What This Category of Data Looks Like
State-owned industrial fund business data primarily originates from four categories: filing registration documents, capital contribution agreements, project due diligence reports, and post-investment management ledgers. Data update cycles are divided into monthly, quarterly, and annual: post-investment ledgers are updated monthly, filing documents are updated quarterly, and annual overall reports are updated annually. Document structures include structured Excel tables (with fields such as fund name, investor, subscribed amount, project name, etc., amount unit is ten thousand yuan, time format is YYYY-MM-DD), long-form Word reports, and fixed-format PDF filing documents.
Constraints for Document Parsing and Chunking
Structured Excel datasets contain over 10,000 rows with tightly linked fields. Excessively large chunk sizes will break the integrity of individual business data records. Long-form Word due diligence reports and PDF filing documents have fixed headers and footers. Targeted extraction of core business content is required. Frequently updated post-investment ledgers require stable parsing processes. Timeouts or format recognition errors will cause data failure, so stability is critical. Mixed input of different document types requires flexible parsing and chunking rules to accommodate differences between structured and unstructured data.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of individual fund business data records (including linked fields), and avoids merging multiple unrelated business contents into a single chunk |
chunkSplitter | Group by table header + semantic paragraph splitting | Distinguishes individual row data in Excel, preserves chapter logic of Word documents, and avoids damaging the relational structure of business data |
customPdfParse | Enabled | Adapts to fixed headers and footers in state-owned industrial fund filing PDFs, automatically filters redundant content and extracts core business fields |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Meets parsing time requirements for 100,000-character Chinese Word documents or Excel files with over 15,000 rows, and avoids parsing failures due to timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Allows uploading complete annual post-investment ledgers or large due diligence reports, and covers full business data volumes |
chunkSettingMode | Manual configuration | Supports custom chunking rules for mixed input of structured Excel and unstructured documents, adapting to different data types |
This page’s configuration rules are designed for FastGPT v4.17.0.
Common Misconfigurations
- Vector recall results have insufficient precision and few entries, and the interface shows lower-than-expected matching scores. The cause is failure to adjust the
chunkSizeparameter. The default chunk length is too large, merging multiple independent fund business data records into a single chunk, leading to semantic matching deviations. - Document parsing tasks time out and fail, with timeout errors shown in backend logs. The cause is failure to adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter. The default timeout duration is insufficient for processing 100,000-character Chinese Word documents or Excel files with over 15,000 rows. - Extracted Excel data contains field content spliced across multiple rows. The cause is failure to configure
chunkSplitterto group by table headers. The default fixed-character splitting breaks the row-level association of business data, causing fields from different rows to be merged into the same chunk.
How to Verify Proper Configuration
- Upload a single post-investment management Excel ledger with 15,000 rows. Check that the number of parsed chunks matches the number of business rows, and there is no field content spliced across multiple rows.
- Upload a single project due diligence Word report with 100,000 Chinese characters. Check that the parsing task completes within 600 seconds, with no timeout errors.
- Upload a state-owned industrial fund filing PDF document. Check that core fields such as fund code and subscribed amount are extracted accurately, with no redundant header or footer content.
- Test the vector recall process. Input a query containing a specific project name. Check that the recall results include complete business information from the corresponding chunk.
The values provided here are common starting points, and should be measured against the reader’s own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.