What Data for This Category Looks Like
Aerospace equipment marketing content data comes primarily from official model technical documents, test verification reports, public promotional materials, user manuals, and similar sources. Updates are triggered by major model project initiation, formal qualification, or test milestones, with no fixed high-frequency update cycle. Document structures include three types of content: structured parameter fields, process description text, and marketing copy. Fields have clear physical units attached. Individual document lengths range from short promotional phrases of a few hundred characters to dozens of pages of test reports.
What Constraints These Characteristics Impose on Vector Models and Indexing
Structured parameters and clear units in aerospace equipment data require vector models to support joint encoding of numerical and text data. This prevents loss of parameter semantics that occurs with single-text embeddings. The wide range of document lengths requires indexes to support variable-length vector storage and flexible segmented retrieval. Low update frequency triggered only by major milestones means no need for high-frequency full indexing. An incremental update strategy is suitable here. Mixed marketing content and technical documents require configuring differentiated recall weights for different document types to improve retrieval accuracy.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | text-embedding-3-large or bge-large-zh-v1.5 | Aerospace equipment documents contain specialized terminology and numerical parameters. These models adapt to scenario needs by encoding professional text and numerical semantics effectively |
chunk_size | 800–1200 characters | Balances semantic completeness of short marketing phrases and reasonable segmentation of long test reports. Prevents reduced vector retrieval accuracy caused by overly long single segments |
chunk_overlap | 100–150 characters | Prevents critical specialized terminology from being truncated after segmentation, and ensures semantic coherence between adjacent segments |
recall_top_k | Top 10–15 results | Covers a sufficient number of relevant candidate documents, providing a solid filtering basis for subsequent reranking steps |
rerank_top_k | Top 3–5 results | Only performs fine reranking on high-confidence recall results, reducing computational time spent in the reranking phase |
index_update_strategy | Incremental update | Adapts to the low-frequency update characteristics of aerospace equipment documents, reducing resource consumption during index construction |
The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, 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
- Phenomenon: After executing a retrieval, the interface returns a delay exceeding the preset threshold. The console shows that reranking phase time accounts for more than 70% of total time. Cause: Segmentation parameters are not adjusted based on document length, or the number of recall entries is set too large, leading to overloaded reranking computation.
- Phenomenon: A large number of irrelevant model parameter documents appear in retrieval results, with extremely low matching degrees for numerical fields. Cause: Only a general text embedding model is used, without adaptive encoding for structured parameters with units, leading to loss of semantic association of parameters.
- Phenomenon: The specified index model fails to load in a Docker deployment environment. The startup log returns a
model not founderror. Cause: The index model is not mounted to the specified directory of the Docker container, or the model path is not correctly configured in the configuration file.
How to Confirm Proper Configuration
- Upload a typical aerospace equipment marketing document, view the parsed segmentation results, and confirm that segment lengths fall within the preset range, with no critical specialized terminology truncated.
- Initiate a retrieval request with professional parameters, check the number of recall entries and reranked entries returned by the console, and confirm they match the configured parameters.
- Check the Docker container's runtime logs, confirm there are no model loading failures or index construction errors.
- Confirm that the current FastGPT version is v4.8.21-fix or later, to avoid bugs in index model loading and segment parsing from older versions.
- Manually compare the matching degree between retrieval results and query keywords, confirm that semantic associations of numerical fields are correctly identified.
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.