What the data for this category looks like
Energy storage research report data mainly comes from industry associations, power equipment manufacturers, third-party consulting institutions, and securities research institutes. Update frequency adjusts dynamically with industry policy changes, release of core industrial chain data, and project implementation progress. There is no fixed update cycle, but updates are frequent and concentrated at the end of quarters and major policy release nodes. Most documents are in PDF format, containing professional indicator explanations, upstream and downstream industrial chain data, original policy texts, and project cases. Fields include energy storage installed capacity, unit cost, cycle life, and others, with corresponding units such as GW, yuan/kWh, number of cycles, and more. Some documents include structured tables and formulas.
What constraints these characteristics impose on knowledge base retrieval and recall
Scattered data sources require support for multi-source knowledge base access to avoid information limitations from a single data source. High-frequency dynamic updates without a fixed cycle require configuring an incremental update mechanism; full updates cannot meet real-time requirements. Documents containing long sentences, professional formulas, and structured tables require retaining contextual associations during parsing while supporting structured data recall. Professional fields and units require automatic alignment of units and terms during retrieval to avoid retrieval deviations caused by unit mismatches. Large individual document sizes require properly setting segmentation parameters to avoid breaking the logical integrity of professional data.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall_count | Top 8-12 | Energy storage research reports contain a large amount of professional data and long texts. Too many recalled results will exceed the LLM context window, while too few will miss key industrial chain information |
similarity_threshold | 0.72-0.80 | The energy storage field is dense with professional terms. A threshold that is too low will introduce irrelevant content, while a threshold that is too high will miss accurate research report fragments related to the profession |
chunk_size | 800-1200 characters | Most energy storage research reports contain long sentences, formulas, and tables. An overly long segment will break contextual logic, while an overly short segment will damage the integrity of professional data |
PARSE_TABLE_ENABLE | Enabled | Energy storage research reports contain a large amount of structured table data such as installed capacity and unit cost. Enabling parsing enables structured recall and precise matching |
incremental_update_interval | Every 6 hours | Energy storage industry policies and project updates are frequent. Incremental updates ensure the knowledge base synchronizes the latest research report content in a timely manner |
maxContext | 4000-6000 characters | Individual energy storage research reports have large content volume, so sufficient context must be retained for the LLM to integrate professional data when answering questions |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing the settings.
Three common misconfigurations
- Phenomenon: When calling the knowledge base retrieval plugin, the target knowledge base cannot be dynamically switched via variables, and the returned results do not match the preset filter conditions. Cause: The plugin's knowledge base selection variable was not correctly bound, and user-input filter parameters were not mapped to the target knowledge base ID.
- Phenomenon: When deploying version 4.8.9 using docker-compose, creating a new knowledge base triggers a
500 Internal Server Error, and logs show a database connection failure. Cause: The database address and account password were not correctly configured in environment variables, causing the service to fail to initialize knowledge base-related data tables. - Phenomenon: The answer generated by the LLM includes irrelevant content outside the knowledge base, and does not strictly rely on recalled research report data. Cause: The "force use recalled content" configuration item was not enabled, or the similarity threshold was set too low, causing the LLM to call external data from its training corpus.
How to confirm proper configuration
- Upload a test energy storage research report, review the parsed segments and table content to confirm that the
PARSE_TABLE_ENABLEconfiguration is active. - Initiate a retrieval for energy storage professional terms, verify the number of recalled results and similarity matching degree to confirm that the
recall_countandsimilarity_thresholdconfigurations meet expectations. - View the knowledge base update log to confirm that the incremental update task runs according to the set
incremental_update_intervalcycle. - Test the knowledge base switching function bound to variables, enter different knowledge base filter conditions, and confirm that returned results correspond to the correct knowledge base content.
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.