What the data for this category looks like
Marketing-related data for communications equipment comes primarily from three categories: equipment hardware specification documents, real-time operation and maintenance logs, and user consultation history. Hardware specification documents include structured content such as model numbers, firmware versions, and signal parameters. Updates are adjusted on demand alongside firmware iterations. Operation and maintenance logs record metrics such as signal strength, uptime, and packet loss rate. Updates occur near real-time. User consultation history includes common installation, configuration, and marketing-related questions. Updates are synced daily. Data fields include device_id, signal_strength (unit: dBm), uptime (unit: hours). Some documents include image-and-text combined installation guides.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The structured nature of hardware specification documents requires that multi-turn dialogue explicitly retain device model context. This avoids mixing up parameters across different models. The near-real-time updates of real-time operation and maintenance data require that dialogue processes call dynamic APIs to obtain the latest metrics. Relying solely on static knowledge bases cannot cover real-time changing operation data. The diversity of user consultation history requires that prompt engineering explicitly distinguish between technical parameter explanations and marketing script guidance. Additionally, communications equipment parameters use specific units. Prompts must enforce that the model adheres to unit standards. The image-and-text structure of long documents requires that dialogue processes adapt to the content length of segmented retrieval. This avoids overly long single segments that cause context overflow.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Communications equipment marketing dialogue requires retaining multi-turn device model and user question details. This range covers typical context lengths and prevents context overflow |
retrieval count | Top 6–8 entries | A single communications equipment manual includes multiple sets of technical parameters. Too many retrieved entries will exceed context limits; this quantity covers core parameter content |
similarity threshold | 0.72–0.78 | Precise matching of device models and user questions is required to avoid retrieving parameters from old firmware versions or non-target devices |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Communications equipment technical documents include large amounts of structured parameters and diagrams. Parsing requires significant time, preventing document parsing failures due to timeout |
tool_call_enable | Enabled | Real-time device operation and maintenance data APIs must be called to obtain the latest dynamic metrics such as signal strength and uptime, supplementing static knowledge base content |
segment length | 1000–1200 characters | Communications equipment manual chapters have lengthy content. This segment length ensures single-segment content has complete semantics, enabling accurate model retrieval |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Each scenario requires individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: After connecting multiple AI dialogue nodes in a workflow, the final output includes dialogue results from all nodes. Cause: No result filtering rule is set for the workflow, and all node output content is retained by default.
- Symptom: After connecting a third-party dialogue channel, the reply content contains large numbers of meaningless punctuation marks such as # and *. Cause: Markdown format output after knowledge base parsing is not disabled, or channel format conversion logic is not configured.
- Symptom: When calling the dialogue API, the returned result does not include the
ref_knowledge_idfield. Cause: The knowledge base reference log configuration item is not enabled, or the prompt does not explicitly require the model to return referenced knowledge base identifiers.
How to confirm configurations are set correctly
- Initiate a test dialogue with consecutive device model and parameter questions, verify that the output retains the device identifiers mentioned in the context.
- Run a workflow connecting multiple AI nodes, verify that the final output only displays the processing result of the last node.
- Call the dialogue API, verify that the returned result includes relevant reference identifier fields.
- Submit a matching query to a knowledge base configured with image URLs, verify that the reply includes the configured image URL content.
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.