What the Data for This Category Looks Like
Photovoltaic marketing content and customer acquisition-related data mainly come from internal photovoltaic project investment brochures, photovoltaic financial product manuals, investor-facing promotional copy, policy interpretation documents, and public industry policy documents from financial institutions. Updates occur irregularly, tied to new product launches, policy adjustments, or project completions, with wide variation in the volume of materials updated per cycle. Document structures include structured parameter fields (such as project installed capacity, expected return rate, with units MW and %), unstructured text passages (such as promotional copy, project reviews), and metadata fields like fixed-format project numbers and product document numbers.
Constraints for Vector Models and Indexing
The multi-dimensional characteristics of photovoltaic marketing content impose multiple constraints on the vector models and indexing workflow. First, mixed content with structured parameters and unstructured text requires vector models to support both specialized terminology semantic encoding and numerical association of structured fields. Second, irregular update schedules and frequent batch material updates require indexes to support incremental updates without full reconstruction. Third, wide variation in document length — from tens of words of promotional copy to thousands of words of project cases — requires support for variable-length text chunking. Fourth, some content has clear timeliness (such as subsidy policies, product expiration dates) requires indexes to associate metadata fields to support time-based filtering of recall results.
Recommended Configuration Parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | text-embedding-3-large or bge-large-zh-v1.5 | The photovoltaic field contains a large number of specialized terms and financial parameters, and large-scale embedding models can more accurately capture semantic associations |
chunk_size | 800–1200 characters | Adapts to the length span of photovoltaic marketing content, balancing semantic integrity and index storage density |
chunk_overlap | 100–150 characters | Avoids semantic fragmentation of specialized terms across chunks, improving recall accuracy |
index_batch_size | 50–100 items/batch | Balances batch index construction speed and server resource usage, adapting to irregular updates of marketing materials |
recall_top_k | Top 8–12 results | Photovoltaic marketing content has strong professional relevance, and a small number of highly matched results can meet business needs |
similarity_threshold | 0.75–0.85 | Filters low-matching irrelevant content, focusing on information matching current photovoltaic financial product or investment promotion scenarios |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Index construction tasks remain in a pending state for a long time or return timeout errors (status code 504). The cause is failure to configure access keys or network proxies for the index model, resulting in failed embedding request sending.
- The language model configuration items associated with the knowledge base become empty. The cause is that some configuration interfaces mistakenly bind the association logic between the index model and the language model, and creating the index model overwrites the original language model configuration.
- Index construction fails during local deployment of version 4.9.6. The cause is failure to correctly add the call address and quota configuration of external free embedding models in the configuration file.
How to Verify Correct Configuration
- A single photovoltaic marketing document is uploaded, and the parsed text chunking results are checked to confirm the chunking logic complies with configuration requirements.
- A small-scale index construction task is run, and the embedding request return status in system logs is checked to confirm there are no abnormal errors.
- Test query terms related to photovoltaic are entered, and the number of recall results is verified to match the preset recall configuration.
- The knowledge base association configuration page is checked to confirm that the binding relationship between the embedding model and index storage has no abnormalities.
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.