Deployment and Upgrade for Lead Optimization Quality

Data Characteristics for This Category

Lead optimization quality documentation primarily includes compound synthesis records, activity screening reports, toxicity test data, pharmacokinetic (ADME) study reports, and stability study results. Data sources are diverse, encompassing Laboratory Information Management Systems (LIMS), Electronic Lab Notebooks (ELN), and raw files from various analytical instruments. The update frequency is relatively high; new data can be generated daily, especially during compound iteration and screening processes. Document structure typically includes chemical structures, molecular weights, batch information, detection methods, experimental conditions, result values, units (e.g., nM, µg/mL), and conclusions. Field naming conventions are generally standardized, but subtle differences may exist across different experimental platforms.

Constraints Imposed by These Characteristics on "Deployment and Upgrade"

Frequent updates and diverse sources of lead optimization quality documentation require real-time data synchronization capabilities in the deployment environment. FastGPT must ingest the latest data promptly. Chemical structures and extensive numerical data within documents demand specific processing capabilities from text parsing and vector embedding models. Models that support chemical structure parsing or effectively handle complex numerical contexts are necessary. Field discrepancies across different data sources necessitate flexible mapping and cleaning mechanisms during data preprocessing. Additionally, sensitive experimental data in documents impose strict requirements on data security and permission management in the deployment environment. Data isolation and access control must be in place. During upgrades, model and parser compatibility becomes critical, requiring evaluation of new versions' impact on existing data indexes.

Configuration Settings

Configuration Item	Recommended Value	Rationale for Recommendation
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Ensures large report files containing numerous structural diagrams and experimental data can be uploaded.
`maxContext`	`2000 characters`	Accommodates detailed experimental procedures and result descriptions that may be present in a single experimental report.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Handles the longer parsing times for complex charts and tables embedded in PDF and DOCX formats.
`Chunk size` (Segment Length)	`800 characters`	Balances context completeness with retrieval efficiency, preventing critical information from being cut off.
`Recall count` (Recall Count)	`Top 10`	Ensures coverage of multiple relevant experimental results in complex lead optimization queries.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Adapts similarity judgments for numerical data and structural descriptions, avoiding false positives.

Three Common Mistakes

Frontend page inaccessibility with container logs showing network connection errors often results from incorrect Docker container port mapping, such as misconfigured ports preventing external access.
Document parsing timeouts, manifested as files uploading but remaining unresponsive or erroring for extended periods, occur when PARSE_FILE_TIMEOUT_SECONDS is set too low to process experimental reports containing numerous charts and complex tables.
Abnormal retrieval results for some historical documents after an upgrade can stem from incompatibility between new model or parser versions and old data indexes. This requires data rebuilding or re-indexing.

How to Confirm Correct Configuration

Upload multiple lead optimization reports from different sources, including chemical structures and numerical data. Verify successful parsing and vector generation.
Perform searches on uploaded reports using compound names, experimental conditions, and result ranges. Validate the relevance and completeness of recall results.
Simulate high-concurrency file upload scenarios. Monitor system resource utilization and response times to ensure deployment environment stability.
Review log output to confirm the absence of file parsing failures, database connection errors, or permission denied messages.

Note: The values provided are common starting points. Measure 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.