Bid Document and Quotation Preparation for Rubber Mixing and Product Vulcanization: Multi-turn Dialogue and Prompt Engineering

This category’s data comes from formula systems, internal mixers, Mooney rheometers, vulcanizers, and laboratories of rubber product manufacturers

What Data for This Category Looks Like

This category’s data comes from formula systems, internal mixers, Mooney rheometers, vulcanizers, and laboratories of rubber product manufacturers including tire, seal, and rubber hose producers. Daily documents include formula sheets, weighing records, mixing curves, rubber compound rapid test reports, vulcanization process cards, mold logs, and finished product performance reports. Data updates generate synchronously with each raw material batch, each rubber compound batch, and each mold production run. Each document includes fields such as batch number, raw material grade, weighing value, mixing temperature, and vulcanization duration. Units include kilograms, degrees Celsius, minutes, and other standard units.

Constraints on Multi-turn Dialogue and Prompt Engineering

The high-frequency update of data requires filtering expired batch data from dialogue contexts. This prevents calling rubber compound parameters not linked to the current bid. Differences across multiple document types require prompts to clearly specify the target document type. For example, only extract torque and temperature data from mixing curves. The independent nature of single-batch data requires passing the batch identifier for the current bid in variables. This stops confusion between rubber compound parameters from different projects. Multi-turn dialogue must gradually verify formula weighing and vulcanization parameters. This prevents errors in feeding order, temperature, or duration, and ensures alignment with business compliance requirements.

Configuration Settings

Configuration ItemRecommended SettingRationale
chatIdBind to the unique ID of the current bid projectEnsure multi-turn dialogue context for the same project does not mix with other projects
messagesRetain the most recent 20 dialogue messagesCover the full interaction flow for a single bid preparation, avoid context loss
variablesPass batch number, raw material grade, and bid project nameAccurately locate mixing and vulcanization data for the current project
Maximum Context8000–12000 tokenAdapt to batch call demands for multiple document types, calibrated via real-world testing
Question OptimizationEnabledAutomatically organize ambiguous parameters in multi-turn dialogue to ensure precise prompt targeting

Three Common Configuration Mistakes

  • Phenomenon: Custom guide prompts do not work, and output content strays from bid quotation requirements. Cause: The guide prompt is not embedded in the system message field of messages, or the prompt is not bound to exclusive parameters for this category.
  • Phenomenon: Configured dialogue applications cannot connect to integrated plugins. Cause: chatId is not bound to the plugin’s callback interface, so the plugin cannot access context parameters for the current project.
  • Phenomenon: An unauthorized error is returned after calling the knowledge base interface. Cause: Knowledge base permissions linked to the bid project are not bound to the current chatId, or the API key does not have document access scope for this category.

How to Verify Correct Configuration

For FastGPT v4.17.0, correct configuration may be verified via the following actions:

  • A test batch number is entered, and the mixing parameters returned by the dialogue are checked against records for the corresponding batch.
  • Multiple follow-up questions are sent, and the messages context is confirmed to retain prior formula details with no parameter omissions.
  • Different AI models are switched between, and the dialogue is verified to still correctly call rubber product data for the current project.
  • All parameters in variables are checked for completeness, and all fields required for the bid are confirmed to have been passed.

The values provided here are common starting points, and should be evaluated against the reader’s own samples.