Data Characteristics for This Category
Culture media and consumables product data typically originates from official manufacturer product manuals, technical specifications, Safety Data Sheets (SDS), and internal R&D reports. Document update frequency is relatively stable, primarily occurring during new product releases, batch updates, or regulatory changes. Document structure usually includes standardized fields such as product name, catalog number, specifications, batch number, expiration date, storage conditions, main components, application areas, usage methods, and quality control indicators. Common units include milliliters (mL), grams (g), micrograms (µg), units (U), batches (Batch), and boxes (Box), often accompanied by percentages for concentration, purity, or specific numerical values. Some data may exist as images, such as product appearance diagrams or scanned quality inspection reports.
Constraints on Multi-turn Conversation and Prompts Due to These Characteristics
The highly structured and standardized nature of culture media and consumables product data allows for more precise matching of user queries in multi-turn conversations. For example, when a user asks about the expiration date of a specific batch, the system can directly extract this information from structured data. The relatively low update frequency means less pressure on knowledge base synchronization, but timely ingestion is crucial for new product releases. Documents contain numerous technical terms and specific numerical values, requiring prompt design to accurately identify these entities and avoid ambiguity. Image-based data challenges the model's understanding capabilities, potentially requiring additional OCR or image recognition preprocessing. In multi-turn conversations, users may frequently mention short text identifiers like batch numbers and catalog numbers, requiring the retrieval mechanism to prioritize responses to such queries.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 3000 Tokens | Handles multi-turn follow-up questions from users regarding complex issues like product composition, batches, and usage methods, ensuring context completeness. |
Recall count | Top 8 entries | Ensures sufficient coverage of multiple product attributes or related reagent information that users might be interested in. |
Similarity threshold | 0.78 | Balances recall precision and recall rate, avoiding interference from irrelevant information while capturing semantic similarity of technical terms. |
Chunk size | 500 characters | Accommodates common paragraph lengths in technical specifications and product manuals, reducing information overload or incompleteness in a single segment. |
Rerank result count | Top 5 entries | Optimizes response speed for multi-turn conversations, prioritizing high-quality information most relevant to the current conversation. |
CHAT_FILE_EXPIRE_TIME | Calibrate by actual measurement | File expiration policy needs to be balanced against actual user behavior and storage capacity to ensure important files are accessible. |
Three Common Mistakes
- A "product batch information not found" prompt appears in the conversation because the product batch number field in the knowledge base is not effectively indexed or data is not updated in time.
- Product specification units are confused in AI responses, such as mistaking milliliters for grams, because unit expressions in documents are inconsistent or the model's robustness in unit recognition is insufficient.
- Users repeatedly ask about the usage steps of the same product, but the system repeatedly provides the same or incomplete information because prompts do not effectively guide the model to extract information from different angles or more detailed knowledge snippets.
How to Confirm Proper Configuration
- Conduct multi-turn conversation tests for different batches and specifications of culture media and consumables products. Check if the system can accurately identify and provide correct product information.
- Simulate in-depth user questions about professional topics such as product components and storage conditions. Evaluate if the system can maintain contextual coherence and provide professional answers in multi-turn interactions.
- Upload the latest product technical manuals and safety data sheets. Check if the system can immediately cite information from the new documents after the knowledge base update and verify the accuracy of key field extraction.
- Test documents containing images or scanned copies. Verify if the system can identify text information within them, such as batch numbers and expiration dates, and include it in the conversation scope.
Note: The values provided are common starting points and should be measured against your 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.