Data Characteristics for This Category
R&D document data for high-value consumables originates from internal R&D management systems, Laboratory Information Management Systems (LIMS), and external regulatory databases. Data update frequency is relatively low, typically aligning with R&D project milestones or regulatory revision cycles, such as annual or quarterly updates. Document types are diverse, including design documents, test reports, clinical trial data, risk assessment reports, quality management system documents, and registration submission materials. These documents have complex structures, containing extensive unstructured text, charts, tables, and specialized terminology. Fields and units are highly specialized, for example, material biocompatibility indicators (e.g., "cytotoxicity grade"), mechanical performance parameters (e.g., "tensile strength MPa"), sterilization process parameters (e.g., "irradiation dose kGy"), and clinical evaluation indicators (e.g., "implantation success rate %"). Precision and consistency requirements are extremely high.
Constraints Imposed by These Characteristics on "Database and Operations"
The low update frequency of high-value consumable R&D documents means the database does not require frequent full synchronization. Incremental updates or periodic bulk import strategies can be adopted, reducing database write pressure. The complex structure and multimodal nature of documents require the database to effectively store and retrieve unstructured data, supporting the parsing and indexing of chart and table content. Strict requirements for specialized terminology and units make data cleaning and standardization critical steps. This necessitates establishing comprehensive glossaries and unit conversion rules, integrating them into the data preprocessing workflow. Furthermore, data involves intellectual property and regulatory compliance, demanding high security and traceability. This requires fine-grained permission management and audit logging features. Database operations must focus on data consistency validation and historical version management, ensuring every step of the R&D process is traceable.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 2000 | Accommodates the complexity and long text characteristics of high-value consumable documents. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses potentially long parsing times for large PDF or image files. |
Chunk size (Chunk Length) | 800–1200 characters | Balances semantic completeness with model processing length limits. |
Recall count (Recall Count) | Top 5 entries | Controls the volume of recalled data while ensuring relevance. |
Similarity threshold (Similarity Threshold) | 0.75 | Precisely matches specialized terminology and technical indicators. |
chunk_overlap | 100 characters | Ensures contextual continuity between chunks, reducing information loss. |
Three Common Mistakes
- When deploying a local development environment, a MongoDB database connection timeout occurs, with logs showing "connection timeout." This typically results from incorrect network configuration or the MongoDB service not starting correctly.
- When adding a database tool call, the conversation reports a "400 status code (no body)." This may indicate a mismatch in tool call interface parameters or the backend service not responding correctly.
- After importing documents, some key fields are empty or parsing results are inaccurate. This happens because the document parser is not optimized for the unique table structures or specialized abbreviations found in high-value consumables.
How to Verify Correct Configuration
- Upload typical high-value consumable R&D documents via the FastGPT management interface. Check if the parsed chunk content is complete and semantically coherent, paying close attention to the extraction of specialized terminology and data tables.
- Use FastGPT's query function to ask questions about specific technical indicators or material performance parameters within the documents. Verify that the relevance and accuracy of the recall results meet the expected threshold.
- Check database logs to confirm no abnormal errors occurred during data import and structured parsing, and that all data fields are successfully stored according to the predefined schema.
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.