Data Characteristics
CSO (Contract Sales Organization) product data in biopharmaceuticals comes from internal sales reports, marketing activity records, product manuals, clinical study reports, and external market research. This data updates frequently. Sales data typically updates daily or weekly, marketing activity data monthly, and product manuals and clinical reports revise with product lifecycles or regulatory requirements. Document structures vary, including structured database records (e.g., sales figures, customer information), semi-structured documents (e.g., product brochures, training materials), and unstructured text (e.g., email communications, meeting minutes). Field and unit consistency is crucial for model processing. Sales data includes amounts (in 10,000 RMB), quantities (boxes/units), regions, and customer types. Clinical data involves dosages (mg/kg), efficacy indicators (percentages, specific values), and adverse event types. Product manuals detail ingredient content (g/L), indications, and usage.
Constraints from Data Characteristics on Model Access and Configuration
CSO product data's high update frequency requires the model to quickly synchronize and incrementally learn, ensuring timely consultation results. Diverse document structures demand flexible data preprocessing to effectively parse various data formats, such as extracting text from PDFs or structured information from databases. Field and unit standardization directly impacts the model's understanding of numerical values and calculation accuracy. Inconsistent sales unit data, for example, can lead to incorrect revenue forecasts. A large volume of unstructured text challenges the model's information extraction and semantic understanding, necessitating refined text segmentation and vectorization strategies. Product manuals and clinical reports contain specialized terminology and domain knowledge. This requires the model to have a strong background in biopharmaceutical knowledge or use RAG (Retrieval Augmented Generation) to avoid generic or inaccurate answers.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192 token | Accommodates the length of product manuals and clinical reports, ensuring a single query covers most core information. |
Chunk size (Segment Length) | 500 characters (characters) | Balances semantic completeness and recall efficiency. Avoids overly long segments that introduce irrelevant information or overly short segments that lose context. |
Recall count (Recall Count) | 10 entries (items) | Ensures relevant recall while controlling the context length sent to the large model, reducing inference costs. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures high relevance of recalled content and filters out noise, especially in the terminology-rich biopharmaceutical domain. |
Rerank result count (Rerank Return Count) | 3 entries (items) | Refines key information presented to the user, improving answer accuracy and conciseness, and avoiding information overload. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Handles parsing time for large PDF product manuals and clinical reports, preventing file upload failures due to timeouts. |
Three Common Mistakes
- "File parsing timeout" occurs when uploading large product manuals or clinical reports. This usually happens because
PARSE_FILE_TIMEOUT_SECONDSis set too low, not allowing enough time to process complex document structures. - The model provides numbers without units or with incorrect units when asked about specific drug dosages. This stems from a failure during data preprocessing to unify or correctly identify units in the data source, leading to the loss of critical dimensional information during vectorization.
- In workflow orchestration, a multi-step process is expected to show only the final result, but each step outputs intermediate results. This can happen if the "display output" option for each node in the workflow is not configured as expected, or if the model itself outputs unnecessary redundant information in intermediate steps.
Verification Steps
- Upload CSO product data files of different types (PDF, Excel, TXT) and sizes (1MB, 10MB, 100MB). Observe if files parse and vectorize into the database correctly. Check if the segmented content after ingestion meets expectations.
- Construct multi-turn Q&A tests for key information in product manuals (e.g., indications, usage, adverse reactions). Evaluate the model's answer accuracy, completeness, and professionalism.
- Simulate real sales scenarios. Ask questions involving sales data (e.g., "Last week's sales for product X in region Y") and marketing activities (e.g., "How effective was the last online promotion?"). Verify if the model's data matches the original data sources.
- Check system logs for errors or warnings related to file parsing, vectorization, or model inference, especially regarding memory overflow or timeouts.
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.