What this category's data includes
Data for this category primarily comes from formulation systems, raw material batch ledgers, internal mixers, Mooney viscometers, vulcanizers, and laboratory testing equipment at rubber product enterprises including tire, seal, and hose manufacturers. Daily documents include formulation sheets, weighing records, mixing curves, rubber material rapid test reports, vulcanization process cards, mold usage records, and finished product performance reports. Fields covered include material ratios, temperature parameters, time thresholds, batch numbers, and more. Data is updated synchronously following each truckload of raw materials, each production batch, and each molded product.
Constraints on multi-turn dialogue and prompt engineering workflows
High update frequency and fine-grained data require that multi-turn dialogue contexts be isolated by batch or mold run to avoid cross-batch data confusion. Diverse document types and specialized fields require prompts to explicitly specify the field range of retrieved documents to reduce irrelevant content interference. Single conversations need to associate multiple types of data such as mixing curves and vulcanization process cards, so multi-round interactions must gradually guide users to supplement key parameters to avoid overloading single requests. Additionally, equipment faults in this category are strongly tied to process parameters, so prompts must prioritize associating equipment inspection records from the current production batch and focus on dedicated production data.
Configuration Settings
The following configurations are based on FastGPT v4.17.0 and adapted to the business data characteristics of this category:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Pass current production batch, mold number, and equipment operating parameters, formatted as {"batchNo": "XXXX", "moldId": "XXXX"} | Data for this category is updated by batch/mold run. Passing dedicated parameters via variables enables precise matching of corresponding documents and avoids retrieving invalid cross-batch data |
messages | Retain the most recent 3 rounds of conversation content. Total character count of the single-round messages array must not exceed 8000 characters | Business data for mixing and vulcanization is updated frequently. Simplifying context reduces redundant interference and meets real-time requirements for multi-turn interactions |
| Reranking | Enable. Retain the top 3 reranked results | Documents for this category include multiple types of specialized fields. Reranking filters low-relevance general equipment documents and focuses on inspection and maintenance records for the current batch |
| Maximum Context Length | 6000-12000 tokens, calibrated based on actual testing | Multi-turn conversations need to associate multiple types of documents including mixing curves, vulcanization process cards, and finished product performance reports. A reasonable context length covers complete business scenarios |
chatId | Generate a unique chatId per production batch | Data for this category has fine-grained update granularity. A unique chatId isolates conversation contexts across different batches and avoids data confusion |
Three common configuration errors
- Symptom: After calling the API to pass the
variablesparameter, variables within the prompt are not replaced, and returned content is unrelated to the preset mixing/vulcanization scenario. Cause: Thevariableswere not passed in the JSON format required by FastGPT, or variable placeholders were not correctly used in the prompt. - Symptom: After enabling the reranking configuration, the reranked results are not displayed in the conversation details page, and the number of retrieved results does not match the preset configuration. Cause: The reranking model was not correctly bound and configured, or the reranking request timed out without triggering retry logic.
- Symptom: A text content extraction node in a multi-turn conversation fails to retrieve valid content, and the specified reply is used without waiting for subsequent user input. Cause: The context continuation rules for multi-turn conversations were not configured, and the round retention logic for
messageswas not bound.
How to verify successful configuration
- Pass preset batch and equipment parameters as
variablesto initiate a test conversation, and verify that returned content is associated with dedicated documents for the current batch. - View the conversation details page to confirm that the number of retained context rounds matches the configured requirements, with no cross-batch historical data interference.
- After enabling the reranking configuration, check that the retrieval result filtering logic is active and irrelevant general equipment documents are filtered out.
- After configuring English prompts and an English knowledge base, initiate a test request to confirm that output content meets preset language and professional scenario requirements.
The values provided are common starting points and should be evaluated against the reader's own operational samples.