What data for this category looks like
Refractory material financing daily report data comes from national building materials industry association financing dynamic summaries, local industry and information department released enterprise financing announcements, and public data from third-party enterprise financing information platforms. Updates follow a daily schedule. Each daily report covers all financing events for refractory material-related enterprises across the full industry on the given day. Documents mostly exist as PDF embedded tables, editable TXT tables, or structured documents exported from web pages. Core fields include financing party name, refractory material sub-category, financing amount, financing round, investor, and publication date. Financing amount is measured in ten thousand yuan. Financing round terms include standard expressions such as angel round, Pre-A round, A round, and others. Some documents include associated production capacity information for refractory products.
Constraints imposed on document parsing and chunking
The multi-source, multi-format nature of refractory material financing daily reports means general parsing models cannot adapt to all document table layouts. Targeted parsing rule configuration is required. The high-frequency daily update requirement demands high parsing process stability, to avoid delayed daily report processing due to timeouts. Special fields such as abbreviations for refractory material sub-categories (such as high-alumina brick, magnesium-carbon brick) require the parsing model to have industry vocabulary recognition capabilities. Without this, field extraction errors will occur. Non-financing remark rows mixed into structured tables will interfere with chunking logic. This leads to valid financing information being split into unrelated chunks.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
parse_mode | structured_table | Refractory material financing daily reports mostly use structured table formats. Prioritizing table structure recognition improves field extraction accuracy |
chunk_size | 800–1200 characters | A single financing entry is approximately 100-200 characters. This range can hold 4 to 6 complete financing entries, avoiding chunking across entries |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | A single daily report contains dozens to hundreds of financing entries. Parsing takes a long time. This duration prevents mid-process timeout interruptions |
enable_ocr | false | Most financing daily reports use tables in editable text formats. No OCR is needed to recognize image content, which improves parsing speed |
chunk_overlap | 100–150 characters | Retains contextual connections between adjacent financing entries, preventing key information from being split and leading to missing retrieval results |
filter_empty_fields | true | Some documents include empty lines or invalid table rows. Filtering these reduces invalid chunks and improves retrieval efficiency |
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. Each situation requires specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Changing the file suffix of a non-structured Java interface document to TXT before importing it for parsing results in no valid financing entries in the parsed output. Reason: FastGPT format recognition relies on file content structure, not file suffix. TXT format cannot automatically detect the table layout of financing daily reports, so content cannot be split correctly.
- Phenomenon: Using chunk mode to call the pushdata API to upload batch financing daily report data, with the interface displaying an indexing status for an extended period. Reason: The
chunk_sizeparameter was not adjusted to match the length of a single financing entry. This causes chunked data to exceed the system's default processing threshold, leading to a backlog in the indexing queue. - Phenomenon: Uploading a PDF-format financing daily report, where embedded image tables cannot be parsed, and only plain text content is extracted. Reason: The
enable_ocrparameter was not enabled. Tables in the document are embedded as images, so general parsing models cannot recognize the table structures within the images.
How to Confirm Proper Configuration
- Upload a single test refractory material financing daily report document. Review the parsed text content to confirm all core fields are correctly extracted.
- View the chunked result list. Confirm each chunk contains complete, continuous financing information, with no mixed content across rounds or categories.
- Call the query interface of the pushdata API. Check the indexing status of uploaded data to confirm there are no timeout or failure markers.
- Run retrieval tests using special vocabulary related to refractory material sub-categories. Verify that relevant chunks are accurately 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.