Workflow Orchestration for Recombinant Protein Clinical

Data Characteristics

Recombinant protein clinical trial pre-screening primarily uses data from public databases (e.g., GenBank, UniProt, PDB, ClinicalTrials.gov), patent literature, academic papers, and internal experimental data. Update frequencies vary. Public databases typically update weekly or monthly. Internal experimental data generates in real-time. Recombinant protein structural information often stores in PDB format, including atomic coordinates and residue sequences. Functional and characterization data often presents in tabular form. Fields include molecular weight, isoelectric point, solubility, immunogenicity prediction, expression host, and purification method. Clinical trial data typically includes study design, inclusion criteria, exclusion criteria, dosage, administration route, adverse event reports, and efficacy indicators. This data commonly appears in XML or JSON format within clinical trial registration documents.

Constraints from Data Characteristics on Workflow Orchestration

Diverse data sources and complex formats for recombinant proteins place high demands on workflow orchestration. First, data heterogeneity requires the workflow to integrate multiple data source connectors. It must also support parsing and standardization of different data formats. For example, PDB file parsing needs specialized bioinformatics tool nodes. Structured extraction from clinical trial documents relies on advanced text processing modules. Second, differences in data update frequency, especially periodic updates from public databases, mean the workflow needs to support scheduled triggers and incremental update strategies. This ensures the timeliness and accuracy of pre-screening results. Finally, specific recombinant protein fields (e.g., immunogenicity, solubility) are often multi-dimensional. The workflow needs complex logical judgment and multi-parameter screening capabilities. This includes comprehensive evaluation based on multiple prediction model results and handling missing or uncertain data.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`6`	Clinical trial pre-screening conversations typically require reviewing recent interactions to maintain context continuity. This avoids repetitive questioning or information omission.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing large PDB files or complex clinical trial documents (e.g., XML format) can require significant time for parsing and structured extraction.
`Chunk size`	`800–1200 characters`	Recombinant protein literature and clinical trial documents are content-dense. An appropriate segment length helps the RAG model more accurately capture key information. It also prevents single segments from becoming too long and diluting the topic.
`Recall count`	`top 5`	The pre-screening stage balances recall rate and result relevance. Recalling the top few most relevant document snippets usually covers core information and reduces unnecessary computation.
`Similarity threshold`	`0.75`	Precise matching of specialized terminology in the biomedical field requires a higher similarity threshold. This helps filter highly relevant documents and reduces noise interference.
`Rerank result count`	`3`	Re-ranking further optimizes the order of recalled results. This ensures that the most relevant clinical trial or protein characteristic information is presented first to the engineer.

Common Pitfalls

Symptom: Workflow stops mid-run, logs show "Node execution timed out". Cause: Some nodes (e.g., complex molecular simulation or bioinformatics analysis of large datasets) exceed the default NODE_EXECUTION_TIMEOUT parameter setting.
Symptom: After multiple turns of conversation, the model's response disconnects from previous turns. Cause: The maxContext parameter is set too low. This truncates chat history context, preventing the model from accessing the full conversation background.
Symptom: Plugin node returns empty or incomplete results when processing user input. Cause: Plugin input parameters are configured incorrectly. For example, the expected field name does not match the actual field name passed by the workflow, or the 聊天记录 parameter is not passed correctly.

Verification Steps

Use the FastGPT workflow debugging interface. Step through each node to check if intermediate outputs meet expectations, especially the output format of data parsing and structured extraction nodes.
Use different types of recombinant proteins (e.g., known and unknown structures) as input. Verify the workflow's stability and accuracy on diverse data. Check if the final pre-screening results include all necessary information fields.
Simulate multi-turn conversation scenarios. Observe the impact of the maxContext parameter on conversation coherence. Ensure the model provides reasonable responses to subsequent questions based on previous interactions.
For typical queries, examine the document snippets recalled by the knowledge base. Evaluate whether the Chunk size, Recall count, and Similarity threshold settings effectively filter irrelevant information and retain key data. The recalled results should support the model's judgment on recombinant protein characteristics.

The values provided are common starting points. Measure them against your 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.