What the data for this category looks like
The data for chemical raw material intelligent due diligence reports comes primarily from public industry association yearbooks, customs import and export clearance data, annual compliance reports of production enterprises, and batch quality inspection documents from third-party testing institutions. Data is updated on a monthly or quarterly basis. Core documents include multi-page PDF quality inspection reports, supply ledgers with multiple worksheets, and structured production capacity and price data tables.
Document fields include CAS registry numbers, purity percentages, annual production capacity, import and export volumes, environmental compliance levels, and more. Most field units use standard industrial units such as ten thousand tons per year, tons, and percent. Some custom fields require adaptation to exclusive batch identifiers for raw materials.
What constraints these characteristics impose on deployment and upgrade
The long documents, multiple data sources, and multiple fields of chemical raw material due diligence reports impose multiple constraints on the deployment and upgrade process. Long documents require longer parsing timeouts to avoid parsing interruptions. Multiple data sources require configuration of parsing rules adapted to different formats, supporting file types such as Excel and PDF. Monthly updated data sources require synchronization of the knowledge base refresh cycle during upgrades to avoid data lag. The precise matching requirement for multiple fields requires adjustment of recall and similarity thresholds to prevent irrelevant data from being included in results.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Chemical raw material due diligence reports often include multi-page long PDF quality inspection reports and supply ledgers with multiple worksheets, which require longer parsing time |
maxContext | 8000–12000 characters | The core field section of a single due diligence report is lengthy, and sufficient context must be retained for precise recall and question answering generation |
KNOWLEDGE_REFRESH_INTERVAL | 7 days | Compliance and production capacity data in the chemical raw material industry is updated monthly, and a 7-day refresh cycle covers the standard update rhythm |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Bulk imported industry yearbooks and cross-regional supply ledger files have large file sizes |
RECALL_TOP_K | Top 8 entries | Chemical raw material due diligence reports require matching multi-dimensional fields such as CAS numbers and purity, and an appropriate number of recall entries improves accuracy |
SIMILARITY_THRESHOLD | 0.75–0.85 | Raw material data with similar CAS numbers must be distinguished to avoid recalling irrelevant entries |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: The number of split question answering results is lower than expected after an upgrade, and core fields are not fully split. Cause:
maxContextis not adjusted to adapt to long documents. The default split length is too short to cover the complete field section of chemical raw material due diligence reports. - Phenomenon: An error occurs after adding a knowledge base in a locally deployed v4.8.14 version, and the issue appears across multiple model tests. Cause:
KNOWLEDGE_BASE_API_KEYis not configured correctly, the mounting path of the local model's knowledge base is incorrect, or there are conflicts in version dependencies. - Phenomenon: A 403 token unauthorized error is returned when calling the model interface. Testing works normally on a third-party model transit platform. Cause: The token permission scope is not correctly bound in the FastGPT model configuration, or the token is not added to the model whitelist for the corresponding scenario.
How to confirm that configurations are set correctly
- Upload a typical chemical raw material due diligence PDF report, check the parsed text segments, and confirm that the segment length matches the configured expectations.
- Initiate a query for a specific CAS number raw material, verify that the number and similarity of recall results meet the configuration requirements.
- Trigger a knowledge base refresh task, check the background task logs, and confirm that the refresh task does not time out and data synchronization is complete.
- Call the bound model interface, verify that the returned results are normal and there are no permission-related errors.
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.