What Data Looks Like for This Category
Ordnance equipment marketing content sources primarily include official equipment technical documents, authorized marketing promotional materials, dealer promotion assets, and compliance review documents. Update cadence follows bulk updates during major equipment iterations or annual marketing plan adjustments, with small incremental tweaks made daily based on compliance requirements or market feedback. Document structures typically include equipment model identifiers, core performance parameters, application scenario descriptions, marketing copy text, and compliance review markers. Fields include equipment numbers with fixed prefixes, performance parameters with legal units of measurement, and security classification fields, among others.
Constraints These Characteristics Impose on Vector Models and Indexing
Fixed prefixes on equipment numbers require vector models to deliver high semantic recognition accuracy for structured identifiers, to avoid confused matching of similar numbers. Core parameters are attached with legal units of measurement. Unit associations must be retained during indexing to prevent matching deviations between unlabeled parameters and actual equipment. Irregular bulk update requirements require indexes to support incremental update modes, avoiding excessive resource usage from full reindexing. Compliance review markers used as metadata must be included in the index for permission filtering during subsequent recall. Marketing copy text varies widely in length, from tens to thousands of characters, requiring flexible segmentation strategies.
Configuration Settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the mixed structure of long parameter paragraphs and marketing copy in ordnance equipment documents, avoiding truncation of critical performance parameters |
embedding_batch_size | 16–32 | Balances video memory usage for local deployments and processing speed of embedding tasks, adapts to bulk uploads of marketing materials |
rerank_top_n | Top 8–12 results | Covers recall needs for cross-model comparisons in ordnance equipment marketing content, avoiding omission of key information for equipment in the same series |
index_refresh_interval | Calibrated via actual testing | Adapts to irregular bulk update cadences, can be adjusted based on actual update frequency, supports incremental indexing mode |
similarity_threshold | 0.72–0.80 | Filters low-similarity irrelevant equipment parameter content, retaining marketing materials with high matching to query intent |
metadata_filter_enabled | Enabled | Filters classified or unauthorized marketing content based on compliance review marker fields, meeting compliance requirements for ordnance equipment |
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 Mistakes
- Issue: After local deployment, the knowledge base cannot automatically index image content, with error prompt
IMAGE_PARSE_FAILED. Cause: The FastGPT image OCR parsing plugin is not enabled. Parameter charts in ordnance equipment marketing materials cannot obtain valid information via plain text indexing. - Issue: In version 4.9.0, the Chat model runs normally, but connecting the Embedding model to OneAPI returns
500 Internal Server Error. Cause: OneAPI has not configured forwarding rules for the corresponding open-source Embedding model, or the locally deployed port is not open to the OneAPI access network segment. - Issue: After enabling the Rerank model and indexing switches, online recall test results do not undergo reranking sorting. Cause: The calling priority of the rerank model is not set in the recall configuration, or the number of rerank return entries is lower than the initial recall number of entries.
How to Confirm Configuration Is Complete
- Upload a single ordnance equipment marketing document with performance parameter charts, check if the parsed text includes chart parameter content extracted via OCR.
- On the FastGPT model management page, test the Embedding model connection, check if vector data is normally generated with no error logs.
- Build a test query containing equipment model keywords, compare the sorting differences of recall results with and without the Rerank model enabled.
- Check the metadata filtering logs on the index management page, confirm that the compliance review marker field is correctly applied to recall filtering rules.
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.