Data Characteristics for This Category
Recombinant protein product data originates from diverse sources. These include production batch reports, quality control (QC) analysis reports, stability study data, customer feedback, and literature. Data typically exists in a mixed format, combining structured data (e.g., database records, CSV files) and unstructured data (e.g., experimental logs, PDF reports, Word documents). Data update frequency depends on product development stages, production cycles, and market feedback. New batch production, QC result releases, and new application research trigger data updates. Document structures for QC reports often include fields such as batch number, production date, purity, activity, endotoxin content, and molecular weight. Units involve %, U/mg, EU/mg, and kDa. These fields and their units are crucial for understanding recombinant protein biological characteristics and quality control. Literature may contain more complex experimental conditions, result descriptions, and charts.
Constraints Imposed by These Characteristics on Workflow Orchestration
The diversity of recombinant protein product data requires workflows with robust multi-format file processing capabilities. This is especially true for text extraction and structured parsing from PDF and Word documents. The non-periodic nature of data updates dictates that workflows must support both manual triggers and automatic triggers based on specific events (e.g., new file uploads). This ensures knowledge base timeliness. Key fields in QC reports, such as batch number and purity, require precise extraction and tagging during orchestration. This supports subsequent semantic search or conditional logic. For example, when a user queries the purity of a specific batch, the system must accurately identify the batch number and extract the purity value from the corresponding report. Numerical data like molecular weight and activity may require range comparisons or trend analysis within the workflow. The complex structure of literature requires workflows to effectively distinguish sections like titles, abstracts, methods, and results during segmentation. This prevents critical information from being truncated.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Ensures complete capture of critical data blocks in QC reports (e.g., a full test item result) or a single independent viewpoint in literature. |
Recall count (Recall Count) | Top 5–8 items | Balances recall rate with computational cost, ensuring coverage of main information points relevant to user queries. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Appropriately increases the threshold to reduce interference from irrelevant content, given the specialized terminology and rigorous descriptions of recombinant proteins. |
Rerank result count (Rerank Return Count) | 3 items | Provides three core and highly relevant pieces of information, avoiding information overload while maintaining precision. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accounts for the parsing time of large PDF reports, providing ample time to prevent parsing timeouts that could lead to knowledge base update failures. |
maxContext | 3000 Tokens | Accommodates multi-faceted user inquiries, such as simultaneously asking about purity, activity, and storage conditions, requiring a larger context window. |
Three Common Mistakes
- After a knowledge base update, newly uploaded batch report content is not retrievable. This typically occurs due to file parsing timeouts or format incompatibility, leading to text extraction failure and an empty knowledge base entry.
- When a user asks about the purity of a specific recombinant protein batch, the system returns data from other batches. This happens because entity recognition and association logic for key fields like batch numbers are improperly configured in the workflow, leading to inaccurate semantic matching.
- During multi-turn conversations, the system fails to remember previous discussions about recombinant protein application scenarios, repeatedly asking questions or providing disjointed answers. This indicates incorrect context transfer configuration in the workflow's conversation management module, causing the model to lose historical information in subsequent turns.
How to Confirm Proper Configuration
- Upload a batch of recombinant protein data in various file formats (PDF, Word, CSV). Verify that all files are successfully parsed and generate retrievable text segments in the knowledge base. Confirm that key fields (e.g., batch number, purity) are correctly identified.
- Conduct multi-turn simulated queries for recombinant protein products of varying complexity. Observe if the system can answer accurately and maintain conversational coherence, especially for queries involving specific batches or detailed test metrics.
- Use FastGPT's log viewing feature to check for parsing errors, timeouts, or model call failures during workflow execution. Ensure all nodes are operating correctly.
Note: The values provided are common starting points. They 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.