What Data in This Category Looks Like
Ordnance equipment-related due diligence data is primarily sourced from public channels including defense manufacturing industry association reports, official finalized equipment information released by defense industry groups, equipment technical specification manuals, and industry press releases. Data update schedules adjust with new equipment finalization and annual industry report releases, with no fixed cycle. Single document structures typically include fields such as equipment model, core performance parameters, development progress, and fielded scope. Units mostly use professional defense metrology standards such as millimeters, kilometers per hour, and tons. Some technical documents include multi-page charts and formulas.
Constraints on Knowledge Base Retrieval and Recall
Publicly available data is scattered across documents in different formats. Some documents contain complex charts and formulas, which places requirements on the format adaptation capabilities of parsing tools. Professional terminology is dense and highly differentiated. Basic keyword searches are prone to matching errors, so enhanced association with professional term banks is needed. Document length varies widely, from single-page news articles to dozens of pages of technical manuals. Segmentation strategies must balance context integrity and retrieval efficiency. Data updates have no fixed cycle. New equipment information must be synchronized to the knowledge base in a timely manner, otherwise recall results will lag behind industry developments.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Ordnance equipment technical documents often contain long, specialized descriptions. Segmentation that is too long will lose context connections, while segmentation that is too short will damage the logical integrity of parameters. This range adapts to most defense industry document structures. |
recall_top_k | Top 10–15 results | Due diligence reports need to cover multi-dimensional parameters of equipment. Too few recalls will miss key performance indicators, while too many will introduce irrelevant information. |
similarity_threshold | 0.75–0.85 | The similarity differentiation of defense industry professional terminology is high. A threshold that is too low will mix in unrelated documents, while a threshold that is too high will miss precisely matched detailed parameters. |
parse_timeout | 300–600 seconds | Parsing large equipment technical manuals takes a long time. Timeouts will cause parsing failures. This range adapts to most single document sizes. |
rerank_top_k | Top 5–8 results | Perform secondary filtering on recall results to retain core parameters that best align with due diligence requirements, and avoid redundant information interfering with responses. |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: The AI response only returns general conclusions, and does not reference equipment parameters retrieved from the knowledge base. No
rag_contextfield appears in logs. Cause: The knowledge base associated with retrieval is not configured in the conversation node, or recall results are not correctly injected into context variables. - Phenomenon: After uploading a PDF or Word document containing images, the knowledge base only extracts text content, and image areas display as empty. Cause: Image OCR parsing configuration is not enabled, or the storage path for image transfer is not configured, resulting in images being unable to generate usable URLs.
- Phenomenon: When deploying
Qwen2-72B, single-round response time exceeds 30 seconds, or video memory usage exceeds80 GB. Cause: Model quantization configuration is not enabled, or context window cropping is not configured, resulting in loading excessive redundant recall content that exceeds hardware carrying capacity.
How to Confirm Correct Configuration
- Upload a single-page ordnance equipment technical manual, and check if the parsed segments retain complete professional parameter paragraphs, with no obvious truncation or splicing errors.
- Enter a precise professional search term, such as "a certain type of armored vehicle maximum range", and check if the number of recall results falls within the set range.
- Trigger the AI conversation flow, and check if the response content includes marked recall context fragments, confirming that knowledge base content is correctly referenced.
- Test large model loading and response, confirm that video memory usage matches the configured quantization parameter range, and that single-round response time meets the expected threshold.
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.