What the data for this category looks like
Defense electronics industry financial report data primarily comes from publicly disclosed periodic reports, temporary announcements, and supporting industry statistical documents. Update cycles fall into two categories: fixed and irregular. Quarterly reports are updated each quarter. Annual reports are updated each year. Temporary announcements such as major contracts and research and development progress updates have no fixed release schedule. Document structures include structured financial statements, research and development project details, defense electronics order disclosures, production capacity operation data and other fields. Common units include ten thousand yuan, hundred million yuan, person/year and others. Some content related to national security is desensitized. Public documents are mostly long-text formats, with individual annual reports having considerable length.
What Constraints These Characteristics Impose on Vector Models and Indexing
The mixed structured and semi-structured nature of defense electronics financial reports requires vector models to adapt to both technical term-dense financial text and structured field retrieval. The mixed update rhythm of fixed-cycle updates and temporary announcements requires indexes to support both incremental synchronization and full update modes. Splitting long-text documents must preserve business context. It must avoid damaging the semantic integrity of core business logic such as research and development and orders. Desensitization of sensitive fields also requires indexes to retain field-level matching rules during the recall phase. This prevents irrelevant content from interfering with accurate retrieval.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to complete semantic units of research and development projects and order details in defense electronics financial reports, avoids splitting that damages business logic |
chunk_overlap | 100–150 characters | Preserves contextual connection between segments, resolves semantic fragmentation caused by long text splitting |
vector_store_type | milvus / zilliz | Adapts to external index deployment requirements of FastGPT 4.9 and above, supports high-concurrency incremental updates |
recall_top_k | Top 10–15 entries | Balances recall coverage and accuracy, avoids excessive irrelevant documents interfering with financial report analysis results |
similarity_threshold | 0.75–0.85 | Filters low-match irrelevant content, adapts to the technical term-dense characteristics of defense electronics financial reports |
rerank_model_url | Privately deployed reranking model address | Meets security and compliance requirements for defense electronics data, avoids sensitive information leakage through third-party services |
The parameter values provided on this page are conventional recommendations used as a starting point 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 conduct tests on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: FastGPT returns the
ECONNREFUSEDstatus code when connecting to an external index. Cause:vector_store_typeis not correctly configured to the corresponding external index type, or network policies restrict access ports of the index service, preventing connection establishment. - Phenomenon: The number of knowledge base recall results is far lower than the set
recall_top_k. Cause:similarity_thresholdis set too high, filtering out eligible matching results, or incremental indexing is not enabled, resulting in temporary announcement data not being included. - Phenomenon: The privately deployed reranking model returns empty results after being called. Cause: The access key for the privately deployed reranking model is not configured, or the input recall text length exceeds the maximum context limit supported by the model.
How to Confirm Proper Configuration
- Enter the knowledge base vector storage settings page in FastGPT, click the connection test button, and confirm that the prompt message indicating a successful connection is returned.
- Upload a defense electronics quarterly financial report document, wait for parsing to complete, and check whether the single segment length in the segment list matches the set
chunk_sizerange. - Initiate a retrieval request containing defense electronics technical terms, check whether the number of recall results falls within the set
recall_top_kinterval, and whether the similarity score meets the threshold requirements. - After enabling the reranking function, check whether the sorting of retrieval results matches the technical term matching degree, with no obvious irrelevant content mixed in.
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.