What this category’s data looks like
Marketing documents for engineering consulting primarily come from project bidding documents, feasibility study reports, annual marketing case collections, and technical proposal documents. Data updates have no fixed cycle, triggered by new awarded projects, updated industry standards, or adjusted annual marketing plans. Most documents are multi-page Word or PDF files, with fixed chapter structures such as project overview, technical parameters, pricing details, and schedule plans. Fields include professional items like project number, cost unit (ten thousand yuan / square meter), and construction period (days). Paragraphs are mostly long-form technical descriptions, with nested tables and professional charts embedded.
What constraints do these characteristics impose on the document parsing and chunking process?
The structured nature of engineering consulting documents requires the parsing step to preserve title hierarchies and chapter associations, and avoid splitting cross-chapter technical logic. Professional fields are tied to specific units, so parsing must accurately link fields and units to prevent semantic loss. Document formats vary across sources, so the system must adapt to nested tables and long paragraphs with different layouts. Batch upload scenarios with no fixed update cycle need support for distinguishing and parsing multiple document versions. These characteristics directly restrict the choice of parsing mode, chunk length settings, and batch upload tag configuration. Parameters must be adjusted specifically to ensure parsing accuracy and retrieval usability.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
parse_mode | structured_document | Adapts to the fixed chapter structure of engineering consulting documents, preserves title hierarchies and paragraph associations |
chunk_max_length | 800–1200 characters | Balances the integrity of professional paragraphs and retrieval granularity, avoids splitting continuous logic of core technical proposals |
chunk_overlap | 150–200 characters | Preserves contextual association across chunks, prevents semantic breakage caused by professional terms being split between different chunks |
parse_file_timeout_seconds | 300 seconds | Handles single feasibility study or bidding documents over 50 pages, prevents parsing timeout interruptions |
enable_table_parse | true | Accurately extracts pricing detail tables and schedule plan tables from documents, preserves numerical associations between cells |
file_source_tag | Enable and bind to uploaded file name | Distinguishes multiple engineering documents uploaded in batches, prevents confusion of parsing results |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: After uploading multiple engineering documents, parsing results cannot distinguish source files, and all chunks share the same identifier. Cause: The
file_source_tagconfiguration item is not enabled, and no unique source identifier is bound to each uploaded document. - Phenomenon: Knowledge base search tests fail to hit target professional content, and returned results do not match input keywords. Cause:
chunk_max_lengthis set too small, splitting complete professional technical paragraphs into overly short chunks, making it impossible to match complete semantics during retrieval. - Phenomenon: Calling the Feishu multi-dimensional table HTTP interface returns a
400 Bad Requeststatus code. Cause: TheContent-Typerequest header is not correctly configured asapplication/json, or correct field formats required by the interface are not passed.
How to confirm the configuration is correctly set
- Upload a single typical engineering consulting document, view the parsed chunk list, confirm each chunk contains complete professional paragraphs or table fragments, with no semantic breakage.
- Trigger a knowledge base search test, enter core professional terms from the document, check the matching degree between returned chunk content and original text semantics, confirm no key content is omitted.
- Upload multiple different types of engineering documents in batches, confirm that the parsing results of each document have clear source identifiers, and chunk content from different files can be distinguished via the identifiers.
- Call the Feishu multi-dimensional table HTTP interface for a write test, check that the returned status code matches the interface documentation description, confirm that request fields are passed in compliance with requirements.
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.