What the data for this category looks like
General equipment investment research data mainly comes from industry association public reports, listed company regular announcements, official technical documents from equipment manufacturers, third-party testing institution parameter reports, and public materials from industry exhibitions. Update cycles vary by data source type. Industry association reports are updated quarterly or semi-annually. Manufacturer documents are updated in real time with new product launches. Listed company announcements are updated at fixed quarterly and annual nodes. Document structures include structured parameter tables, working condition analysis documents, competitor benchmarking reports, and supply chain breakdown documents. Core fields include equipment model, rated voltage, rated power, operating noise, weight, and more. Most fields have clear attached units.
What constraints do these characteristics place on the model access and configuration process
The multi-source and heterogeneous characteristics of general equipment investment research data require the model access link to support mixed input of structured parameters and unstructured text. The varying update cycles of different data sources require configurable incremental update trigger rules to avoid invalid repeated synchronization. The dedicated field and unit system requires the model to have accurate professional term recognition capabilities to prevent parameter matching errors. The coexistence of long documents and short parameter tables requires configurable adaptive adjustment of segmentation and context carrying parameters to ensure complete parsing.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | General equipment investment research documents include short parameter entries and long working condition analysis texts. This range balances single-segment parsing accuracy and context carrying capacity |
CHUNK_OVERLAP_RATE | 15–20% | Equipment parameter tables have continuously associated fields. The overlap rate ensures contextual coherence of parameter segments and avoids field splitting breaks |
SIMILARITY_THRESHOLD | 0.75–0.85 | General equipment investment research requires strict matching of precise fields such as model and rated parameters. A higher threshold filters irrelevant recall results |
RECALL_TOP_N | Top 8–12 entries | A single round of investment research requires multi-dimensional information including equipment parameters, competitor benchmarking, and working condition data. This quantity balances information completeness and context occupancy |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Parsing large equipment 3D drawings and annual industry reports takes a long time. Reserve sufficient time to ensure complete parsing |
UPLOAD_FILE_MAX_SIZE | 2000 MB | The general equipment field has large-capacity technical manuals and supply chain data documents. This upper limit covers most upload requirements |
The parameter values provided on this page are all conventional recommendations used to determine starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: An error is returned when calling the speech recognition link, prompting that no available channel is available for model whisper-1 under the default group. Reason: No dedicated model group is configured for the general equipment investment research scenario, or the specified speech recognition model is not bound to the corresponding group. This causes the default group to have no available call channels.
- Phenomenon: After configuring the "voices" field of the TTS model, the generated speech does not match the expected pronunciation of professional terms. Reason: The standard voice identifier format supported by the platform is not used, or parameters are not adjusted in combination with the pronunciation habits of equipment professional terms, resulting in abnormal field parsing.
- Phenomenon: After importing historical investment research records to train a custom model, the model has low recognition accuracy for equipment parameters. Reason: The preprocessing rules of the training data are not adjusted for the dedicated field and unit system of general equipment, causing the model to fail to accurately match professional parameter items.
How to confirm the configuration is complete
- Upload a single general equipment parameter table document, check whether the parsed segments completely cover all parameter fields without field breaks.
- Initiate a retrieval request related to equipment parameters, verify whether the number and matching accuracy of recall results meet preset requirements.
- After configuring the speech recognition and TTS models, upload an audio clip of the equipment technical document, check the field accuracy of the recognition result and the matching degree of the speech output.
- Test the incremental update process, confirm that newly added equipment manufacturer documents can be normally accessed and parsed without timeout or parsing failure.
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.