What this type of data looks like
Data in the defense electronics sector for investment research scenarios comes from multiple sources: public announcements from defense industry groups, project approval documents, supply chain supporting files, professional industry research reports, component parameter manuals, and test reports. Updates occur irregularly, aligned with project milestones, industry exhibitions, and financial report releases. New test data and project updates are added at any time. Document structures fall into three categories: long-form research reports, structured parameter tables (with fields such as model number, voltage rating, power), and scattered test notes. Fields include security classification labels, model numbers, physical parameters and their associated units. No unified fixed format exists for these documents.
Constraints for knowledge base retrieval and recall
The security classification attribute of defense electronics investment research data requires that recall results strictly match user permissions to prevent sensitive information leaks. Structured parameter tables have special fields and units, so retrieval must support precise matching of parameter types and units to avoid confusion across different units. The mixed document structure of long-form research reports and short test notes requires a splitting strategy that balances contextual integrity and information granularity. Irregular update cycles require support for flexible batch re-embedding workflows, to adapt to temporarily added model data and research reports. The large number of domain-specific professional terms and included bilingual model numbers requires embedding models to adapt to industry vocabulary, ensuring effective multilingual recall and meeting cross-language information needs for investment research.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the mixed structure of long research report paragraphs and structured parameter tables in defense electronics documents, preserving contextual logic while avoiding information overload within chunks |
embedding_model | Multilingual embedding model supporting professional Chinese and English terms | Meets retrieval requirements for bilingual model numbers and parameter terms in the defense electronics domain, solving multilingual recall bias issues |
recall_top_k | Top 10–15 results | Covers scattered model-related information in defense electronics research reports, retaining sufficient candidate results for subsequent reranking |
rerank_top_k | Top 5 results | Focuses on core information required for investment research, returning the most relevant document chunks to the analysis scenario |
file_parse_mode | Paragraph first + structured field extraction | Adapts to mixed document structures, preserving the logical flow of research reports while extracting structured fields such as component parameters for precise retrieval |
permission_filter_enabled | Enabled | Aligns with the security classification requirements of defense electronics documents, filtering recall results based on user permissions |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material formats, 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
- An error prompt for return parameters occurs when calling the interface to create a text collection. The cause is failure to follow the
paragraph_firstparameter requirements of version 4.9.10, with an incorrectly passed string-type parameter value. - Some files do not appear in the selectable list after Feishu knowledge base synchronization. The cause is failure to grant read permissions for the corresponding Feishu documents, or documents not being set to accessible status, resulting in the interface being unable to pull file content.
- Recall effectiveness does not improve after batch re-embedding. The cause is failure to clear old embedded data from the original vector database, with direct appending of new embeddings causing vector conflicts, or failure to re-split document chunks before performing re-embedding, resulting in chunk structures that do not match the new embedding model.
How to confirm the configuration is correct
- Upload a defense electronics component parameter manual, check if the split text chunks retain model numbers, parameters and their corresponding units, verifying that the
chunk_sizeandfile_parse_modeconfigurations take effect. - Enter mixed Chinese and English professional terms for retrieval, check if relevant documents are recalled, verifying that the
embedding_modeladapts to domain-specific professional vocabulary. - Simulate retrieval initiated by users with different permissions, check if documents with security classification labels are only visible to authorized users, verifying that the
permission_filter_enabledconfiguration takes effect. - Call the external upload interface to upload defense electronics documents, check if files can be properly parsed and added to the knowledge base, verifying the correctness of interface call parameters.
- Perform a batch re-embedding operation, check the vector database update log to confirm that old data has been cleared and new embedded data has been properly written, verifying the correctness of the re-embedding workflow.
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.