What Data for This Category Looks Like
Data sources for semiconductor intelligent due diligence reports include wafer process parameter documents, supply chain quotation ledgers, patent application texts, industry association production capacity statistics, and public financial disclosure documents of listed companies. Update cadences vary across sources:
- Supply chain quotations are updated weekly
- Patent applications are published in real time
- Production capacity statistics are released monthly
- Financial reports are updated quarterly or annually
Document structure mixes structured tables and unstructured text. Structured fields include wafer process nodes, single-wafer production capacity, unit cost, and more. Corresponding units are nm, ten thousand wafers per month, USD per wafer. Unstructured text includes long technical analysis and market interpretations. Individual reports range from several pages to dozens of pages in length.
Constraints on Vector Models and Indexing From These Characteristics
Mixed structured and unstructured data requires vector models to adapt to semantic representation of semiconductor industry-specific terminology, and support metadata binding for structured fields to distinguish unit information. Data sources with multiple update cadences require the indexing system to support batched incremental refresh strategies. This avoids excessive resource usage from full index rebuilding. The wide range of document lengths requires chunking strategies to balance semantic completeness of long texts and indexing density of short segments. This prevents semantic fragmentation or indexing redundancy. Unit differences across fields require unit standardization before vector ingestion. This avoids semantic matching errors caused by unit confusion.
How to Configure
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | Doubao-embedding-large | Adapts to vector representation of semiconductor industry-specific terminology, supports mixed processing of long texts and structured fields |
chunk_size | 800–1200 characters | Balances semantic completeness of long technical texts and fragment density of compact parameter tables in semiconductor due diligence reports |
index_refresh_interval | 1 hour (supply chain data), 7 days (financial report data) | Matches update frequencies of different data sources, balances real-time performance and indexing construction efficiency |
top_k_retrieval | Top 8–12 results | Semantic correlation of semiconductor professional parameters is strong; this range filters redundant recalls and covers core relevant information |
similarity_threshold | 0.75–0.85 | Semantic similarity differentiation of professional terminology is high; this range accurately filters highly relevant index results |
vector_db_batch_size | 32–64 | Semiconductor data has high field complexity; this batch size balances ingestion efficiency and memory usage |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Issue: In version V4.14.3, after enabling the
Doubao-embedding-largemodel and entering a custom request address and APIKey, a401 Unauthorizederror is returned. Cause: The path suffix of the custom request address is not configured correctly, or the APIKey does not have permission to call the vector model. - Issue: After batch importing semiconductor due diligence reports, unit confusion appears in recall results for structured parameter fields. Cause: Unit information is not bound to field metadata, preventing the vector model from distinguishing parameters with the same numerical value but different units.
- Issue: After initiating a due diligence query, the professional term matching accuracy of recall results is low. Cause: The
chunk_sizesetting is too large, splitting short-text parameter tables into overly long fragments, leading to deviations in vector representation of semantic units.
How to Confirm Proper Configuration
- Navigate to the embedding model configuration page, click the test button, and verify that the returned vector dimensions match the official published parameters of the selected model.
- Upload a semiconductor due diligence document containing a structured parameter table, check the index generation logs, and confirm that field metadata is correctly bound to corresponding units.
- Initiate a query targeting semiconductor parameters, and check whether the similarity scores of recall results fall within the preset range.
- Wait for the preset index refresh interval, then check the index update logs for supply chain data, and confirm there are no abnormal error records.
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.