What This Category of Data Looks Like
Steel trade investment research data sources include real-time quotes from spot trading platforms, market data from futures exchanges, ex-factory price announcements from steel mills, inventory ledgers from traders, logistics freight rate disclosures, industry regulatory policy documents, and professional research reports. Update rhythms vary significantly: spot quotes are updated daily, futures market data is pushed in real time, steel mill ex-factory prices are updated weekly or monthly, and policy documents and research reports are updated irregularly. Document structures include structured quote tables (with fields including product name, specification, origin, price, date), unstructured PDF research reports, and Excel-format inventory statistical ledgers. Core field units are mostly yuan/ton, ten thousand tons, cubic meters, and similar units.
What Constraints Do These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering?
Steel trade data characteristics impose multiple constraints on multi-turn dialogue and prompt engineering configurations. First, structured data across multiple product categories and specifications requires prompts to explicitly restrict queries to specific steel product categories and specifications, to avoid cross-category confusion. Second, differing update frequencies across data sources require multi-turn dialogue to distinguish call logic between real-time market data and historical ledgers, to ensure timeliness of returned content. Third, coexistence of long-form research reports and short-form quote sheets requires adapting to retrieved content of varying lengths, to avoid breaking coherence of professional terminology. Fourth, industry-specific terms (such as basis spread, finished product yield rate, forward locked price) require prompts to predefine unified explanations, to reduce dialogue ambiguity.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single-segment retrieved content for steel trade investment research is relatively long, needs to accommodate multi-turn dialogue history and multiple retrieved documents, to avoid context overflow |
chatHistoryRetainCount | 8–12 entries | Steel trade investment research dialogues often involve historical inquiries and policy changes across multiple product categories. Retaining 8 or more entries allows complete tracking of category and parameter changes in context |
Recall count | 6–8 entries | Structured quote data and unstructured research reports carry large amounts of information. Too many entries will exceed the context window, while too few will miss critical market information |
Similarity threshold | 0.72–0.78 | Steel trade product categories and specifications are highly segmented. A threshold that is too low will retrieve irrelevant categories, while a threshold that is too high will miss valid quote data for similar specifications |
Chunk size | 1500–2000 characters | Steel industry research reports and inventory ledger documents are relatively long. Too long a segment will lose context association, while too short a segment will break the coherence of professional terminology |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Parsing large steel industry research report PDFs takes a long time, to avoid file parsing failure due to timeout |
The parameter values provided on this page are general recommendations for starting point configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: For FastGPT v4.8.10, using the Text Content Extraction plugin with the configuration
聊天记录：6 entriesin a workflow results in lost multi-turn dialogue context. Cause: The chat history configuration for workflow nodes only applies within the individual node, and is not bound to the global dialogue context, so it cannot transfer dialogue state across nodes. - Phenomenon: Calling the API to access a locally deployed FastGPT application returns a
400 Bad Requesterror. Cause: ThechatIdparameter was not correctly included, so the system cannot identify the current dialogue session and cannot reuse historical context. - Phenomenon: The steel product quotes returned in multi-turn dialogue do not match the specifications requested by the user. Cause: The
Similarity thresholdwas set too high, failing to retrieve structured quote data for similar specifications, or the prompt did not explicitly require matching specific steel product specification parameters.
How to Verify Proper Configuration
- Initiate a multi-turn dialogue covering different steel product specifications, check that each reply is linked to the historical queries of the current session, and no context jumps occur.
- Upload an Excel file of a steel trade inventory ledger, initiate a dialogue about inventory queries, check that the reply includes correct fields and units.
- Call the API to start a session, copy the returned
chatIdparameter, call the API again with this parameter, check that both replies belong to the same dialogue context. - Adjust the
Similarity thresholdto 0.75, initiate a query for rebar with similar specifications, check that the reply retrieves quote data for the corresponding origin.
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.