Data Characteristics for This Category
R&D documents in the retail chain industry primarily cover new product formulations, process flows, quality standards, and supply chain traceability. Data sources are diverse, including raw material specifications from suppliers, internal lab test reports, production line operating procedures, and user experience records from stores. These documents update frequently, especially with new product iterations, regulatory changes, or supply chain shifts; some sections may update weekly or even daily. Document structures typically include structured fields like formulation lists, ingredient content, production batches, expiration dates, and storage conditions. They also contain extensive unstructured descriptions such as flavor profiles, taste evaluations, and packaging recommendations. Field units involve grams, milliliters, percentages, and temperature units (Celsius), and may have multiple representations, such as "20 grams" or "20 g".
Constraints Imposed by These Characteristics on "Multiturn Conversation and Prompts"
The high update frequency of retail chain R&D documents requires the multiturn conversation system to quickly synchronize with the latest knowledge. This prevents incorrect guidance based on outdated information. The mix of structured and unstructured information, along with diverse unit expressions, means prompt design must balance precise extraction with semantic understanding. This prevents data parsing failures due to unit misidentification. Users may mention specific product batches or store feedback in conversations, requiring the system to handle time and location-constrained queries. Furthermore, queries about sensitive information like expiration dates and storage conditions demand higher standards for multiturn conversation context management and security, ensuring information is not leaked or confused during multiple interactions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
maxContext | 800–1200 characters | Balances multiturn conversation context length and inference efficiency, ensuring coverage of key business information. |
Chunk size (Segment Length) | 400 characters | Accommodates fine-grained information like formulations and process steps in R&D documents, improving recall precision. |
Recall count (Recall Count) | 8 entries | Ensures coverage of enough relevant document segments, handling complex queries that may involve multiple knowledge points. |
Similarity threshold (Similarity Threshold) | 0.78 | Balances recall and accuracy, filtering out irrelevant document segments and reducing noise. |
Rerank result count (Reranked Return Count) | 3 entries | Prioritizes displaying the most relevant key information, improving user efficiency in obtaining valid information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses the parsing time for large specification sheets or experimental reports, preventing timeouts. |
Common Pitfalls
- When querying global variables saved in historical conversations from a new conversation, the system fails to load them correctly, resulting in incomplete query results. This occurs due to a lack of explicit global variable passing mechanisms in the workflow design or incorrect variable scope configuration.
- Calling an external database tool to query formulation information returns a
400 status code (no body)error. This happens when database connection parameters are misconfigured or the query statement format does not meet database requirements. - When a user wants to switch AI models or enable internet access, the conversation system does not provide corresponding options or responses. This prevents the user from making adjustments as needed. This occurs when the front-end interface does not integrate these functional control points, or the back-end workflow is not configured for model switching and external tool invocation logic.
How to Verify Configuration
- Perform multiturn conversation tests for typical new product formulation queries. Verify if the system's returned ingredient content and process steps match the latest documentation.
- Randomly select time-constrained and location-constrained queries from documents (e.g., "quality feedback for a specific batch of products in East China stores"). Verify if the system accurately identifies and provides relevant information.
- Simulate scenarios where users frequently modify query intent during a conversation. Observe the system's ability to understand and maintain context across multiple interactions, ensuring key fields like product names and units remain accurate after context switching.
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.