What the data for this category looks like
Papermaking investment research data mainly comes from publicly available statistical materials from the China Papermaking Association, periodic reports of listed papermaking enterprises, quotation data from commodity trading platforms, and industry research reports. Industry statistical materials are updated quarterly, enterprise announcements are released according to business milestones, spot quotations are updated daily, and research reports are released as needed. The data includes two categories: structured tables and unstructured text. Structured data takes paper type, monthly output, raw material unit consumption, and unit selling price as core fields, with corresponding units of type, ton, kilowatt-hour/ton, and yuan/ton. Unstructured research reports are mostly long texts, containing modules such as industry trends and enterprise analysis, with some attached standardized production capacity data attachments.
What constraints do these characteristics impose on the "model access and configuration" link
The multi-source and multi-frequency characteristics of papermaking investment research data require the model access link to support scheduled synchronization configuration for multiple data sources, and adapt to request headers and parsing rules of different data sources. The clear fields and units of structured data require that the original units be retained and field integrity be verified during model invocation, to avoid inference errors caused by unit conversion or missing fields. The existence of long-text research reports requires appropriate text segmentation and context window parameters to be configured, to avoid exceeding the model's context limit. Real-time quotation data streams require low-latency request timeout and retry rules to be configured, to ensure the timeliness of data synchronization. In private deployment scenarios, intranet access and cross-domain configuration are also required to ensure data transmission security.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Papermaking industry research reports have a relatively long average length per document, and sufficient context must be retained to correlate cross-chapter paper type production capacity and price data |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing structured production capacity tables and long research reports takes a long time, to avoid parsing failure caused by early timeout |
Recall Count | Top 8–10 entries | Papermaking investment research data has multiple dimensions, and sufficient associated data must be recalled to cover multi-dimensional information such as paper type, raw materials, and costs |
Similarity Threshold | 0.75–0.85 | Papermaking industry data has clear fields, so a relatively high threshold is required to filter irrelevant general industry information and retain accurately matched investment research data |
Custom Request Address | Intranet IP and port of the deployed model, or a filed domain name address | Adapt to the private deployment scenario of papermaking investment research knowledge bases, and ensure data transmission security |
RECALL_RERANK_TOP_N | Top 3–5 entries | The most relevant core data must be retained after reranking, to avoid redundant information interfering with model inference |
The parameter values provided on this page are all common recommended starting points for configuration. The actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to conduct actual tests on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: After configuring a domain name access address, the page prompts "model connection failed", and the knowledge base data loads normally when accessed via IP. Cause: The domain name has not been added to FastGPT's allowed cross-origin request list, and the domain name has not been correctly mapped to the port of the FastGPT deployment server, causing the model request to fail to complete the handshake.
- Phenomenon: When deploying the vllm model (port 8000) using Docker Compose, filling in
http://localhost:8000as the custom request address prompts a connection timeout. Cause: The 8000 port of vllm has not been mapped to the host machine in the Docker Compose configuration, or the FastGPT container cannot access the localhost address of the host machine. Use the container intranet IP or service name instead. - Phenomenon: When configuring the model, the configuration cannot be saved if both the
API_KEYandCustom Request Addressfields are empty. Cause: The FastGPT model access interface enforces mandatory verification of required parameters, and configuration submission cannot be completed if the fields are not filled in.
How to Confirm the Configuration is Complete
- Initiate a knowledge base recall test, verify that the number of returned documents matches the configured
Recall Count, and that the document content matches papermaking investment research data. - Submit a segment of papermaking industry research report text, check that the parsed segment length conforms to the configured
segment lengthparameter, with no truncation or parsing failure prompts. - View the model invocation log, confirm that the request address matches the configured
Custom Request Address, and that the return status code is 200. - Switch to the domain name access address, test the model invocation and data synchronization functions, and confirm that the results are consistent with those obtained via IP access.
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.