What data for this category looks like
Data sources for the plastics and rubber industry include futures contract market data from the Shanghai Futures Exchange and Dalian Commodity Exchange, industry statistical reports from the China Plastics Processing Industry Association and China Rubber Industry Association, import and export declaration data from the General Administration of Customs, and daily quotes from spot traders.
Update frequencies vary: futures market data updates in real time, spot quotes update daily, industry monthly reports release monthly, and import and export data updates weekly.
Document structures include structured market data tables (with product names, delivery grades, prices, trading volumes), unstructured industrial analysis articles and policy interpretation documents.
Field and unit standards are as follows: price fields use yuan/ton as the unit, inventory, output and import and export volumes use ten thousand tons as the unit, contract codes use six-character format, and delivery grades must indicate national standard brand numbers.
What constraints these characteristics impose on database and operations workflows
Low-latency writing requirements for real-time market data require configuring asynchronous database write queues to avoid blocking the main thread.
Mixed storage of structured and unstructured data requires enabling index optimization for relational databases and similarity retrieval functions for vector databases at the same time.
Field differences across multiple data sources require configuring unified mapping rules to prevent data chaos after ingestion.
Data sources with different update frequencies require split scheduling tasks, with synchronization cycles set separately.
A large number of product name enumeration values require establishing joint indexes for core fields to improve retrieval efficiency.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
VECTOR_STORE_TYPE | Localization-compatible vector storage (such as Dameng Vector Plugin, Kingbase ES Vector Extension) | Meets localization replacement requirements, avoids usage restrictions of foreign open-source components |
RECALL_TOP_K | Top 10 to 15 entries | There are many associated documents in the plastics and rubber industry chain, so sufficient recall volume is needed to cover core associated data |
PARSE_FILE_TIMEOUT_SECONDS | 300-600 seconds | Large industry research report documents take longer to parse, avoid import failures caused by timeouts |
CONTEXT_WINDOW_SIZE | 8000-12000 characters | Supports context association for multi-turn continuous conversations, resolves off-topic outputs in cross-turn question answering |
DB_CONNECTION_POOL_SIZE | 20-30 | Balances concurrent retrieval requirements for investment research scenarios and database resource usage |
UPLOAD_FILE_MAX_SIZE | 500 MB | Adapts to batch import requirements for large industry database documents |
The parameter values provided on this page are all conventional recommendations, used as a starting point for configuration determination. 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 the user’s own samples before finalizing settings.
Three common misconfigurations
- Symptom: A
DatabaseConnectionRefusederror is thrown when a workflow node executes. Cause: The IP whitelist of the business database is not added to the FastGPT server configuration, or the port parameter in the connection string is configured incorrectly. - Symptom: In multi-turn conversations, the second and subsequent questions cannot associate context, and the model outputs content unrelated to the current question. Cause: The
CONTEXT_WINDOW_SIZEparameter value is too small, failing to retain the context vector from the previous conversation, or the context cache configuration is not resynced after upgrading the version. - Symptom: Some documents fail to parse when batch importing industry research reports, and the log shows
FileParseTimeout. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter value is lower than the parsing time required for large research report documents, or the parsing engine's chunking strategy is not adjusted for unstructured documents.
How to verify correct configuration
- Access the database management page in the FastGPT backend, verify that the connection status of the target vector storage and business database is normal, and check whether the fields in the connection string match the field mapping relationship of the business data source.
- Initiate two consecutive rounds of questions to confirm that the model can associate content from the previous conversation, and verify that the context window configuration meets multi-turn conversation requirements.
- Import a standard plastics and rubber industry research report to verify that the parsed document field completeness and parsing time meet expectations.
- Simulate retrieval requests with a specified concurrency level, observe the occupancy of the database connection pool, and confirm that the connection pool parameter values can support the current business load.
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.