What the data for this category looks like
Data for defense electronics financing daily reports is sourced from public financing announcements of defense electronics enterprises, industry regulatory disclosure documents, and public reports from professional research institutions. Updates are published daily. Each daily report includes standardized fields: full name of the financing entity, financing round, financing amount (unit: ten thousand yuan or hundred million yuan), list of investors, disclosure date, affiliated defense electronics sub-sector (such as semiconductor components, military communications), and brief financing background description. Most documents follow a semi-structured format, with format variations in some fields. Preprocessing is required to standardize these fields.
Constraints imposed on vector models and indexing
Defense electronics financing daily reports have high specialized terminology density, mix structured and semi-structured content, and generate incremental data daily. Vector models must adapt to embedding accuracy for defense industry-specific terminology to avoid semantic recognition bias. The high frequency of incremental updates requires indexes to support low-latency batch inserts, preventing performance losses from full index reconstruction. Field standardization requirements need preprocessing to ensure consistent fields during embedding, reducing noise interference during retrieval.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Matches the average length of individual text entries in defense electronics financing daily reports, avoiding loss of key fields such as financing round and financing amount during text splitting |
embedding_batch_size | 16–32 | Balances embedding processing speed and API call limits, avoiding timeouts caused by overly large single request data volumes |
embedding_threads | 2–4 | Reduces the risk of exceeding embedding rate limits, adapting to the processing volume of single-batch incremental data |
| Top recall count | Top 10–15 results | Covers associated financing information from the same sub-sector and round, while keeping context length within model limits |
INDEX_REFRESH_INTERVAL | 3600 seconds | Matches the daily update schedule, ensuring timeliness of retrieved data while reducing server load |
| Similarity threshold | 0.72–0.80 | Filters irrelevant retrieval results, retaining associated financing information for defense industry sub-sectors |
The parameter values provided on this page are general recommendations used as a starting point for configuration. 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 configuration mistakes
- Phenomenon: Text chunks are lost when
chunk_sizeis set to 3000. Cause: Some long paragraphs in defense electronics financing daily reports exceed the maximum input length of the vector model. Forced truncation during splitting cuts off key information, resulting in lost chunks. - Phenomenon: An error occurs during vectorization, with logs showing embedding rate limit exceeded. Cause: The
embedding_threadsorembedding_batch_sizeparameters are not adjusted. The single-batch data processing volume is too large, triggering API call frequency limits. - Phenomenon: Knowledge base retrieval speed is slow, or question-answer pair extraction tasks remain in the "indexing" state without becoming ready. Cause: Incremental index updates are not enabled, full index reconstruction frequency is too high, or the
embedding_threadsconfiguration is too low, leading to task queue backlog.
How to confirm correct configuration
- Upload a single sample of defense electronics financing daily report, check that vectorized text chunks are complete, with no loss of key fields.
- View embedding call logs, confirm there are no rate limit exceeded errors, and processing time meets expectations.
- Run a retrieval test, input industry-related search terms, check that the relevance and quantity of returned results meet business requirements.
- View the index status dashboard, confirm that incremental update tasks can be completed within the set cycle, with no long-term backlog of index tasks.
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.