What this category of data looks like
Specialized equipment investment research data sources include public technical specifications from equipment manufacturers, operation logs from industry end users, performance test reports from third-party testing institutions, and technical requirement documents for equipment bidding from government procurement. Update cadence: Manufacturer specifications are updated annually or semi-annually, operation logs are updated in real time, and bidding documents are updated on a project-triggered basis. Documents are mostly a mix of structured and semi-structured content, including equipment models, rated operating condition parameters, fault code correspondence tables, parts lists, and other content. Some long documents include text descriptions of equipment disassembly. Common fields include rated power, maximum operating speed, and pressure rating, with corresponding units of kW, r/min, and MPa.
What constraints do these characteristics impose on vector models and indexing workflows
The mixed structured and semi-structured document structure requires vector models to support mixed encoding of structured fields and unstructured text, to avoid semantic separation of parameters and their descriptions caused by generic chunking. Real-time updated operation log data requires the indexing system to support incremental writing and real-time synchronization, to avoid performance loss from full index rebuilding. Differences in document formats across multiple sources require the indexing module to support parsed field formats from different sources, and unify metadata storage structures. Long parameter tables and fault code tables within single documents require chunking strategies to preserve field boundaries, preventing loss of association between parameters and their descriptions after splitting.
Configuration guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | text-embedding-3-large | Supports high-dimensional vector encoding, can accurately distinguish subtle differences in equipment parameters, and adapts to mixed encoding requirements for structured fields and unstructured text |
chunk_size | 800–1200 characters | Adapts to the mixed length of parameter tables and descriptive text in specialized equipment documents, avoiding breaking parameters and their corresponding descriptions during chunking |
chunk_overlap | 50–80 characters | Preserves semantic association between adjacent chunks, preventing loss of context when long parameter tables are split |
index_type | HNSW | Meets the fast recall requirement for high-dimensional equipment parameter vectors; HNSW indexes have better query efficiency than flat indexes |
similarity_threshold | 0.75–0.85 | Filters low-similarity irrelevant equipment parameter documents, ensuring matching between recall results and investment research topics |
retrieval_top_k | Top 10 results | Balances information breadth required for investment research and query latency, avoiding excessive redundant results that interfere with analysis |
enable_structured_field_embedding | Enabled | Encodes structured fields such as equipment model and rated power separately, improving recall accuracy for parameter comparison across devices in the same category |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Each scenario requires individual analysis, and it is recommended to test on samples relevant to the actual deployment before finalizing settings.
Three common configuration mistakes
- Symptom: An index creation task triggers a
401 Unauthorizederror when connecting an embedding model via One API. Cause: API key and interface address for the embedding model are not configured correctly, resulting in authentication failure. - Symptom: A large number of irrelevant equipment model parameters appear in index recall results, and the number of recalled entries does not match the set
retrieval_top_kvalue. Cause: Structured field encoding is not enabled; generic vector encoding cannot distinguish parameter differences between different equipment models, leading to semantic confusion. - Symptom: Index synchronization delay exceeds the preset threshold when incrementally updating operation logs. Cause: Incremental indexing mode is not configured, and full index rebuilding is triggered for each update, consuming significant computing resources.
How to confirm proper configuration
- Upload an equipment technical manual that includes a structured parameter table, check if the parsed document retains metadata for fields such as rated power and rotational speed, and confirm that the
enable_structured_field_embeddingconfiguration is active. - Submit a parameter comparison retrieval request, verify that the number of recall results matches the set
retrieval_top_kvalue, and that all results are relevant to the target equipment model. - Upload a new operation log document, check if the indexing system completes the update within the preset time, and confirm that the incremental indexing mode is configured correctly.
- Test the embedding model invocation request, confirm that the returned vector dimensions match the standard dimensions of the selected model, with no format errors.
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.