What This Type of Data Looks Like
Power investment research data comes primarily from public operational monitoring data of national and regional power grids, monthly reports from power industry associations, periodic financial reports of listed companies, real-time trading data from power trading centers, and industry policy documents. Update frequency: Real-time operational data updates hourly. Trading data updates daily. Financial reports and policy documents update quarterly or upon release. Document structures include structured load curve data tables, unit parameter manuals, unstructured industry research reports, and policy interpretation PDF files. Fields and units include active power (megawatts, MW), power generation (megawatt-hours, MWh), on-grid electricity price (yuan per megawatt-hour), and other specialized industry terminology.
Constraints on Model Access and Configuration
Real-time, hourly operational data requires models to support high-frequency vector updates. Retrieval processes must adapt to time-sensitive data sources. Coexisting structured unit parameters, load curves, and unstructured research reports requires configuring parameters for both structured and unstructured parsing. Specialized terminology has high semantic similarity. This requires adjusting entity recognition and similarity threshold configurations. Wide variation in document size ranges from short parameter tables to dozens of pages of long research reports. This requires dynamically adjusting segment length and parsing timeout settings.
Recommended Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
CHUNK_SIZE | 800–1200 characters | Power industry documents include structured tables and long-form research reports. This segment length balances content completeness and model context window utilization |
RECALL_TOP_N | Top 6–10 results | Power investment research requires covering multi-dimensional associated data including load, units, and policies. This quantity ensures comprehensiveness of retrieval results |
SIMILARITY_THRESHOLD | 0.75–0.85 | Power-specific terminology has high semantic similarity. This threshold filters irrelevant general industry data and retains accurately matched content |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing large power research reports or unit parameter documents takes significant time. This timeout covers the parsing process for most long documents |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Some power industry standard documents have large file sizes. This upper limit supports complete upload of core documents |
VECTOR_MODEL_EMBEDDING_DIM | 768 dimensions | Commonly used general vector models adapt to the embedding dimension required for power terminology, ensuring accuracy of vector representations for specialized terms |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: After switching to a new large language model, knowledge base question answering still returns errors from the old model, and only uses the first model in the
llmModelsarray in the configuration file. Cause: The newly selected model was not bound in the FastGPT application configuration. Only the global configuration file was modified without updating the application association. - Phenomenon: After deploying a GPU-free vector model, the model cannot be added via the OneAPI channel. The interface prompts that the channel configuration failed. Cause: The correct local service address and port were not filled in the FastGPT vector model settings, or network access permissions for the corresponding port were not enabled.
- Phenomenon: The model cannot be called via the OneAPI channel. The interface prompts an API key error. Cause: The API key in the FastGPT configuration was not updated after resetting the password via the OneAPI root account, or the key input had a formatting error.
How to Verify Successful Configuration
- Enter the FastGPT model management interface, check the channel status of configured large language models and vector models, and confirm the status shows normal connection.
- Upload a typical power unit parameter document, and verify that the parsed content segments match the preset segment configuration.
- Launch a targeted power investment research question-and-answer test, and confirm that the number of retrieval results matches the preset retrieval configuration.
- View system operation logs, and confirm there are no error messages related to model calls, and that the called model matches the configured model.
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.