What this type of data includes
Solid waste treatment industry research reports are a subset of financial research reports. Data sources include industry analysis reports from securities firm research institutes and fund companies, public financial reports of environmental protection enterprises, and regulatory announcements from ecological environment departments. Update frequency varies by content type: policy documents update when policies are released, corporate financial data updates quarterly, and industry trend reports are released quarterly or semi-annually. Document structure includes industry policy interpretations, corporate operation data, pollutant emission indicators, treatment process parameters, cost accounting tables, facility diagrams, and monitoring data charts. Fields and units include solid waste generation volume (tons/year), treatment cost (yuan/ton), emission standards (mg/m³), equipment models, and revenue proportion. Most content is presented in tables or structured data.
Constraints on document parsing and chunking
The multi-type data characteristics of solid waste treatment industry research reports impose clear constraints on the parsing and chunking process. First, large volumes of structured tables and numerical fields with units require the parsing process to retain row-column associations and semantic integrity. Splitting the same group of business data would disrupt corporate revenue and operation analysis in financial scenarios. Second, embedded content such as facility diagrams and monitoring charts requires extraction of text from images. Failure to do so would lose key monitoring data and process descriptions, preventing complete retrieval of investment logic. Third, the hierarchical structure of long documents, including chapter numbers and corporate case IDs, requires retaining context associations during chunking. This avoids forced splitting of cross-chapter or cross-paragraph corporate data. Finally, batch uploads of similar research reports require the parsing process to have stable adaptability, preventing parsing failures due to differences in document formats.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Segment Length | 800–1200 characters | Solid waste research reports contain a large number of technical terms and numerical values with units. This range balances semantic integrity and retrieval accuracy, avoiding excessive chunk length causing retrieval redundancy, or insufficient chunk length causing semantic breaks |
Segment Overlap | 10–15% of segment length | Retains context across chunks. For example, sentences like "XX environmental protection company’s solid waste treatment volume is 1000 tons/year, with 60% coming from incineration" avoid splitting key corporate data across different chunks |
Enable OCR Parsing | Enabled | Solid waste research reports include facility diagrams, monitoring data charts and other image content. Enabling this extracts embedded text from images, enabling simultaneous retrieval of text and images |
Table Structured Parsing | Retain row-column associations | Operational ledgers and cost comparison tables in solid waste research reports contain multi-dimensional business fields. Retaining row-column structures allows accurate matching of associated data during retrieval, avoiding retrieval bias caused by scattered text |
File Parsing Timeout | 300 seconds | A single solid waste research report may contain multiple pages of tables and images. A longer timeout prevents task interruptions due to large parsing volumes |
Recalled Chunk Count | Top 6–8 entries | Solid waste research reports contain many technical terms. An appropriate number of recalled chunks covers complete process or policy context, improving retrieval practicality for financial analysis scenarios |
The parameter values provided on this page are common starting points 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 configuration mistakes
- Scenario: After uploading a PDF-format solid waste research report, retrieval results do not include monitoring data text from images. Cause: The
Enable OCR Parsingconfiguration is not enabled, so only native text in the PDF is extracted, and text embedded in images is not recognized. - Scenario: After setting chunk length using tokens as the unit, retrieved chunks split continuous descriptions related to "solid waste treatment process parameters". Cause: The counting units of characters and tokens are confused. Solid waste research reports contain many technical terms, so token counting causes chunk length to not match actual semantic blocks.
- Scenario: After uploading an Excel-format solid waste operation ledger, retrieval results only show scattered cell values, and row-column associations are not retained. Cause: The
Table Structured Parsingconfiguration is not enabled, so Excel content is split directly as plain text, losing context associations of the data.
How to verify correct configuration
- Upload a sample solid waste research report that includes images and tables, check the parsed chunk content to confirm that text from images has been extracted and integrated into the corresponding chunks.
- Select a continuous description that includes a company name and numerical values, confirm that the content appears completely in a single chunk with no splitting.
- Check the parsing result after uploading an Excel file, confirm that the row-column structure of the table is retained, and no scattered single-line text appears.
- Search for technical terms related to solid waste treatment, confirm that the recalled chunks cover relevant process descriptions and parameter data, with no obvious semantic breaks.
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.