What the data for this category looks like
Cement industry investment research data sources include monthly industry briefs released by the China Building Materials Federation, regular reports of listed cement enterprises, regional market price monitoring data, and industrial policy documents from the National Development and Reform Commission. Update frequencies cover daily (raw material prices, regional average prices), monthly (production capacity, output data), quarterly (corporate financial reports), and on-demand released policy documents. Document types include structured statistical tables, image-integrated research report PDFs, plain-text policy interpretations. Some documents include multi-page regional breakdown data. Fields and units include clinker output (10,000 tons), cement average price (yuan/ton), coal procurement cost (yuan/ton), and project tender scale (10,000 yuan).
What constraints do these characteristics bring to the context and token link
The multi-source and multi-update frequency characteristics of cement industry investment research data require the knowledge base to recall content with different time granularities, such as daily prices, monthly production capacity, and quarterly financial reports. The length of different types of documents varies significantly. Ultra-long research reports or structured tables will consume more tokens. Regional breakdown data generates a large number of independent data blocks. If context splicing rules do not match data association relationships, information fragmentation will occur, and token consumption will increase. Parsing differences across multiple format data sources require context segmentation to adapt to different document structures. Otherwise, field loss or chaotic context splicing may occur. This will trigger the large model’s token upper limit, or prevent the context from being effectively passed to the large model.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 1500–2000 characters | Adapt to the chapter structure of cement industry research reports and structured table fields. Avoid single segments that are too long and consume excessive tokens, while retaining complete data association |
similarityTopN | Top 6–8 results | Balance recall coverage of multi-dimensional investment research data and token consumption. Avoid excessive regional breakdown data exceeding the context window |
rerankTopN | Top 3–5 results | Screen the most relevant core data of prices, production capacity, and policies. Reduce redundant recall content and optimize token usage efficiency |
maxContext | 10000–14000 characters | Match the context window upper limit of mainstream large models. Accommodate splicing of multiple types of data, and prevent the context from being unable to be fully passed |
PARSE_CHUNK_OVERLAP | 100–150 characters | Retain context association between segments. Prevent fields in structured tables from being split and broken, and ensure coherent context information |
UPLOAD_FILE_MAX_SIZE | 500 MB | Allow uploading large industry collection documents. Adapt to the demand for batch import of multiple research reports in the cement industry |
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 the settings.
Three common mistakes
- Phenomenon: After setting the
similarityTopNconfiguration to a value greater than 10, the knowledge base search results appear normal, but the large model’s reply does not include the recalled context. Cause: The total recalled tokens exceed themaxTokenPerMessageupper limit configured for the large model, causing the system to automatically truncate the context and fail to pass valid content to the large model. - Phenomenon: After parsing an uploaded large cement research report, segmented content has field breaks. For example, regional output data is split into two segments. Cause: The
chunkSizesetting is too small, andPARSE_CHUNK_OVERLAPis not configured with sufficient length. This causes the field association of structured tables to be split. - Phenomenon: After the workflow generates an ultra-long text, submitting it to the large model triggers a token overflow error. Cause: No segmentation processing is performed on the ultra-long text, and the
maxContextparameter is not adjusted to adapt to the ultra-long input. This causes the total tokens to exceed the large model’s limits.
How to confirm the configuration is correct
- Enter the knowledge base preview test page, enter core search terms for the cement industry, and check the number and content of recalled results. Adjust the corresponding configurations until the recalled content covers core investment research dimensions and has no obvious redundancy.
- Upload a typical cement monthly research report, check the parsed segment preview, and confirm that the overlapping content between segments meets expectations, with no structured field breaks.
- After adjusting context-related parameters, initiate a query containing multi-dimensional data to the application, and check whether the large model’s reply fully includes the recalled context content.
- Trigger the workflow to generate an ultra-long text and submit it to the large model, confirm that no token overflow error occurs, and that the large model can correctly process the ultra-long input content.
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.