What Data for This Category Looks Like
Core data for this category comes from process and quality departments at foundries and die casting workshops. Daily documents include casting process cards, batching lists, heat records, mold histories, first-piece inspection records, material reports, and nonconforming product review forms. Data sources cover ERP material master data, melting furnace thermometers, spectrometers, MES heat records, mold management systems, and customer technical drawings. Update frequency is per heat, per batch of castings, or daily collation. Document fields include heat number, alloy composition percentage, melting temperature, pouring time, mold cycle count, defect type, and more. All fields must strictly match unit standards collected on-site.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Frequent data updates require multi-turn dialogue to recall the latest heat and batch documents in real time, and avoid using expired process parameters. There are many specialized fields with strict unit requirements, so prompts must explicitly limit responses to only the content of uploaded documents, and must not fabricate parameters or change units. Multi-turn dialogue must track business identifiers such as current query heat number and mold cycle count, to prevent confusion of process data from different batches. Document lengths vary widely, so reasonable chunking is needed to preserve complete specialized terminology, and avoid information truncation during Q&A that reduces answer accuracy.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the single-entry length of casting process cards and heat records, adapts to FastGPT v4.17.0 knowledge base chunking logic, and prevents truncation of specialized terminology |
| Overlap Length | 100–200 characters | Covers continuous specialized terms such as cross-chunk alloy compositions and defect names, and ensures completeness of retrieved content |
| Similarity Threshold | 0.75–0.85 | Filters low-match irrelevant documents, and retains content highly relevant to foundry process questions |
variables | Bind business identifiers such as heat number and mold cycle count | Passes core business parameters for current queries during multi-turn dialogue, and prevents confusion of process data from different batches |
messages | Filter redundant content that is not business interaction | Only retains valid dialogue between users and assistants, and excludes irrelevant information such as plugin logs and system prompts |
chatId | Bind the unique identifier of the current session | Tracks context association for multi-turn dialogue, and ensures queries within the same session revolve around the same business scenario |
Three Common Mistakes
- Phenomenon: The model adds extra spaces when outputting, and converts lowercase letters to uppercase, which breaks the original format of document links. Cause: The prompt does not explicitly require strict retention of character format and case from input documents, and the model follows general text optimization rules by default.
- Phenomenon: File parsing tool calls fail, and documents such as casting process cards and heat records cannot be loaded. Cause: Parsing rules for corresponding document formats are not configured, or uploaded file formats do not meet platform-supported standards.
- Phenomenon: Plugin call logs are mixed into historical dialogue, which interferes with context for current multi-turn Q&A. Cause: Historical message filtering rules are not set, and non-user and assistant interaction content is included in the context window.
How to Confirm Configuration Is Correct
- Upload a real foundry heat record document, and initiate the query "Please check the alloy batching ratio for heat number L20240512". Confirm that the returned result includes the exact numerical values and units from the document.
- Initiate two consecutive queries: first ask about the mold history for a specific mold cycle count, then ask about the cause of casting defects corresponding to that mold cycle count. Confirm that the assistant can associate context to provide the correct answer.
- View the
chatIdandvariablesparameters of the session, and confirm that the business identifier for the current session (such as heat number) has been correctly bound and passed. - Test triggering a plugin call, and confirm that plugin logs are not included in the dialogue history, and the assistant only generates responses based on user questions and knowledge base content.
All provided configuration values are common starting points, and should be validated against the reader's own samples.