Internal Office and Employee Services for Investment Project Approval and Post-Evaluation: Document Parsing and Chunking

Internal Office and Employee Services for Investment Project Approval and Post-Evaluation: Document Parsing and Chunking

What this category of data looks like

This category of data serves central enterprise strategic investment departments, project companies, state-owned asset supervision departments, and external consulting and evaluation institutions. Daily processed documents include annual investment plans, project proposals, feasibility study reports, due diligence reports, asset evaluation reports, and post-investment evaluation reports. Data primarily comes from state-owned assets supervision and administration commission investment supervision systems, enterprise ERP and project management systems, industry market data, audit and financial statements. Update frequency is quarterly. Document structures contain multi-column structured tables, long-paragraph business descriptions, and hierarchical project information. Fields cover investment amounts, progress milestones, evaluation indicators, and more. There is no unified fixed format, but all content centers on capital investment and state-owned asset preservation and appreciation.

Constraints imposed by these characteristics on document parsing and chunking

Batch documents updated quarterly require parsing workflows to support bulk processing and avoid repeated loading of historical files. Document structures mixing multi-column structured tables and long text require chunking rules that do not break business data relevance. The rigor of state-owned asset related documents requires that parsing results do not lose key fields. Additionally, large feasibility study reports and multiple evaluation reports have a large total file size. This imposes clear requirements on parsing timeout limits and single-file upload maximum sizes. Differences in document formats from various sources also require the parsing module to support structured extraction for multiple file types.

Configuration Settings

Configuration ItemRecommended SettingRationale
chunkSize800–1200 charactersCategory documents are mostly long texts containing financial and project data. This range preserves the integrity of individual business logic
chunkSplitterSplit by table rows + paragraphsCategory documents contain multi-column financial statements and project lists. Splitting by rows preserves data relevance
PARSE_FILE_TIMEOUT_SECONDS600 secondsLarge feasibility study reports and evaluation reports take longer to parse. This avoids interrupting the parsing workflow due to timeout
UPLOAD_FILE_MAX_SIZE1000 MBThe total size of bulk-uploaded annual investment plan collections and multiple feasibility study reports falls within this range
customPdfParseEnable structured parsingCategory PDF documents are mostly formatted official reports. Structured parsing preserves field hierarchy and table structures
chunkSettingModeManual configurationCategory documents have relatively fixed structures. Manual configuration prevents automatic chunking from disrupting business data relevance

Three Common Mistakes

  • Issue: When parsing multi-column Excel financial statements, automatic chunking merges cross-row and cross-column content into a single paragraph. Cause: The chunking basis by table rows is not specified, and the default paragraph splitting rule is used. This breaks business data relevance.
  • Issue: When bulk uploading target documents, parsing results include content from non-target files uploaded previously. Cause: The historical file loading switch for bulk parsing is not turned off. The system defaults to parsing all previously uploaded documents cumulatively, which does not meet the requirement of only processing the current attachment in a single operation.
  • Issue: After uploading project site photos or asset evaluation photos, the model cannot parse the text information in the images. Cause: Image parsing related configurations are not enabled. Only parsing rules for text-based documents are configured, so image format input content cannot be processed.

How to Verify Proper Configuration

  • In the FastGPT v4.17.0 environment, upload a single multi-column Excel financial statement. Wait for parsing to complete, then check the chunking results. Confirm that each row of content is an independent chunk.
  • Upload a single feasibility study report larger than 500 MB. View the parsing logs. Confirm that no timeout error is triggered, and the parsing workflow completes normally.
  • After uploading a single target attachment, manually trigger parsing. Check that the parsing results only include content from the currently uploaded file, with no residual content from historical documents.
  • Upload a single project image containing text. Confirm that the parsing results include the visible text information in the image, and the text can be called for subsequent retrieval.

The values provided 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.