Data Characteristics for This Category
Biopharmaceutical retail chain product and reagent data is often a mix of highly structured and semi-structured information. Data sources are diverse, including supplier product manuals, internal inventory management systems, point-of-sale transaction records, and user feedback. Update frequency is typically high. New product launches, inventory changes, promotional activities, and regulatory adjustments all trigger data updates. Some core data, like inventory levels, may update hourly. Document structures vary. Product manuals are often PDF or Word files, containing detailed ingredients, indications, dosage, and contraindications. Inventory and pricing data commonly exist as database tables or CSV files, with fields like SKU, ProductName, BatchNumber, ExpiryDate, Price, and StockQuantity. Units are clearly defined, such as mg, ml, box, tablet, and yuan.
Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts
High-frequency data updates require an efficient knowledge base synchronization mechanism. This ensures product information (e.g., inventory, price) provided in multi-turn conversations is real-time. The presence of structured data (e.g., SKU, Price) allows prompt design to leverage precise field queries for quick information retrieval, reducing hallucinations. However, semi-structured product manuals (e.g., side effect descriptions, usage precautions) demand stronger text understanding capabilities. Prompts must guide the model to extract key information from long texts. User inquiries often involve multiple products or different dimensions (e.g., comparison, recommendation). This requires multi-turn conversations to effectively manage context, understand shifts in user intent, and integrate structured and unstructured data for comprehensive answers. For example, when a user asks if a certain medicine "has any promotions," the system must accurately identify the medicine name and query the promotions database.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
maxContext | 8 | In retail chain scenarios, user inquiries often involve comparing multiple products or complex questions. Retaining sufficient context helps understand user intent. |
Chunk size (Segment Length) | 500 characters (characters) | Key information density is high in product manuals. Overly long segments can dilute focus, while overly short segments risk losing context. |
Recall count (Recall Count) | Top 5 entries (top 5) | Ensures coverage of the most relevant document segments during product information queries, reducing information loss due to insufficient recall. |
Similarity threshold (Similarity Threshold) | 0.75 | Medical product inquiries demand high accuracy. A higher threshold filters for more relevant knowledge, lowering the risk of incorrect answers. |
Rerank result count (Rerank Return Count) | Top 3 entries (top 3) | Further refines the initial recall, ensuring the most relevant and core knowledge points are presented to the user. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Processing large product manual PDF files can take a long time. Sufficient timeout is needed to prevent interruptions. |
Common Pitfalls
- Uploaded attachments are not recognized, and no summary or analysis appears in the conversation. This occurs when file parsing service dependencies (e.g.,
llama-cpp-pythonorunstructured) are not correctly installed or configured in the Docker container of the deployment environment. The file parsing module fails, even if the frontend upload succeeds, the backend cannot process it. - In advanced orchestration, the AI chat node after a judgment node fails to receive the initial user question. This happens when the judgment node does not pass the original user input as a variable to the subsequent AI chat node, resulting in an empty or incomplete input for the AI chat node.
- The conversation log only shows anonymous users, without Feishu usernames. This indicates that the system is not correctly configured with OAuth authorization or user identity mapping with the Feishu platform. Consequently, it cannot retrieve detailed user identity information from Feishu and record it in the logs.
How to Verify Configuration
- Upload product manuals or inventory lists in different formats (PDF, Word, CSV). Ask questions in the chat window. Observe if the model can correctly extract key information (e.g., ingredients, price, inventory quantity) and compare it with the original documents.
- Design multi-turn conversation scenarios with two or more turns where the intent gradually narrows. For example, first ask about a category of medicine, then follow up with a question about the side effects of a specific medicine within that category. Check if the model maintains contextual coherence and provides accurate answers.
- Intentionally insert a small amount of outdated or incorrect product information into the knowledge base. Then, ask questions and observe if the model prioritizes recalling correct information. Evaluate its ability to correct errors or refuse to answer, to determine the reasonableness of the
Similarity threshold(Similarity Threshold). - Review conversation logs. Confirm that each conversation's user input, model output, and knowledge base recall content are fully recorded. Verify if user identification information from integrated platforms (like Feishu) is included.
The values provided are common starting points. Measure them against specific 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.