Data Characteristics for This Category
Consultation data for laboratory services, particularly in biological reagents, lab consumables, and customized analysis, originates primarily from product manuals, technical whitepapers, SOP (Standard Operating Procedure) documents, SDS (Safety Data Sheets), experimental reports, and online vendor databases. These documents typically exist as PDF, Word, Excel, or structured JSON files. Data update frequency varies by product line and regulatory requirements. New product releases, batch updates, and regulatory revisions trigger data updates, usually quarterly or semi-annually. Document structures often include fields such as product name, catalog number, specifications, batch, storage conditions, application areas, experimental procedures, and technical indicators in manuals. SDS documents focus on chemical composition, hazard identification, and first aid measures. Field values may contain complex chemical formulas, biological macromolecule names, units (e.g., mg/mL, IU/μL, OD600), and specific experimental conditions (e.g., 37°C, pH 7.4).
Constraints Imposed by These Characteristics on "Deployment and Upgrade"
The diversity, specialized nature, and update cycle of laboratory service data impose specific requirements on FastGPT deployment and upgrades. Complex document structures, including numerous tables, images, and specialized terminology, necessitate optimized file parsing capabilities to ensure accurate extraction of critical information. Data update frequency dictates the vector database synchronization strategy; overly frequent reconstruction consumes significant computational resources, while delays can lead to outdated consultation results. Additionally, many specialized field values include specific units, requiring the model to accurately identify and differentiate them to avoid unit conversion errors or confusion. The deployment environment must provide sufficient storage and computing resources to handle vectorization storage and querying of large document volumes. During upgrades, new version improvements to parsers and embedding models require thorough validation on real data to ensure good compatibility with existing knowledge bases and enhanced processing capabilities for new data types.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 100 MB | Technical documents for laboratory services, especially PDFs with high-resolution images and charts, can have large individual file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing complex PDF documents, particularly those involving table recognition and multi-language content, may require extended time. |
chunkSize | 800–1200 characters | Ensures individual segments contain complete experimental procedures or technical indicator descriptions while avoiding excessive length that leads to information redundancy. |
maxContext | 6000 token | Laboratory service consultations often require a longer context to understand complex experimental scenarios or product specifications. |
Similarity threshold | 0.78 | Increase the similarity threshold to filter out irrelevant recall results, ensuring accuracy in specialized consultations. |
Rerank result count | Top 5 entries | After reranking, the top 5 results typically cover the user's core needs, reducing the model's processing burden. |
Three Common Mistakes
- Application fails to respond, logs show
network error: This usually indicates a Docker container network configuration issue, preventing FastGPT from accessing external resources or internal service ports. - After file upload, knowledge base content is empty or lacks critical information: The file parser has insufficient support for specific formats (e.g., scanned PDFs, complex nested tables), leading to failed or incomplete content extraction.
- Query results do not match the latest product information: The knowledge base failed to timely synchronize the latest data updates from vendors, causing the model to respond based on outdated data.
How to Confirm Correct Configuration
- Upload a batch of typical product manuals and SDS documents in PDF, Word, and Excel formats. Check if the knowledge base segment preview accurately extracts product names, catalog numbers, key technical parameters, and experimental procedures.
- Based on the uploaded document content, simulate inquiries about product applications, storage conditions, and technical indicators. Verify if the model provides accurate answers that include critical fields (e.g., units).
- Regularly check data sources (e.g., vendor databases) for updates. Trigger knowledge base synchronization tasks and observe synchronization logs and results to ensure new data is correctly indexed and queryable.
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.