Data Characteristics
CMC (Chemical Manufacturing and Control) research and development document data originates primarily from experimental records, analysis reports, batch production records, and stability study reports. These documents update infrequently, typically at key project milestones or when phased results are produced. Document structures are complex, containing large amounts of unstructured text, semi-structured tables (e.g., spectral data, test results), and charts. Field names vary and often include compound names, batch numbers, test items, test methods, result values, and units (e.g., mg/mL, ℃, pH, %). They also contain numerous industry-specific abbreviations and terminology. Data accuracy and traceability are critical; even minor deviations can impact subsequent R&D decisions.
Constraints Imposed by Data Characteristics on Database and Operations
The data characteristics of CMC R&D documents impose specific requirements on database and operations. First, the complex document structure and mixed data types require databases with strong unstructured data processing capabilities, such as vector databases or document databases capable of storing complex JSON objects. Second, data updates are infrequent, but each update may involve a large amount of related information, necessitating transactional support to ensure data consistency. Professional terminology and units within documents require customized text preprocessing and entity recognition rules to ensure accurate structured parsing. High traceability requirements mean that historical versions of each parse and modification must be recorded. Additionally, due to data sensitivity, database access control and data encryption are essential configurations.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 2048 Tokens | Ensures coverage of critical contextual information for most CMC R&D documents. |
Chunk size (Segment Length) | 800-1200 characters (characters) | Balances semantic completeness with vector embedding efficiency, suitable for lengthy reports. |
Recall count (Recall Count) | Top 10 entries (top 10) | Improves recall rate of relevant information, covering multi-dimensional data points. |
Similarity threshold (Similarity Threshold) | 0.78 | Balances recall and precision, reducing interference from irrelevant results. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Accommodates parsing large PDFs or scanned documents, preventing timeouts. |
VECTOR_DB_TYPE | Milvus or Elasticsearch | Supports complex vector retrieval and adapts to multimodal data storage requirements. |
Common Pitfalls
- Returning
tool_callserrors when calling database query tools often indicates incorrect credentials or host addresses in the database connection configuration, or insufficient permissions to access the specified database. - Excessive document parsing time, or even timeout errors, usually results from
PARSE_FILE_TIMEOUT_SECONDSbeing set too low, failing to account for the processing time of large or complex documents. - Lack of critical numerical or unit information in Q&A results often occurs because the text preprocessing stage did not customize recognition and extraction for CMC-specific fields and units, leading to loss of structured information.
Configuration Verification
- Upload a typical CMC R&D document and observe its parsing progress and results. Ensure the document content is correctly segmented and key information is extracted.
- Use the FastGPT application interface to perform queries containing specialized terminology and units. Verify that the system accurately recalls relevant document segments and data points.
- Check database logs to confirm that during peak periods or concurrent requests, the database connection pool and query response times are within reasonable limits, with no significant number of connection failures or query timeouts recorded.
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.