What Data for This Category Looks Like
Data comes from public periodic reports, temporary announcements, and compliance documents required by industry regulators from cybersecurity enterprises. Storage formats are mostly PDF and structured spreadsheet files. Update cadence: annual reports are released once per year, quarterly reports are updated each quarter, and temporary announcements are triggered by major cybersecurity business events or compliance changes. Document structure includes audit opinion pages, core financial data tables, business segment revenue breakdowns, R&D investment details, cybersecurity service delivery data, and more. Field units are mostly monetary or integer count units. Some fields use specialized technical or compliance text descriptions.
What Constraints Do These Characteristics Impose on Vector Models and Indexing
The mixed structured and unstructured data of cybersecurity financial reports requires vector models to adapt to both financial numeric fields and business description text. This avoids semantic recognition bias from general models for vertical cybersecurity terminology. Irregular temporary announcement updates require indexing systems to support incremental synchronization mechanisms. This prevents excessive hardware resource usage from full index rebuilding. Professional technical terms and long compliance texts in documents require chunking strategies to retain complete term blocks. Vector dimensions must also adapt to the semantic complexity of the vertical domain. Additionally, differences in document formats across multiple sources require indexing configurations to support both structured and unstructured output from PDF parsing.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
EMBEDDING_MODEL | Select text-embedding-3-small or a cybersecurity-domain fine-tuned vector model | Adapts to cybersecurity terminology and financial professional phrasing in financial reports, improving semantic recall accuracy |
VECTOR_DB_TYPE | Select pgvector | Compatible with lightweight vector databases deployed via Docker, supports low-resource operation without GPU environments |
INDEX_CHUNK_SIZE | 800–1200 characters | Balances long text chunking and term integrity in financial reports, prevents truncation of critical content such as CVE IDs and compliance clauses |
INDEX_INCREMENTAL_SYNC | Enable, with trigger conditions set to new file uploads or specified time intervals | Adapts to irregular updates of temporary announcements, avoids resource consumption from full index rebuilding |
RECALL_TOP_K | Top 10–15 results | Covers business and financial information across multiple segments of financial reports, prevents key data from being missed by single recall results |
EMBEDDING_BATCH_SIZE | 32–64 | Adapts to 8c16G hardware configurations of non-GPU virtual machines, prevents batch requests from exceeding memory limits |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, 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: PG vector database index creation fails, with
connection refusederror returned in logs. Cause: Port mapping for the pgvector service is not configured in docker-compose.yml, or the database initialization script did not execute correctly. - Symptom: Embedding model call returns an error with
401 Unauthorizedstatus code. Cause: An API key for the indexing model is not configured separately, and theCHAT_API_KEYof the chat model is used instead, leading to permission verification failure. - Symptom: Container is forcibly terminated during index construction, with exit status code
137. Cause:EMBEDDING_BATCH_SIZEis not adjusted, and batch vector requests exceed the memory limits of the 8c16G non-GPU host machine.
How to Confirm Configurations Are Set Properly
- Run a vector database connection test, check whether a connection can be established normally and an index table can be created, and confirm that
VECTOR_DB_TYPEmatches the actually deployed database type. - Upload a small cybersecurity financial report document, check the index construction logs, confirm that the chunk length matches the
INDEX_CHUNK_SIZEconfiguration, and no critical terms are truncated. - Initiate a recall test, enter a query related to financial reports, check the number of returned results, and confirm that the
RECALL_TOP_Kconfiguration meets business requirements. - Upload a temporary announcement document, check whether the indexing system triggers incremental synchronization, and confirm that the
INDEX_INCREMENTAL_SYNCconfiguration is active and full rebuilding is not performed.
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.