What the data for this category looks like
Data sources for communications equipment investment research include carrier centralized procurement announcements, equipment manufacturer technical white papers, 3GPP series standard documents, industry exhibition reports, quarterly shipment statistics, patent publication documents, and more. Update frequency varies by document type: centralized procurement announcements are released quarterly or semi-annually, standard documents are updated with version iterations, patent documents are published in real time, and shipment data is updated monthly. Document structures include structured parameter tables, long-form technical descriptions, bid-winning lists for bidding sections, and more. Fields cover frequency band (unit: MHz), transmit power (unit: dBm), shipment volume (unit: 10,000 units), bid-winning quotation (unit: 10,000 yuan), and others.
What constraints do these characteristics impose on the "document parsing and chunking" link
Multi-source, heterogeneous document types include scanned archived files, formatted Word documents, and structured PDF tables. This requires the parsing module to support both native text extraction and high-precision OCR functionality. Frequently updated documents such as monthly shipment data and quarterly centralized procurement announcements require the chunking process to support incremental parsing and breakpoint resume, to avoid repeated parsing of full document sets. Semantic unit boundaries for structured parameter tables and long standard documents are clearly defined. The chunking logic must adapt to chapter structures, avoid hard splitting based on fixed character counts, and prevent breaking the binding relationship between split fields and their units.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_OCR_ENABLE | Enable for scanned documents, enable on demand for others | A large number of scanned archived centralized procurement announcements and old standard documents exist in communications equipment investment research data. OCR can extract native text |
PARSE_CHUNK_SIZE | 800–1200 characters | Semantic units of 3GPP standard documents and technical white papers mostly fall within this range, avoiding splitting cross-chapter content |
PARSE_CHUNK_OVERLAP | 100–150 characters | Cross-chapter semantic connections in long technical documents are strong, overlapping fragments can retain context coherence |
UPLOAD_FILE_MAX_SIZE | 2000 MB | A single 3GPP standard document can reach hundreds of pages, large-capacity files require normal upload support |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Long document parsing takes a long time, avoid interrupting the parsing process due to timeout |
PDF_MARKER_HIGH_PRECISION | Enable | Technical parameter tables in communications equipment documents require high-precision parsing to reduce OCR recognition error rates |
The parameter values provided on this page are conventional starting point recommendations. 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 mistakes
- Symptom: An
OCR Erroris returned when uploading a scanned technical white paper. Cause: High-precision OCR configuration is not enabled, or the document scan resolution is insufficient, leading to text recognition failure. - Symptom: After uploading a document in simple mode of version 4.8.9, the parsing process is not triggered. Cause: The file type whitelist for triggering parsing is not set, or the model context threshold is not adapted to the long document length.
- Symptom: The
miner-uparsing function entry cannot be found after an upgrade. Cause: The enable switch for third-party parsing plugins is not turned on in system settings, or the plugin version does not match the current deployment version.
How to confirm the configuration is correct
- Upload a scanned communications equipment document, check whether the parsed log contains recognized text fragments, to confirm that the OCR configuration takes effect.
- Upload a 3GPP standard document, check whether the chunked text retains the binding relationship between chapter titles and main text, to confirm that the chunking logic adapts to long document structures.
- Enter the knowledge base search test page, enter specific frequency band parameter keywords in the document, confirm that the corresponding semantic blocks can be retrieved, to verify that the chunking and retrieval logic match.
- Upload a 1500 MB test document, confirm that it can be uploaded and parsed normally, to verify that the
UPLOAD_FILE_MAX_SIZEconfiguration takes effect.
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.