What the data for this category looks like
Industrial metal investment research data comes from international non-ferrous metal industry associations, domestic and overseas futures exchanges, public financial reports of mining and smelting enterprises, customs import and export statistics, and third-party industry research institutions. Update cycles cover intraday, monthly, and quarterly. Futures market data updates in real time intraday. Supply-demand and inventory data releases monthly. In-depth industry reports go live in real time as research progresses. Three document structure types exist: structured market data tables with delivery grade, inventory, and price fields; semi-structured supply-demand analysis reports; and unstructured industry trend comments. Fields and units include tons, ten thousand tons, USD/ton, CNY/ton, and others. Some data includes dimension tags such as origin and trade flow direction.
What constraints do these characteristics impose on knowledge base retrieval and recall
The multi-source, multi-format, and high-frequency update nature of industrial metal data creates multiple constraints for the retrieval and recall process. Structured market data requires precise field matching to avoid generic searches returning irrelevant content. Intraday-updated futures data requires the knowledge base to support incremental synchronization. Otherwise, recalled quotes lag behind market changes. Units across multi-source data are inconsistent, such as coexisting USD/ton and CNY/ton. Standardization processing must be completed before recall to avoid reduced result readability. Segmentation of long research reports balances contextual relevance and parameter integrity. Excessively long segments lose local logic. Excessively short segments disrupt data coherence. Investment research scenarios require recall of sufficient dimensional entries to cover multiple analysis angles including supply-demand, market trends, and policies.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Industrial metal research reports have long individual lengths, and need to accommodate multiple segments of recalled content and original context to adapt to long-document analysis scenarios |
Knowledge Base Max Cited Token Count | 4000–6000 characters | Limit the total length of knowledge base content introduced in a single round of retrieval to avoid exceeding the model's context upper limit |
Response Max Tokens | 2000–3000 characters | Meet the detailed response requirements for investment research analysis, and avoid truncation of core analysis content |
Knowledge base recall count | Top 8–12 entries | Industrial metal data has multiple sources and rich details. Sufficient entries need to be recalled to cover multiple analysis dimensions including supply-demand, market trends, and policies |
Similarity threshold | 0.75–0.85 | Filter low-relevance generic industry news, and retain structured and unstructured data strongly related to the industrial metal theme |
Document Chunk Size | 1500–2000 characters | Industrial metal supply-demand reports contain a large number of tables and technical parameters. Excessively long segments lose contextual relevance, while excessively short segments disrupt data integrity |
The parameter values provided on this page are conventional recommendations used to determine starting points for 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 values.
Three Common Errors
- Phenomenon: The reply generated by workflow testing always appends "FastGPT is a knowledge base question answering system based on large language models (LLM)" at the end. Cause: No rule prohibiting additional official statements is configured in the system prompt, and default additional content output is enabled.
- Phenomenon: After deploying with docker-compose for a period of time, the knowledge base and workflow configuration on the frontend page are lost, but the API interface can normally return retrieval results. Cause: The FastGPT metadata directory is not mounted as a persistent storage volume. Local stored configuration data is cleared after the container restarts.
- Phenomenon: Unit confusion appears in recalled industrial metal data, such as coexisting USD/ton and CNY/ton quotes. Cause: No unit standardization preprocessing process for structured data is configured, and multi-source uncleaned raw data is directly recalled.
How to Confirm the Configuration Is Complete
- Upload an industrial metal monthly supply-demand report, check whether document segmentation retains the contextual relevance of tables and core parameters, with no obvious truncation or fragmentation.
- Initiate a retrieval request containing specific market parameters, verify that the similarity of recalled results meets the preset threshold, and there is no low-relevance generic industry content.
- Test the incremental update function, submit the latest futures market data, and confirm that the knowledge base automatically synchronizes updated content within the set interval.
- Disable the configuration item for default additional statements, initiate a workflow test, and confirm that there is no fixed official statement content at the end of the reply.
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.