What this type of data looks like
Photovoltaic investment research data is sourced primarily from public reports by industry associations, regular financial reports of listed companies, technical white papers from component manufacturers, meteorological solar radiation monitoring data, and local new energy policy documents. Update rhythms vary across data sources: industry research reports are updated weekly, monthly, or at project milestones; policy documents are released irregularly per regulatory requirements; component technical parameters are updated quarterly alongside production capacity iterations. Document formats include long-form research reports (dozens of pages), structured parameter tables, and single technical indicator entries. Fields cover component conversion efficiency, installed capacity, irradiance, subsidy standards, and other relevant metrics. Units include percentage, gigawatt, watts per square meter, and other professional measurement standards.
Constraints on vector models and indexing
The multi-source heterogeneous nature, mixed short and long documents, and frequently updated parameter characteristics of photovoltaic investment research data impose multiple constraints on the vector models and indexing workflow. Long-form industry research reports require reasonable context window splitting to avoid semantic fragmentation. Structured parameter tables must be split by field before embedding, to prevent reduced recall accuracy caused by mixed indexing. Frequently updated technical parameters require an incremental indexing workflow, to reduce resource consumption and time costs from full index rebuilding. Multi-source data includes text, numerical values, and time-series indicators, requiring adaptation to different vector encoding logic. Numerical fields must first undergo normalization processing before embedding, to ensure reasonable similarity calculation.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Matches the semantic completeness of single paragraphs in photovoltaic industry research reports, avoids context overflow from embedding models for overly long text, and prevents disruption of semantic associations for technical terms if too short |
chunk_overlap | 100–150 characters | Preserves contextual continuity between segments, prevents loss of critical logical connections between split research report paragraphs |
index_type | HNSW | Suits scenarios with large volumes of photovoltaic data, HNSW indexes balance recall speed and accuracy in high-dimensional vector retrieval |
top_k | Top 10–15 entries | Covers multi-source reference information required for photovoltaic investment research, prevents omission of key parameters or policy entries from too few recalled results |
similarity_threshold | 0.75–0.85 | Filters low-correlation photovoltaic technical parameters or industry news, ensures matching between recalled results and investment research needs |
enable_incremental_index | Enabled | Suits the frequently updated characteristics of photovoltaic technical parameters, reduces resource consumption and time costs from full index rebuilding |
The parameter values provided on this page are common 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 values.
Three Common Configuration Mistakes
- Phenomenon: After configuring the local
m3evector model, the knowledge base remains stuck in the "indexing" state with no progress during index creation. Cause: API port mapping for the local model was not configured correctly, or the vector model service failed to start normally, preventing FastGPT from obtaining embedding vector generation results. - Phenomenon: After adding
text-embedding-ada-002via OneAPI, the knowledge base returns the "no retrievable vectors" error when called. Cause: The corresponding channel's API key was not correctly bound in FastGPT's system configuration, or the model name in the channel configuration does not match the actual invocation name. - Phenomenon: When retrieving photovoltaic module parameters, recalled results include irrelevant policy document fragments. Cause:
chunk_sizeandchunk_overlapwere not configured according to the structure of photovoltaic data, leading to chaotic segmentation logic for different document types and disrupted semantic associations.
How to confirm proper configuration
- Navigate to the vector model management page in FastGPT, verify that the configured model name matches the actual invoked model, and confirm that the API key or local service address was filled correctly.
- Upload a sample photovoltaic industry research report to trigger index construction, check the index progress logs to confirm no errors occurred during segmentation and indexing processes.
- Enter a professional photovoltaic search term, review the number of recalled results and similarity scores to confirm compliance with preset retrieval rules.
- Upload an updated photovoltaic module parameter document, verify that the index update process triggers normally, and completes updates without requiring a full rebuild.
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.