Data Characteristics of This Category
Textile manufacturing investment research data sources include monthly production and sales statistics from industry associations, customs import and export declaration data, periodic reports of listed companies, yarn and fabric spot price databases, and equipment operation log documents. Update frequencies cover real-time (equipment operation parameters), daily (spot prices), monthly (production and sales data), and quarterly or annual (financial reports). Document structures include structured tables (such as yarn count, gram weight, production capacity data), unstructured research reports, supply chain contracts, and equipment manuals. Fields include yarn count (unit: Ne), gram weight (unit: g/㎡), production capacity (unit: tons/month), customs declaration number, quotation date, and others. Some fields have unit differences across data sources.
Constraints on Database and Operations
The multiple update frequencies and structure types of textile manufacturing investment research data create multiple constraints for the database and operations link. Real-time high-frequency equipment and price data requires support for high-concurrency writes. Reasonable connection pool and index strategies must be configured. Structured multi-dimensional data requires sharded database storage to avoid query delays caused by excessive single-table data volume. Unit differences across data sources require standardized verification processes during operations to prevent unit mismatch errors during retrieval. Long documents such as research reports and contracts require adaptation to large-file sharded storage. The vector index dimensions must match the text shard length.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supply chain contracts, equipment manuals and other single files for textile manufacturing have large sizes, to avoid truncation during upload |
MONGO_MAX_POOL_SIZE | 50–80 | Adapt to the high-frequency write requirements of real-time spot prices, to avoid service interruptions caused by connection exhaustion |
RECALL_TOP_K | 10–15 | Balance the comprehensiveness and latency of retrieval recall. Textile manufacturing data has multiple dimensions, and excessive recall increases subsequent processing load |
RERANKER_TOP_N | 3–5 | Focus on core production capacity, supply and demand, and price data, to avoid redundant results affecting investment research judgments |
PARSE_CHUNK_SIZE | 800–1200 characters | Preserve the semantic integrity of process parameters and price tables in textile research reports, to avoid damage to data relevance from sharding |
TOOL_SELECT_CONCURRENCY | 1 | Limit the number of concurrent executions for tool selection, to avoid repeated calls |
VECTOR_DB_TYPE | mongodb@opengauss | Adapt to the database storage requirements of trusted innovation environments, replacing native MongoDB |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material forms, 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: Docker deployment results in startup failure, with logs showing
Authentication failed for user, and database connection cannot be established. Cause: Only the database password in container environment variables was modified, and theDB_PASSWORDparameter in the FastGPT configuration file was not updated synchronously, resulting in failed service and database verification. - Phenomenon: After enabling question optimization and result reranking, retrieval response time exceeds 30 seconds. Cause: The
RECALL_TOP_KandRERANKER_TOP_Nparameters were not adjusted reasonably. Excessive recall of redundant textile manufacturing segmented data increases the computational load of the reranking model. - Phenomenon: The vector database fails to load the trusted innovation-compatible storage driver, and knowledge base synchronization fails. Cause: The
VECTOR_DB_TYPEparameter was not configured as a trusted innovation-compatible database type, and driver deployment and verification were not completed.
How to Verify Successful Configuration
- Log in to the database management interface, run the connection test script, verify that the configured
DB_PASSWORDparameter matches the container environment variables, and confirm successful connection. - Upload a textile manufacturing spot price table document, check that the upload progress does not trigger the
UPLOAD_FILE_MAX_SIZElimit, and that the parsed segment length matches thePARSE_CHUNK_SIZEsetting. - Initiate a retrieval request including production capacity data queries, check that the response time meets expectations, and that the number of reranked returned results matches the
RERANKER_TOP_Nparameter. - Simulate 2-3 concurrent retrieval requests, check that the tool selection log only triggers one call, with no repeated execution records.
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.