What the data for this category looks like
Photovoltaic category marketing data primarily comes from three sources: publicly available product specification documents from photovoltaic equipment manufacturers, technical disclosure materials for distributed photovoltaic projects, and grid connection guidance documents released by local energy management departments. Product specification documents are updated quarterly alongside new product launches or parameter adjustments. Policy documents are updated irregularly to reflect changes in local policies. Project case documents are supplemented in real time as projects are completed. Most documents use structured tables paired with brief explanatory text. Core fields include product model, rated output power, component external dimensions, total system weight, and applicable installation scenarios. Corresponding units are kW, mm, and kg for physical parameters, while applicable scenarios are described in plain text.
What Constraints These Characteristics Impose on Multiturn Conversations and Prompting
The structured data format of photovoltaic category data requires multiturn conversations to interact using precisely bound preset fields, to avoid generating unsubstantiated parameter information. Fixed-cycle updated product data requires the conversation system to synchronize parsed structured content from the knowledge base, to prevent the output of outdated parameters. Irregularly updated policy data requires explicit instructions in the prompt to prioritize calling the latest parsed policy documents, to avoid referencing invalid clauses. Multi-scenario marketing requirements require multiturn conversations to retain contextual information such as the user’s input installation scenarios and power usage needs, to provide a basis for subsequent product matching. The coexistence of different measurement units requires unified unit expression rules in the prompt, to prevent confusion over power units.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Photovoltaic marketing conversations must retain multiturn context such as user installation requirements and product parameters, to avoid losing key interaction information |
RECALL_TOP_N | Top 6–8 entries | Photovoltaic product parameters have many fields, so enough structured data must be recalled to cover user question dimensions while controlling redundant information |
PROMPT_TEMPLATE | Organize responses in the order of product model, rated power, and applicable scenario, and only use field data from the current knowledge base | Core marketing information for photovoltaics is concentrated in these three fields, which can improve response relevance and compliance |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Photovoltaic product manuals are mostly multi-page structured tables, which take longer to parse, so sufficient time must be reserved for complete parsing |
JSON_SCHEMA | {"type":"object","properties":{"product_model":{"type":"string"},"rated_power":{"type":"number"},"adapt_scene":{"type":"string"}},"required":["product_model","rated_power"]} | Standardize the return format of marketing content to ensure that core product fields are returned completely |
AUTO_REFRESH_KNOWLEDGE | Triggered quarterly | Photovoltaic product parameters are updated quarterly, so parsed knowledge base content must be refreshed synchronously to avoid outputting outdated information |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes in Configuration
- Phenomenon: Deployed conversation workflows run slower than during local debugging. Cause: After deployment, the system must call knowledge base data parsed in real time on the cloud, and multiturn context verification logic increases overall processing time.
- Phenomenon: Returned JSON formats do not comply with the preset
JSON_SCHEMA, with missing fields or incorrect value types. Cause: The prompt does not explicitly require strict adherence to preset structural rules, or unstructured content in the knowledge base interferes with format generation logic. - Phenomenon: Multiturn conversations cannot retain contextual information such as the user’s previously provided installation scenarios and power usage needs. Cause: The character count set in the
maxContextconfiguration is too small, so earlier interaction content is automatically truncated and cannot be retained for subsequent conversation segments.
How to Verify Configurations Are Correctly Set
- Launch a test conversation with multiple interaction turns. Check whether subsequent turns can correctly call installation scenario information entered earlier, to verify context retention effectiveness.
- Submit a query about product parameters. Check whether returned content only uses fields from the current knowledge base and does not include unsubstantiated parameter information.
- View the knowledge base’s automatic refresh logs. Confirm that document parsing and updates have completed according to the configured cycle, to verify that the
AUTO_REFRESH_KNOWLEDGEconfiguration is active. - Generate a test response. Check whether the format complies with the preset
JSON_SCHEMArules, and verify field completeness and type correctness.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.