What the data for this category looks like
Data sources for aquaculture financial reports include monthly production ledgers of aquaculture enterprises, regional monitoring data from provincial fishery technology promotion stations, and industry reports released by the Ministry of Agriculture and Rural Affairs. Update frequency follows this schedule: internal enterprise production data is updated monthly, quarterly financial reports are compiled quarterly, and annual financial reports are released once per year.
The document structure has two categories: single pond/batch breeding ledgers and official financial reports. Ledgers record feed input, seedling quantity, and water quality monitoring indicators by date. Official financial reports include three modules: production indicators, cost expenditures, and revenue and profit.
Fields and units include "total feed input" (kilograms), "finished product market volume" (tons), "unit area breeding cost" (yuan/mu), "single batch breeding cycle" (days), and other similar items.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Data sources for aquaculture financial reports are scattered, including internal enterprise ledgers and public industry reports. Multi-turn dialogue must first clarify the data subject and time range required by the user.
The single pond/batch document structure requires prompts to guide users to specify a specific pond or batch, to avoid generalized analysis. Most fields are concrete production and cost indicators, so prompts must clearly require detailed output of corresponding fields.
The relatively high update frequency requires multi-turn dialogue to repeatedly confirm the data time period, to avoid mixing data across months or quarters.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single pond data documents for aquaculture financial reports often reach several thousand characters, so context about pond and time range in multi-turn dialogue must be retained |
recall_top_k | Top 6–8 entries | Aquaculture financial reports have many fields, so enough relevant production and cost field data must be recalled |
similarity_threshold | 0.72–0.78 | Differentiate between industry general data and enterprise-specific production data, to avoid mixing irrelevant fields |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports uploading Excel and PDF files of annual summarized breeding ledgers |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing large multi-pond financial reports requires a longer time |
max_round | 8–12 turns | Aquaculture financial report analysis requires multiple rounds of information clarification, including pond, cycle, comparison dimensions, and other details |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: Calling the dialogue interface returns a
401 Unauthorizedstatus code, or prompts "invalid application key". Cause: Mistaking FastGPT'sappIdfor the call key, and failing to correctly obtain theapiKey. - Phenomenon: After uploading a financial report file, the parsing result lacks pond detail data. Cause:
UPLOAD_FILE_MAX_SIZEis not set to adapt to large files, causing some pond ledgers to fail to complete parsing. - Phenomenon: In multi-turn dialogue, subsequent questions deviate from the financial report analysis theme, generating irrelevant content. Cause: The prompt does not limit the dialogue scope to the production, cost, and revenue fields of aquaculture financial reports.
How to confirm the configuration is correct
- Upload a single pond quarterly financial report file, check if the parsing result includes all preset fields, and verify that
UPLOAD_FILE_MAX_SIZEcovers the actual file size. - Initiate a test dialogue, ask about pond range, time cycle, and cost composition in sequence, check if the dialogue context is correctly retained, and verify that the
maxContextandmax_roundconfigurations match the dialogue turns. - Call the test interface, pass the correct
apiKeyandappId, check if the returned result includes expected financial report analysis content, and confirm that the call parameter configuration is correct. - Adjust the
similarity_threshold, compare the relevance of the recall results, and confirm that the threshold matches the document characteristics of the current knowledge base.
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.