results further improves result precision.

Three Common Pitfalls

During FastGPT testing, a permission error occurs, while OneAPI testing passes. This usually indicates incorrect Authorization header transmission or API Key configuration when FastGPT integrates with OneAPI.
After deploying the deepseek8b model with ollama, a 404 error appears in FastGPT configuration. This might be due to the ollama service port or path not being correctly exposed to FastGPT, or FastGPT's model address configuration not matching the actual service address provided by ollama.
After uploading documents, the model fails to accurately answer deployment-related questions and instead uses content from other documents. This could be due to an improper knowledge base segmentation strategy, leading to key deployment steps being split or buried in a large amount of non-core information, affecting recall accuracy.

How to Confirm Correct Configuration

Upload a PDF experimental report containing compound structures, IC50 values, and target information. Check if the knowledge base can correctly extract and display these key fields.
Use a query with a specific compound ID and biological activity to verify if FastGPT accurately recalls corresponding compound library records and experimental report segments. Check the similarity score of the recalled items.
Simulate a question about "DeepSeek 8B model local deployment." Observe if the model provides specific steps or configuration suggestions based on the uploaded deployment documentation. Check if the answer includes key parameter names from the document.

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.