What data for this category looks like
Data for optoelectronics financing daily reports is sourced from public disclosure databases of industry self-regulatory organizations, public announcements of listed or quoted companies, and structured extracts from professional financial news platforms. Data updates on a daily T+1 schedule, with financing transaction information from the prior day published before the update window. Most documents are structured Excel files with fixed header fields. Core fields include: financing entity name, affiliated sub-sector, financing round, financing amount, financing amount unit, investor list, financing completion date, and disclosure media source. Most units are RMB ten thousand yuan. Some cross-border financing projects will specify foreign currency units.
Constraints for knowledge base retrieval and recall
Structured fixed fields require retrieval to match specific fields. Full-text fuzzy searches must be avoided, otherwise irrelevant non-financing information will be included. The daily incremental update feature requires configuring incremental index synchronization logic, to avoid resource consumption and delays caused by full index rebuilding. Single documents contain over 10,000 rows of data. If document chunks are not split properly, recalled content will exceed the large model context window limit. Financing amount fields include multiple units. If units are not unified during preprocessing, retrieval errors with mismatched values and units will occur, reducing matching accuracy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_length | 800–1200 characters | The combined field length of a single structured entry in optoelectronics financing daily reports is moderate. This range covers the complete financing information for a single enterprise, while avoiding oversized chunks that cause context overflow |
recall_count | Top 3–5 results | Most retrieval needs for financing daily reports involve precise matching of specific enterprises or rounds. Too many recalled results introduce irrelevant data, while too few may miss valid information |
similarity_threshold | 0.75–0.85 | Structured data has high requirements for field matching accuracy. A threshold that is too low introduces incorrect matches, while a threshold that is too high may fail to retrieve valid results |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing a single Excel file with 10,000+ rows takes significant time. 300 seconds ensures complete parsing without timeout |
incremental_sync_enabled | Enabled | Financing daily reports use daily incremental updates. Enabling incremental synchronization reduces resource consumption from index rebuilding and improves update efficiency |
unit_unification_preprocessing | Enabled | Financing amount fields include multiple units. Unifying units to ten thousand yuan during preprocessing avoids unit mismatch errors during retrieval |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Each scenario requires individual analysis. It is recommended to test with your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Retrieval results display the text "citation marker: [1]". Cause: The citation marker output configuration for knowledge base retrieval results was not disabled, causing the large model to automatically append source data citation identifiers when generating content.
- Phenomenon: Parsing fails for a single Excel file with 10,000+ rows, with the task status showing timeout. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted, and the default timeout duration is insufficient to complete parsing of large structured files. - Phenomenon: Recalled content chunks exceed the large model context window limit, resulting in truncation or errors. Cause: A reasonable
chunk_lengthparameter was not set, and the complete Excel file was uploaded without splitting document chunks, leading to oversized individual chunks.
How to confirm configurations are set correctly
- Upload a test Excel file with fewer than 1,000 rows, review the parsed document chunk lengths, and adjust the
chunk_lengthparameter to achieve the expected chunk size. - Submit a test query targeting a specific financing entity or financing round, verify that the number of recalled results matches the configured
recall_countparameter. - Check the knowledge base synchronization logs to confirm that daily incremental update tasks are automatically triggered and completed.
- Review parsed field information to confirm that financing amount units have been unified to the standard unit.
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.