What data for this category looks like
Ordnance and equipment research report data sources include:
- Public research reports from securities firm military industry research teams
- Public information disclosed by national defense science and technology industry authorities and military industry groups
- Industrial analysis documents released by industry associations
Update rhythms fall into two categories:
- Scheduled updates, primarily covering monthly and quarterly reports
- Event-triggered updates, released alongside milestones such as equipment commissioning, military exercises, and major technological breakthroughs
Document structures typically include:
- Report titles, issuing institutions, and release times
- Core equipment model parameters, performance indicators, and industry supply and demand data
- Risk warnings
Fields include professional units such as equipment model, maximum range (km), commissioning quantity (units/sets), and research and development cycle (months).
Individual document lengths vary widely, ranging from thousands to tens of thousands of characters.
Constraints on knowledge base retrieval and recall from these characteristics
Multi-source data access creates format and field differentiation issues. Unified parsing rules must be configured for different sources such as securities firm research reports and official disclosure documents.
Non-fixed update rhythms require the knowledge base to support incremental synchronization and event-triggered updates. This avoids resource waste from full repeated parsing.
The presence of professional fields and units requires the retrieval and recall link to support precise field matching and professional term vector encoding. This prevents retrieval failure caused by term splitting or unit mismatches.
The wide range of long document lengths requires chunking strategies to retain contextual associations of professional parameters. This avoids damage to core information integrity from improper chunking.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Ordnance and equipment research reports contain professional parameters and long sentence descriptions. This chunk length preserves professional term context and avoids breaking parameter associations |
recall_top_k | Top 8–12 results | Research report content is professional and has low redundancy. Too many recall results introduce irrelevant information, while too few fail to cover core arguments |
similarity_threshold | 0.75–0.85 | Professional term matching requires a high similarity threshold to avoid recalling unrelated general military documents |
rerank_top_n | Top 3–5 results | Perform secondary filtering on recall results to retain core research report content that best matches the query |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Individual ordnance and equipment research reports may contain large numbers of charts and data attachments, requiring support for large file uploads |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Long document parsing requires extended time to avoid parsing timeout failures |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis, and testing on local samples should be completed before finalizing settings.
Three Common Mistakes
- Issue: Knowledge base retrieval response times out, and reply speed is slow. Cause:
chunk_sizeandrecall_top_kparameters are not adjusted. Excessively long chunks and too many recall entries increase vector calculation and context splicing time. - Issue: Retrieval results do not include content related to the research report title. Cause: Title retrieval configuration items are not enabled. Only main body content is vector encoded, so documents matching the title cannot be recalled.
- Issue: API calls fail automatically due to carried Bearer tokens. Cause: FastGPT’s automatic Bearer token supplement function is not disabled, causing conflicts with third-party API authentication logic.
How to Confirm Configurations Are Correct
- Upload a single ordnance and equipment research report, check the number and length of parsed chunks to confirm they fall within the configured
chunk_sizerange. - Enter a query for a professional equipment model, verify the number and similarity of recall results to confirm alignment with the
recall_top_kandsimilarity_thresholdconfiguration logic. - Call the API interface, check for extra Bearer tokens in the request header to confirm the automatic supplement function has been adjusted as needed.
- Enter a query containing only research report title keywords, confirm recall results include documents with matching titles to verify normal operation of the title retrieval function.
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.