What Data for This Category Looks Like
The data for specialized equipment intelligent due diligence reports primarily comes from equipment factory certificates, quarterly operation and maintenance logs, annual quality inspection reports, industry compliance standard documents, and supply chain purchase ledgers. Data update rhythms are divided into static and dynamic categories. Basic equipment parameters such as model and rated power are statically updated. Operation and maintenance records and fault counts are updated monthly. Compliance ratings are updated quarterly.
The document structure usually includes four modules: equipment basic information page, parameter ledger table, operation and maintenance cycle summary, and compliance verification checklist. Fields include equipment number (string), rated power (kilowatt), cumulative operating duration (hours), last maintenance date (YYYY-MM-DD format), compliance level (character type), etc. Each field is bound to clear physical units or format requirements.
Constraints Imposed by These Characteristics on Model Access and Configuration
The feature that multiple fields of specialized equipment due diligence reports are bound to units requires that model access must support semantic recognition of structured fields and unit verification to avoid parameter confusion. Dynamically updated data needs to match the incremental refresh frequency of the vector database, aligning with monthly/quarterly update rhythms to avoid recalling outdated data.
The long document structure and multi-module combination require that the model context window must adapt to complete semantics after sharding, while requiring reasonable sharding rules to retain field integrity. In addition, the demand for cross-document association analysis requires that model access must support context splicing of multiple data sources, and cannot only handle independent parsing of a single document.
How to Set Configuration Values
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000-16000 characters | Specialized equipment due diligence reports contain multi-module content, and the post-parsing text length is long. This range can cover complete context splicing requirements |
parse_chunk_size | 1000-1500 characters | It is necessary to retain the integrity of equipment parameter fields and units. This range avoids splitting that destroys the semantic association of structured fields |
embedding_batch_size | 32-64 | Specialized equipment data contains many structured fields. Batch embedding can improve processing efficiency and reduce the delay of single embedding |
similarity_threshold | 0.75-0.85 | It is necessary to accurately match core parameters such as equipment model and rated power. This threshold balances recall accuracy and coverage |
rerank_top_n | Top 5-8 entries | Specialized equipment due diligence analysis needs to focus on core compliance and parameter indicators. Too many recall results will increase the model inference burden |
llm_model_timeout | 120-180 seconds | Cross-document compliance verification and association analysis require long inference time. This range covers the processing cycle of most complex tasks |
The parameter values provided on this page are all conventional recommendations used to determine the starting point 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.
Three Common Mistakes
- After replacing a large language model, the original model error persists. The root cause is failure to synchronously update the first model entry in the
llmModelsarray in the FastGPT system configuration. Modifying only the configuration file without synchronizing the front-end configuration leads to this issue. - Vector retrieval results are empty or have excessive matching deviations. The cause is failure to configure custom field mapping rules for the physical unit fields of specialized equipment, which prevents the model from correctly recognizing the semantics corresponding to parameter units.
- Model calls frequently time out. The cause is failure to adjust the
llm_model_timeoutparameter. Multi-document association analysis for specialized equipment due diligence exceeds the default timeout threshold, leading to task interruption.
How to Confirm Successful Configuration
- Access the FastGPT model management page. Confirm that the configured LLM and embedding model status is marked as "Connected", and verify that the model names in the
llmModelsarray match the actually connected models. - Upload a standard specialized equipment due diligence report sample. Check that the sharded content after parsing retains the unit information of core fields such as equipment number and rated power.
- Initiate a simulated due diligence analysis request. Check that the returned result correctly matches equipment parameters and compliance requirements, with no obvious unit confusion or missing fields.
- View the vector database operation logs. Confirm that the batch processing volume of embedding tasks matches the configured
embedding_batch_sizeparameter, with no abnormal 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.