What the Data for This Use Case Looks Like
Data for photovoltaic intelligent due diligence reports comes primarily from project approval documents, component factory inspection reports, power station grid connection acceptance materials, quarterly operation and maintenance ledgers, and irradiation monitoring data. There are two data update cycles: new project data is imported in one-time batches, while existing operation and maintenance data is updated synchronously each quarter.
Document structures include four modules: basic project information, equipment parameters, power generation performance, and compliance verification. Fields include installed capacity (unit: MWp), component peak power (unit: Wp), annual equivalent utilization hours (unit: h), and total irradiation (unit: kWh/㎡). A complete single report is split into multiple independent text chunks.
What Constraints These Characteristics Impose on the Vector Models and Indexing Link
The multi-source, scattered nature of photovoltaic due diligence data requires vector models to adapt to both structured parameter text and unstructured compliance description content, to avoid semantic coding deviations.
The dual update rhythm of batch and incremental updates requires the indexing system to support two configurations: full batch reconstruction and incremental synchronous update. This meets the different update needs of new projects and existing operation and maintenance data.
Fixed field units and numerical range characteristics require the vector coding process to retain field semantic associations, to prevent similarity calculation errors caused by unit confusion.
Text chunks split from a single report have strong project correlations. The indexing recall logic must balance inter-chunk relevance to avoid recalling irrelevant content.
How to Configure the Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the conventional length of parameter descriptions and compliance text in photovoltaic due diligence reports, avoiding semantic breaks |
chunk_overlap | 50–100 characters | Retains semantic associations between adjacent text chunks, avoiding semantic separation of cross-chunk parameters such as installed capacity and corresponding component model |
retrieve_top_k | Top 8–12 entries | Matches the number of valid text chunks split from a single photovoltaic due diligence report, balancing recall accuracy and resource consumption |
similarity_threshold | 0.72–0.80 | Adapts to the semantic similarity characteristics of photovoltaic parameters, filtering low-relevance non-project text |
enable_duplicate_removal | Enabled | Reduces indexing redundancy caused by repeated compliance clauses and parameter references in due diligence reports |
incremental_update_batch_size | 50 documents | Adapts to the scale of operation and maintenance data updated incrementally each quarter, balancing synchronization efficiency and server resource usage |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: After custom splitting photovoltaic due diligence documents, duplicate chunks are automatically deleted during index storage, but the original splitting order is lost. The arrangement of recalled chunks does not match the original document logic. Cause: The unique identifier of the split chunks is not bound to the index metadata, or the deduplication configuration that retains order is not enabled.
- Phenomenon: When connecting the m3e vector model, a 401 unauthorized status code is returned, and vector coding cannot be completed. Cause: The API key and request domain name of the vector model are not configured correctly, or the key does not have call permissions for the vector service.
- Phenomenon: After adding a vector model through a custom channel, the actual request still triggers the large language model call logic. Cause: The request routing of the vector model is not separated from the large language model routing, and no dedicated vector service call path is specified in the channel configuration.
How to Verify Successful Configuration
- Upload a test sample of a single photovoltaic due diligence report, check whether the number of parsed text chunks matches the preset splitting rules.
- Trigger a full index build, check the index statistics log to confirm that the duplicate chunk deduplication logic complies with the configuration requirements.
- Enter project core parameter keywords to initiate a recall test, verify that the semantic relevance of the recall results matches the preset threshold logic.
- Call the dedicated vector model test interface to confirm that there are no unauthorized error reports, and the returned vector coding format meets expectations.
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.