What the Data for This Category Looks Like
Papermaking industry research reports mainly come from domestic securities firm research institutes, papermaking industry association official websites, listed companies’ periodic reports, and public industry databases. Regular reports are released quarterly and annually. When the industry sees abnormal changes such as raw material price fluctuations or capacity adjustments, temporary urgent reports are issued with irregular update frequencies. Document structures typically include modules such as institution attribution, release date, industry overview, supply and demand analysis, key enterprise operating data, and investment advice. Core fields include release institution, release date, core paper type, raw material price (yuan/ton), gross profit per ton (yuan/ton), production capacity scale (10,000 tons/year), and some reports include production and sales data for segmented product categories.
Constraints Imposed by These Characteristics on Model Access and Configuration
The long-text structure and structured data features of papermaking research reports require model access to support long-text embedding, to avoid truncation of key capacity and price data. Research report update rhythms are irregular, with both regular batch upload requirements and temporary new data source scenarios. Configurations must therefore support flexible scheduling rules and incremental update logic. Additionally, research reports contain a large number of professional terms and structured fields. Without targeted parsing rule configuration, semantic segmentation will be inaccurate, which affects subsequent retrieval and question answering accuracy.
How to Set Configurations
| Configuration Item | Recommended Value | Basis for This Value |
|---|---|---|
embedding_model | Prioritize long-text capable vector models, such as text-embedding-v3 | The length of a single papermaking research report is mostly 3000-8000 characters, and long-text embedding adaptation is required to avoid truncation of core data |
Chunk size | 800–1200 characters | Adapt to the semantic units of papermaking research reports, avoid splitting structured data into different segments, and ensure data relevance |
Recall count | Top 10–15 entries | Core information of papermaking research reports is concentrated in distribution. Excessive recall will introduce redundant content, while insufficient recall will fail to cover relevant analysis |
Similarity threshold | Calibrate based on actual tests | Professional terms in the papermaking industry are highly specialized, and adjustments need to be made based on actual retrieval results to balance recall accuracy and coverage |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | A single research report has a large amount of content, which takes a long time to parse. Extending the timeout time can avoid parsing failures |
rerank_top_n | Top 5–8 entries | Perform secondary screening on recall results, focus on the most matching research report content, and improve question answering accuracy |
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 specific analysis, and it is recommended to conduct tests on available samples before finalizing.
Three Common Misconfigurations
- Phenomenon: All retrieved results have similarity scores higher than 0.85, and some results are unrelated to the papermaking industry. Cause: The similarity threshold has not been adjusted for papermaking professional terms, or the semantic segmentation logic of the vector model has not been adapted to the long-text structure of research reports, leading to excessive amplification of local semantic matching.
- Phenomenon: A
tls: failed to verify certificate: x509error occurs when configuring the overseas version of FastGPT to access a third-party model. Cause: Custom certificate verification rules have not been configured, or there is an incompatibility issue between the certificate chain of overseas nodes and domestic model services. - Phenomenon: Multiple sets of structured data in a single research report (such as wood pulp price, boxboard paper production capacity) cannot be recalled independently, only full document segments are returned. Cause: The field-by-field vector storage configuration has not been enabled, or limited by the v4.8.7 version of the single vector model, the multi-vector generation rules have not been configured, resulting in multiple sets of data being unable to generate independently associated vectors.
How to Confirm Successful Configuration
- Upload a standard papermaking research report, check the parsed segmented content, confirm that the segment length meets expectations, and no key structured data is truncated.
- Enter professional query terms for the papermaking industry, check the relevance and quantity of recall results, and adjust the number of recalled entries and similarity threshold to a reasonable range.
- After configuring third-party model access, initiate a test request to confirm that there are no connection timeouts or certificate verification errors.
- View the segment details of the vector storage, confirm that multiple sets of structured data are split into independent vector segments that can be accurately retrieved and matched.
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.