Data Characteristics for This Category
CSO (Contract Sales Organization) product data primarily originates from pharmaceutical companies. This includes product manuals, clinical trial reports, drug registration approvals, marketing materials, and internal sales training documents. The data is largely unstructured, commonly found in formats like PDF, Word documents, and PowerPoint presentations, with some structured Excel spreadsheets. Data update frequency aligns with drug lifecycles and market strategy adjustments. Revisions occur when new drugs launch, indications expand, adverse reactions update, or competitors emerge, with update cycles ranging from weeks to months.
Document content is highly specialized and in-depth, containing extensive medical terminology, pharmacological mechanisms, clinical data (e.g., P-values, confidence intervals), dosage instructions, and contraindications. Specific fields and units precisely describe active pharmaceutical ingredient content (e.g., mg, g), administration routes, and pharmacokinetic parameters (e.g., AUC, Cmax).
Constraints Imposed by These Characteristics on Multiturn Conversations and Prompts
The highly specialized and unstructured nature of CSO product data places strict demands on the accuracy of multiturn conversations and prompt design. The abundance of medical terminology and detailed data requires the model to possess strong semantic understanding to avoid misinterpretations of specialized vocabulary that could lead to incorrect information.
Irregular document updates necessitate a knowledge base that can quickly synchronize with the latest materials, ensuring the timeliness of conversational content. Failure to do so could result in compliance risks from advice based on outdated information. Complex tables and charts within documents, such as clinical trial data, challenge information extraction and structured processing. This directly impacts the ability to respond to data comparisons and analyses in multiturn conversations. Furthermore, precise dosage and unit information requires prompt design to guide the model in focusing on these details, preventing vague or erroneous statements regarding critical parameters.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
Chunk Size | 500–800 characters | Balances contextual completeness of medical text with retrieval efficiency, avoiding irrelevant information from overly long chunks. |
Recall Count | 8–12 chunks | Addresses complex queries by covering more relevant document segments, increasing information comprehensiveness. |
Similarity Threshold | 0.78–0.85 | Ensures recalled document segments are highly relevant to the query, filtering out content with similar medical terms but differing semantics. |
Rerank Count | 5 chunks | Refines recalled results, prioritizing the most core and accurate response basis. |
Max Token Count | 4096 Tokens | Accommodates accumulated context in multiturn conversations, ensuring the model has sufficient space to process specialized information. |
Temperature | 0.3–0.5 | Ensures professionalism and accuracy of responses, reducing the risk of generating hallucinations. |
Common Pitfalls
- Incorrect drug dosages or indications appear in conversations. This occurs when the knowledge base is not updated promptly or key fields are not highlighted during indexing.
- The model provides a generic answer directly after a user's question, without invoking specific tools or knowledge. This happens when the conditional logic for HTTP requests in the workflow is too lenient or internet search trigger words are not configured correctly.
- Speech input is not recognized or is misidentified. This is due to missing audio processing dependencies within the FastGPT container or
ffmpegnot being installed correctly.
Verification of Configuration
- Conduct multiturn conversation tests for CSO product questions of varying complexity (simple queries, data comparisons, contraindication inquiries). Check the accuracy, completeness, and professionalism of responses, especially for critical information like dosage and usage.
- After knowledge base document updates, verify that relevant queries immediately retrieve the latest information, confirming the data synchronization mechanism functions correctly.
- Review logs to confirm whether the workflow executed HTTP requests or external API calls as expected for questions involving internet searches or external tool invocations. Validate that the returned results are correctly parsed and utilized.
- Simulate typical user scenarios to test speech input functionality. Check if speech recognition results are accurate and if the conversation flow is smooth.
The values provided are common starting points. Measure performance against your own samples to determine optimal settings.
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.