Data Characteristics for this Category
Biopharmaceutical stability study data primarily originates from long-term experimental monitoring reports. This data typically appears as structured tables, graphs, or text descriptions. It records changes in physical, chemical, and biological properties of drugs, reagents, or biological products over time under various conditions such as temperature, humidity, and light. Data update frequency is generally low, potentially monthly, quarterly, or annually. Document formats are diverse, including PDF reports, Excel spreadsheets, or CSV files exported from LIMS (Laboratory Information Management Systems). Key fields include batch number, sample ID, test date, storage conditions, test items (e.g., content, purity, dissolution, pH value), test results and units (e.g., %, mg/ml, IU/mg), as well as judgment criteria and conclusions.
Constraints from these Characteristics on "Forms and Interactions"
The low update frequency of stability study data means a large initial data ingestion volume when building the knowledge base, but fewer subsequent incremental updates. The focus is on the accuracy and completeness of historical data. Diverse document formats require FastGPT to have robust document parsing capabilities during data ingestion, especially for structured extraction of tabular data from PDFs and Excel. The standardization of fields and units, such as distinguishing between "%" and "mg/ml" for content, and defining the range for pH values, directly impacts validation rules for user input and unit selection controls during form design. Additionally, unique identifiers like batch numbers and sample IDs are fundamental for building effective retrieval and associated queries. These must be highlighted as core query items in form design.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Stability study reports often contain numerous charts and raw data, leading to large individual file sizes. |
Chunk Length | 800 characters | Ensures each chunk contains sufficient contextual information to understand the relationship between experimental conditions and results. |
Recall Count | Top 5 | In stability study reports, relevant information is often concentrated and highly correlated. Recalling too many results can introduce noise. |
Similarity Threshold | 0.75 | Guarantees high relevance between recall results and user queries, reducing inaccurate experimental data or conclusions. |
maxContext | 3000 Tokens | Accommodates complex queries users might pose, such as comprehensive stability trend analysis across multiple batches. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing large PDF or Excel files can take a long time; this prevents parsing timeouts. |
Three Common Mistakes
- After a user enters a batch number, the system returns "No relevant information found." This usually occurs because the batch number field was not correctly identified or indexed during data ingestion, leading to a retrieval mismatch.
- A user queries historical data for a specific test item, but the results are missing data for certain time points. This might be due to inconsistent timestamp formats in the original reports or incomplete extraction of all time-series data during parsing.
- A user enters "content" in the form, but the system prompts "unit mismatch." This happens because the form configuration does not provide options for common units like "%" and "mg/ml," or the input validation rules are too strict.
How to Confirm Proper Configuration
- Upload typical stability study reports (PDF, Excel, CSV) and verify that key fields such as batch number, sample ID, test item, result, and unit are correctly extracted into the knowledge base.
- Obtain the application's initialization information via API calls and check if the opening remarks and preset form fields align with common consultation scenarios for stability studies.
- Simulate user queries, such as asking about the dissolution trend for a specific batch under particular storage conditions. Confirm that the system accurately recalls relevant data and provides a reasonable response, and verify that the form input function correctly passes data to the backend service.
Note: 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.